BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Business Development Institute (BDI) - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://bdionline.com X-WR-CALDESC:Events for Business Development Institute (BDI) REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20240310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20241103T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20250309T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20251102T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20260308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20261101T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20270314T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20271107T060000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20250309T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20251102T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20260308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20261101T090000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20270314T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20271107T090000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20240310T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20241103T070000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20250309T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20251102T070000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20260308T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20261101T070000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20270314T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20271107T070000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20260415T120000 DTEND;TZID=America/New_York:20260415T140000 DTSTAMP:20260416T034623 CREATED:20260210T152932Z LAST-MODIFIED:20260406T212044Z UID:119965-1776254400-1776261600@bdionline.com SUMMARY:Virtualization in the Age of AI: Building a Flexible Hybrid Cloud Foundation DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/041526/ LOCATION:Monterey\, 37 E 50th St\, New York City\, NY\, 10022\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2026/02/Untitled-1-1.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260414T173000 DTEND;TZID=America/Los_Angeles:20260414T200000 DTSTAMP:20260416T034623 CREATED:20260415T152814Z LAST-MODIFIED:20260415T154511Z UID:121481-1776187800-1776196800@bdionline.com SUMMARY:Event Recap: Operationalizing AI at Scale: The Enterprise AI Factory Playbook DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/041426_event_recap/ LOCATION:The Sea by Alexander’s Steakhouse\, 4269 El Camino Real\, Palo Alto\, CA\, 94306\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/02/hpe-nvidia-palo-alto-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260414T173000 DTEND;TZID=America/New_York:20260414T200000 DTSTAMP:20260416T034623 CREATED:20260210T204518Z LAST-MODIFIED:20260331T162015Z UID:120090-1776187800-1776196800@bdionline.com SUMMARY:Operationalizing AI at Scale: The Enterprise AI Factory Playbook DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/041426/ LOCATION:The Sea by Alexander’s Steakhouse\, 4269 W El Camino Real\, Palo Alto\, CA\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/02/hpe-nvidia-palo-alto-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260331T120000 DTEND;TZID=America/New_York:20260331T140000 DTSTAMP:20260416T034623 CREATED:20260401T151737Z LAST-MODIFIED:20260402T211049Z UID:121260-1774958400-1774965600@bdionline.com SUMMARY:Event Recap: From Insight to Foresight: How Senior HR Leaders are Using AI to Anticipate Change and Shape Culture DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/033126_culture_amp_event_recap/ LOCATION:Oceana\, 120 W 49th St\, New York\, NY\, 10020\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/02/Culture-amp-nyc.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20260331T120000 DTEND;TZID=America/Chicago:20260331T140000 DTSTAMP:20260416T034623 CREATED:20260401T140410Z LAST-MODIFIED:20260401T152619Z UID:121239-1774958400-1774965600@bdionline.com SUMMARY:Event Recap: Virtualization in the Age of AI: Building a Flexible Hybrid Cloud Foundation DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/033126_hpe_event_recap/ LOCATION:Gibsons Italia\, 233 N Canal St\, Chicago\, 60606\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2026/02/hpe_greenlake-template.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260331T120000 DTEND;TZID=America/New_York:20260331T140000 DTSTAMP:20260416T034623 CREATED:20260209T172850Z LAST-MODIFIED:20260402T163958Z UID:119923-1774958400-1774965600@bdionline.com SUMMARY:Virtualization in the Age of AI: Building a Flexible Hybrid Cloud Foundation DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/033126_hpe/ LOCATION:Gibsons Italia\, 233 N Canal St\, Chicago\, 60606\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2026/02/hpe_greenlake-template.