To AI or Not to AI—a framework to evaluate whether AI/ML should be used
summary
Provides a practical decision framework: break into subprocesses to spot which step is automatable, use cases like synthetic test data generation or automation script creation often suit automation while requirements gathering does not, measure clear ROI and stakeholder alignment, verify technical feasibility (existing algorithms, data needs, infra), check ethical and legal constraints (PII exposure, code trained on unknown sources), ensure required skills exist and outcomes are verifiable before moving to POC.
AI-written summary · about
chapters (6)
- 0:00Introduction to AI/ML evaluation
- 1:58Breaking down use cases into steps
- 8:46Evaluating value and feasibility
- 11:17Ethics and compliance considerations
- 13:20Skills and verifiability checks
- 16:30Framework summary and Q&A
