AI adoption starts with the work

People already use machine learning through everyday software, often without thinking about it. Deliberately introducing generative AI at work is a different decision. It needs a purpose, a place in the workflow and a way to judge whether it helps.

Choose a recurring task. Describe what makes a good result and where the time goes today. Then ask whether the proposed tool makes that work easier without creating a larger checking or administration burden.

Ease of access matters, but so does fitting the routine. A tool that requires people to move information between several systems may struggle even when its individual outputs look impressive.

A proposal can make an individual task look effortless. The useful question is what happens around it. Who finds the source material? Who checks the answer? Where does the approved result go? Include those steps in the comparison, otherwise the demonstration can hide the work it creates.

Take a weekly client report. A fast first draft is only one part of the job. The figures may sit in a spreadsheet, the explanation in meeting notes and the commitments in email. Someone still needs to reconcile them. If the draft invents an explanation for a missed target, checking it may take longer than writing the paragraph.

Write down three tests: can the person doing the job use it without repeated help; does it fit the systems and approvals already in place; and is the finished, checked result better than the current approach? A failure on any one test gives you something specific to fix.

For that reporting example, choose a few completed reports with their original inputs. Use approved or anonymised material. Ask the team to recreate the reports with the proposed workflow, including review and corrections. Compare the finished work with the original, rather than comparing drafting time alone.

Agree what would stop the trial: unsupported figures, information shown to the wrong person or an approval step that can be bypassed. Record exceptions as carefully as successes. You can then decide whether to improve the workflow, use a simpler tool or leave the task alone.

Expansion should follow a result you can explain. If the only evidence is that people enjoyed the demonstration, you have more to learn before buying wider access.