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DTSTART;TZID=America/New_York:20260729T173000
DTEND;TZID=America/New_York:20260729T200000
DTSTAMP:20260721T142932Z
CREATED:20260602T174204Z
LAST-MODIFIED:20260721T142932Z
UID:122311-1785346200-1785355200@bdionline.com
SUMMARY:From AI Pilots to Production: Building a Hybrid Cloud Foundation with HPE GreenLake
DESCRIPTION:Summary\nPanel\nCompanies\nThemes\nTakeaways\nPhotos\n\n\n\n\nPost-Event Recap\nFrom AI Pilots to Production\nBuilding a Hybrid Cloud Foundation with HPE GreenLake. \n\n\nEvent details\nDateTuesday\, July 28\, 2026Time5:30–8:00 PM CTVenueFleming’s SteakhousePlano\, TexasFormatPrivate executive dinner & wine tasting\n\n\n\n\n\n\nExecutive summary What leaders discussed\nEnterprise AI strategies are shifting toward hybrid operating models that balance cloud flexibility with security\, control\, performance\, and predictable economics. \n\n\n\nEnterprise AI strategies are increasingly shifting toward hybrid operating models that balance the flexibility of public cloud with the security\, control\, performance\, and predictable economics of private infrastructure. The discussion emphasized that workload placement should be determined by business requirements\, including data sensitivity\, latency\, regulatory obligations\, capacity needs\, and long-term cost\, rather than by a single cloud-first policy. Moving AI from pilot to production requires more than infrastructure. Successful initiatives begin with a clearly defined business outcome\, are tested with realistic production data\, and scale gradually through controlled user groups. Many pilots stall when idealized inputs meet complex real-world conditions\, when ROI is unclear\, or when governance\, user experience\, and operational readiness are addressed too late. The conversation also reinforced that enterprise AI is a people and governance transformation. Organizations need employees who understand both the technology and the business process\, leaders who can identify meaningful use cases\, and governance frameworks that evolve as models and architectures change. Collaboration across internal teams\, industry peers\, technology partners\, and regulatory bodies can help organizations build capability faster without recreating every solution independently. \n\n\n\n\n\n\nModerator & panel Perspectives from the room\nThe panel examined how enterprises can build the infrastructure\, governance\, data\, and operating practices required to move AI from controlled pilots into production. \n\n\n\n\n\n\nModerator\nPaul Squyres\nHybrid Cloud Sales Director \nHPE\n\n\n\n\n\n\n\n\n\n\n\n\n\nPanelist\nMalcolm Ferguson\nDistinguished Technologist \nHPE\n\n\n\n\n\n\n\n\n\n\n\n\n\nPanelist\nHari Kishan\nDirector of Cloud Engineering \nManulife\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOrganizations represented A cross-industry peer group\nLeaders from 23 organizations joined the conversation\, bringing perspectives from financial services\, telecommunications\, technology\, healthcare\, hospitality\, consulting\, and enterprise services. \n\nAmdocsLiberty Mutual InsuranceAbrigoDigifieddHumanaJohnhancockPepsiCoMsquare SystemsJPMorganChaseNTSquaredAT&TDeloitteGlobal PaymentsAlvarez & MarsalBank of AmericaGoDaddyRobloxVerizon BusinessResonate TechnologiesEricsson IncTexas InstrumentsResolute GridMarriott International\n\n\n\n\n\nKey themes What shapes production-ready AI\nThe conversation consistently returned to five requirements for building enterprise AI that can move beyond experimentation and operate reliably at scale. \n\n01 / HYBRID AIHybrid AI Is Becoming the Enterprise DefaultOrganizations are placing workloads across private cloud\, public cloud\, colocation\, and edge environments based on security\, performance\, economics\, and data requirements. 02 / BUSINESS VALUEBusiness Value Determines Production ReadinessAI initiatives scale when they solve an achievable business problem and demonstrate measurable value\, not simply because the technology is available. 03 / GOVERNANCEGovernance Must Evolve with the TechnologyStatic policies are insufficient when models\, data pipelines\, and AI architectures change rapidly. Responsible AI requires continuous review and updated controls. 04 / DATAData Control and Connectivity Are CriticalEnterprises need secure access to proprietary data while retaining the ability to connect with external models\, platforms\, and public data sources. 