Author: David Ohnstad

Three months after deployment, Fortune 500 teams can’t answer basic questions about their AI systems. David Ohnstad explains the fundamental mismatch: vendors design platforms for teams with skills that rarely exist in legacy enterprise environments.

ServiceNow’s latest AI platform promised to manage everything—until real production hit. David Ohnstad explores why proof-of-concepts succeed while deployments fail, and the unglamorous data work required to make enterprise AI actually work.

A single approved AI tool led to 847 unauthorized database uploads in three months. David Ohnstad reveals why enterprise AI governance lags behind deployment speed, and how classification rules, accountability checkpoints, and cross-functional oversight close the gap.

A Fortune 500 company’s 40-page AI policy was ignored within 72 hours of launch. David Ohnstad examines why 68% of enterprise AI governance frameworks fail and the single technical mechanism that separates success from costly failure.

A $2.1M AI integration stalled for six months—and that delay saved the project. David Ohnstad explains why organizational readiness and team architecture matter more than technology when deploying machine learning in enterprise software systems.

We had $2.3M approved for AI-integrated PLM, but one question stopped everything: who decides when AI recommendations conflict with engineer judgment? That silence revealed the real challenge of enterprise AI adoption.

When your ML engineer leaves mid-project, panic is optional. Learn how one finance SaaS team shipped automated anomaly detection on schedule despite losing seven months of institutional knowledge three weeks before production launch.

Eight months into production, an AI anomaly detection system was delivering sophisticated features—but nobody could prove its business value. A CFO’s simple question revealed a critical gap in how enterprises measure AI success.

SAP’s Q2 2026 AI release shipped impressive features, but three months later a CFO asked the question that kills most initiatives: ‘What did we actually get for that spend?’ Adoption metrics don’t answer it.

A CTO faced an impossible choice: spend $4.2 million migrating systems or abandon AI entirely. But this false dichotomy haunts enterprises everywhere. The real path forward? Integrating AI without replacing your core infrastructure—and why 47% of leaders struggle to see it.