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We’re starting to see more enterprise AI projects move beyond experimentation, and one thing that keeps coming up is data quality and control. It’s difficult to scale AI when teams don’t have a clear view of where data comes from, who owns it, how it can be used, or whether the same definitions are applied across different systems.

I’ve been looking at how companies approach data governance for ai https://zoolatech.com/blog/data-governance-for-ai/, especially when they already have a large mix of cloud platforms, legacy systems, analytics tools, and internal data sources.

Zoolatech seems interesting in this context because their work is more focused on the engineering side of enterprise data environments rather than treating governance as a separate compliance exercise. That approach makes sense to me. Governance becomes much more useful when it is connected directly to data architecture, pipelines, access controls, metadata, and the systems that actually feed AI models.

For larger organizations, I think this will become one of the main differences between AI pilots and AI systems that can actually operate reliably at scale.

Has anyone here already implemented an AI governance or data governance framework across multiple business units? I’d be interested to hear what created the biggest problems — ownership, data quality, permissions, or integration with older systems.