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260331T120000 DTEND;TZID=America/New_York:20260331T140000 DTSTAMP:20260416T034623 CREATED:20260205T201121Z LAST-MODIFIED:20260324T202846Z UID:119444-1774958400-1774965600@bdionline.com SUMMARY:From Insight to Foresight: How Senior HR Leaders Are Using AI to Anticipate Change and Shape Culture DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/33126/ LOCATION:Oceana\, 120 W 49th St\, New York\, NY\, 10020\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/02/Culture-amp-nyc.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260326T120000 DTEND;TZID=America/New_York:20260326T140000 DTSTAMP:20260416T034623 CREATED:20260330T193334Z LAST-MODIFIED:20260330T210118Z UID:121135-1774526400-1774533600@bdionline.com SUMMARY:Event Recap: Operationalizing AI at Scale: The Enterprise AI Factory Playbook DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/032626_event_recap/ LOCATION:Capital Grille (Atlanta)\, 255 E Paces Ferry Rd NE\, Atlanta\, GA\, 30305\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/01/hpe-nvidia-atl.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260326T120000 DTEND;TZID=America/New_York:20260326T140000 DTSTAMP:20260416T034623 CREATED:20260203T182557Z LAST-MODIFIED:20260326T160723Z UID:119335-1774526400-1774533600@bdionline.com SUMMARY:Operationalizing AI at Scale: The Enterprise AI Factory Playbook DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/032626/ LOCATION:Capital Grille (Atlanta)\, 255 E Paces Ferry Rd NE\, Atlanta\, GA\, 30305\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/01/hpe-nvidia-atl.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20260325T120000 DTEND;TZID=America/Chicago:20260325T140000 DTSTAMP:20260416T034623 CREATED:20260330T200325Z LAST-MODIFIED:20260330T210421Z UID:121177-1774440000-1774447200@bdionline.com SUMMARY:Event Recap: Virtualization in the Age of AI: Building a Flexible Hybrid Cloud Foundation DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/032526_event_recap/ LOCATION:Del Frisco’s (Houston)\, 5061 Westheimer Rd Ste 8060\, Houston\, TX\, 77056\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/02/grennlakehpe_Dallas.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20260325T120000 DTEND;TZID=America/Chicago:20260325T140000 DTSTAMP:20260416T034623 CREATED:20260206T162127Z LAST-MODIFIED:20260324T135422Z UID:119645-1774440000-1774447200@bdionline.com SUMMARY:Virtualization in the Age of AI: Building a Flexible Hybrid Cloud Foundation DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/032526/ LOCATION:Del Frisco’s Double Eagle Steak House\, 5905 Legacy Dr Suite A120\, Plano\, TX\, 75024\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/02/grennlakehpe_Dallas.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260304T173000 DTEND;TZID=America/New_York:20260304T200000 DTSTAMP:20260416T034623 CREATED:20260127T182147Z LAST-MODIFIED:20260304T191646Z UID:118802-1772645400-1772654400@bdionline.com SUMMARY:Operationalizing AI at Scale: The Enterprise AI Factory Playbook for Financial Institutions DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/030426/ LOCATION:Butter\, 70 W 45th St\, New York\, NY\, 10036\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/01/HPE-NVIDIA-NYC1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260304T080000 DTEND;TZID=America/New_York:20260304T170000 DTSTAMP:20260416T034623 CREATED:20260306T165226Z LAST-MODIFIED:20260311T170209Z UID:120547-1772611200-1772643600@bdionline.com SUMMARY:Event Recap: Operationalizing AI at Scale: The Enterprise AI Factory Playbook for Financial Institutions DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/030426_event_recap/ LOCATION:Butter\, 70 W 45th St\, New York\, NY\, 10036\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/01/HPE-NVIDIA-NYC1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260122T120000 DTEND;TZID=America/New_York:20260122T140000 DTSTAMP:20260416T034623 CREATED:20251125T152256Z LAST-MODIFIED:20260206T154110Z UID:117995-1769083200-1769090400@bdionline.com SUMMARY:The Hidden Cost of Risk: Eliminating Security and Compliance Blind Spots DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/012226/ LOCATION:Del Frisco’s Double Eagle Steakhouse\, 5905 Legacy Dr Suite A120\, Plano\, Texas\, 75024\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/11/envoydallas.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260122T080000 DTEND;TZID=America/New_York:20260122T170000 DTSTAMP:20260416T034623 CREATED:20260126T175225Z LAST-MODIFIED:20260127T195946Z UID:118774-1769068800-1769101200@bdionline.com SUMMARY:Event Recap: The Hidden Cost of Risk: Eliminating Security and Compliance Blind Spots DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/012226_envoy_event_recap/ LOCATION:Del Frisco’s Double Eagle Steak House\, 5905 Legacy Dr Suite A120\, Plano\, TX\, 75024\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/11/envoydallas.