How global enterprises are operationalizing AI beyond the pilot stage
Most enterprise AI programs stall in the same place: a promising pilot that never becomes a production system. The gap usually isn't the model — it's the governance, data infrastructure, and change management that never got built alongside it.
Across the 40+ AI engagements our teams have run this year, the enterprises that made it to production shared three traits: a data foundation built before the model was chosen, a clear owner accountable for model risk, and a rollout plan that treated end users as a design constraint, not an afterthought.
Governance is the part most teams underinvest in. Model registries, audit trails, and human-in-the-loop review points aren't exciting, but they're what convinces a risk committee to sign off on a production deployment.
The pattern that works: start with a narrow, well-instrumented use case, prove the operating model — not just the model accuracy — and only then expand scope. Enterprises that skip that step end up rebuilding their AI platform twice.