05 / ADOPTIONAdoption Depends on Talent\, Education\, and ExperienceTechnical expertise alone is not enough. Leaders\, frontline teams\, and users must understand the capabilities\, limitations\, and practical applications of AI. \n\n\n\n\n\nActionable takeaways What enterprise leaders can do next\nThese actions translate the discussion into practical decisions for technology\, data\, security\, risk\, operations\, and business leaders. \n\nClassify workloads before choosing infrastructureEvaluate each AI workload based on data sensitivity\, latency\, compliance\, performance\, scale\, and cost before deciding where it should run. Use public cloud selectivelyTake advantage of cloud flexibility for experimentation or temporary capacity while maintaining tighter control over sensitive production data and persistent workloads. Start with small\, controlled user groupsTest AI solutions with representative users\, gather feedback\, improve the experience\, and expand only after value and reliability are demonstrated. Define ROI before scalingEstablish the expected financial\, operational\, or customer outcome and determine how success will be measured before making significant infrastructure investments. Test with real-world data and conditionsAvoid relying only on clean or idealized pilot inputs. Production testing should account for incomplete data\, unexpected scenarios\, varied users\, and operational complexity. Build governance into the architectureEstablish access controls\, data policies\, ethical standards\, compliance reviews\, monitoring\, and decision rights at the beginning of the initiative. Review governance when the technology changesReassess controls whenever an organization changes models\, data flows\, interfaces\, or AI architectures rather than assuming previous approvals still apply. Create a secure enterprise AI environmentGive employees access to approved AI capabilities connected to internal data without requiring them to place sensitive information into unmanaged public tools. Prioritize user experienceEnsure AI solutions are intuitive\, natural\, and embedded into existing workflows. Technical capability does not create value if employees or customers cannot use it effectively. Educate business leaders before requesting use casesProvide practical workshops that help leaders understand what AI can do\, where it creates value\, and what limitations must be considered. Develop cross-functional AI teamsCombine data scientists\, engineers\, domain experts\, security\, compliance\, operations\, and user experience professionals around shared business outcomes. Use external ecosystems strategicallyCollaborate with technology partners\, peer organizations\, industry consortia\, and regulatory groups to access expertise and proven approaches. Scale incrementally as value becomes clearStart with focused models and manageable infrastructure\, then expand capacity\, sophistication\, and autonomy as adoption and business value increase. \n\n\n\n\n\nEvent moments Inside the conversation\nThe evening combined a moderated panel with candid peer discussion\, giving attendees space to compare strategies for hybrid cloud\, data\, governance\, and production AI. \n\nTechnology leaders connected over dinner and wine tasting before the panel discussion.The evening brought together technology leaders for a candid exchange on hybrid cloud and enterprise AI.The panel explored how enterprises can move AI from experimentation into dependable production environments.Peer discussion continued around the dinner tables as attendees compared strategies and operating realities.Leaders from across industries shared practical perspectives on data\, governance\, infrastructure\, and adoption.HPE GreenLake welcomed guests to Fleming’s Steakhouse in Plano.\n\nView the full photo gallery ↗\n\n\n\n\n\n\n\n\n\n\nEvent sponsor \nBuild for the AI era with HPE GreenLake\nUnlock 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. \nLearn more about HPE GreenLake ↗
URL:https://bdionline.com/event/072926/
LOCATION:Harry Caray’s\, 33 W. KINZIE STREET\, CHICAGO\, IL\, 60654\, United States
CATEGORIES:Event Calendar,No Header
ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/06/greenlake-chicago.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260729T173000
DTEND;TZID=America/New_York:20260729T200000
DTSTAMP:20260721T143707Z
CREATED:20260602T175513Z
LAST-MODIFIED:20260721T143707Z
UID:122334-1785346200-1785355200@bdionline.com
SUMMARY:From AI Pilots to Production: Building a Hybrid Cloud Foundation with HPE GreenLake
DESCRIPTION:Summary\nPanel\nCompanies\nThemes\nTakeaways\nPhotos\n\n\n\n\nPost-Event Recap\nFrom AI Pilots to Production\nBuilding a Hybrid Cloud Foundation with HPE GreenLake. \n\n\nEvent details\nDateTuesday\, July 28\, 2026Time5:30–8:00 PM CTVenueFleming’s SteakhousePlano\, TexasFormatPrivate executive dinner & wine tasting\n\n\n\n\n\n\nExecutive summary What leaders discussed\nEnterprise AI strategies are shifting toward hybrid operating models that balance cloud flexibility with security\, control\, performance\, and predictable economics. \n\n\n\nEnterprise AI strategies are increasingly shifting toward hybrid operating models that balance the flexibility of public cloud with the security\, control\, performance\, and predictable economics of private infrastructure. The discussion emphasized that workload placement should