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/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:20260331T120000 DTEND;TZID=America/New_York:20260331T140000 DTSTAMP:20260416T054657 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:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 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:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20260330T193334Z LAST-MODIFIED:20260330T210118Z UID:121135-1774526400-1774533600@bdionline.com SUMMARY:Event Recap: Operationalizing AI at Scale: The Enterprise AI Factory Playbook DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20260203T182557Z LAST-MODIFIED:20260326T160723Z UID:119335-1774526400-1774533600@bdionline.com SUMMARY:Operationalizing AI at Scale: The Enterprise AI Factory Playbook DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 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:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 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:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 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:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 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:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20251125T152256Z LAST-MODIFIED:20260206T154110Z UID:117995-1769083200-1769090400@bdionline.com SUMMARY:The Hidden Cost of Risk: Eliminating Security and Compliance Blind Spots DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 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:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20260113T154709Z LAST-MODIFIED:20260127T195621Z UID:118731-1764696600-1764705600@bdionline.com SUMMARY:Event Recap: AI-Powered Creativity: Elevating Brand Experiences DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20250926T150429Z LAST-MODIFIED:20260203T172158Z UID:117051-1764696600-1764705600@bdionline.com SUMMARY:AI-Powered Creativity: Elevating Brand Experiences with Adobe DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20250924T163115Z LAST-MODIFIED:20260203T172200Z UID:116830-1763659800-1763668800@bdionline.com SUMMARY:AI & ERP - From Hype to Impact DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20260112T193332Z LAST-MODIFIED:20260203T172203Z UID:118705-1763573400-1763582400@bdionline.com SUMMARY:Event Recap: The Enterprise Compute Advantage: Enabling Agentic AI DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20250919T153844Z LAST-MODIFIED:20260203T172205Z UID:116446-1763573400-1763582400@bdionline.com SUMMARY:The Enterprise Compute Advantage: Enabling Agentic AI DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20260109T191522Z LAST-MODIFIED:20260127T194142Z UID:118572-1763487000-1763496000@bdionline.com SUMMARY:Event Recap: Beyond Silos: Unifying ERP & AI for Smarter Business DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20250923T180404Z LAST-MODIFIED:20260203T172206Z UID:116737-1763487000-1763496000@bdionline.com SUMMARY:Beyond Silos: Unifying ERP & AI for Smarter Business DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 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:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20250903T163530Z LAST-MODIFIED:20260203T172208Z UID:115523-1763055000-1763064000@bdionline.com SUMMARY:From Disruption to Advantage: AI-Powered Resilience in Supply Chains DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 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:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20250918T190532Z LAST-MODIFIED:20260225T173650Z UID:116347-1762968600-1762977600@bdionline.com SUMMARY:Reimagining the Digital Core: AI-Powered Transformation for the Enterprise DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20250925T132604Z LAST-MODIFIED:20260224T171346Z UID:116935-1762948800-1762956000@bdionline.com SUMMARY:AI-Powered Creativity: Elevating Brand Experiences with Adobe DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 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:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20250911T161040Z LAST-MODIFIED:20260203T172218Z UID:115914-1762450200-1762459200@bdionline.com SUMMARY:Agents\, Autonomy\, and the Future of Enterprise AI DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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:20260416T054657 CREATED:20250912T185820Z LAST-MODIFIED:20260203T172223Z UID:116068-1762363800-1762372800@bdionline.com SUMMARY:Equinix Engage: Real-World Insights From Today’s Leaders in AI DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake 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 BEGIN:VEVENT DTSTART;TZID=America/New_York:20251028T173000 DTEND;TZID=America/New_York:20251028T200000 DTSTAMP:20260416T054657 CREATED:20250825T183226Z LAST-MODIFIED:20260203T172227Z UID:115265-1761672600-1761681600@bdionline.com SUMMARY:GenAI Roundtable for Enterprise Innovation - An Executive Dinner & Wine Tasting for Technology Leaders DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/102825/ LOCATION:Butter\, 70 W 45th St\, New York\, NY\, 10036\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2025/08/AMDNYCfeaturedimage.