The most common mistake when choosing an enterprise AI tool is confusing general capabilities with business outcomes. A system that writes fluent text or correctly answers a few sample questions may not work reliably with your documents. Accuracy alone is not enough, either: The tool may expose sensitive information, present incorrect output extremely convincingly, or initiate unexpected actions when granted automation privileges. A sound purchasing decision should therefore be based not on a product demo, but on a pilot with clearly defined boundaries.
1. Define the task before the product
“Employees using AI” is not a measurable goal. Instead, choose a single workflow, such as classifying support requests, extracting specific clauses from lengthy contracts, or drafting responses from approved documents. Document the input, expected output, end user, and impact of an incorrect result.
Measure the task’s success using the current method. How many minutes does an employee take to complete the same work today? How many corrections are made? What is the critical error rate? Without this baseline, you cannot tell at the end of the pilot whether the observed increase in speed is a genuine gain or merely excitement about a new tool.
Exclude high-impact decisions from the first pilot. Incorrect output in areas such as hiring, credit, health, law, or employee performance can have serious consequences for individuals. The pilot should begin with a low-risk, reversible task; AI should not be the final decision-maker, but the producer of a draft submitted for review.
2. Prepare a representative test set
Create at least several dozen examples that reflect real work and have been stripped of personal and confidential information. Do not select only easy examples. Include edge cases such as missing information, spelling errors, conflicting instructions, different document formats, Turkish characters, long contexts, and unanswerable questions. Test not only whether the system gives the correct answer, but also whether it refrains from answering when there is insufficient evidence.
Define the acceptance criteria for each example before testing. Changing the criteria afterward creates the risk of moving the goalposts to fit favorable-looking results. The evaluation table can use separate columns for factual accuracy, source attribution, adherence to instructions, restraint when information is missing, avoidance of harmful content, and the time a person spends making corrections.
A single overall score can hide flaws. For example, a 95 percent success rate may look impressive while the remaining 5 percent consists of critical cases in which customer data is sent to the wrong person. Ordinary and critical errors should therefore be separated; for critical errors, use an acceptance threshold close to zero rather than merely counting them.
3. Test security boundaries realistically
NIST’s generative AI profile does not reduce risk management to model performance alone; it addresses governance, content integrity, privacy, security, and human oversight throughout the system’s life cycle. The pilot team should also map the data flow from end to end: Where are prompts processed, how long are they retained, are they used for training, who are the subprocessors, and who can view the logs?
Verify contractual promises through the product settings. Actually test single sign-on, multifactor authentication, role-based access, audit logs, retention periods, and bulk data deletion options. Do not rely solely on training materials to prevent users from pasting customer secrets, source code, or health information; apply data classification and technical restrictions.
OWASP’s current LLM risks highlight threats such as prompt injection, sensitive information disclosure, supply chain issues, improper output handling, and excessive agency. Turn these topics into test scenarios. What happens when a document containing malicious instructions is uploaded to the system? Is model output executed as a command in another application? If the tool can send email or delete files, is human approval required before the action? Give plugins and connections only the minimum permissions required for the task.
4. Measure Turkish-language quality separately
A provider’s success in general benchmark tests does not guarantee the quality of your Turkish-language workflow. Use separate examples for suffixes, negation, date and currency formats, internal terminology, formal forms of address, and different regional expressions. Flag text that sounds as though it was produced through translation, punctuation problems that alter the meaning, and incorrect Turkey-specific answers based on foreign-language sources.
When testing a source-grounded system, check whether every claim is actually supported by the document provided. A link or page number alone is not evidence; the cited section must match the claim. For tasks requiring current information, also record the knowledge cutoff date, search capabilities, and how frequently sources are updated.
5. Do not treat cost as merely the token price
Add the time spent on integration, employee training, security reviews, output verification, and error correction to the license fee. Calculate potential quota, API, storage, and support costs under three different usage scenarios. OWASP’s “Unbounded Consumption” category shows how uncontrolled use can lead to both service disruption and unexpected bills. Per-user and per-transaction limits, budget alerts, and abuse-monitoring mechanisms should be active during the pilot.
6. Base the decision on a body of evidence
At the end of the pilot, review not only the average score but also failed examples, critical errors, time spent on human corrections, and security tests together. Write down the thresholds for “buy,” “continue with limited use,” and “stop” decisions in advance. If the product succeeds, clearly specify the data types, user groups, and human approval requirements under which it may be used.
Finally, remember that models and services can change. Make rerunning the test set part of the contract and operating process whenever the provider changes the model version, data policy, or pricing. A good pilot does not prove that AI is flawless; it makes clear the conditions under which it is sufficiently useful, controllable, and safe.