When the executive team signs off on a new AI initiative, the energy is electric. Business leaders see speed. Efficiency. Innovation. Competitive advantage. The pressure to move quickly — especially as competitors roll out their own AI programs — feels existential.
But in too many organizations, fragmented governance that tethers control and visibility to specific data systems, sources or compute platforms prevents companies from scaling data and AI use cases safely.
In that rush to production, one assumption takes hold: that the models will work because the data already exists. And on paper, they do — until the model starts hallucinating. Until it can’t explain its answers. Until an audit raises questions no one can answer.
That’s where the trouble begins.
Before unpacking the risks, it’s worth noting that AI doesn’t just reveal data problems — it multiplies them. And for many teams, those problems are already well understood. In fact, 53% of data professionals say that data reliability is a top challenge when building and deploying AI models. Other challenges include ensuring compliance, assessing risk and balancing pressure with risk minimization. These challenges don’t just slow progress, they erode trust in AI outcomes.
