Stop Funding Data Governance! Start Climbing the Maturity Ladder with Agentic Data Governance
Future-Grade AI & Data
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Stop Funding Data Governance! Start Climbing the Maturity Ladder with Agentic Data Governance<br>For two decades we answered broken data governance with more boards, more policies, more headcount but got meetings instead of data quality. Agentic AI finally changes the economics.
Mario Meir-Huber<br>Jul 19, 2026
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Data Governance didn’t die. It can finally grow up. Thanks to AI, not because of it. Data People expected a lot of attention on the Topic of Data Governance, but in fact the expectations fell short; it started with arguments “we need good Data Quality for AI”. This is wrong, data people didn’t understand what AI is about. AI is about (process) automation largely and it only touches data on the side. You can get impressive results without even touching Data.<br>For two decades, the poor Data People argued for Data Quality and nobody listened. The board didn’t want to fund it; odd, considering the same board expected reliable numbers every quarter. I know this uphill battle from the inside, because I fought it the traditional way. I set up governance boards, gremiums, steward networks. They produced policies, the policies produced meetings, and the meetings produced very little data quality.<br>Future-Grade AI & Data is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
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So today I argue for something close to the opposite of what I once built: strip governance back to what actually needs a human, and let the rest run where the work happens. DAMA gave us a framework that is theoretically complete and organisationally unfundable. Nobody staffs it fully, so it half-exists everywhere and works nowhere.<br>But “unfundable framework” and “no governance” are not the same conclusion, and this is where I’ve changed my mind about my own hot take. The failure mode was never too little governance or too much. It was uniform governance. We reviewed the experimental chatbot with the same ceremony as automated credit decisioning. Heavy process applied evenly kills velocity on the harmless stuff and still under-serves the genuinely dangerous stuff. The fix is proportionality: governance intensity should match exposure: blast radius and reversibility but not a one-size-fits-all policy.<br>This is where AI enters — and no, it doesn’t kill governance (the dash is on purpose :D). It finally makes the proportional version affordable. Agents can write the documentation, watch quality, classify sensitive data, and review every change directly in the pipeline. All the toil that once justified an entire governance organisation is now automatable, which means governance stops being a department you fund and becomes a capability that runs.<br>One honest caveat before the recommendation, because it’s the thing that separates a LinkedIn take from something that survives contact with your actual org: this is a capability you have to earn. Agent-run governance sits on prerequisites: a clean corpus, documented APIs, real guardrails. Most organisations are somewhere between “we have a RAG chatbot” and “our core systems have documented APIs.” Deploy governance agents on top of that and you don’t get automation, you get an agent that fails unpredictably. Do the honest assessment of which rung you’re on first.<br>If you’ve earned the rung, here’s the shape I now recommend with three changes to the team setup.
The Exception Layer
This is done by a handfull of humans as a side task. They are here for the decisions that genuinely need humans: accepting risk, settling conflicts, saying yes to the thing the agent flagged and can’t decide alone. This is the one job the department was always secretly for. No standing meetings, no agenda, agents prepare the case, people decide the exception. Define the risk appetite once (”we accept up to X% error on internal document search”) and it becomes a boundary the agents operate inside, not a debate you re-run every sprint.<br>The Exception Layer is automated and doesn’t run in regular meetings. If the agents (I’ll describe them below) flag something, automated alerts are sent to those impacted and potentially the CDO. There is no need for a dedicated Data Governance Team, Data Governance becomes a capability people need to know.<br>The Agent Fleet
All the steward work nobody ever wanted, such as documenting, tagging, writing quality rules, goes to agents, running continuously, right where the data work happens. The catalogue stays current on its own (i’d argue we might even don’t need one as we know them, they would need to evolve!). Frontier models are extremely capable here, and tuned well, errors are rare (humans also make errors, possibly at an even higher rate as frontier models now!)<br>But there is the part I’d have skipped a year ago: the fleet is itself a system you govern. An agent that classifies...