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.
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.
The failure modes are known and the path is repeatable. Getting to production is a different and harder discipline than getting to a demo, and the winners staff and sequence for it.
Define the business outcome as a number and stand up evals. Most stuck pilots never had either, which is why they cannot prove they are worth scaling.
Because a pilot runs on a clean sample and production runs on live, messy, changing data. If the data foundation is not built for production, the system degrades the moment real data hits it. Gartner expects this to kill 60% of projects by the end of 2026.
Almost never. The models work. What breaks is everything around the model: the data, the operational layer, the absence of measurement, and the unwillingness to change how work gets done.
You need the capability, not necessarily the headcount right away. Many teams bring in a partner to build the operational layer and transfer it, then hire against a roadmap they have de-risked.
Put kill points into the path. If a use case cannot clear a stage, stop it there. Ruthless stopping is what frees budget for the use cases that actually pay.