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251202T173000 DTEND;TZID=America/New_York:20251202T200000 DTSTAMP:20260416T034623 CREATED:20260113T154709Z LAST-MODIFIED:20260127T195621Z UID:118731-1764696600-1764705600@bdionline.com SUMMARY:Event Recap: AI-Powered Creativity: Elevating Brand Experiences DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/120225_adobe_event_recap/ LOCATION:Toca Vez\, 95 Morristown Rd\, Basking Ridge\, NJ\, 07920\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2025/09/AdobeNJ.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251202T173000 DTEND;TZID=America/New_York:20251202T200000 DTSTAMP:20260416T034623 CREATED:20250926T150429Z LAST-MODIFIED:20260203T172158Z UID:117051-1764696600-1764705600@bdionline.com SUMMARY:AI-Powered Creativity: Elevating Brand Experiences with Adobe DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/120225/ LOCATION:Toca Vez\, 95 Morristown Rd\, Basking Ridge\, NJ\, 07920\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2025/09/AdobeNJ.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251120T173000 DTEND;TZID=America/New_York:20251120T200000 DTSTAMP:20260416T034623 CREATED:20250924T163115Z LAST-MODIFIED:20260203T172200Z UID:116830-1763659800-1763668800@bdionline.com SUMMARY:AI & ERP - From Hype to Impact DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/112025/ LOCATION:Butter\, 70 W 45th St\, New York\, NY\, 10036\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/09/answerthink.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20251119T173000 DTEND;TZID=America/Chicago:20251119T200000 DTSTAMP:20260416T034623 CREATED:20260112T193332Z LAST-MODIFIED:20260203T172203Z UID:118705-1763573400-1763582400@bdionline.com SUMMARY:Event Recap: The Enterprise Compute Advantage: Enabling Agentic AI DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/111925_hpe_event_recap/ LOCATION:Del Frisco’s Double Eagle Steak House\, 5905 Legacy Dr Suite A120\, Plano\, TX\, 75024\, United States CATEGORIES:Event Calendar,Event Recap,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/09/hpenvidiadallas.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251119T173000 DTEND;TZID=America/New_York:20251119T200000 DTSTAMP:20260416T034623 CREATED:20250919T153844Z LAST-MODIFIED:20260203T172205Z UID:116446-1763573400-1763582400@bdionline.com SUMMARY:The Enterprise Compute Advantage: Enabling Agentic AI DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/111925/ LOCATION:Del Frisco’s Double Eagle Steakhouse\, 5905 Legacy Dr Suite A120\, Plano\, Texas\, 75024\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/09/hpenvidiadallas.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20251118T173000 DTEND;TZID=America/Chicago:20251118T200000 DTSTAMP:20260416T034623 CREATED:20260109T191522Z LAST-MODIFIED:20260127T194142Z UID:118572-1763487000-1763496000@bdionline.com SUMMARY:Event Recap: Beyond Silos: Unifying ERP & AI for Smarter Business DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/111825_sap_vistavu_event_recap/ LOCATION:Del Frisco’s Double Eagle Steak House\, 5905 Legacy Dr Suite A120\, Plano\, TX\, 75024\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/09/Vistavu-dallas.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251118T173000 DTEND;TZID=America/New_York:20251118T200000 DTSTAMP:20260416T034623 CREATED:20250923T180404Z LAST-MODIFIED:20260203T172206Z UID:116737-1763487000-1763496000@bdionline.com SUMMARY:Beyond Silos: Unifying ERP & AI for Smarter Business DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/111825/ LOCATION:Del Frisco’s Double Eagle Steak House\, 5905 Legacy Dr Suite A120\, Plano\, TX\, 75024\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/09/Vistavu-dallas.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20251113T173000 DTEND;TZID=America/Chicago:20251113T200000 DTSTAMP:20260416T034623 CREATED:20260109T192948Z LAST-MODIFIED:20260127T200930Z UID:118589-1763055000-1763064000@bdionline.com SUMMARY:Event Recap: From Disruption to Advantage: AI-Powered Resilience in Supply Chains DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/111325_sap_nttdata_event_recap/ LOCATION:Gibsons Rosemont\, 5464 N River Rd\, Rosemont\, IL\, 60018\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/09/ntt-sap-flipped.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251113T173000 DTEND;TZID=America/New_York:20251113T200000 DTSTAMP:20260416T034623 CREATED:20250903T163530Z LAST-MODIFIED:20260203T172208Z UID:115523-1763055000-1763064000@bdionline.com SUMMARY:From Disruption to Advantage: AI-Powered Resilience in Supply Chains DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/111325/ LOCATION:Gibsons Rosemont\, 5464 N River Rd\, Rosemont\, IL\, 60018\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/09/ntt-sap-flipped.