> For the complete documentation index, see [llms.txt](https://doc.duaer.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.duaer.com/guides/ai-vendor-evaluation-checklist.md). # AI Vendor Evaluation Checklist 2027: Seven Checks Before You Sign Adapted from the Duaer documentation site, this guide explains how to start from the job to be done, treat data handling as a hard gate, test vendors with your own documents, and weigh integration, exit options and post-launch operations before signing an enterprise AI contract. ## Why AI Vendor Evaluation Needs a Different Design Most AI vendor evaluations begin with a demo, and demos are usually built to impress. The vendor shows a clean interface, answers a few questions fluently, and presents a customer success story. The procurement committee leaves feeling good and signs a pilot agreement. A few months later, the team discovers that the tool cannot read the company's real documents, the data terms are vague, or switching away would cost more than the original contract. The problem is rarely that the vendor lied. The problem is that the evaluation measured the wrong things. A useful checklist keeps the team focused on what will still matter after the sales cycle ends. Traditional software procurement compares features, pricing and security certifications. Those still matter, but AI products add three layers of complexity. First, output quality depends on your data and your tasks, so a vendor's benchmark results say little about your specific workflow. Second, the underlying model may change without notice, altering processes the team has already built around it. Third, many AI products quietly send data to third-party model providers, so the real supply chain is longer than the vendor's logo suggests. For this reason, a single scorecard with equal weights often rewards polished demos. A better approach is to split the evaluation into stages, each with clear pass conditions, so that a vendor who fails a basic requirement cannot advance to the next round on the strength of a good presentation alone. ## Start From the Job, Not the Feature List Before opening a single vendor website, describe the job in one paragraph: who will use the tool, what input they start with, what output they need, how they will check that output, and what happens when it is wrong. This paragraph should be the first item on the checklist, because every later criterion is measured against it. Many teams skip this step and end up comparing vendors on features that have nothing to do with the task. A support team needs to retrieve information reliably from policy documents and hand cases cleanly to a human agent. A legal team needs precise citations to specific clauses and an audit trail. Both may be sold as an 'AI assistant', but they are not interchangeable. Define the role first, then ask each vendor to explain how its product handles that specific job. ## Check Where Data Goes and Who Can See It For most companies, this is the first hard gate. Ask vendors to answer in writing: where customer data is stored, which regions and subprocessors can access it, whether data is used to train shared models, how long logs are retained, and how deletion works in practice. Also ask whether the product can run in a configuration that keeps data inside the company's own environment, and what that configuration costs in capability and operational effort. Separate contractual promises from technical controls. A statement that 'data is not used for training' is useful, but follow up by asking which controls enforce access permissions, encryption and tenant isolation. If a vendor cannot describe these clearly, record that as a warning sign, even if the rest of the product performs well. ## Test With Your Own Documents, Not the Vendor's Demo Set This is the weakest point in most checklists. Vendor demos use carefully selected, well-structured content designed to show the product at its best. Real company environments contain outdated PDFs, duplicate files, scanned spreadsheets and policies that contradict one another. A test only means something if it uses a sample of your actual material. Build a test set of thirty to fifty realistic questions drawn from real work, and attach the answers a qualified person would consider correct. Have every shortlisted vendor answer the same questions under the same conditions, and score the results on accuracy, whether answers are traceable to sources, and whether the system admits uncertainty instead of guessing. Run the test again a week later to see whether the results hold steady. This is also where a structured workspace helps. Teams can set up the same workflow in Duaer and compare how each candidate handles the same questions and the same documents, which is far more concrete than a slide deck. The goal is not to pick a winner from a single run, but to make the differences clear enough that the committee can discuss them based on evidence. ## Check Integration and Exit Paths Before Signing, and Plan for Operations After Launch Every AI vendor says its product integrates with your systems. The useful question is how. Ask which connectors are native and which require custom development, who maintains them, and what happens when a source system's API changes. Request a written list of the systems the pilot will touch and the permissions it needs. The principle of least privilege is easy to promise and hard to maintain, so check how access is granted and revoked. Exit planning matters just as much. Ask how you can export your prompts, workflows, knowledge indexes, evaluation results and logs in usable formats, and whether content can move to another platform without being rewritten. A vendor that makes leaving difficult is not necessarily a bad choice, but that difficulty should be counted as a cost of the decision. For a closer look at how dependency builds up across an AI stack and which signals to watch for, see our guide [AI Vendor Lock-In Guide](https://doc.duaer.com/guides/vendor-lock-in-ai/). A pilot runs in a controlled environment; production does not. The checklist should also cover what the vendor provides after launch: monitoring of answer quality, alerts when retrieval quality drops, an incident review process, and notification of model changes. Ask how often the underlying model is updated, whether you can pin a version, and how regression testing is done before an update reaches users. Commercial terms deserve equal attention. Define response times for incidents, who is responsible for wrong answers that reach customers, and how usage-based pricing changes as volume grows. Many contracts look reasonable at pilot scale and become expensive in production, so ask vendors for a cost model at three usage levels rather than a single quote. If the provider is a consulting or transformation partner rather than a software vendor, the criteria shift toward delivery capability and knowledge transfer. Our guide [Enterprise AI Transformation Services Selection Guide](https://doc.duaer.com/guides/enterprise-ai-transformation-services-2027/) covers this case in more depth. ## Score Vendors, Record Your Reasons, and Act This Week Finally, turn the research into a simple scorecard. Anchor it to the job description, treat data handling and integration as gates rather than points, and score output quality using your own test set. Record why each vendor passed or failed each gate. Written reasons are more valuable than a number, because when circumstances change, such as new regulations or a new model release, the committee can revisit the decision on the same basis. Here is a practical next step. Pick an upcoming AI purchase and write its job description in one paragraph. Then assemble thirty questions from real tickets, documents or requests to form your test set, and send the same question list and sample files to every shortlisted vendor. Before the next demo round, ask each vendor for written data handling terms and an exit procedure. You can run this process in Duaer and have the team compare candidates on identical materials before committing budget. To see whether this workflow fits your evaluation, you can start by registering for a trial at https://www.duaer.com. A good AI vendor evaluation checklist is not meant to predict which product will impress in a demo. It is meant to show which product will still work correctly, stay compliant and remain replaceable a year after signing. ## Questions ### Which criteria matter most when evaluating AI vendors? Start with the job to be done, and treat data handling, access controls and exit paths as pass/fail gates. Once a vendor clears those gates, use your own test set to assess accuracy, whether answers can be traced to sources, and whether the system is honest when it is uncertain. Feature lists and pricing are useful secondary references. ### Why shouldn't I decide based only on a vendor demo? Demos use idealized, carefully selected content that does not reflect the outdated files, duplicate material and conflicting policies found in real company documents. Testing with your own samples under the same conditions, then retesting after a interval, is the only way to tell whether results are stable. ### How can I tell whether an AI vendor creates lock-in risk? Ask whether prompts, workflows, knowledge indexes, evaluation results and logs can be exported in usable formats, whether content can move to another platform without being rewritten, and whether you can pin model versions. Counting migration costs in the decision is far better than discovering later that leaving is hard.