be determined by business requirements\, including data sensitivity\, latency\, regulatory obligations\, capacity needs\, and long-term cost\, rather than by a single cloud-first policy. Moving AI from pilot to production requires more than infrastructure. Successful initiatives begin with a clearly defined business outcome\, are tested with realistic production data\, and scale gradually through controlled user groups. Many pilots stall when idealized inputs meet complex real-world conditions\, when ROI is unclear\, or when governance\, user experience\, and operational readiness are addressed too late. The conversation also reinforced that enterprise AI is a people and governance transformation. Organizations need employees who understand both the technology and the business process\, leaders who can identify meaningful use cases\, and governance frameworks that evolve as models and architectures change. Collaboration across internal teams\, industry peers\, technology partners\, and regulatory bodies can help organizations build capability faster without recreating every solution independently. \n\n\n\n\n\n\nModerator & panel Perspectives from the room\nThe panel examined how enterprises can build the infrastructure\, governance\, data\, and operating practices required to move AI from controlled pilots into production. \n\n\n\n\n\n\nModerator\nPaul Squyres\nHybrid Cloud Sales Director \nHPE\n\n\n\n\n\n\n\n\n\n\n\n\n\nPanelist\nMalcolm Ferguson\nDistinguished Technologist \nHPE\n\n\n\n\n\n\n\n\n\n\n\n\n\nPanelist\nHari Kishan\nDirector of Cloud Engineering \nManulife\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOrganizations represented A cross-industry peer group\nLeaders from 23 organizations joined the conversation\, bringing perspectives from financial services\, telecommunications\, technology\, healthcare\, hospitality\, consulting\, and enterprise services. \n\nAmdocsLiberty Mutual InsuranceAbrigoDigifieddHumanaJohnhancockPepsiCoMsquare SystemsJPMorganChaseNTSquaredAT&TDeloitteGlobal PaymentsAlvarez & MarsalBank of AmericaGoDaddyRobloxVerizon BusinessResonate TechnologiesEricsson IncTexas InstrumentsResolute GridMarriott International\n\n\n\n\n\nKey themes What shapes production-ready AI\nThe conversation consistently returned to five requirements for building enterprise AI that can move beyond experimentation and operate reliably at scale. \n\n01 / HYBRID AIHybrid AI Is Becoming the Enterprise DefaultOrganizations are placing workloads across private cloud\, public cloud\, colocation\, and edge environments based on security\, performance\, economics\, and data requirements. 02 / BUSINESS VALUEBusiness Value Determines Production ReadinessAI initiatives scale when they solve an achievable business problem and demonstrate measurable value\, not simply because the technology is available. 03 / GOVERNANCEGovernance Must Evolve with the TechnologyStatic policies are insufficient when models\, data pipelines\, and AI architectures change rapidly. Responsible AI requires continuous review and updated controls. 04 / DATAData Control and Connectivity Are CriticalEnterprises need secure access to proprietary data while retaining the ability to connect with external models\, platforms\, and public data sources. 05 / ADOPTIONAdoption Depends on Talent\, Education\, and ExperienceTechnical expertise alone is not enough. Leaders\, frontline teams\, and users must understand the capabilities\, limitations\, and practical applications of AI. \n\n\n\n\n\nActionable takeaways What enterprise leaders can do next\nThese actions translate the discussion into practical decisions for technology\, data\, security\, risk\, operations\, and business leaders. \n\nClassify workloads before choosing infrastructureEvaluate each AI workload based on data sensitivity\, latency\, compliance\, performance\, scale\, and cost before deciding where it should run. Use public cloud selectivelyTake advantage of cloud flexibility for experimentation or temporary capacity while maintaining tighter control over sensitive production data and persistent workloads. Start with small\, controlled user groupsTest AI solutions with representative users\, gather feedback\, improve the experience\, and expand only after value and reliability are demonstrated. Define ROI before scalingEstablish the expected financial\, operational\, or customer outcome and determine how success will be measured before making significant infrastructure investments. Test with real-world data and conditionsAvoid relying only on clean or idealized pilot inputs. Production testing should account for incomplete data\, unexpected scenarios\, varied users\, and operational complexity. Build governance into the architectureEstablish access controls\, data policies\, ethical standards\, compliance reviews\, monitoring\, and decision rights at the beginning of the initiative. Review governance when the technology