Understand the work as it happens today
When a new AI tool catches attention, it is tempting to begin with the technology and search for a place to use it. A better starting point is the work itself. Follow the task from beginning to end: what information comes in, which steps repeat, where people need to make a judgment, and what happens when an exception appears?
That view can uncover a simpler improvement. A clearer handoff, a useful form, or a small workflow change may solve the problem without introducing a new model or adding operational complexity.
Choose a task that is suitable for assistance
If automation could help, define the role it should play. Is it organizing information, drafting a starting point for a person to review, or completing a predictable step? Start with a bounded task where the expected input and output are understood and the value can be evaluated.
Make sure the information used is appropriate for that purpose and that people understand how to use the result. Be clear about uncertainty: an automated output should not look more certain than it is, and a person should be able to check or correct it.
Design for exceptions and accountability
Decide who owns the final decision, which cases require review, and how a person can pause or override the process. Consider what should happen when information is missing, an answer is unreliable, or the workflow changes. These paths are part of the design, not edge cases to leave for later.
Finally, observe whether the change actually helps. Gather feedback from the people who use it, monitor errors and exceptions, and revisit the process as needs evolve. Responsible automation should make work more useful and understandable—not simply move a decision out of sight.
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