Start by auditing and cataloging the terms most central to your highest-priority use case — not every term in the business, just the three or four that the agent will actually touch. Force the departments that use those terms to sit in a room and agree on one definition, even when that conversation is uncomfortable and political, because it usually is. Then encode that agreed definition somewhere machine-readable and enforced — not just written down in a glossary document nobody opens again after the kickoff meeting — so every model, dashboard and agent that touches that term resolves it the same way.

None of this needs to wait for a multi-year data governance overhaul, and I’d actively discourage treating it that way. The mistake I made early on was assuming semantic work had to be comprehensive to be useful — mapping every term across every system before touching a single agent. It doesn’t. Scoping it tightly to one workflow, getting the two or three business functions that actually touch that workflow to agree, and encoding that narrow agreement is a project measured in weeks, not quarters. The comprehensive enterprise glossary can, and probably should, come later, built one validated use case at a time rather than designed top-down in a conference room before anyone has tested it against a real agent.

CDOs and CIOs would have to ensure that this is also a mandatory shift for data teams themselves, not just a technical build. The job used to be building reports and charts for humans to interpret with their own judgment layered on top. It’s increasingly about building and maintaining the context that lets a machine interpret data correctly without a human in the loop to catch the ambiguity. That’s a different skill set than most data teams were hired for, and it’s worth saying so explicitly when planning headcount and training for the next year, rather than assuming the existing analytics team will absorb it by osmosis.