AI, Machine Learning or Automation: Choosing the Right Approach
Match the technology to the task before investing in a more complex solution.
Key takeaways
- Use clear rules when the process is predictable.
- Evaluate generative AI and predictive models against different needs.
- Account for maintenance, oversight and failure.
Start with the task, not the label
Describe the input, required output and acceptable variation. A fixed approval rule, a demand forecast and a draft customer response are different problems. The fact that a product contains AI does not make it a better fit.
Compare approaches against the same business objective. Include the existing process and straightforward process improvements in the comparison.
Use rules for predictable work
Rules-based automation can route a completed form, send a reminder or apply a defined calculation. It works best when the conditions and permitted actions can be specified clearly.
Document exceptions and ownership. If rules change frequently, someone must maintain them. Simple automation still needs access controls, testing and a fallback.
Use generative AI where draft variation is useful
Consider bounded tasks such as preparing an outline or summarising approved material. Test whether the output preserves the source and whether review effort is acceptable.
An illustrative meeting-summary workflow should check decisions, names and deadlines before distribution. See our human review guide for deciding where approval belongs.
Consider predictive models when evidence supports them
A predictive task needs a defined outcome, relevant historical examples and a way to test performance on cases outside model development. Check whether the data represents the setting in which the model will be used.
The data readiness guide helps identify gaps before integration. Avoid custom model development when an existing tool or simpler method meets the need.
Choose an approach you can operate
Compare total cost, transparency, reliability and the team’s ability to maintain the workflow. NIST’s AI RMF 1.0 provides a risk managementThe ongoing process of identifying, assessing and addressing reference for AI systems.
Test the preferred approach through a limited business pilot. A combination of rules, AI drafts and human approval may be more suitable than one technology handling every step.
Sources and further reading
Discuss the right approach for your workflow
Discuss your business process, team needs and next practical step with Obizmax.