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: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/Denver BEGIN:DAYLIGHT TZOFFSETFROM:-0700 TZOFFSETTO:-0600 TZNAME:MDT DTSTART:20250309T090000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0600 TZOFFSETTO:-0700 TZNAME:MST DTSTART:20251102T080000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0700 TZOFFSETTO:-0600 TZNAME:MDT DTSTART:20260308T090000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0600 TZOFFSETTO:-0700 TZNAME:MST DTSTART:20261101T080000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0700 TZOFFSETTO:-0600 TZNAME:MDT DTSTART:20270314T090000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0600 TZOFFSETTO:-0700 TZNAME:MST DTSTART:20271107T080000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:America/Chicago 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:20260416T173000 DTEND;TZID=America/New_York:20260416T200000 DTSTAMP:20260416T054906 CREATED:20260210T193653Z LAST-MODIFIED:20260401T162859Z UID:120028-1776360600-1776369600@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/041626/ LOCATION:Morton’s The Steakhouse Rosemont\, 9525 W Bryn Mawr Ave\, Rosemont\, 60018\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/02/HPE-NVIDIA-CHICAGO-26.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Denver:20260423T120000 DTEND;TZID=America/Denver:20260423T140000 DTSTAMP:20260416T054906 CREATED:20260209T175944Z LAST-MODIFIED:20260320T154507Z UID:119934-1776945600-1776952800@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/042326_greenlake/ LOCATION:Guard And Grace\, 1801 California Street\, Denver\, CO\, 80202\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2026/02/hpe_greenlake-template1.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260430T173000 DTEND;TZID=America/New_York:20260430T200000 DTSTAMP:20260416T054906 CREATED:20260310T181614Z LAST-MODIFIED:20260323T171937Z UID:120631-1777570200-1777579200@bdionline.com SUMMARY:AI Agent Identity Security: Governing Autonomous Access Across 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/043026/ 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/2026/03/nyc-aws-akeyless1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260505T173000 DTEND;TZID=America/New_York:20260505T200000 DTSTAMP:20260416T054906 CREATED:20260317T204319Z LAST-MODIFIED:20260410T171055Z UID:120903-1778002200-1778011200@bdionline.com SUMMARY:Operationalizing AI at Scale: Moving Enterprise AI from Pilot to Production 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/050526/ LOCATION:Davio’s Boston Seaport\, 26 Fan Pier Boulevard\, Boston\, MA\, 02210\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/03/BOSTON-.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260514T173000 DTEND;TZID=America/New_York:20260514T200000 DTSTAMP:20260416T054906 CREATED:20260318T192503Z LAST-MODIFIED:20260413T185713Z UID:120957-1778779800-1778788800@bdionline.com SUMMARY:Operationalizing AI at Scale: Moving Enterprise AI from Pilot to Production 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/051426/ LOCATION:Aqimero\, 10 Ave Of The Arts\, Philadelphia\, PA\, 19102\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/03/hpe-nvidia-philly.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260519T120000 DTEND;TZID=America/New_York:20260519T140000 DTSTAMP:20260416T054906 CREATED:20260330T195659Z LAST-MODIFIED:20260415T160905Z UID:121176-1779192000-1779199200@bdionline.com SUMMARY:Managing Risk Beyond the Traditional Perimeter: Building Consistent Controls Across Physical and Digital Environments 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/051926/ LOCATION:Noa\, 77 S 7th St\, Minneapolis\, MN\, 55402\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/03/Envoy-Minnepolis.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260520T080000 DTEND;TZID=America/New_York:20260520T170000 DTSTAMP:20260416T054906 CREATED:20260319T172049Z LAST-MODIFIED:20260413T224622Z UID:120978-1779264000-1779296400@bdionline.com SUMMARY:Operationalizing AI at Scale: Moving Enterprise AI from Pilot to Production 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/052026/ LOCATION:Harry Caray’s\, 33 W. KINZIE STREET\, CHICAGO\, IL\, 60654\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/03/hpe-nvidia-chicago2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Chicago:20260602T120000 DTEND;TZID=America/Chicago:20260602T140000 DTSTAMP:20260416T054906 CREATED:20260320T182535Z LAST-MODIFIED:20260415T183053Z UID:121008-1780401600-1780408800@bdionline.com SUMMARY:Ignoring Culture in the Age of AI: The Hidden Cost During Times of Uncertainty 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/060226/ LOCATION:Remington’s\, 20 N Michigan Ave\,\, Chicago\, IL\, 60602\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2026/03/Untitled-1-20-03-2026-20-31-43.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260603T173000 DTEND;TZID=America/New_York:20260603T200000 DTSTAMP:20260416T054906 CREATED:20260325T193949Z LAST-MODIFIED:20260414T165746Z UID:121062-1780507800-1780516800@bdionline.com SUMMARY:Managing Risk Beyond the Traditional Perimeter: Building Consistent Controls Across Physical and Digital Environments 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/060326/ LOCATION:Davios Back Bay\, 75 Arlington Street\, Boston\, MA\, 02116\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/03/envoy-boston.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260604T173000 DTEND;TZID=America/New_York:20260604T200000 DTSTAMP:20260416T054906 CREATED:20260414T184927Z LAST-MODIFIED:20260415T162233Z UID:121390-1780594200-1780603200@bdionline.com SUMMARY:The Rise of Agentic Financial Operations 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/060426/ LOCATION:Capital Grille (New York City)\, 120 W 51st St\, New York\, NY\, 10020\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/04/Blackline-nyc.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20260611T173000 DTEND;TZID=America/New_York:20260611T200000 DTSTAMP:20260416T054906 CREATED:20260413T194503Z LAST-MODIFIED:20260413T201847Z UID:121347-1781199000-1781208000@bdionline.com SUMMARY:Building the Enterprise AI Factory: From Experimentation to Execution 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/061126/ 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/2026/04/Andela-Palo-Alto.png END:VEVENT END:VCALENDAR