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20251112T173000 DTEND;TZID=America/Chicago:20251112T200000 DTSTAMP:20260416T034623 CREATED:20260109T195731Z LAST-MODIFIED:20260127T200412Z UID:118609-1762968600-1762977600@bdionline.com SUMMARY:Event Recap: Reimagining the Digital Core: AI-Powered Transformation for the Enterprise DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/111225_sap_navisite_event_recap/ LOCATION:Ocean Prime\, 87 E Wacker Dr\, Chicago\, IL\, 60601\, United States CATEGORIES:Event Recap,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/09/navisite-flipped.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251112T173000 DTEND;TZID=America/New_York:20251112T200000 DTSTAMP:20260416T034623 CREATED:20250918T190532Z LAST-MODIFIED:20260225T173650Z UID:116347-1762968600-1762977600@bdionline.com SUMMARY:Reimagining the Digital Core: AI-Powered Transformation for the Enterprise DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/111225/ LOCATION:Ocean Prime\, 87 E Wacker Dr\, Chicago\, IL\, 60601\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/09/navisite-flipped.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251112T120000 DTEND;TZID=America/New_York:20251112T140000 DTSTAMP:20260416T034623 CREATED:20250925T132604Z LAST-MODIFIED:20260224T171346Z UID:116935-1762948800-1762956000@bdionline.com SUMMARY:AI-Powered Creativity: Elevating Brand Experiences with Adobe DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/111225adobe/ LOCATION:CRAVE\, 825 Hennepin Ave\, Minneapolis\, 55402\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2025/09/AdobeMinn.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251106T173000 DTEND;TZID=America/New_York:20251106T200000 DTSTAMP:20260416T034623 CREATED:20250912T193704Z LAST-MODIFIED:20260203T172217Z UID:116089-1762450200-1762459200@bdionline.com SUMMARY:Equinix Engage - Reimagining IT: From cost center to growth engine with Distributed AI DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/110625equinix/ LOCATION:Mastros Ocean Club\, 25 Fan Pier Boulevard\, Boston\, MA\, 02210\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2025/09/EquinixbostonFeaturedImage.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251106T173000 DTEND;TZID=America/New_York:20251106T200000 DTSTAMP:20260416T034623 CREATED:20250911T161040Z LAST-MODIFIED:20260203T172218Z UID:115914-1762450200-1762459200@bdionline.com SUMMARY:Agents\, Autonomy\, and the Future of Enterprise AI DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/110625/ LOCATION:Taverna\, 800 Emerson St\, Palo Alto\, CA\, 94301\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2025/09/Andela-Palo-Alto.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251105T173000 DTEND;TZID=America/New_York:20251105T200000 DTSTAMP:20260416T034623 CREATED:20250912T185820Z LAST-MODIFIED:20260203T172223Z UID:116068-1762363800-1762372800@bdionline.com SUMMARY:Equinix Engage: Real-World Insights From Today’s Leaders in AI DESCRIPTION:Operationalizing AI at Scale: The Enterprise AI Factory Playbook\n \n \n \n \n Palo Alto\, CA | The Sea by Alexander’s Steakhouse | April 14th\, 2026    \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Moderator & Panel \n \n \n \n \n \n \n \n \n \n \n \n \n Chad Smykay \n \n \n Hewlett Packard Enterprise \n \n \n \n \n \n \n \n AI CTO & Distinguished Technologist\, Industry Verticals\, North America \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tarik Hammadou \n \n \n NVIDIA \n \n \n \n \n \n \n \n Director Developer Relations\, AI for Retail & CPG \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Mitalee Gujar \n \n \n Amazon \n \n \n \n \n \n \n \n Director of Engineering \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sriram Madhavan \n \n \n Applied Materials \n \n \n \n \n \n \n \n Design Engineering Director \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Patrick McQuillan \n \n \n Visa \n \n \n \n \n \n \n \n Global Head of AI & Data Governance \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Executive Summary \n \n \n \n Enterprises are moving from experimentation with AI to operational deployment\, but many are encountering friction in scaling initiatives effectively. While foundational models and tooling have advanced rapidly\, organizations are struggling with integration into legacy systems\, unclear ownership of use cases\, and inconsistent alignment between AI initiatives and business objectives. The gap between technical capability and operational execution is emerging as the primary constraint to realizing value. A recurring challenge is balancing standardization with flexibility. Over-standardization can limit innovation\, particularly in emerging areas such as generative and agentic AI\, while lack of governance introduces risk\, inefficiency\, and inconsistent outcomes. Leading organizations are adopting a hybrid approach\, applying structured controls for repeatable AI use cases while allowing more flexibility in exploratory and high-innovation environments. This is particularly relevant as enterprises transition from traditional AI/ML to more dynamic\, agent-driven systems. At the same time\, organizations are recognizing that AI success is less about technology selection and more about problem definition\, data readiness\, and cultural adoption. Misaligned incentives\, such as deploying AI for visibility rather than value\, are leading to failed initiatives and low ROI. The enterprises making progress are those that prioritize clear use cases\, align AI with business strategy\, and build feedback loops that enable continuous learning and improvement. