BOTSCOUT / DECISION LAB
Find the value.
Test the assumptions.
Turn an interesting AI workflow into a decision you can explain. Model the workload, inspect the evidence, and leave with a small test to run.
Workload → trade-offs → evidence → test
How the model works ↓02 / YOUR DECISION BRIEF
A useful answer includes
the unknowns.
What could change?
Capacity value, tool spend, setup recovery, and a downside scenario.
What supports the idea?
Published reports and the measurements still missing for this project.
What should you test?
A repeatable 20-case pilot protocol and a blank measurement worksheet.
No account needed. No results are invented when evidence is missing.
METHOD 1.0.0 / INSPECT THE ASSUMPTIONS
Every failed attempt still counts.
Time and cost
Every attempt incurs usage cost and human review. An unaccepted result also incurs the full manual task. Released hours are valued at your hourly input, then tool costs are deducted. Setup includes cash and valued staff time.
Sensitivity, not certainty
Downside: acceptance falls 20 percentage points and review takes 50% longer. Upside: acceptance rises 10 points and review is 25% faster. Acceptance stays between 0% and 100%. These are scenarios, not statistical intervals.
Evidence stays separate
We inspect up to 20 latest published reports for the selected project. Environment, age, cost, and review-time coverage remain visible. We never pool unlike tests or turn sponsorship into a quality score.
Released time may be useful capacity without reducing payroll. Retries, maintenance, and unentered costs are excluded. A favorable estimate is a reason to investigate, not a promise of savings.