Informal networking and welcome, setting the tone for a strategic day.

Enterprise AI is moving from experimentation to production. Melissa Crichton, VP of AI Specialty Sales at Dell Technologies, explores the forces shaping this shift: inference-led growth, rising enterprise AI spend, tokenomics, and sovereign AI. The takeaway: scale AI with economic discipline and control—on your terms, across a distributed, sovereignty-aware future.

Most enterprise AI initiatives stall. Not because the models are wrong, but because the data underneath them is fragmented, ungoverned, and inconsistent. In this session, we share how global organizations like Pfizer are scaling AI across 27 markets and 15,000 users by investing first in a governed, unified data foundation. You’ll leave with a practical framework for building the AI-ready data infrastructure that turns pilots into enterprise-wide transformation.

Data is emerging as a core driver of enterprise strategy, fueling innovation, operational excellence, and competitive differentiation. Organizations that treat data as a strategic asset are positioned to unlock new value, enable intelligent decision-making, and build resilient, future-ready business models.

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Hosted by Dell Technologies + AMD

Hosted by Boomi

Hosted by CodeRabbit

Hosted by Rocket Software

Hosted by Strategy

Time off to think, network and share insights.

Building trustworthy AI requires embedding ethics, bias mitigation, and transparency into every stage of the AI lifecycle. Organizations that operationalize these principles not only strengthen compliance and stakeholder trust but also position themselves for responsible innovation and long-term success.

As organizations move from experimenting with agentic AI to deploying it at scale, trust becomes the limiting factor. This session explores what it takes to deploy AI agents at scale without sacrificing control: governed data, transparent decision-making, and guardrails built for autonomy. Boomi shares insights on unlocking the highest-value use cases and safely enabling agents to take on more workloads with trusted autonomy.

Earlier in the day, roundtable participants will explore the practical challenges slowing AI adoption, including fragmented data, disconnected knowledge, governance concerns, and trust. In this discussion, IDC’s Marlanna Harrington and Rocket Software’s Ray Sullivan examine what those challenges reveal about the future of enterprise data architecture and the capabilities organizations will need as AI moves closer to decisions and actions.

As AI adoption matures, trust, context, governance, lineage, and accessibility are becoming increasingly important. Join us for a discussion on what leading organizations are doing differently, where enterprise data strategies are evolving, and what leaders should prioritize now to support AI at scale.

AI agents now open pull requests alongside engineers, but review and governance haven’t scaled with them. As more of the codebase is written by machines, who validates it, who’s accountable, and how does trust stay intact at speed? This candid peer discussion covers agentic AI readiness: the controls, guardrails, and team models leaders use so autonomous coding speeds delivery without adding risk, drawing on attendees’ own questions and what’s working (or not) in their organizations.

SAP Business AI Platform brings together applications, data, and AI to help organizations connect business processes, extend and build applications, and embed AI across the enterprise. As the technology foundation for SAP Business Suite, the platform helps unlock the full potential of your applications, data, and AI. Learn how SAP Business AI Platform can accelerate business agility, drive innovation, and help future-proof your business for what’s next.

Lightning Insights: Enterprise networks are constrained by a widening skills gap. Senior administrators hold deep institutional knowledge, while junior staff face complex environments with limited context. As networks grow more dynamic, this imbalance slows response and increases risk. AI assistants change the operating model by translating telemetry, correlating signals, and guiding remediation through natural language. The result is faster resolution, more consistent execution, and better use of scarce expert time.

Enterprise AI adoption is no longer limited by deployment, but by what happens when agents make mistakes: inaccurate planning, failed tool use, knowledge gaps and loss of trust. This Lightning Insight shows recent techniques to turn errors, escalations and expert interventions into governed memory, operational reliability and continuous performance improvement.

The hard part of AI is not building an impressive demo; it is turning that demo into a trusted, scalable business solution. Drawing on real implementation experience, OGI shares what it takes to move from pilot to production: AI-ready data, governance by design, observability, clear ownership, and controlled costs. Learn the critical decisions and practical lessons that help teams reduce risk, accelerate implementation, and deliver business value at scale.

AI is moving beyond assistance. In this five-minute session, Glean explores how enterprises can turn generative and agentic AI into trusted action—connecting people, knowledge, and workflows with the context and permissions required to operate safely at scale. We’ll unpack practical guardrails, risk and cost trade-offs, hybrid model strategies, and the path from experimentation to measurable business value.

Hosted by Glean

Hosted by OGI

Hosted by Syllotips

Hosted by BlueCat

AI and data will fundamentally redefine competitive dynamics, business models, and workforce expectations. Leaders must anticipate disruptive technological shifts, invest in adaptive strategies, and cultivate organizational resilience to harness the full potential of an AI-driven future.

Enjoy more food and drinks with our partners and new contacts