Understand the process first; choose automation second
Digital transformation is not complete when existing work moves onto a screen. I first understand the people, decisions, exceptions, inputs and approvals involved. Then I identify which steps to simplify, which to automate and which decisions should remain with people.
AI is one possible solution, not the objective. Conventional automation can handle repetitive, clearly defined tasks more reliably. AI is worth considering for uncertain tasks such as interpreting language, classification or drafting, where a person can check the result.
Autonomous workflows need visible boundaries
- A clear purpose, input data and success measure for every automation
- Access and action permissions based on least privilege
- Human approval for important decisions, outbound messages and irreversible actions
- Auditable records of inputs, outputs, errors and decisions
- Incorrect results, exceptions, service outages and safe shutdown scenarios
- Evaluation sets that can be rerun when models or tools change
Autonomous does not mean uncontrolled. Define what the workflow can and cannot do, when it must stop and who can intervene before it starts.
Potential deliverables
- A process map and assessment of automation potential
- Responsibility boundaries for people, AI and conventional automation
- Permission, approval, logging, evaluation and shutdown rules
- A technical plan, risks and evaluation criteria for a limited pilot
- A plan to turn a successful pilot into an operable product and transfer knowledge to the team
I measure productivity gains rather than assume them
I agree measures such as processing time, errors and corrections, human review effort, model/API unit costs and user satisfaction before and after a pilot. If results are not meaningful, I stop or redesign rather than scale the automation.