Many enterprises are accelerating AI adoption, yet the real constraint is whether people trust the data enough to use it - especially in industries where sensitive personal information raises the stakes. In long-established organizations shaped by acquisitions, legacy platforms, and fragmented definitions, “technically correct” data work can still fail if it doesn’t earn adoption and shared ownership.

In this opening keynote, Matthew Busbee shares the leadership turning point that changed his approach to enterprise data: shifting from treating data as back-end delivery to productizing data around users, outcomes, and trust. He describes how momentum emerged by building clear decision rights and nurturing advocates across the organization - so better data became a shared priority, not a mandate from the data team. He then connects that journey to the AI era, where opportunity is real but must be paired with responsible guardrails and cross-functional accountability.

This session sets the tone for open, practical dialogue across industries: how leaders turn complex data landscapes into trusted assets - and how that trust becomes the foundation for responsible AI.

AI's effectiveness depends on the quality of data. Many organizations face architectural bottlenecks, inconsistent data quality, and unclear lineage, hindering AI-driven decision-making. This theme discusses how leaders are rebuilding modern, transparent, high-performance data foundations that provide the speed, reliability, and scale AI needs to generate real enterprise value.

Turning insight into action is now the main competitive edge. As organizations embrace streaming data, smart workflows, and automation, the focus shifts from simply generating analytics to ensuring they drive quick, high-quality decisions. This theme explores how teams are rapidly operationalizing insights, managing constant data flow, and developing real-time systems that bridge the gap between “knowing” and “doing”.

As AI becomes integrated into daily operations, the need for transparency, accountability, and ethical safeguards grows quickly. Responsible intelligence is no longer just a compliance requirement but a strategic necessity for sustainable innovation. This theme emphasizes how organizations are incorporating responsible AI principles into workflows, clarifying decision accountability, and developing governance that promotes progress without causing analysis paralysis.

As a continuation of the Catalyst Mission track, these sessions will establish a strategic blueprint to chart the pathway toward identifying a solution.

Track 1: The Modern Data Core
Track 2: From real-time to real results
Track 3: Data as an Ecosystem
Track 4: Responsible Intelligence

Continuing your mission tracks, having built the blueprint we now enter the reckoning — a decisive phase where insights are rigorously challenged and crystallised into solution-led outcomes.

Track 1: The Modern Data Core
Track 2: From real-time to real results
Track 3: Data as an Ecosystem
Track 4: Responsible Intelligence

As AI agents take on more responsibility in your enterprise, many teams confront a hard truth: AI is only as reliable as the data behind it. In large organizations, data is typically distributed, inconsistently governed, and difficult to interpret, making it risky for AI agents to operate with confidence.

This session looks at what it truly means to make your data ready for AI agents, beyond simple access or scale. We’ll explore how context, lineage, and governance help agents discover, understand, and safely use enterprise data. Join our session to discover a practical approach to building data foundations your AI agents can rely on for trustworthy, auditable outcomes.

Classic Management, first identified in the 1940's and codified and corporatized in the 1990's cannot deliver you to AI nirvana. It fails on several levels, with these three the being the most important. It presumes a known destination, when the future of AI is unknown. It's driven by Change Management Experts (or worse PMOs) at the behest of Executives who don't understand AI or how it impacts the work to be done. And it's designed to change actions and 'behavior' when AI changes the way you think - thinking precedes behavior and action. Let's learn from tech transformations that didn't need Change Management and let's look at a new, not yet proven way that at least gets us out of denial.