changesReassess controls whenever an organization changes models\, data flows\, interfaces\, or AI architectures rather than assuming previous approvals still apply. Create a secure enterprise AI environmentGive employees access to approved AI capabilities connected to internal data without requiring them to place sensitive information into unmanaged public tools. Prioritize user experienceEnsure AI solutions are intuitive\, natural\, and embedded into existing workflows. Technical capability does not create value if employees or customers cannot use it effectively. Educate business leaders before requesting use casesProvide practical workshops that help leaders understand what AI can do\, where it creates value\, and what limitations must be considered. Develop cross-functional AI teamsCombine data scientists\, engineers\, domain experts\, security\, compliance\, operations\, and user experience professionals around shared business outcomes. Use external ecosystems strategicallyCollaborate with technology partners\, peer organizations\, industry consortia\, and regulatory groups to access expertise and proven approaches. Scale incrementally as value becomes clearStart with focused models and manageable infrastructure\, then expand capacity\, sophistication\, and autonomy as adoption and business value increase. \n\n\n\n\n\nEvent moments Inside the conversation\nThe evening combined a moderated panel with candid peer discussion\, giving attendees space to compare strategies for hybrid cloud\, data\, governance\, and production AI. \n\nTechnology leaders connected over dinner and wine tasting before the panel discussion.The evening brought together technology leaders for a candid exchange on hybrid cloud and enterprise AI.The panel explored how enterprises can move AI from experimentation into dependable production environments.Peer discussion continued around the dinner tables as attendees compared strategies and operating realities.Leaders from across industries shared practical perspectives on data\, governance\, infrastructure\, and adoption.HPE GreenLake welcomed guests to Fleming’s Steakhouse in Plano.\n\nView the full photo gallery ↗\n\n\n\n\n\n\n\n\n\n\nEvent sponsor \nBuild for the AI era with HPE GreenLake\nUnlock 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. \nLearn more about HPE GreenLake ↗
URL:https://bdionline.com/event/072926tysons/
LOCATION:2941\, 2941 Fairview Park Dr.\, Falls Church\, VA\, 22042\, United States
CATEGORIES:Event Calendar,No Header
ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/06/tysons-greenlake.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260915T173000
DTEND;TZID=America/Chicago:20260915T203000
DTSTAMP:20260729T174017Z
CREATED:20260728T174737Z
LAST-MODIFIED:20260729T174017Z
UID:123876-1789493400-1789504200@bdionline.com
SUMMARY:Beyond the Pilot: Operationalizing Agentic AI
DESCRIPTION:Summary\nPanel\nCompanies\nThemes\nTakeaways\nPhotos\n\n\n\n\nPost-Event Recap\nFrom AI Pilots to Production\nBuilding a Hybrid Cloud Foundation with HPE GreenLake. \n\n\nEvent details\nDateTuesday\, July 28\, 2026Time5:30–8:00 PM CTVenueFleming’s SteakhousePlano\, TexasFormatPrivate executive dinner & wine tasting\n\n\n\n\n\n\nExecutive summary What leaders discussed\nEnterprise AI strategies are shifting toward hybrid operating models that balance cloud flexibility with security\, control\, performance\, and predictable economics. \n\n\n\nEnterprise AI strategies are increasingly shifting toward hybrid operating models that balance the flexibility of public cloud with the security\, control\, performance\, and predictable economics of private infrastructure. The discussion emphasized that workload placement should be determined by business requirements\, including data sensitivity\, latency\, regulatory obligations\, capacity needs\, and long-term cost\, rather than by a single cloud-first policy. Moving AI from pilot to production requires more than infrastructure. Successful initiatives begin with a clearly defined business outcome\, are tested with realistic production data\, and scale gradually through controlled user groups. Many pilots stall when idealized inputs meet complex real-world conditions\, when ROI is unclear\, or when governance\, user experience\, and operational readiness are addressed too late. The conversation also reinforced that enterprise AI is a people and governance transformation. Organizations need employees who understand both the technology and the business process\, leaders who can identify meaningful use cases\, and governance frameworks that evolve as models and architectures change. Collaboration across internal teams\, industry peers\, technology partners\, and regulatory bodies can help organizations build capability faster without recreating every solution independently. \n\n\n\n\n\n\nModerator & panel Perspectives from the room\nThe panel examined how enterprises can build the