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251022T173000 DTEND;TZID=America/New_York:20251022T200000 DTSTAMP:20260416T054657 CREATED:20250828T160329Z LAST-MODIFIED:20260203T172231Z UID:115403-1761154200-1761163200@bdionline.com SUMMARY:Equinix Engage: Real-World Insights From Today’s Leaders in AI DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/102225/ LOCATION:Fin & Fino\, 135 Levine Avenue of the Arts\, Charlotte\, NC\, 28202\, United States CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2025/08/EquinixCharlotteFeaturedImage.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251021T173000 DTEND;TZID=America/New_York:20251021T200000 DTSTAMP:20260416T054657 CREATED:20260108T200720Z LAST-MODIFIED:20260127T193616Z UID:118393-1761067800-1761076800@bdionline.com SUMMARY:Event Recap : Finance & ERP Transformation in the Age of Gen AI: Driving Innovation\, Governance\, and Change DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/102125_sap_protiviti_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/2025/08/protiviti-flipped.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251021T173000 DTEND;TZID=America/New_York:20251021T200000 DTSTAMP:20260416T054657 CREATED:20250825T182434Z LAST-MODIFIED:20260203T172344Z UID:115243-1761067800-1761076800@bdionline.com SUMMARY:GenAI Roundtable for Enterprise Innovation - An Executive Dinner & Wine Tasting for Technology Leaders DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/102125amd/ LOCATION:Black & Blue\, 130 King St W\, Toronto\, ON M5X 2A2\, Canada\, Toronto\, ON\, Canada CATEGORIES:Event Calendar,No Header ATTACH;FMTTYPE=image/webp:https://bdionline.com/wp-content/uploads/2025/08/AMDTORONTOfeaturedimage2.webp END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20251021T173000 DTEND;TZID=America/New_York:20251021T200000 DTSTAMP:20260416T054657 CREATED:20250818T181707Z LAST-MODIFIED:20260203T172347Z UID:114752-1761067800-1761076800@bdionline.com SUMMARY:Finance & ERP Transformation in the Age of Gen AI: Driving Innovation\, Governance\, and Change DESCRIPTION:Event Recap: Virtualization in the Age of AI:\nBuilding a Flexible Hybrid Cloud Foundation\n \n \n \n \n Dallas\, Tx | Del Frisco’s | March 25\, 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 Paul Squyres \n \n \n HPE \n \n \n \n \n \n \n \n Greenlake Sales 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 Ananth Hegde \n \n \n JPMorgan Chase & Co. \n \n \n \n \n \n \n \n Head of Data 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 Saad Khan \n \n \n \n \n \n \n \n \n \n \n Leader Solution Architect\, Investment Banking\, Senior IEEE Member\, ex VP of JP Morgan \n \n \n \n \n \n LinkedIn \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Hari Kishan \n \n \n Manulife. \n \n \n \n \n \n \n \n Director of Cloud 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 Venu Vidyashankar \n \n \n Heartland Payments Systems \n \n \n \n \n \n \n \n Leader - Enterprise Data Architecture \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 Speaker \n \n \n \n \n \n \n \n \n \n \n \n \n Hunter Nordyke \n \n \n HPE \n \n \n \n \n \n \n \n Hybrid Cloud Enterprise Architect \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 SummaryEnterprise IT leaders are navigating a structural shift in virtualization strategy driven by rising costs\, vendor consolidation\, and the growing demands of AI workloads. The traditional model of a single\, dominant virtualization platform is breaking down\, forcing organizations to reassess long-term dependencies and adopt more flexible\, heterogeneous environments. While virtualization remains foundational\, it is no longer sufficient on its own to support emerging workloads\, particularly those driven by AI\, which introduce new requirements around data locality\, latency\, and infrastructure design. At the same time\, organizations are balancing modernization with operational risk. Large enterprises with legacy systems are prioritizing incremental transformation\, leveraging hybrid architectures that combine on-premise\, cloud\, and edge environments. This approach enables continuity while allowing teams to experiment with new platforms\, AI capabilities\, and cost optimization strategies. However\, complexity is increasing as organizations manage multiple environments\, governance models\, and tooling layers simultaneously. A clear trend is emerging toward platform diversification\, cost awareness\, and workload-specific architecture decisions. Enterprises are moving away from one-size-fits-all infrastructure strategies and instead aligning infrastructure choices to workload requirements\, regulatory constraints\, and financial outcomes. AI is accelerating this shift\, exposing gaps in existing architectures and forcing organizations to rethink how and where workloads are deployed. \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 The virtualization reset and vendor reassessment.