3 posts tagged with "ai-strategy"

A product manager pulls up the next-quarter roadmap. Three features are marked "depends on next-gen model." Nobody asks what happens if next-gen slips, arrives 20% smaller than the demo suggested, or ships gated behind an enterprise tier your customers do not qualify for. Six months later, all three of those scenarios have happened, and the team is now rebuilding two quarters of architecture against the model that actually shipped — a different shape from the one they planned for.

This is the model-of-the-week roadmap: treating unreleased capability claims as committed dependencies. It is one of the most reliable ways to turn a twelve-month plan into a thirty-month plan, and it rarely looks risky in the moment because every vendor demo feels inevitable. The schedule damage is invisible until the slip compounds.

Your model achieved 91% accuracy on the held-out test set. Latency is under 200ms at p95. You've cut the error rate by 40% compared to the previous rule-based system. By every technical measure, the project is a success. Six months later, leadership cancels it.

This is not a hypothetical. Eighty percent of AI projects fail to deliver intended business value, and the majority of those failures are not caused by model performance. They are caused by the gap between what engineers measure and what decision-makers understand. The technical team speaks a language that executives cannot evaluate — and in the absence of comprehensible signal, leadership defaults to skepticism.

The metrics translation problem is not a communication soft skill. It is an engineering discipline that most teams treat as optional until the funding review.

Forty-two percent of companies abandoned most of their AI initiatives in 2025, up from 17% a year earlier. The striking part isn't the abandonment rate — it's the delay. Most of those projects had been in various stages of "almost ready" for six to twelve months before someone finally pulled the plug. The demo worked. The metrics looked plausible. The team was invested. And so the feature lingered, burning budget and credibility, long after the evidence pointed toward shutdown.

The hardest product decision in AI isn't what to build. It's when to stop building something that technically works but practically doesn't.