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Key Themes \n \n \n \n Key Themes \n \n \n \n \n\n\n \n \n Balancing standardization and innovation.\n Structured frameworks are required for reliability and compliance\, but excessive standardization can limit experimentation and slow progress in emerging AI use cases.\n \n\n \n AI as a force multiplier\, not a standalone solution.\n AI delivers value when applied to clearly defined problems and embedded into workflows\, rather than deployed for its own sake.\n \n\n \n Data readiness and feedback loops are critical.\n Incomplete\, outdated\, or poorly governed data limits model performance\, making continuous data pipelines and feedback mechanisms essential for maintaining relevance.\n \n\n \n Legacy systems and technical debt as barriers.\n Fragmented architectures and siloed data environments continue to slow AI adoption\, requiring modernization alongside deployment.\n \n\n \n Misalignment between AI initiatives and business value.\n Many AI projects are driven by external pressure or internal visibility rather than customer needs\, resulting in low adoption and limited ROI.\n \n \n\n Read more\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Anchor AI initiatives to clear business problems.\n Define specific use cases tied to measurable outcomes before deploying AI solutions.\n \n\n \n Adopt a dual approach to governance.\n Apply strict controls for repeatable\, high-risk use cases while maintaining flexibility for experimentation in emerging areas.\n \n\n \n Invest in data pipelines and feedback loops.\n Continuously update models with new data and validate outputs against real-world outcomes to prevent performance degradation.\n \n\n \n Modernize selectively to enable AI integration.\n Prioritize modernization efforts that unlock data accessibility and interoperability rather than attempting full system overhauls.\n \n\n \n Avoid deploying AI for visibility or trend alignment.\n Evaluate whether initiatives deliver tangible value to customers or operations\, not just internal or market signaling.\n \n\n \n Empower domain teams to experiment responsibly.\n Identify AI champions within business units and provide them with tools and autonomy to test and scale use cases.\n \n\n \n Define accountability for AI outputs.\n Maintain human oversight and clear ownership\, particularly in customer-facing or high-risk applications.\n \n\n \n Start with repeatable\, high-impact workflows.\n Focus initial deployments on processes that are repetitive\, data-driven\, and constrained by human capacity.\n \n\n \n Prepare for iterative failure and learning.\n Treat early initiatives as learning cycles\, using failures to refine models\, processes\, and governance structures.\n \n \n\n Read more\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n EVENT PHOTOS \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your boldest ambitions with Hewlett Packard Enterprise\, your essential partner for the AI era. HPE uses the power of AI\, cloud\, and networking to help you move faster\, work smarter\, and achieve more. With deep expertise and bold ingenuity\, we empower organizations to turn data into foresight\, elevate performance\, and drive real-world impact—at scale. Rooted in decades of innovation\, we focus on helping companies adapt\, grow\, lead\, and challenge the limits of what’s possible. www.hpe.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Our Story NVIDIA is a full‑stack\, accelerated computing company that delivers the AI infrastructure and software powering the world’s most demanding enterprises\, from cloud to data center to factory floor. We combine industry‑leading GPUs\, high‑performance networking\, and optimized software into integrated platforms that enable you to build\, deploy\, and scale generative AI\, digital twins\, and advanced analytics with unmatched performance and efficiency. As the engine behind many of the world’s largest clouds and AI initiatives\, NVIDIA helps organizations transform their data into a competitive advantage\, modernize their core systems\, and accelerate innovation while reducing total cost of ownership and time to value. URL:https://bdionline.com/event/110525equinix/ LOCATION:Shooters Waterfront\, 3033 NE 32nd Ave\, Fort Lauderdale\, 33308\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2025/09/EquinixfloridaFeaturedImage.webp END:VEVENT END:VCALENDAR