\n Rising costs and licensing changes are forcing organizations to reevaluate long-standing dependencies on single virtualization vendors\, accelerating interest in alternative platforms and more flexible hybrid strategies.\n \n\n \n Heterogeneous environments as the new standard.\n Enterprises are operating across legacy virtualization\, containers\, cloud services\, and bare metal simultaneously\, increasing complexity in governance\, visibility\, and day-to-day operations.\n \n\n \n AI workloads redefining infrastructure requirements.\n AI introduces fundamentally different demands\, including high data throughput\, GPU dependency\, and low-latency processing\, requiring architectures that extend beyond traditional virtualization models.\n \n\n \n Hybrid cloud as a practical operating model.\n Organizations are combining public cloud\, private infrastructure\, and edge deployments to balance performance\, cost\, and regulatory requirements\, rather than pursuing full cloud migration.\n \n\n \n Cost and FinOps becoming strategic capabilities.\n As AI and cloud usage expand\, enterprises are formalizing FinOps practices to manage spend\, optimize resource allocation\, and evaluate infrastructure trade-offs with greater precision.\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 Actionable Takeaways for Enterprise Leaders\n \n \n \n \n Actionable Takeaways for Enterprise Leaders\n \n \n \n \n \n \n \n Audit and reassess virtualization dependencies.\n Evaluate licensing exposure\, platform utilization\, and feature adoption to identify opportunities to reduce cost and limit vendor lock-in.\n \n\n \n Design for a multi-platform future.\n Build architectures that support interoperability across virtualization\, containers\, cloud\, and bare metal to avoid rigid infrastructure decisions.\n \n\n \n Align infrastructure decisions to workload requirements.\n Place workloads based on latency\, data sensitivity\, and performance needs rather than defaulting to cloud-first or on-prem-first strategies.\n \n\n \n Introduce centralized governance across environments.\n Implement unified visibility\, access control\, and reporting layers to manage increasingly fragmented infrastructure landscapes.\n \n\n \n Prioritize data locality and security for AI workloads.\n Keep sensitive data close to where it is generated and processed\, minimizing unnecessary movement that increases cost and compliance risk.\n \n\n \n Adopt FinOps early for AI and cloud initiatives.\n Establish cost monitoring\, usage controls\, and accountability frameworks before scaling workloads to prevent uncontrolled spend.\n \n\n \n Start with targeted\, high-impact use cases.\n Focus on AI applications that deliver measurable business value quickly\, then scale based on proven outcomes.\n \n\n \n Plan for latency-sensitive architectures.\n For real-time and customer-facing applications\, invest in edge or on-prem solutions that meet strict performance requirements.\n \n\n \n Leverage proven platforms to accelerate modernization.\n Where internal capabilities are limited\, adopt established tools and infrastructure to reduce time-to-value and execution risk.\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 Sponsors \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Unlock your organization’s next phase of innovation with HPE Greenlake\, the edge-to-cloud platform designed for the AI era. HPE Greenlake brings cloud agility to applications and data wherever they live\, combining scalable infrastructure\, built-in security\, and intelligent operations. With deep expertise across AI\, cloud\, and networking\, HPE helps enterprises turn data into insight\, improve performance\, and operate with greater speed and control. Backed by decades of innovation\, HPE Greenlake enables organizations to modernize\, scale\, and lead with confidence. www.hpe.com/greenlake URL:https://bdionline.com/event/102125/ 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/2025/08/protiviti-flipped.png END:VEVENT END:VCALENDAR