How to Evaluate an Enterprise AI Assistant

Decide what a successful task looks like

An assistant that answers a demonstration question fluently has not yet shown that it can support a business process. Begin by defining one task, its users, the permitted information and the expected result. Specify the conditions under which the correct response is to ask for clarification, decline an action or escalate to a person.

For a hypothetical procurement assistant, a successful answer might identify the current approved purchasing policy and explain which department handles an exception. It should not invent an approval threshold when the policy is missing. A refusal in that case may be more useful than a confident answer.

Write acceptance criteria before comparing tools. Include factual accuracy, completeness, source support, access behavior and total effort to finish the task. These dimensions can move independently. A concise answer might omit a critical condition, while a comprehensive one may take longer to verify than reading the policy directly.

Build cases from the work, not the demo

Collect representative questions with authorization from the relevant data owners. Use synthetic or minimized material where possible. Include routine tasks, rare exceptions and cases that previously confused employees. Keep part of the evaluation set separate from prompt development so the team cannot simply optimize for known examples.

For each case, record the intended user, available evidence, acceptable answer elements and disallowed outcomes. Some questions have several valid answers. Use a rubric explaining what makes them acceptable instead of requiring an exact text match.

The NIST AI Metrology Center organizes measurement resources around AI use contexts. Selecting a metric remains the organization's responsibility; a public benchmark does not establish suitability for your own process.

Separate retrieval, generation and action failures

When an answer is wrong, identify which step failed. Did the system miss the relevant document? Did it retrieve an outdated copy? Did the generated answer contradict the correct passage? Did a tool execute a different action from the one approved? A single quality score makes these problems difficult to distinguish.

A useful review record captures the question, permitted source version, retrieved passages, answer, any proposed action and reviewer judgment. Minimize sensitive content in this record and define access and retention. Evaluation logs can become a second collection of confidential business information if they are copied casually.

Test evidence rather than the appearance of citations. Open the cited source and check whether it supports the specific statement. A valid link to a long handbook does not prove that an answer's numerical limit is correct. Include a case where no source supports the requested claim and check whether the assistant admits the gap.

Evaluate Arabic and English as real work

A bilingual interface requires more than translating an English test list. Include natural Arabic requests, mixed Arabic and English product names, dates, currencies, abbreviations and terminology used by the actual team. Ask fluent reviewers to assess meaning, instructions and tone, not only grammar.

Check whether the assistant silently changes a number, interprets an ambiguous date incorrectly or loses a negative condition during translation. Compare equivalent questions across languages while preserving language-specific cases. An average across both languages can conceal a weak Arabic experience.

Avoid treating a model's self-assigned confidence as a measured probability of correctness. Establish any escalation rule using observed results on relevant tasks, and test whether reviewers understand its limitations.

Make severe errors visible

Maintain explicit failure gates for unauthorized information disclosure, fabricated commitments and actions outside permission. These should not disappear inside a high average accuracy score. Test an employee asking for another department's restricted record, a misleading instruction in a document and a request that requires an unavailable approval.

OWASP's Misinformation guidance explains the danger of credible-looking false output. Our recommendation is to compare reviewers with and without the assistant, including their correction effort, so apparent speed gains do not conceal misplaced trust.

Track result variation as well. Repeat a selected set of difficult cases under controlled conditions and record model, prompt, tool and source versions. A changed configuration requires targeted regression testing; last month's approval should not silently cover a different system.

Choose a limited release on evidence

End the pilot with a documented decision: approved scope, excluded tasks, remaining failure modes, owner and review date. A limited read-only assistant may be acceptable while action-taking remains blocked. Rejection or further testing is a valid outcome when the evidence is insufficient.

Monitor real use for new failure patterns and allow staff to report unsuitable answers. Agree how to disable the assistant without losing business records. PRAESYNX can discuss software quality and integration requirements, starting with the work you need to verify rather than a general promise of AI accuracy.