Almost every organization we talk to has run an AI pilot. Far fewer have an AI capability in production that a business unit would fight to keep. The gap between those two states is rarely about the technology. Pilots stall for organizational reasons: no owner, no baseline, no integration plan, and no answer to the question "what happens when it is wrong?"
Why pilots stall
- The pilot proved the demo, not the workflow. A model that summarizes documents impressively in a sandbox has not yet met the document formats, edge cases, and volume of the real process.
- Nobody owns the outcome. Innovation teams can build pilots, but only the business unit that lives with the process can own adoption, and they were often not asked what they need.
- No baseline was measured. If nobody recorded how long the task takes today, or its error rate, then the pilot cannot prove improvement, and the CFO is right to be skeptical.
- The error story is missing. Every model is sometimes wrong. Production readiness means knowing the failure modes, their cost, and the correction path. Pilots usually skip this entirely.
Pick problems where AI's failure mode is affordable
The best first production use cases share a shape: high volume, moderate stakes per item, a human already in the loop, and an output that is easy to check. Drafting responses a person reviews before sending. Classifying and routing inbound requests. Extracting fields from documents that a downstream step validates anyway. In each case, a wrong answer costs a correction, not a crisis.
Conversely, be suspicious of first projects where an error is expensive or invisible: unsupervised customer communication, financial commitments, anything with regulatory exposure. Those can come later, after the organization has built the evaluation and monitoring muscles that make error rates knowable.
The path from pilot to production
- Baseline first. Measure the current process: cycle time, cost, error rate, volume. One week of honest measurement beats a quarter of debate.
- Define acceptance before building. Agree with the business owner what accuracy or quality threshold makes the tool worth using, and how it will be measured. Write it down.
- Build the evaluation set early. Collect a few hundred real examples with known-good answers. Every model change, prompt change, and vendor change gets scored against it.
- Integrate into the existing workflow. A separate tool people must remember to open loses to the tab they already work in. Meet the process where it lives.
- Instrument from day one. Log inputs, outputs, user corrections, and overrides. Corrections are free training data for the next iteration and an early-warning system for drift.
- Plan the human loop deliberately. Decide which outputs auto-complete, which require review, and how that boundary moves as measured confidence grows.
Governance that helps instead of blocks
AI governance has a reputation for being where projects go to die, because it is often bolted on at the end as a review gate. It works better as a set of defaults established once: which data classes may be sent to which model providers, what gets logged, who approves new use cases, and what the escalation path is when an output causes a problem. With defaults in place, the tenth AI feature ships faster than the first, instead of slower.
The honest timeline
For a well-chosen first use case, the pattern we consider healthy looks like: two to four weeks to baseline and scope, four to eight weeks to a production-integrated version behind a review step, then a measured loosening of review as the numbers earn it. Organizations that try to skip to the end tend to arrive later than the ones that walk through the middle.