Before automating a workflow with AI, map the work

## Map the current process first Write down the trigger, inputs, steps, decisions, systems, outputs, and exception paths of the workflow as it exists today. Include who performs each step and where delays or rework occur. If the process is inconsistent or poorly understood, automating it may simply make inconsistency faster. Start with a narrow, frequent, well-defined task rather than a broad promise to “use AI.”

## Decide whether AI is necessary Many repetitive tasks are better solved with ordinary rules, templates, search, or a small software integration. Use AI where language variation, classification, summarization, or generation provides a real advantage. Identify the quality threshold and compare it with a simpler baseline. Consider whether the task can tolerate occasional errors and how those errors would be detected and corrected.

## Classify data and risks List the information the system will process and whether it includes personal, confidential, regulated, or copyrighted material. Review vendor retention, access, and security terms with qualified stakeholders. Do not send sensitive information to a service without authorization. Consider bias, prompt injection, inaccurate outputs, and unintended disclosure. Document which use cases are disallowed and how users can raise concerns.

## Keep accountability visible Decide where human review is required, who is responsible for the final decision, and what the system must do when uncertain. A confidence score alone is not proof of correctness. Provide a safe fallback and make generated content recognizable to the people who need to review it. Preserve an audit trail appropriate to the risk, while minimizing unnecessary data retention.

## Pilot and measure realistically Test on representative examples, including difficult cases, not just ideal demonstrations. Measure accuracy against a defined rubric, time saved, correction effort, failure severity, and user experience. Set a threshold for stopping or reverting. Begin with a limited pilot, monitor changes in underlying models or vendors, and retrain staff on escalation. AI deployment is ongoing operational work; it should have an owner, review schedule, and documented limitations.

Continue with AI Workflow Automation ↗

Every business and audience is different; treat these ideas as a starting point and validate decisions against your own users and goals.