Why Enterprise AI Stalls, and How to Ship It
Enterprises have adopted AI. Almost none run it dependably in production.
That gap is expensive. Gartner ties poor data quality to $12.9 million a year per organization, and MIT found 95% of GenAI deployments return nothing measurable.
The blocker is rarely the model. It is the data underneath it. Teams that fix the foundation first ship AI faster and cheaper. Teams that chase models first spend a year on rework.
This report shows the pattern across 161 enterprise engagements and 414 measured outcomes, all from delivered client work, not surveys.
Key Takeaways from the Report
- Win user adoption, where most AI value leaks.
- Move the goal from adoption to dependable production.
- Fix the data foundation before scaling any AI.
- Expect large gains, +47% median across 414 outcomes.
- Embed governance early, the entry price in regulated sectors.
The Gap, the Payoff, the Playbook
The report runs in three parts.
Part one sizes the production gap and gives you a five-stage maturity model, from ad hoc data to agents on governed enterprise data.
Part two breaks down verified outcomes across 161 engagements, including the spread behind the +47% median. 76% of engagements improved a metric by 50% or more, and 3 in 10 cleared 90%.
Part three hands you a readiness checklist, the five failure modes that sink most AI programs, and five predictions for the year ahead.
Foundation First, Then Governed AI
The enterprises pulling ahead share one habit. They modernize and govern their data before scaling AI, and they measure outcomes at delivery.
The ones stuck in pilots do the reverse. They chase models on shaky data, then spend the next year on rework.
Read the report to benchmark where you stand, then move from stalled pilots to governed AI in production.
