Your AI Is Only as Good as Your Data Stack

AI is all anyone wants to talk about in boardrooms these days. But the companies actually trying to deploy it at scale are running into a problem that’s far less glamorous than a chatbot demo: their data is a mess.

Consumer AI tools feel effortless. You type, it answers. Fast, slick, impressive. Behind the scenes at an enterprise, though, things look very different. Data is scattered across legacy systems, locked inside SaaS platforms, sitting in formats that don’t talk to each other. You can’t feed that into an AI and expect anything reliable to come out.

Bavesh Patel, SVP at Databricks, puts it bluntly: “The quality of that AI and how effective that AI is, is really dependent on information in your organization.” And in most organizations, that information is fragmented. Silos everywhere. Different teams use different tools, different schemas, different definitions of what a “customer” even means. AI doesn’t handle ambiguity well. Feed it messy data, you get messy outputs.

Patel doesn’t mince words about what happens next: “You’re actually going to end up having terrible AI.”

So what does “AI-ready data” actually look like? It’s not about buying a new tool or slapping a vector database on top of your existing mess. It’s about consolidation into open formats, governance that actually enforces access controls, and making data accessible across functions without losing context. Structured, unstructured, real-time streams, historical records — all of it needs to live in a unified architecture.

That’s a big lift. Most companies are still running on a patchwork of disconnected dashboards and siloed applications. They’ve spent years optimizing for departmental reporting, not for AI consumption. Now they need to rethink the whole stack.

Rajan Padmanabhan, unit technology officer at Infosys, makes a point that I think is often overlooked: this has to tie directly to business metrics. AI initiatives shouldn’t be treated as isolated innovation projects. They need governance frameworks that measure what’s actually delivering value and what should be killed off fast. Otherwise you end up with expensive experiments that never make it to production.

“We see this big opportunity just with AI literacy with business users,” Patel adds. “They’re very eager to understand how they should be thinking about AI.” That’s encouraging, but literacy alone won’t fix broken infrastructure. You need both the technical foundation and the organizational buy-in.

The real opportunity here isn’t just about making existing processes more efficient. Padmanabhan describes a shift from “systems of execution” and “systems of engagement” toward “systems of action.” That means AI agents that don’t just recommend or assist, but actually execute workflows and transactions autonomously. That’s where the ROI gets serious.

But none of that works if the data underneath is unreliable. You can’t have autonomous agents making decisions based on stale, incomplete, or contradictory information. The foundation has to be solid before you start building on top of it.

This episode of Business Lab was produced in partnership with Infosys Topaz, and the full transcript includes a deeper conversation between the hosts and both guests. But the core message is clear: the companies that win with AI will be the ones that get their data house in order now. Not next quarter. Not after the next hype cycle. Now.