6 Checks Before You Hire an AI Implementation Partner

Before hiring an AI implementation partner, ask each candidate to show how it would turn one of your actual business problems into a working process your team can operate. Compare the evidence in six areas: problem definition, technical fit, testing, decision ownership, adoption, and handover. A strong presentation can open the conversation; the proposed delivery team and its working evidence should decide the shortlist.

The six checks below are Clairvance's proposed evaluation method, not a validated predictor of project success. Use them after you have defined the problem and before you commit to an implementation. They apply whether the supplier is a large consultancy, a specialist firm, a software vendor, or an existing technology partner.

Choosing well matters. In MIT NANDA's July 2025 report, The GenAI Divide, pilots built with external partners reached deployment about 67% of the time, against about 33% for internally built tools, in the authors' interview sample of 52 organizations. The authors caution that the figures are self-reported and may reflect the organizations that chose partners rather than the approach itself. Treat the gap as a reason to evaluate partners carefully, not as proof that any partner will succeed.

Use the AI Vendor Evaluation Scorecard to organize your questions.

The six checks at a glance:

1. Can they define the work and the business case?

Ask for a map that starts with an incoming request and ends with an outcome the business recognizes as complete. It should show the systems involved, the people doing the work, where items wait, and who can resolve an exception. A box labeled “AI agent” does not explain how an order gets approved or an invoice dispute gets closed.

Then ask how the team would establish a baseline. The answer should separate handling time, waiting time, rework, and transaction volume. If a proposal describes hours saved, ask what would happen to that capacity: less overtime, fewer outsourced tasks, a backlog cleared, or more work handled by the same team. Those are different business cases.

Evidence to request: a sample process map and a calculation with explicit assumptions. Unresolved concern: a return estimate derived entirely from an industry average, without your volumes or operating constraints.

2. Will they test what your existing systems can already do?

Have your IT owner identify the relevant system editions, configured modules, permissions, interfaces, and supported ways to read or write data. Ask the supplier to explain what it would configure, buy, connect, and build. It should also explain why a simpler option would not adequately solve the problem.

A supplier may have experience with the name of your ERP while still needing to investigate your version and customizations. Request a demonstration against a suitable test environment or a documented integration assessment. Treat the proposed connector as unverified until that work is complete.

Evidence to request: a map of system connections and dependencies, including licensing and access requirements. Unresolved concern: “we integrate with everything” without a defined write operation, error response, or owner for the connection.

3. What would make their demonstration fail?

Ask to see ordinary work and difficult cases. Keep some evaluation examples separate from the examples used to configure the system. Have the business owner agree on the expected result before the demonstration, including cases that should stop for review.

Do not accept a single accuracy percentage without its denominator. Correctly reading a customer name is different from creating an order with the right customer, address, item, quantity, price, and status. Evaluate completion at the level where an error affects the business.

Evidence to request: a test plan, sample results, and a list of unresolved failures. Unresolved concern: every example succeeds, but nobody can explain how the system handles an unreadable attachment or a timed-out write.

4. Who is allowed to decide, and who handles the exception?

Ask for explicit boundaries between a suggestion, an approved action, and a completed system change. Identify the person authorized to resolve each consequential exception, the information they receive, and the time at which an unresolved item is escalated.

The NIST AI Risk Management Framework is voluntary guidance for incorporating trustworthiness into AI design, use, and evaluation. A supplier citing it should still explain the controls proposed for your workflow; citing a framework is not evidence that those controls operate.

Some suppliers cite ISO/IEC 42001, the international standard published in December 2023 that specifies requirements for establishing, implementing, maintaining and continually improving an AI management system. Ask who assessed the supplier against it and whether the team and services proposed to you fall within that scope.

Evidence to request: an access and decision-rights map, an example activity record, and a stop or recovery procedure. Unresolved concern: a “human in the loop” appears on a diagram, but the person has no queue, context, time, or authority to act.

5. Can your team actually use it during a working day?

Include the people who will operate the process in the evaluation. Ask them to complete an unfamiliar case, explain the result, and recover from a failure. Observe whether the proposed process reduces work or creates a second system that staff must keep synchronized manually.

Training should include role-specific tasks, not just a product tour. Ask how the supplier will collect feedback, update instructions, and support new starters. A named business owner should decide whether the new process is ready to replace the old one.

Evidence to request: a role-based walkthrough and an adoption plan tied to actual tasks. Unresolved concern: the delivery plan ends at technical deployment, with operating changes left for your team to invent.

6. What will you own when the engagement ends?

Put the proposed handover in writing. Review access to configurations, prompts, integration code where applicable, documentation, logs, test cases, and data exports. Identify third-party subscriptions and who can administer them. Agree what support is included, what requires a separate arrangement, and who investigates a failed run.

Ask the proposed delivery team to demonstrate a recovery or routine change. A file handed over at the end is less useful if nobody on your side can understand or maintain it.

Evidence to request: a handover inventory and responsibilities after launch. Unresolved concern: your process depends on an account, undocumented step, or key person you cannot access when something fails.

Give every candidate the same evaluation packet

Here is an illustrative packet for distributor order intake. It is a proposed exercise, not a client case or a completed supplier test. Use sanitized or synthetic records until your team has agreed the appropriate data access.

Change the packet for your business. An accounting firm could use a missing client document and a revised upload; an insurer could use conflicting submission details. The point is to make suppliers explain the same real decision, rather than compare demonstrations selected for different purposes.

Make the selection from evidence and open questions

For each of the six checks, record demonstrated, documented but untested, or unresolved, with a link to the evidence and an owner for the next action. Do not let several attractive features cancel out an unresolved requirement such as access control or recovery. Decide your non-negotiable requirements before reviewing proposals.

The AI Vendor Evaluation Scorecard provides a separate planning aid for organizing comparisons. Its scores are prompts for discussion, not certification. Once a candidate meets the essential requirements, compare the complete delivery plan, your team's required participation, dependencies, and ongoing ownership.

Clairvance provides AI transformation assessment, implementation, and adoption support. Apply these same checks to us. The useful next conversation starts with a process, its current systems, and what your team needs to improve.