AI features work best when they solve a specific task that people already recognize. A broad request to ‘add AI’ can hide the actual work, the quality bar and the risk of a wrong answer.
Map the existing task
Watch how the task happens today. Note inputs, decisions, exceptions and the point where a person needs to act. Repeated summarization or classification may be promising, but repetition alone does not make a workflow a good candidate.
Check the consequences of mistakes
Ask what happens if the system omits a detail, misroutes a request or generates a confident but incorrect response. Start with work where the result can be reviewed before it affects a customer or an important record.
- Define who owns the final decision.
- List sensitive data the workflow may encounter.
- Plan an easy path for correction or escalation.
Test on representative examples
Collect examples that include ordinary cases, edge cases and incomplete inputs. Compare an assisted workflow with the current process. Measure time saved alongside accuracy, rework and user trust; speed is not useful if correction takes longer.
Design the surrounding product
People need to know when an output was generated, which source informed it and how to change it. Build review, feedback and fallback into the interface. The workflow around the model is often what makes the feature dependable.
Practical next step
Choose a narrow, measurable task with manageable failure costs. Validate the complete workflow before expanding automation.
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