Scaling AI is about governance, not technology
Data governance is unglamorous work. It is also the reason most AI strategies stall before they scale.
Spending on models, platforms and use cases keeps growing. But the disciplines that make those investments effective – data quality, ownership and governance – often receive far less attention.
Part of the challenge is that data governance is neither “fun” nor “sexy.” It lacks the excitement of new technologies and the appeal of quick wins, so it is consistently deprioritized.
Yet as organizations scale their AI ambitions, governance is increasingly the factor that determines whether those efforts succeed or stall.
The imbalance in attention is now starting to show. While AI adoption continues to grow, many organizations still struggle to move beyond pilot stages into enterprise-scale deployment. The gap between ambition and execution is widening, and weak data governance is often at the center of it.
The issue is not awareness. Most business...
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