Six months after an AI tool goes live, the most useful review is not a replay of the buying decision. The business now has something better than demonstrations and promises: evidence from ordinary work. Staff have found shortcuts, awkward cases have appeared, supplier features may have changed and the original use case may have expanded quietly. A six-month review should use that evidence to decide whether the tool remains useful, controlled and worth the work around it.
Start with the job the tool was bought to do
Return to the original problem. Was the tool intended to reduce repetitive drafting, help classify enquiries, retrieve information or support another defined task? Compare that purpose with how people actually use it now.
Scope drift is not automatically bad. Staff may discover a genuinely useful adjacent use. But unplanned expansion deserves a fresh decision, particularly if the tool now sees different information, communicates externally or influences more consequential work. Record what changed rather than allowing today's use to inherit approval from a different six-month-old use case.
Look at outcomes rather than activity
High usage does not prove that the tool is helping. Review whether it removes useful work, shortens a meaningful process, improves consistency or makes information easier to use. Include the effort spent checking, correcting and maintaining it.
Ask staff where the tool saves attention and where it merely moves effort. A generated draft that requires extensive repair may look productive in an activity report while adding little to the finished workflow. Conversely, a modest feature used on a repetitive bottleneck may deliver considerable practical value without producing dramatic usage numbers.
Study corrections, exceptions and near misses
The difficult cases are often the richest evidence after six months. Group recurring corrections and escalations. Are outputs failing because source information is incomplete, instructions are weak, the task is ambiguous or the use case simply demands human judgement?
The UK government's introduction to AI assurance describes assurance as measuring, evaluating and communicating the trustworthiness of AI systems. A review should therefore examine limitations and risks as deliberately as successful outputs, rather than treating failure evidence as an embarrassment to exclude.
Recheck information access and retention
Integrations and permissions often expand during implementation. Review which systems the tool can now access, whether each connection remains necessary and who has administrative control. If interaction histories or prompts contain personal information, ask why they are retained and for how long.
The ICO's AI data-minimisation audit guidance calls for reviewing the relevance of personal information and justifying its retention. The six-month point is a sensible opportunity to remove access or data collection that seemed useful during setup but has proved unnecessary in practice.
Check what changed outside your business
The product you reviewed before purchase may not be identical to the product in use now. Examine material supplier changes to models, integrations, controls and dependencies, as well as changes to your own process. Confirm that staff guidance still matches the current system.
The NCSC's secure AI operation guidance recommends monitoring system behaviour and inputs and taking a secure approach to updates. That reinforces the need to treat deployed AI as a changing operational system rather than a purchase that can be forgotten once configured.
Ask the people around the workflow
Speak to users, supervisors and people who receive the output. They experience different failure modes. A user may see repetitive corrections; a manager may see uneven adoption; a colleague downstream may discover that summaries omit context they need.
Where customers interact with the system, review complaints, repeated contacts and hand-offs rather than assuming silence means satisfaction. The aim is not a popularity vote. It is to understand consequences that a technical dashboard cannot reveal.
Finish with a decision and named actions
A six-month review should end with more than “continue”. Decide what to retain, change, restrict, test or stop. Assign owners and dates to important actions, especially source updates, permission changes, staff guidance and unresolved failure patterns.
Also define the next review trigger. A major new integration or customer-facing use may justify reassessment before another calendar milestone. Six months is valuable because enough real work has accumulated to challenge assumptions. Use it to turn experience into a better operating decision, not simply to confirm that the subscription is still active.