Dr. Neil's Notes
Software > Development > AI in Delivery Systems
Thin-slice experimentation
Introduction
Large AI programs often fail because they are too broad to learn from quickly. Thin-slice experimentation keeps risk low and learning fast by testing one narrow workflow at a time in real delivery conditions.
Pick a thin slice
Choose one repeatable task, one team, and one measurable outcome. Keep the scope small enough to complete in days, one sprint, or monthly, not quarters. This makes the learning clearer to see and understand.
Design the experiment
Define success criteria, failure criteria, and a stop condition before starting. Agree who is accountable for interpreting results. The point is not to prove AI is good, it is to discover whether this change improves this flow.
Scale only after evidence
When a thin slice works, codify it as a reusable pattern and then extend to adjacent work. If it does not work, document why and move on. Delivery maturity grows from evidence, not enthusiasm.
However, consider that what worked yesterday might not work tomorrow, and what failed yesterday might work tomorrow. When the pace of change is high knowledge needs to be refreshed and revalidated on a regular basis.
Part of the AI in Delivery Systems series.