The foundation an AI strategy can stand on

What tipped them over

Renata didn't go looking for MyDataWork. She found it already running. An analytics lead in supply chain had been using it to catalog her assets and frame use cases, and the FP&A group in finance had quietly done the same. Two pockets of the company, on their own, had started keeping a clean record of what their data work was, what it depended on, and who it served. Renata had spent two years trying to impose that kind of discipline from the center and getting polite nods. Here it had grown from the bottom up because it fit how people already worked. Nobody had to leave SQL, Excel, Power BI, or the warehouse. MyDataWork sat alongside those tools as a work-context layer, holding only metadata, names, types, paths, relationships, value, stakeholders, and never the underlying data itself. So instead of fighting it or standing up a competing standard, Renata decided to make it the standard.

The first problem they solved

The moment that convinced her came in a leadership review. For the first time, Renata could open the aggregates-only "Architecture & Value" view and show the CEO and CFO where the value in the company's data work actually sat, what it depended on, and where the key-person risk was concentrated, without opening a single analyst's private working files. Everyone's day-to-day work stayed private by default; leadership saw only the rollup. That distinction mattered. It meant Renata could be accountable to the board about the estate as a whole while the teams stayed trusting enough to keep their records honest.

"For the first time I could tell the CEO where our value and our risk actually lived, without opening anyone's private work."

Scaling from here

Standardizing was less disruptive than Renata expected, precisely because there was nothing to rip out. She co-funded the rollout with IT under enterprise terms — multi-team, per-org limits, invoice billing — and extended it across supply chain, commercial, and finance. Each function kept its own tools and its own private workspace. What changed was that the individual team stories now rolled up into one org-wide capability. Renata finally had a substrate underneath the whole organization's data work, governed and consistent, instead of a scatter of disconnected spreadsheets and tribal knowledge. Within two quarters, a clear majority of the company's material data assets were cataloged and tied to a named owner and a stated use case.

Toward agentic & generative AI

The real payoff was the AI roadmap. Renata's view is blunt: agentic and generative AI are only as trustworthy as the context you feed them, and most companies feed them either nothing or everything. MyDataWork gave her a third option. Approved agents get read-only, metadata-only context through governed Agent Access, enough to understand the shape of the estate, never the sensitive values inside it. Before any function builds, Renata uses Agent Studio to help them scope an agentic use case responsibly, defining what an agent may see and do while it's still a proposal. That governed, metadata-only estate became the on-ramp for the company's entire AI strategy, a foundation solid enough to build on and safe enough to defend.