Case study 05 of 15
The Best AI Use Case Was Not AI
A team considered AI for stale data identification and cleanup — and found a better answer.
Business challenge
The underlying need was deterministic metadata analysis, reporting, and rules-based archiving, not language understanding.
Architecture approach
Scripting and automation came first, with AI reserved for classification tasks that might genuinely need it later.
How the work moved from request to production
- Business need
- Intake & risk classification
- Select reusable pattern
- Build / configure
- Review & validate
- Deploy / enable
- Monitor usage, risk, cost, value
Governance considerations
Deterministic automation was far easier to explain, test, and approve than a model-based approach would have been.
Results
The team found a faster, lower-risk path to the same value, without a model in the loop.
Lesson learned
AI maturity includes knowing when not to use AI.
Discussion questions
- Which tasks are actually deterministic?
- When does classification justify reaching for a model?
- How do you avoid choosing AI for its own sake?