Business process guide
Decision Automation
Decision automation is appropriate when the rule is clear, data is available and the consequences are understood.
Practical note: process design depends on organization, data, controls, laws, contracts and technology. Treat examples and calculators as educational planning aids, then validate changes against your own requirements.
Deterministic rules first
Many business decisions do not need machine learning. Explicit rules are easier to test and explain.
Escalate uncertainty
Missing, contradictory or unusual data should trigger a defined human review rather than an arbitrary default.
Measure decision outcomes
Track overrides, false positives, complaints and downstream corrections to see whether the automated policy is working.