Why 75% of AI Projects Fail (And How to Avoid Being One of Them)
Three out of four AI projects never make it past the pilot. This isn't a technology problem — it's a category error. Most teams treat AI like a science project: run an experiment, learn something, move on. Production systems don't work that way.
The experiment trap
When leadership greenlights an “AI pilot,” the implicit contract is that the outcome is uncertain. That uncertainty gives everyone an easy exit: if the pilot fails, nobody expected it to succeed anyway. The downside is that success is rarely defined clearly enough for the system to be owned, operated, and trusted after the pilot ends.
What production-grade looks like
A production-grade automation is stress-tested, documented, and owned by the team that uses it — not by a consultant on retainer. It fits inside existing workflows. It has clear failure modes and clear recovery paths. And its success is measured in hours reclaimed, not models trained.
What to do instead
Start from the business outcome and work backwards. Pick workflows where the judgment call is low and the repetition is high. Build for the team that will own it. And commit to a 90–120 day window — long enough to do it right, short enough to force discipline.
