Most teams that run into AI reliability problems assume the issue is the model. They adjust the prompt, try a different LLM, add more context. The outputs improve slightly, then drift again.
The problem is almost never the model.
When Cristian Badet, Data Lead Solution Architect at mindit.io, opened a recent live webinar on data governance with Databricks, he described a pattern that shows up across almost every data platform he works with. The platform is in place. The sources are connected. The business uses the data every day. And then the platform grows, and with it, the chaos.
Every new team brings its own definition of a business term. Every new use case creates its own copy of a dataset. An analyst opens the catalog and finds three tables: customer, customer_old, and customer_v2. They ask around to find out which one is right. The answer lives in someone’s head, or in an email thread from 18 months ago.
That is the environment you are asking AI to work in when you skip governance.
What AI actually needs from your data
AI does not need perfect data. It needs data with a clear context. When a Genie agent is asked about net revenue by customer segment, it needs to know what net revenue means in this organisation, which table holds the authoritative version, which columns are relevant, and whether the data it is looking at is certified and current.
Without that context, the agent picks something and answers. The answer may look plausible. It may even be correct sometimes. But it will not be consistent, and the business will not be able to trust it.
Cristian framed it simply: every AI answer reflects the quality of the data governance underneath it. If the governance is missing, the AI will not give you the right answers. Not because the model is wrong, but because it was never given the information it needed to be right.
Three governance problems that break AI first
Based on experience across banking, retail, and aviation data platforms, Cristian identified three recurring situations that cause AI outputs to break down.
The first is duplicate tables with no certification. When the same data exists in multiple versions and none of them is marked as authoritative, the agent cannot know which one to use. It will pick one. It may pick a different one next time.
The second is undefined or inconsistent metric definitions. When three teams define the same KPI differently and none of those definitions is recorded in a shared location, the agent inherits the ambiguity. The answer it returns is technically based on something, but nobody can verify whether it was the right something.
The third is ungoverned sensitive data. When columns are not classified and access rules are not enforced consistently, AI access to sensitive data follows no policy. This is not a model problem. It is a governance gap that creates compliance exposure.
The shift governance needs to make
For most of its history, data governance was about compliance, access management, documentation, and data quality. Those requirements still exist. But AI adds three new demands.
Data must be governed so that AI can understand it. That means business definitions, metadata, lineage, quality scores, and clear ownership recorded in a way the platform can read and apply, not locked inside documents or email threads.
Data access must be governed the same way for AI as it is for people. The same tag-based masking rules that protect sensitive columns from human users need to apply when an agent queries the same table.
And governance must be automated, because AI operates at a pace and scale that manual governance cannot keep up with. If quality checks run once a month and an agent is querying the table every hour, the gap between those two rhythms is where problems accumulate.
The shift is from governance as documentation to governance as architecture. A set of decisions that the platform enforces on every load, every query, and every deployment, rather than a set of documents that someone updates when they remember.
That is what makes AI outputs reliable. Not a better prompt. A better foundation.
Read more about the mindit.io + Databricks partnership on our dedicated page.
