In brief
- Pick one workflow, name its owner and define what good output looks like.
- Test the cases where failure would matter, not just the easy ones.
- Write down what the workflow does, what it does not do and when to stop it.
Interest in AI often starts with a broad goal: use it everywhere, save time across the business, keep up with competitors. Broad goals are hard to test, and they make it difficult to say who is responsible when something goes wrong.
A smaller start works better. One workflow, with clear boundaries and an accountable owner, can be measured, improved and explained to staff and customers. Once it works, the same approach can be repeated for the next one.
Name the workflow, not the tool
Describe the task, who owns it and what acceptable quality looks like. A tool choice comes after the problem and the permitted data inputs are clear.
Good first candidates are frequent, well understood and easy to check: summarizing meeting notes, drafting first replies to common enquiries, tagging documents or preparing first drafts of routine content. Avoid starting with decisions that affect people’s rights, money or safety.
Write down which data the workflow may use and which it must never see, such as personal or confidential information protected by your policies or client agreements.
Test where failure would matter
Include routine cases, ambiguous cases and cases where a person should take over. Time saved is not useful if quality or responsibility becomes unclear.
Build a small test set from real examples, each with the answer a skilled person would give. Run the workflow against it before launch and again whenever the tool, the prompt or the data changes. Record where it fails as carefully as where it succeeds.
A useful test set includes:
- typical cases the workflow should handle well
- unclear cases where it should ask or escalate
- cases it must refuse or pass to a person
- examples in the language and tone your customers use
Keep a person in the loop
Decide where a person reviews the output before it reaches a customer or a decision, and make that review part of the job rather than an afterthought. Reviewers need time, guidance on what to look for and a simple way to flag problems.
Tell people when they are dealing with AI-assisted work where it matters to them, and make it easy to reach a person.
Measure what changed
Before the pilot starts, record how the workflow performs today: how long it takes, how often it needs rework and how the people who rely on it rate the result. Measure the same things during the pilot.
Look beyond time saved. Accuracy, consistency, the review effort the workflow creates and the experience of staff and customers all count. If the gains only appear when nobody checks the output, the workflow is not ready.
Define the handover
Document what the workflow does, what it does not do and when it should be stopped. Training and ownership determine whether the pilot becomes useful work or another abandoned experiment.
A one-page workflow card is enough: its purpose, owner, permitted data, review step, known limits and the measures you will track. Review it on a set schedule, because tools and prompts change over time.
Questions to ask before you start
- Which single workflow are we improving, and who owns it?
- What data may it use, and what must it never see?
- What does acceptable quality look like, and how will we check it?
- Where does a person review the output?
- When would we pause or roll back the workflow?
Where to start
Pick the workflow your team repeats most often and can check most easily. Make it work well before you make it bigger.