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We built an AI-powered vendor risk platform to automate 340 security questionnaires. Fourteen months later, we deprecated it entirely. Here’s what we learned about when enterprise AI fails, and the hidden costs of automation theater.

We built an AI-powered vendor risk tool with impressive metrics—then watched 89% of users ignore it for manual checklists. The problem wasn’t the model. It was how we approached enterprise adoption. Here’s what we learned.

We built an AI anomaly detector that worked perfectly—until 89% of users disabled it on first login. The problem wasn’t accuracy. It was inserting sophisticated prediction into a workflow that never needed it. Here’s why most enterprise AI pilots never escape the PoC phase.

A $340,000 AI anomaly detection feature launched with eleven users—eight from the product team. David Ohnstad exposes why enterprise AI budgets are wasted and what separates successful implementations from expensive failures.

Most companies automating with AI agents skip a critical step: mapping actual decision trees. This article breaks down why autonomous systems fail in practice and what preparation prevents expensive mistakes.

A $2.3M AI platform launch resulted in zero adoption after 90 days. David Ohnstad explains the critical mistake: building infrastructure before understanding what problems it actually solves. The real roadmap starts with user demand, not infrastructure.

Your organization doesn’t need AI agents right now. You need better data infrastructure, clearer decision frameworks, and the discipline to solve operational chaos first. Most enterprises are building AI agents before addressing fundamental problems that guarantee failure.