The Truth About Innovation in Databases

Why the same old data infrastructure playbook won't survive contact with agentic AI, and what actually has to change underneath. In this episode of AI Radicals, host Satyen Sangani talks with IBM Software CTO Anant Jhingran about why enterprise AI's biggest bottleneck isn't the models, it's the decades-old data and integration infrastructure sitting underneath them. Anant and Satyen dig into why data fundamentals (provenance, metadata, systems of record) haven't changed, even as humans and fixed workflows give way to agents reasoning on the fly. They contrast "AI for data" with "data for AI," why AI's tolerance for messiness still doesn't excuse bad data, and why centralization may matter less as agents run quick, discovery-driven queries instead of big fixed reports. Anant also shares a new focus at IBM: rethinking whether one "golden" code path per product still makes sense when AI makes forking and personalizing variants easy. "If you think that agents are just going to do the same thing that you're doing except machines instead of people, it's kind of boring, and I don't think that's going to happen. The real change is they're doing something different that we haven’t done before." Listen to this episode to learn: Why "AI for data" and "data for AI" are two distinct problems enterprises need to solve separately Why agentic, discovery-driven workloads may reduce the need to centralize all your data, but raise the stakes on metadata Why forking products into many tailored variants, instead of one shared code path, could reshape how software gets built