A convincing AI demonstration can answer a prepared question in seconds. A useful business tool must handle incomplete requests, outdated documents and the moment when it should ask a person for help. The gap between those two experiences is what a pilot should investigate.
A four-week pilot is a planning framework, not a promise that every integration can be delivered in a month. Its purpose is to produce evidence for a decision: expand, revise or stop. Keep the scope to one team, one workflow and a small set of authorized information sources.
Week one: choose a task and measure the baseline
Start with a task whose output a person can assess. Drafting an answer from an approved support knowledge base is easier to evaluate than “improving productivity.” Another candidate is extracting requested fields from incoming documents, provided the team can check the extracted values.
Ask whether the task needs AI at all. If every input is structured and the decision follows fixed rules, ordinary automation may be simpler to test and operate. AI is more relevant when interpreting language or variable documents is a meaningful part of the work.
For the selected task, record volume, handling time, corrections and escalation reasons. Use a mix of routine and difficult examples. Nominate a process owner who can decide what an acceptable answer looks like, and write down which actions remain outside the pilot.
Week two: prepare information and a fair evaluation
For an assistant answering questions about company material, retrieval-augmented generation, or RAG, retrieves relevant passages and provides them to a language model as context. Microsoft’s RAG design and evaluation guide distinguishes evaluation of retrieval from evaluation of the resulting answer.
Begin with a small collection of current documents. Record an owner and review date for each one. Remove superseded instructions or label them so they cannot be mistaken for the current process. A model should not be expected to resolve an undocumented disagreement between two departments.
Keep evaluation examples separate from the examples used to tune the assistant. One practical starting set is 100 cases: 60 routine, 20 ambiguous, 10 with no supported answer and 10 testing access restrictions or hostile instructions. These proportions are an example, not a universal standard; adjust them to the workflow’s actual risks.
For every case, define the expected result, the supporting source and what counts as a serious mistake. Include Romanian, English and German if all three will be used. A strong English result does not demonstrate that a Romanian request retrieves the right German source document.
Week three: run alongside the existing process
Let staff review draft outputs before anything reaches a customer or changes a business record. Show the source passages beside the draft so that a reviewer can check the substance efficiently. Capture whether the draft was accepted, edited or rejected, along with the reason.
Use normal application authorization to decide which documents each user can retrieve. Instructions in a prompt are not a substitute for permissions. OWASP documents how instructions embedded in retrieved content can redirect model behaviour and recommends constrained privileges and human approval for consequential actions. See its prompt injection guidance.
For this pilot, keep action-taking disabled unless it is explicitly part of the tested scope. A support draft assistant does not need permission to issue refunds. If a later phase adds that capability, it needs a separate authorization and confirmation flow with its own acceptance tests.
Make failure useful: unsupported questions should route to a person with the available context. Requiring an answer to every request encourages confident guesses and makes the evaluation less informative.
Measure the time people actually recover
Token usage and response speed are only part of operating cost. Include review, corrections, escalation, source maintenance and monitoring. Measure complete handling time against comparable work, rather than comparing model generation time with the entire old process.
Consider this illustrative result for 100 requests. The old process takes six minutes each, or 600 minutes. With the assistant, 60 requests take two minutes each and 40 still take six minutes: 360 minutes in total. Another 40 minutes of evaluation and maintenance brings the total to 400 minutes. The net saving is 200 minutes per 100 requests.
At 500 comparable requests per month, that would be about 16.7 hours. It is a scenario calculation, not a forecast. Recheck it if request complexity, review effort or the share of usable drafts changes. The team must also decide what productive work can use the recovered time.
Week four: make the expansion decision explicit
Review a small scorecard: supported-answer quality, serious errors, correct escalation, time per completed request and total operating cost. Inspect results by language and request type so that an overall average cannot hide a weak category.
Set acceptance criteria before reviewing the final results. Any observed unauthorized disclosure should block expansion until the cause is fixed and retested. For less critical quality issues, agree a tolerance suited to the workflow and assess whether failures are easy to detect. A pilot with no observed serious error still does not prove such errors are impossible.
Finish with a decision record, a named operational owner and a list of the evidence needed for the next phase. Our AI development and automation service can help define the workflow, evaluation and integration together. Bring one repetitive task and examples of good results, and we can shape a pilot around work your team can measure.