AI, Data & Analytics Network Brings Enterprise AI Decision-Makers Into Focus at Chief AI Officer Exchange Chicago

Enterprise AI is entering a phase where expectations no longer tolerate drift. For three years, intelligence was treated like an upgrade path, add it, demo it, figure out the value later. That window is closing fast. Boards are staring at outcomes, not experiments, and 5% success rates are no longer dismissed as early innings. They’re flagged as systemic failure. Accountability is tightening, and that shift is cascading directly into the startup ecosystem, where enterprise alignment is becoming the only currency that holds.

That is the backdrop for The Chief AI Officer Exchange, hosted by AI, Data & Analytics Network, landing May 12–13 in Chicago. This is not a conference built for observation. It is constructed for correction. A closed-door environment operating under Chatham House Rules, where senior leaders are expected to bring what is working, what is failing, and what is next. The mandate is clear. Take back control of AI before innovation outruns governance. Tie initiatives to P and L. Build systems that scale, not pilots that stall. In a market where enterprise buyers are recalibrating spend, this room becomes a directional signal for the startup ecosystem on what actually survives procurement.

The room itself carries weight. Invite-only. Carefully qualified. C-suite and VP-level leaders responsible for AI, data, and digital outcomes inside organizations that do not have the luxury of getting this wrong. Financial institutions, telecom giants, retailers, industrial operators, public sector leaders, and infrastructure players all converging with a shared problem set. The format removes noise. No expo floor distractions. Instead, curated one-to-one meetings, boardroom sessions, and working discussions where conversations move quickly from theory to implementation. The kind of environment where a single insight can redirect a roadmap before another 12 months is lost to misaligned execution.

The speaker roster reads like a real-time map of enterprise AI authority. Miguel Navarro, Chair of the Exchange, framing 2026 as a defining year where AI shifts from capability to responsibility. Pascal Belaud, Chief AI and Data Officer at Truist, grounding strategy inside regulated financial systems. Dr. Ricki Koinig, CIO at Wisconsin Department of Natural Resources, bringing public sector constraints into focus. Rana M. Dalbah, Senior Director of AI & Data Governance at BAE Systems, anchoring governance inside defense-grade environments. Kristina Milovanovic Khan, Director of AI Transformation at AT&T, translating AI at network scale. Tanushree Mittal, Head of AI/ML for WW Infrastructure Engineering Operations at Amazon, connecting AI to operational backbone.

Then the operators shaping execution at scale. Amit Shivpuja driving Data and AI Enablement at Walmart. Radha Kuchibhotla leading AI solutions design at CVS Health. Praveen Moturu building enterprise digital platforms at Mars. Scott Malone advancing automation and AI at U.S. Bank. Walter Leverett representing NVIDIA’s enterprise channel motion. Stuart Benington from Dell and Khalid Kark from Cloudflare grounding infrastructure and cloud realities. Alongside them, voices like Sabaita Mohsin at Caterpillar, Katie Heupel at CNA, and Arjun Srinivasan at WWEX Group bring sector-specific weight that turns conversation into application. This is not a lineup built for visibility. It is built for accountability, and that distinction matters for the startup ecosystem trying to understand where enterprise budgets are actually moving.

What separates this Exchange is not just who is in the room, but how the room operates. Every interaction is intentional. Delegates are vetted. Conversations are protected. The structure is designed to accelerate clarity, not prolong discovery. Sponsors and solution providers engage through curated meetings, not crowded booths, creating a direct line between enterprise demand and market supply. That compression of time and signal is where real decisions begin to form.

Zoom out and the pattern becomes clear. The rise of the Chief AI Officer is not symbolic. It is structural. Enterprises are consolidating accountability for AI into roles that sit at the intersection of technology, risk, and revenue. These leaders are not experimenting. They are defining operating models, governance frameworks, and investment priorities that will shape the next decade. Rooms like this are where those decisions are tested, refined, and quietly aligned.

Because the next phase of AI will not be determined by who builds the most advanced model. It will be determined by who integrates intelligence into systems that hold up under pressure, scrutiny, and scale. And the signals coming out of Chicago will not stay in Chicago. They will ripple outward, setting direction for the startup ecosystem that depends on getting enterprise AI right the first time.