Build AI workflows that improve over time.
A useful AI workflow should not depend on someone remembering the perfect prompt. It should begin with approved context, follow a defined job, stop for human judgement and keep the corrections that improve the next run.
The short version
- Design the work before choosing the tool.
- Give AI the current facts, a clear output standard and defined boundaries.
- Keep a person responsible for exceptions and consequential decisions.
- Save useful corrections into the source material or workflow brief so the lesson survives the chat.
Stop rebuilding the instructions every time.
The loop is simple enough to use with ChatGPT or Claude today. More advanced tools can automate parts of it later, once the workflow is stable.
Use current source material instead of relying on memory or an old chat.
Make the outcome and the point of human responsibility explicit.
Handle exceptions and record the reason for material changes.
The next task starts with stronger instructions, so the team spends less time re-explaining the work and fixing the same mistake.
An AI workflow is a repeatable way to move a defined piece of work from an input to a reviewed output. The AI may prepare most of the work, but a person still owns the result.
Separate the workflow from the tool.
A workflow is not “use ChatGPT for customer service.” That names a tool and a broad department. A usable workflow is closer to: draft replies to routine delivery questions using the current policy, flag any exception and leave every reply for a support lead to approve.
This distinction matters because tools change. A clear workflow can move between models or platforms without losing the business rules that make it useful.
- a.Trigger: what starts the work.
- b.Inputs: the request, data and approved reference material.
- c.Procedure: what the AI should do and what it must not decide.
- d.Output: the required format and standard.
- e.Review: who checks the result and handles exceptions.
Give the AI less room to guess.
Good workflow instructions answer four practical questions: what does the AI receive, what should it produce, which rules apply and where must it stop?
Use a small set of approved examples to show the expected standard. State the common exceptions. If the AI lacks information, tell it to flag the gap rather than invent an answer.
“Reply to this customer.”
“Draft a reply using the current delivery policy. Answer only questions covered by the policy. If the order is late, the customer requests compensation or the policy is unclear, flag the message for a person. Do not send the reply.”
Turn a weekly report into a repeatable workflow.
Assume a founder spends ninety minutes every Friday collecting performance numbers, explaining changes and writing an update for the team.
The first useful version
The AI receives an exported metrics sheet, the agreed KPI definitions, last week’s report and the current reporting format. It calculates the movement, drafts a plain-English summary and flags missing or unusual data. The founder checks the interpretation and adds decisions.
What improves after each run
If the AI misreads a metric, correct the KPI definition. If the commentary is too vague, add an approved example. If a data source regularly arrives late, update the exception rule. The improvement belongs in the workflow, not only in Friday’s chat.
The founder still owns the judgement. AI removes collection, formatting and first-draft work, while each correction reduces the effort required next week.
Do not save every correction.
Some feedback is specific to one customer or one unusual week. Saving all of it creates clutter and conflicting instructions. Keep a correction when it is likely to change future work.
- a.Update the source when a policy, fact or definition was wrong or missing.
- b.Update the workflow when a step, boundary or review point was unclear.
- c.Add an example when the expected tone, structure or judgement was hard to describe.
- d.Leave it out when the correction applies only to one exceptional case.
Choose a task stable enough to improve.
Start with work that occurs often, follows a recognisable pattern and has an owner who can judge quality. Avoid automating a process the team cannot yet explain.
- ✓The task happens often enough for saved effort to matter.
- ✓The required inputs and approved source material are available.
- ✓A good output can be described or shown with examples.
- ✓Common exceptions and human approval points are known.
- ✓Someone owns the workflow and can keep its context current.
If several of these are missing, clarify the process first. AI cannot make an undefined workflow reliable.
Write these six things down before you automate anything.
One page is usually enough for a first version. Test it manually with your chosen AI tool, review the result and improve the brief before adding connections or autonomy.
What business result should improve?
Name the time, quality, revenue or risk outcome.
What starts the work?
List the request, data and source material required.
What should the AI do?
Describe the essential steps without unnecessary detail.
What does good look like?
Set the format, quality checks and approved examples.
Where must the AI stop?
State prohibited actions, sensitive decisions and exceptions.
Who reviews and improves it?
Name the approver and where reusable corrections are saved.