1. Give AI a defined job
“Use AI” is not a business requirement. Choose a specific recurring task such as summarising meeting notes, preparing a first draft, categorising routine information or helping staff retrieve approved guidance.
Describe what good output looks like, what source information the system can use and what decision remains with a person. A narrow task is easier to test and govern than a broad assistant expected to know everything.
2. Start low risk and high frequency
Frequent internal work gives the business enough examples to judge whether the workflow actually saves time. Avoid starting with decisions that materially affect customers, employees or finances when the process has not yet been proven.
A successful pilot should be useful even if the business decides not to expand it. That keeps experimentation connected to operational value rather than technology theatre.
3. Improve the information first
AI output reflects the quality and consistency of its inputs. If service information, templates or internal guidance conflict, the workflow inherits that confusion.
Organise the underlying knowledge, identify the approved source and make ownership clear. This improves both AI-assisted work and ordinary human work.
4. Design human review
Decide who checks output, what they are checking and when approval is mandatory. “Someone will look at it” is not a control.
Review should match the risk. A draft internal summary may need a quick accuracy check. Customer-facing or sensitive material may need a named approver and a record of the source used.
5. Protect sensitive information
Understand where prompts and uploaded information go, who can access them, how long they are retained and what contracts or account settings apply. Do not put customer, employee or commercially sensitive information into a workflow simply because the tool makes it convenient.
Set practical rules staff can follow. Governance works best when the safe route is part of the normal process rather than a policy nobody can apply.
6. Test the benefit and keep a fallback
Measure time saved, correction rate and whether the output actually helps the next step. If staff spend more time fixing drafts than creating them, the workflow may not be worthwhile.
Keep a clear manual fallback and document the configuration. AI should support a resilient business process, not become a hidden dependency that stops work when a tool changes.
