Choose a useful first AI application
Start with a recurring task and a way to check the result. Choose AI only when it helps more than a simpler approach.
Start with work, not a model
Look for a task that happens often, takes effort and has an output someone can review. Examples might include drafting a response, organising unstructured feedback or finding relevant passages in approved documents. Name the user and the decision the output supports.
Define the boundary. Reading a document to suggest a draft is different from automatically sending a message or changing a customer record. Begin where review is practical and mistakes can be caught before they cause harm.
Illustrative situation: a support team drafts similar replies from approved documentation. AI may help with the first draft while a person checks and sends it. Copying a confirmed order between systems is usually better served by ordinary automation.
A checklist to reuse
- A real user with a recurring task
- Examples of current inputs and acceptable outputs
- An owner who can judge quality
- An explicit boundary on what the system may do
Compare simpler options first
If the task follows stable rules with structured inputs, a form, template, search filter or integration may be easier to test and maintain. AI becomes more interesting when language or variation makes fixed rules difficult. It still needs an operational reason to exist.
Compare the total workflow, including review time, correction and exceptions. A fast draft that takes longer to verify is not a saving. Include the cost of preparing and maintaining the information the system needs.
A checklist to reuse
- Could a template or better search solve it?
- Could a deterministic rule handle most cases?
- How much review and correction is required?
- What new maintenance work would AI introduce?
Run a small, representative evaluation
Keep a set of examples that includes ordinary cases, ambiguous inputs and cases the system should decline. Decide what counts as acceptable before looking at results. Compare with the current process on the same cases.
Use data you are authorised to process. Check the chosen provider’s actual retention, access and training terms before sending confidential information. During an early test, synthetic examples can help explore the workflow without exposing real customer details. Removing names alone does not make real data anonymous; assess whether people can still be identified.
A checklist to reuse
- A fixed test set including difficult cases
- Quality criteria and unacceptable failure types
- Time measured including human review
- Data access, retention and provider terms checked
Decide whether to expand, change or stop
An experiment should end with a decision. If the improvement is small or unreliable, adjust the task or keep the simpler process. A useful outcome can be deciding that AI is unnecessary.
If the test is promising, define the review owner, fallback, monitoring and a way to disable the feature. Recheck the examples when changing the model, prompt or source material. Training your team to recognise limits is part of implementation, not an optional extra.
A checklist to reuse
- Documented evidence for the decision
- A human review and escalation path
- A working fallback when AI is unavailable
- An owner for future evaluation and updates
Sources and upkeep
These sources inform the guidance above. The examples and checklists are modeleven’s editorial recommendations, not promises of results. Sources reviewed 8 September 2026.