Start with a costly or frustrating workflow, not a model purchase. A useful AI project has a specific user, an observable problem, accessible information and a way to judge the result. The first goal is evidence that the workflow improves.
Find work worth improving
Ask teams where they repeatedly search for information, retype records, classify requests or draft similar responses. Estimate the frequency and effort using actual examples. Identify the consequence of an incorrect result and the person who owns the process.
A policy search assistant, document-intake helper or support-draft tool can be easier to evaluate than a broad “AI employee.” If the source information is inconsistent or nobody owns the process, resolve that before expecting automation to work reliably.
Match the technique to the task
Use retrieval when people need answers from approved knowledge. Consider extraction and validation for document intake. Use an agent when a task needs tools and decisions across steps. Use conventional rules where the process is deterministic and those rules are easier to verify.
Not every project requires generative AI. A better form, clearer policy or simple integration may remove the bottleneck at lower cost. The technology should earn its place by improving the job.
Define a pilot agreement
Write the intended users, sources, allowed actions, exclusions and acceptance criteria. Establish the current baseline and decide how results will be reviewed. A pilot should have an owner who can approve source corrections and decide whether the outcome is useful.
For a support assistant, measure accepted responses, correction time, repeat enquiries and customer experience. For an internal knowledge assistant, measure successful task completion and evidence quality. Avoid using the number of generated answers as the main measure of value.
Plan data and deployment
Classify the information involved and choose approved tools and hosting. Check whether personal, confidential or sector-restricted records are necessary. Minimise the data used in experiments and document who can access it.
If a private deployment is required, include infrastructure, operating support and model evaluation in the scope. If a managed service is appropriate, review its actual configuration and contract. Neither route removes the need for access controls and source ownership.
Expand with evidence
After the pilot, compare benefits with the full cost of review, maintenance and operation. Improve the weakest part of the workflow, then extend to similar tasks. Staff training and feedback matter: a tool that users cannot trust or correct will struggle even if its underlying model performs well.
A good first project leaves the company with a repeatable method for selecting and evaluating the next one—not just a demonstration that depends on its original builder.
Sources & further reading
Primary references for the technical background and regional statements in this guide. Planning examples and checklists are Novacom’s practical guidance; examples are illustrative unless explicitly identified as project experience.