Executives are betting on agentic AI to drive growth, yet many enterprises still struggle to move from pilot to production. The hard part has never been the models. It is about building trust in underlying data assets, human-AI workflows, and outcome accountability. Enter Trust Engineering, the emerging discipline you need to consistently scale AI beyond pilots. It brings together the trust infrastructure required to support agentic AI in production, design patterns that improve human-AI decision making in operational workflows, and best practices for creating a culture of accountability around AI-driven outcomes. Through real-world case studies and examples, attendees will receive a practical framework for building and deploying agentic systems that preserves trust and delivers value at scale.

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Learn what key AI transformations mean for data teams, architecture teams, product strategy, governance, and investment planning—and how to prepare today.

Five Transformations Reshaping Enterprise GenAI: What Leaders Should Prepare For

GenAI is evolving faster than most enterprises can adapt. What matters now is not chasing individual tools or models but understanding the structural trends that will define how AI is built, deployed, governed, and valued. This talk outlines the five transformations that will reshape enterprise AI architectures and operating models over the next 3–5 years. Each trend is grounded in real enterprise experience, market analysis, and architectural patterns emerging across the industry.

Get a candid look at lessons every leader needs to understand to make AI a competitive advantage.

Learnings From the Front Lines of AI

This candid and practical session separates the signal from the noise, exploring what’s truly working in AI implementations across industries and where companies are falling short. From the common pitfalls that derail well-intentioned efforts to the design principles behind AI initiatives that create measurable value, Frederick shares the lessons that every leader needs to understand to make AI a competitive advantage rather than a costly experiment.

Get actionable frameworks and questions to ask to accelerate the responsible deployment of GenAI and agentic AI.

From Pilot to Production: The Executive Playbook for Scaling GenAI & Agentic AI

GenAI and agentic AI are accelerating fast, but many enterprises still struggle to navigate to real-world production systems that are reliable, governable, and aligned with business objectives. This discussion is designed for technical and business leaders who want proven patterns for scaling their AI initiatives. Understand what “production-ready” means for AI systems, how to incorporate GenAI and agentic AI into enterprise workflows seamlessly, how to handle shadow AI, and a pragmatic set of KPIs that help leaders scale what works and stop what doesn’t.

It’s not just about technology; Successful AI adoption requires strong, visionary leadership. This panel of thought leaders will share insights and guidance on how to translate strategy into desired business outcomes in a rapidly changing, uncertain landscape.

Recognize 10 critical AI risks, detect emerging issues early, and implement practical measures to mitigate them.

AI Risks & Risk Mitigation Strategies

As organizations embrace AI technologies, they face growing challenges related to fairness, privacy, and unpredictable system behavior. This session uses real-world examples to help attendees recognize 10 critical AI risks, detect emerging issues early, and implement practical measures to mitigate them while maximizing business value. The discussion also emphasizes the role of AI governance as a foundation for responsible and sustainable AI adoption.

Join us during drinks, light bites and networking in the showcase! Featuring leading industry vendors, the Data + AI Solutions Showcase offers a vibrant marketplace to explore cutting-edge solutions in data management, data architecture and engineering, analytics, and AI. Whether you have a specific project that needs help or you just want to keep up with the latest tools, strategies, and trends, the showcase is a can't miss destination.

The modern data stack was built for a world of dashboards and batch pipelines. AI agents are breaking it. The scarcest resource is no longer compute or storage, but the talent to reduce platform complexity. Further, traditional boundaries between operational and analytical databases are dissolving. The separation between roles and use is collapsing into a single operational discipline as AI systems blur the line between applications and data pipelines. This talk examines trends reshaping every layer of the AI-driven data foundation, from storage to governance to operations.

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Learn how to uncover and manage shadow AI, design governance that accelerates adoption, and build programs that balance innovation with accountability.

This session challenges the common “AI-first” mindset and presents a practical, data-centric perspective on why AI success is fundamentally dependent on business alignment, data readiness, and operational governance. Attendees learn why AI is not software-driven but data-driven, how unclear data ownership and quality derail AI investments, and why AI systems require continuous iteration rather than one-time deployment. Drawing from enterprise and public-sector experience, the session introduces a structured approach that connects business intent to data readiness, model development, evaluation, and long-term operationalization.

Learn about real-world case studies demonstrating successful implementation of agentic AI solutions, best practices, and how to measure ROI.

Building With Purpose: Leveraging Agentic AI to Transform Enterprise Data Into Business Outcomes

In today's rapidly evolving technological landscape, organizations are sitting on vast amounts of untapped data potential. This session explores how agentic AI-driven systems are revolutionizing the way enterprises transform raw data into measurable business outcomes. Attendees leave well-informed with a solid understanding of the fundamental principles of agentic AI and its practical applications in enterprise settings and be ready to drive unparalleled business impacts.

This session examines the leadership patterns that shape whether AI strengthens credibility or quietly erodes it as adoption scales.

Leadership Trust in 2026: Executive Judgment When AI Is Everywhere

As AI becomes more embedded in everyday decision making, expectations rise, ambiguity increases, and decisions move faster. Trust becomes the make-or-break factor for adoption and impact, because leadership accountability does not disappear when AI enters the workflow. This session examines the leadership patterns that shape whether AI strengthens credibility or quietly erodes it as adoption scales. It offers leaders a clearer lens for what to watch for, what to question, and how to lead decisively without losing trust.

Get a road map for transforming fragmented data landscapes into dynamic, AI-ready environments that fuel smarter decisions and sustainable growth.

AI-Ready Data: Building the Foundation for Intelligent Insights

AI thrives on high-quality, well-structured data—but many organizations struggle with data fragmentation, limited access to operational data, and inconsistent data accessibility. This session explores how to overcome these challenges and design an AI-ready data architecture that accelerates innovation and drives business value. Using the Data City Plan as a guiding framework, we’ll cover: a blueprint for AI-ready data, data fragmentation, operational data access, Data City Principles in action, and modernization for AI enablement.

Despite unprecedented investment, most enterprise AI initiatives still struggle to deliver sustained business value. This keynote draws on our 2026 market research across 200-plus organizations to explain why—and what successful organizations are doing differently. We analyze how organizations are progressing from experimentation to measurable business impact and where initiatives most commonly stall. The session surfaces the key challenges limiting AI outcomes—including data readiness, platform complexity, organizational alignment, and trust—and examines the critical role of governance in enabling responsible, scalable AI. Drawing on evidence from across industries, we identify the architectural and operating patterns that consistently support AI success while balancing innovation with enterprise risk, compliance, and accountability.