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A $2.4M AI system predicted customer churn at 91% accuracy. It worked perfectly. Yet six months later, executives couldn’t name a single decision it influenced. The problem wasn’t technical—it was definitional.

Six months into 2026, the gap between AI pilot success and production deployment has widened. Deloitte data shows 68% of organizations with successful proof-of-concepts haven’t moved models into production—revealing a critical execution problem in enterprise AI strategy.

Six months in, fourteen ML models were live—but none had automated retraining. Three still used hardcoded paths from a laptop. When compliance flagging hit 40% false positives, an eleven-day investigation traced it back to an undocumented schema change. This is where enterprise AI projects actually break.

Most organizations celebrate their ML deployment—then face unexpected costs and performance failures within weeks. David Ohnstad breaks down the post-deployment governance gap that catches enterprises off guard and provides the framework to prevent it.

Vendors pitch $4.7M platform replacements to unlock AI capabilities. But you don’t need a rip-and-replace strategy. David Ohnstad shows how smart teams layer AI functionality onto existing stacks—saving millions while avoiding implementation freezes.

A 94% accurate model means nothing if nobody defined what business outcome it should drive. David Ohnstad exposes the critical gap between data science validation and executive expectations that derails nine months of enterprise AI development.

Most teams believe AI requires tearing out legacy infrastructure. David Ohnstad explains why this myth costs companies weeks of debate and delayed shipping. Your existing SaaS foundation can support enterprise AI—here’s how.

Most enterprise AI initiatives stumble not because of poor models, but because teams skip the critical step of defining what success actually looks like. Without clear business outcomes first, even production-ready ML systems deliver no value.