infrastructure\, governance\, data\, and operating practices required to move AI from controlled pilots into production. \n\n\n\n\n\n\nModerator\nPaul Squyres\nHybrid Cloud Sales Director \nHPE\n\n\n\n\n\n\n\n\n\n\n\n\n\nPanelist\nMalcolm Ferguson\nDistinguished Technologist \nHPE\n\n\n\n\n\n\n\n\n\n\n\n\n\nPanelist\nHari Kishan\nDirector of Cloud Engineering \nManulife\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOrganizations represented A cross-industry peer group\nLeaders from 23 organizations joined the conversation\, bringing perspectives from financial services\, telecommunications\, technology\, healthcare\, hospitality\, consulting\, and enterprise services. \n\nAmdocsLiberty Mutual InsuranceAbrigoDigifieddHumanaJohnhancockPepsiCoMsquare SystemsJPMorganChaseNTSquaredAT&TDeloitteGlobal PaymentsAlvarez & MarsalBank of AmericaGoDaddyRobloxVerizon BusinessResonate TechnologiesEricsson IncTexas InstrumentsResolute GridMarriott International\n\n\n\n\n\nKey themes What shapes production-ready AI\nThe conversation consistently returned to five requirements for building enterprise AI that can move beyond experimentation and operate reliably at scale. \n\n01 / HYBRID AIHybrid AI Is Becoming the Enterprise DefaultOrganizations are placing workloads across private cloud\, public cloud\, colocation\, and edge environments based on security\, performance\, economics\, and data requirements. 02 / BUSINESS VALUEBusiness Value Determines Production ReadinessAI initiatives scale when they solve an achievable business problem and demonstrate measurable value\, not simply because the technology is available. 03 / GOVERNANCEGovernance Must Evolve with the TechnologyStatic policies are insufficient when models\, data pipelines\, and AI architectures change rapidly. Responsible AI requires continuous review and updated controls. 04 / DATAData Control and Connectivity Are CriticalEnterprises need secure access to proprietary data while retaining the ability to connect with external models\, platforms\, and public data sources. 05 / ADOPTIONAdoption Depends on Talent\, Education\, and ExperienceTechnical expertise alone is not enough. Leaders\, frontline teams\, and users must understand the capabilities\, limitations\, and practical applications of AI. \n\n\n\n\n\nActionable takeaways What enterprise leaders can do next\nThese actions translate the discussion into practical decisions for technology\, data\, security\, risk\, operations\, and business leaders. \n\nClassify workloads before choosing infrastructureEvaluate each AI workload based on data sensitivity\, latency\, compliance\, performance\, scale\, and cost before deciding where it should run. Use public cloud selectivelyTake advantage of cloud flexibility for experimentation or temporary capacity while maintaining tighter control over sensitive production data and persistent workloads. Start with small\, controlled user groupsTest AI solutions with representative users\, gather feedback\, improve the experience\, and expand only after value and reliability are demonstrated. Define ROI before scalingEstablish the expected financial\, operational\, or customer outcome and determine how success will be measured before making significant infrastructure investments. Test with real-world data and conditionsAvoid relying only on clean or idealized pilot inputs. Production testing should account for incomplete data\, unexpected scenarios\, varied users\, and operational complexity. Build governance into the architectureEstablish access controls\, data policies\, ethical standards\, compliance reviews\, monitoring\, and decision rights at the beginning of the initiative. Review governance when the technology changesReassess controls whenever an organization changes models\, data flows\, interfaces\, or AI architectures rather than assuming previous approvals still apply. Create a secure enterprise AI environmentGive employees access to approved AI capabilities connected to internal data without requiring them to place sensitive information into unmanaged public tools. Prioritize user experienceEnsure AI solutions are intuitive\, natural\, and embedded into existing workflows. Technical capability does not create value if employees or customers cannot use it effectively. Educate business leaders before requesting use casesProvide practical workshops that help leaders understand what AI can do\, where it creates value\, and what limitations must be considered. Develop cross-functional AI teamsCombine data scientists\, engineers\, domain experts\, security\, compliance\, operations\, and user experience professionals around shared business outcomes. Use external ecosystems strategicallyCollaborate with technology partners\, peer organizations\, industry consortia\, and regulatory groups to access expertise and proven approaches. Scale incrementally as value becomes clearStart with focused models and manageable infrastructure\, then