Why most enterprise AI never makes it out of the demo, and what the one-in-five who succeed do differently. A staged path from a working pilot to something you can actually run.
Fund the exciting demo, starve the part that creates value, and join the 80%+ of projects that fail to deliver business value.
Treat getting to production as its own discipline, separate from getting a demo to work, and do the unglamorous work the other 80% skip.
Data foundations and an operational layer are not glamorous, and they are exactly what the winners invest in before scaling.
Without evals you cannot tell if a change made the system better or worse, cannot catch regressions before users do, and cannot prove value to the people holding the budget.
Don't Bolt AI Onto It
The most counterintuitive finding in the McKinsey data: workflow redesign correlates most strongly with profit impact.
Define the business outcome and how you will measure it before building. If you cannot state success as a number, that is your first problem. Build the smallest version that tests the real hypothesis and stand up evaluation alongside it, so you can tell if anything you do next is an improvement.
Make the data reliable, governed, and fresh. This is usually the longest stage and the one teams most want to skip. Skipping it is why 60% of projects are forecast to die here.
Deployment, monitoring, versioning, rollback, and the human-validation rules. This is the MLOps and LLMOps work that turns a model in a notebook into a system you can run safely.
Do not paste AI onto the old process, rebuild it; this is the stage most correlated with profit and the one most often skipped. Then expand only as the evals and economics hold, bringing cost controls and governance along at every step, not as an end-stage fire drill.
Drop your details and we'll send From Pilot to Production: Scaling Enterprise AI straight to your inbox - no spam, unsubscribe anytime.
Talk through how this applies to your roadmap with our engineering leads - a working session, not a sales pitch.
Download White Paper