Author: David Ohnstad

Enterprises pour billions into artificial intelligence, yet half of the models never see a real user. Discover why machine learning projects stall before deployment and how product teams can bridge the gap between complex data artifacts and the actual decisions users need to drive real business value.

Enterprises spend billions on artificial intelligence, yet half of new models never leave the sandbox. According to recent data, only forty-eight percent of pilot projects reach production within twelve months. David Ohnstad examines this deployment paradox, revealing why feedback loops and real-world data are essential to bridging the gap between heavy investment and actual enterprise value.

What happens when your VP commits to shipping AI by Q1, but the feature doesn’t exist yet? David Ohnstad recounts the October afternoon his team restructured around an unapproved anomaly detection system—and what actually matters when organizations bet on machine learning.

Beautiful dashboards don’t guarantee adoption. David Ohnstad discovered his company’s C-suite was making AI budget decisions in a spreadsheet—bypassing the expensive governance platform entirely. The real problem isn’t technology; it’s alignment between what executives need and what platforms deliver.

Vendors promise unified AI governance, but consolidation actually compounds compliance risks. David Ohnstad breaks down why platform lock-in strategies fail to solve the real governance problems enterprises face with machine learning deployments.

A $4.2M AI platform delivered on time with 94% accuracy. Yet eighteen months later, teams still use Excel in parallel. The real barrier to AI adoption isn’t technology—it’s organizational structure and change management that budgets never address.

A $2.1M AI platform launched successfully with 95% accuracy—yet only 4% of intended users engaged. David Ohnstad explores the organizational readiness gap that determines whether enterprise AI initiatives succeed or stall after deployment.

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.