expand capacity\, sophistication\, and autonomy as adoption and business value increase. \n\n\n\n\n\nEvent moments Inside the conversation\nThe evening combined a moderated panel with candid peer discussion\, giving attendees space to compare strategies for hybrid cloud\, data\, governance\, and production AI. \n\nTechnology leaders connected over dinner and wine tasting before the panel discussion.The evening brought together technology leaders for a candid exchange on hybrid cloud and enterprise AI.The panel explored how enterprises can move AI from experimentation into dependable production environments.Peer discussion continued around the dinner tables as attendees compared strategies and operating realities.Leaders from across industries shared practical perspectives on data\, governance\, infrastructure\, and adoption.HPE GreenLake welcomed guests to Fleming’s Steakhouse in Plano.\n\nView the full photo gallery ↗\n\n\n\n\n\n\n\n\n\n\nEvent sponsor \nBuild for the AI era with HPE GreenLake\nUnlock 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. \nLearn more about HPE GreenLake ↗
URL:https://bdionline.com/event/091526/
LOCATION:Fleming’s Prime Steakhouse – Plano\, 7250 Dallas Pkwy Suite 110\, Plano\, TX 75024\, Plano\, TX\, 75024\, United States
CATEGORIES:Event Calendar,No Header
ATTACH;FMTTYPE=image/png:https://bdionline.com/wp-content/uploads/2026/07/HPENVIDIA-Featured-Image.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260917T173000
DTEND;TZID=America/Los_Angeles:20260917T203000
DTSTAMP:20260729T203257Z
CREATED:20260728T175905Z
LAST-MODIFIED:20260729T203257Z
UID:123916-1789666200-1789677000@bdionline.com
SUMMARY:Beyond the Pilot: Operationalizing Agentic AI
DESCRIPTION:Summary\nPanel\nCompanies\nThemes\nTakeaways\nPhotos\n\n\n\n\nPost-Event Recap\nFrom AI Pilots to Production\nBuilding a Hybrid Cloud Foundation with HPE GreenLake. \n\n\nEvent details\nDateTuesday\, July 28\, 2026Time5:30–8:00 PM CTVenueFleming’s SteakhousePlano\, TexasFormatPrivate executive dinner & wine tasting\n\n\n\n\n\n\nExecutive summary What leaders discussed\nEnterprise AI strategies are shifting toward hybrid operating models that balance cloud flexibility with security\, control\, performance\, and predictable economics. \n\n\n\nEnterprise AI strategies are increasingly shifting toward hybrid operating models that balance the flexibility of public cloud with the security\, control\, performance\, and predictable economics of private infrastructure. The discussion emphasized that workload placement should be determined by business requirements\, including data sensitivity\, latency\, regulatory obligations\, capacity needs\, and long-term cost\, rather than by a single cloud-first policy. Moving AI from pilot to production requires more than infrastructure. Successful initiatives begin with a clearly defined business outcome\, are tested with realistic production data\, and scale gradually through controlled user groups. Many pilots stall when idealized inputs meet complex real-world conditions\, when ROI is unclear\, or when governance\, user experience\, and operational readiness are addressed too late. The conversation also reinforced that enterprise AI is a people and governance transformation. Organizations need employees who understand both the technology and the business process\, leaders who can identify meaningful use cases\, and governance frameworks that evolve as models and architectures change. Collaboration across internal teams\, industry peers\, technology partners\, and regulatory bodies can help organizations build capability faster without recreating every solution independently. \n\n\n\n\n\n\nModerator & panel Perspectives from the room\nThe panel examined how enterprises can build the infrastructure\, governance\, data\, and operating practices required to move AI from controlled pilots into production. \n\n\n\n\n\n\nModerator\nPaul Squyres\nHybrid Cloud Sales Director \nHPE\n\n\n\n\n\n\n\n\n\n\n\n\n\nPanelist\nMalcolm Ferguson\nDistinguished Technologist \nHPE\n\n\n\n\n\n\n\n\n\n\n\n\n\nPanelist\nHari Kishan\nDirector of Cloud Engineering \nManulife\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOrganizations represented A cross-industry peer group\nLeaders from 23 organizations joined the conversation\, bringing perspectives from financial services\, telecommunications\, technology\, healthcare\, hospitality\, consulting\, and enterprise services. \n\nAmdocsLiberty Mutual InsuranceAbrigoDigifieddHumanaJohnhancockPepsiCoMsquare SystemsJPMorganChaseNTSquaredAT&TDeloitteGlobal PaymentsAlvarez & MarsalBank of AmericaGoDaddyRobloxVerizon BusinessResonate TechnologiesEricsson IncTexas InstrumentsResolute GridMarriott International\n\n\n\n\n\nKey themes What shapes production-ready AI\nThe conversation consistently returned to five requirements for building enterprise AI that can move beyond experimentation and operate reliably at scale. \n\n01 / HYBRID AIHybrid AI Is Becoming the Enterprise DefaultOrganizations are placing workloads across private cloud\, public cloud\, colocation\, and edge environments based on security\, performance\, economics\, and data requirements. 