Pilots
How to baseline an AI pilot before you scale
The fastest way to waste an AI rollout is to start with “the company” instead of one workflow. Broad deployments hide whether anything actually improved. A pilot with a baseline makes expansion a decision instead of a hope.
Pick one repeated workflow
Good first pilots share four traits:
- The team already does the work every week
- The answer or action spans more than one system
- Success can be timed or counted
- Someone owns the outcome
Examples that fit Pipeer’s model: IT ticket status answers, policy questions, customer-feedback synthesis, and release or status reporting.
Establish the before numbers
Capture a short baseline before people change behavior:
- Time-to-answer for the recurring question
- Manual steps from request to finished artifact or action
- Interruptions to the human who currently holds tribal knowledge
- Turnaround for the report, brief, or ticket update
Keep the measurement simple enough that a lead can update it weekly without a BI project.
Define the evidence bar for expansion
Before the pilot starts, write down what “good enough to expand” means. For example:
- Time-to-answer drops by a stated percentage
- Adoption holds above a minimum weekly active rate on the pilot team
- Source-linked outputs need fewer clarifying follow-ups
- Security and ops are willing to add the next workflow
If those thresholds are not met, keep the scope narrow. That is not failure. That is the point of a pilot.
Pair product metrics with control metrics
Operational AI is not only about speed. Track whether actions remain inspectable, whether permissions stay user-scoped, and whether people still open the cited sources for consequential decisions.
A pilot that is fast but opaque should not scale. A pilot that is slower at first but trusted and measurable can.