Most AI governance programs have a structural blind spot. Not in the policies. Not in the quality scorecards. In the identity layer — the one piece of infrastructure that determines whether your AI actually knows who it's making decisions about.

In this live 30-minute demo, you will watch a running Senzing Agentic Entity Resolution system do what MDM programs and probabilistic matching consistently fail to do: resolve fragmented entity records across multiple sources in real time, with a complete audit trail and full explainability.

The session builds to a single high-stakes moment — a sanctions record loaded live, resolving against an existing entity through a name variant match that no rule was written for and no batch job would have caught.

This is not a slide deck. It is a running system. Bring your questions about how it handles your data, your sources, and your governance requirements.

Speaker: Gurpinder Dhillon, Head of Data & AI Ecosystem, Senzing

Dr. Gurpinder S. Dhillon brings over 20 years of enterprise data leadership across data governance, AI enablement, and identity resolution. He holds a Doctorate of Business Administration with a focus on data governance, decision intelligence, and entity resolution — and has spent two decades at the intersection of data strategy and business value, including a senior leadership role at Dun & Bradstreet overseeing one of the company's largest data management business units. He is the author of Think Data, Act AI, and publishes a weekly LinkedIn newsletter, From AI Hype to AI Impact, read by data and AI leaders globally. A frequent speaker at Gartner Data & Analytics Summit, Dreamforce, Big Data & AI World London, and Enterprise Data World, he is known for translating complex data concepts into decisions leaders can act on. His core thesis: AI failures in production are identity failures — not model failures.