02 / BUSINESS VALUEBusiness Value Determines Production ReadinessAI initiatives scale when they solve an achievable business problem and demonstrate measurable value\, not simply because the technology is available. 03 / GOVERNANCEGovernance Must Evolve with the TechnologyStatic policies are insufficient when models\, data pipelines\, and AI architectures change rapidly. Responsible AI requires continuous review and updated controls. 04 / DATAData Control and Connectivity Are CriticalEnterprises need secure access to proprietary data while retaining the ability to connect with external models\, platforms\, and public data sources. 05 / ADOPTIONAdoption Depends on Talent\, Education\, and ExperienceTechnical expertise alone is not enough. Leaders\, frontline teams\, and users must understand the capabilities\, limitations\, and practical applications of AI. \n\n\n\n\n\nActionable takeaways What enterprise leaders can do next\nThese actions translate the discussion into practical decisions for technology\, data\, security\, risk\, operations\, and business leaders. \n\nClassify workloads before choosing infrastructureEvaluate each AI workload based on data sensitivity\, latency\, compliance\, performance\, scale\, and cost before deciding where it should run. Use public cloud selectivelyTake advantage of cloud flexibility for experimentation or temporary capacity while maintaining tighter control over sensitive production data and persistent workloads. Start with small\, controlled user groupsTest AI solutions with representative users\, gather feedback\, improve the experience\, and expand only after value and reliability are demonstrated. Define ROI before scalingEstablish the expected financial\, operational\, or customer outcome and determine how success will be measured before making significant infrastructure investments. Test with real-world data and conditionsAvoid relying only on clean or idealized pilot inputs. Production testing should account for incomplete data\, unexpected scenarios\, varied users\, and operational complexity. Build governance into the architectureEstablish access controls\, data policies\, ethical standards\, compliance reviews\, monitoring\, and decision rights at the beginning of the initiative. Review governance when the technology changesReassess controls whenever an organization changes models\, data flows\, interfaces\, or AI architectures rather than assuming previous approvals still apply. Create a secure enterprise AI environmentGive employees access to approved AI capabilities connected to internal data without requiring them to place sensitive information into unmanaged public tools. Prioritize user experienceEnsure AI solutions are intuitive\, natural\, and embedded into existing workflows. Technical capability does not create value if employees or customers cannot use it effectively. Educate business leaders before requesting use casesProvide practical workshops that help leaders understand what AI can do\, where it creates value\, and what limitations must be considered. Develop cross-functional AI teamsCombine data scientists\, engineers\, domain experts\, security\, compliance\, operations\, and user experience professionals around shared business outcomes. Use external ecosystems strategicallyCollaborate with technology partners\, peer organizations\, industry consortia\, and regulatory groups to access expertise and proven approaches. Scale incrementally as value becomes clearStart with focused models and manageable infrastructure\, then expand capacity\, sophistication\, and autonomy as adoption and business value increase. \n\n\n\n\n\nEvent moments Inside the conversation\nThe evening combined a moderated panel with candid peer discussion\, giving attendees space to compare strategies for hybrid cloud\, data\, governance\, and production AI. \n\nTechnology leaders connected over dinner and wine tasting before the panel discussion.The evening brought together technology leaders for a candid exchange on hybrid cloud and enterprise AI.The panel explored how enterprises can move AI from experimentation into dependable production environments.Peer discussion continued around the dinner tables as attendees compared strategies and operating realities.Leaders from across industries shared practical perspectives on data\, governance\, infrastructure\, and adoption.HPE GreenLake welcomed guests to Fleming’s Steakhouse in Plano.\n\nView the full photo gallery ↗\n\n\n\n\n\n\n\n\n\n\nEvent sponsor \nBuild for the AI era with HPE GreenLake\nUnlock 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. \nLearn more about HPE GreenLake ↗
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