Choose an AI vendor through evidence, not a polished demo
AI vendor selection should establish whether a team can deliver and operate a useful system within your constraints. Start with the business task, then compare implementation evidence, testing practices, data handling, operating costs and exit options. A fluent demonstration is a reason to investigate, not proof that a service will work reliably in your environment.
This guide is for buyers comparing implementation partners. It does not rank suppliers or suggest that one company is right for every project. A drafting assistant and a system authorized to submit orders require different controls. Define the consequences of an error before deciding which evidence matters most.
Give every candidate the same brief
Write a short description of the current workflow, intended users, permitted inputs and expected output. Identify what a person must still approve. Include existing software integrations, access constraints and the internal owner who will answer operational questions.
Avoid asking one vendor for a chatbot and another for end-to-end service automation, then comparing their totals. Those are different scopes. If ownership or data availability remains unclear, use the business AI readiness checklist before treating estimates as purchase-ready proposals.
State exclusions explicitly. These might include restricted data, autonomous financial actions or a department outside the pilot. Ask candidates to mark their assumptions and dependencies. A proposal that exposes uncertainty is more useful than one that silently assumes perfect data and unrestricted access.
Verify relevant delivery experience
A list of customer logos cannot explain what a vendor actually delivered. Ask for a comparable project described in terms of workflow, data complexity, integration and operational responsibility. Industry similarity can help, but it is not enough: a prototype and a production service demonstrate different capabilities.
Useful evidence includes a redacted architecture overview, a sample evaluation report, an incident process and an explanation of a limitation encountered after launch. Respect customer confidentiality. A supplier should not need to disclose another client's private records to demonstrate competence.
Meet the proposed delivery team, not only the sales team. Identify the owners of integration, evaluation and support. Ask what happens when a key engineer becomes unavailable and how knowledge is transferred. Record unverified claims as open questions rather than awarding them the same weight as observed evidence.
Turn the demonstration into a shared test
Provide each shortlisted candidate with the same permitted scenarios. Include an ordinary request, an incomplete input, an out-of-scope question and a request for an unauthorized action. Observe when the system asks for clarification, refuses or escalates to a person.
Separate software output from human intervention. Ask vendors to disclose manual preparation, operator corrections, model versions and relevant configuration. A recorded demonstration can establish plausibility, but it cannot replace testing on representative inputs.
For document-grounded assistants, the enterprise RAG evaluation checklist covers retrieval and citation testing in more detail. The supplier-selection question is broader: can this team repeat its tests and explain failures without hiding them behind an aggregate score?
Keep evaluation material separate from examples used to tune the demonstration. Otherwise, a supplier may appear successful because the system has been adapted to the exact questions it will face. The exercise need not be large; it must distinguish a rehearsed presentation from evidence of repeatable behavior.
Make the data and dependency chain visible
The implementation company may use separate model, hosting and document-processing providers. That is not inherently a weakness. The concern is an unclear chain of responsibility. Request a simple diagram showing which data leaves your environment, where it is processed and which parties can access it.
The voluntary NIST AI Risk Management Framework addresses third-party risks and contingency processes in GOVERN 6. Mentioning the framework does not constitute certification or prove that controls work. Ask for evidence tied to the proposed system.
Clarify input retention, logging, training use, deletion procedures and access boundaries. A statement about one service tier may not apply to a different configuration. Identify who can authorize changes to providers and how those changes will be communicated.
Use synthetic or appropriately sanitized examples during selection. Before real deployment, have the relevant technical and legal reviewers assess the actual processing arrangements. This is a purchasing checklist, not jurisdiction-specific legal advice.
Compare a common operating scenario
Separate setup, data preparation, integration, service consumption, hosting, recurring evaluation and support. Include your own review effort and staff time. A development-only quotation is not directly comparable with a proposal that includes operations and training.
Give suppliers a normal-use scenario and a growth scenario with the same request volume, input size, retention needs and support coverage. Ask what happens at consumption limits, who receives budget alerts and which changes require a new estimate. Do not assume a low headline price includes all of these items.
The AI process automation cost guide explains the cost categories. For selection, also make unsuccessful outcomes visible: what would you pay to stop after the pilot, export data or transfer responsibility to another team?
Use minimum requirements before weighted scoring
Set non-negotiable conditions before reviewing proposals. Unresolved data handling or an impermissible autonomous action should not be cancelled out by a beautiful interface. The conditions should reflect the specific project, not a generic checklist copied without review.
For candidates that pass, an illustrative 100-point model could allocate 25 points to workflow fit, 25 to performance evidence, 20 to data safeguards, 15 to delivery and support, and 15 to cost transparency and exit. These are suggested weights, not an official standard. Adjust them before seeing results, particularly for sensitive workflows.
Attach evidence to every score. Distinguish observed behavior, written commitments and sales statements. Business and technical reviewers can score independently first, then resolve disagreements against the evidence. Keep unresolved questions visible in the decision record.
Buy a bounded pilot with an explicit decision
A pilot should resolve a specific uncertainty. Define inputs, users, deliverables, duration, acceptance ownership and stop conditions. Link milestones to inspectable work rather than vague promises of complete transformation. Agree how scope changes will be assessed.
Consider a hypothetical service business seeking draft replies for staff. One candidate produces impressive answers but cannot show an escalation process. Another offers fewer features but keeps responses in draft and records human review. The second may fit the task better, subject to testing. This is an illustration, not a claim about actual vendors or results.
In that pilot, reviewers would observe the entire path from incoming request to draft, staff approval and final record. Critical failures would be reviewed separately from average performance. The outcome could be expansion, a narrower scope or a stop decision. A pilot should not automatically become a production commitment.
Evaluate the handover before signing
The UK government's AI procurement guidelines discuss supplier evaluation, collaboration and avoiding undesirable lock-in. They were developed for public procurement; they are a methodological reference here, not a statement of legal obligations for every buyer.
Ask for a sample handover package: setup documentation, configuration, dependencies, tests and recovery instructions. Clarify which components are custom deliverables and which remain licensed services. Possessing source code does not by itself mean that another team can operate the system.
Define support coverage, incident submission, response versus resolution commitments and responsibility for upstream outages. Require a process for reviewing model or data-source changes. Finally, test a small data export and identify the knowledge, access and time required for a transition.
Make the decision traceable
The right partner can explain both what it can deliver and what remains uncertain. Shortlist against minimum requirements, compare evidence on shared scenarios and use a bounded pilot before a larger commitment. Keep the decision record simple enough that operational staff can understand why the supplier was chosen.
Explore EasySaz AI implementation services and contact the team to discuss your project with a workflow description, a non-sensitive example and your constraints. A testable scope is a stronger starting point than a preferred model name or a long feature list.