What to ask AI vendors before committing your resources

AI buying risk extends beyond product capability

An AI marketing tool can work as promised and still be a poor purchase. The harder procurement question is how much implementation work, uncertainty, and operational risk the company must absorb before value appears. Subscription price and a strong demonstration capture only part of that commitment. Procurement should evaluate the business outcome, evidence, internal workload, data exposure, and contractual allocation of risk together.

This changes the order of diligence. A developing product may require integration, testing, training, troubleshooting, and customer feedback before it fits the intended workflow. Those demands consume employee capacity that has other uses. Product maturity matters because it helps determine how much evidence the buyer should demand and how much uncertainty the company should accept in the deal.

Start with the business outcome

The first useful question for an AI vendor is simple: what business problem does the product solve? A feature matters when it changes an outcome the company values, such as increasing output or identifying tracking gaps so teams can troubleshoot faster. Marketing executives should connect the proposed use case to a problem their organization already recognizes. Feature-heavy explanations without that connection give buyers little basis for calculating value.

Time savings require the same discipline. If automation frees capacity from campaign operations, reporting, or troubleshooting, management should decide how to use it. Employees might handle more accounts, improve campaign quality, investigate performance problems faster, or redirect effort to higher-value work. The buyer can then identify the current workflow, expected change, and business consequence before assigning value to the tool.

That definition creates a test for later evaluation. Procurement can judge performance against a specified operational or commercial result. The result should be concrete enough to determine whether implementation produced the expected change. Once the outcome is defined, the buyer can ask how much evidence the vendor has that it can deliver.

Vendor maturity determines how much proof you should demand

An established vendor should provide specific case studies and results from advertisers with a similar use case, vertical, or organizational size. Buyers should examine how closely those examples match their workflows and constraints before using them to estimate likely value. The vendor benefits from presenting its strongest evidence, so procurement should test the relevance of each example and the degree of independent validation it provides.

An early-stage company presents a different diligence problem when it has limited experience with the buyer’s use case or vertical. Procurement should establish how many relevant deployments the vendor can substantiate and what the customer would need to contribute during adoption. Hands-on testing, access to knowledgeable staff, documented limitations, and clear implementation requirements can help the buyer assess uncertainty before making a larger commitment.

Domain expertise deserves a separate test. A vendor building for media buying should be able to explain how a media buyer spends the day, where the workflow fails, and which constraints affect adoption. If the founders lack firsthand experience, buyers should ask how the company researched those workflows and incorporated the findings into product decisions. A compelling founding story can inform that assessment, but procurement still needs evidence that the product can produce the defined result.

The salesperson does not need to hold all that expertise personally. Serious prospects should, however, be able to reach people who can answer detailed questions about the technology, workflow, limitations, and implementation. The vendor has a commercial interest in making adoption appear straightforward. Buyers need enough access to test those representations before committing their own staff and systems.

Calculate the internal cost of adoption

The economic calculation includes the subscription and the internal capacity required to make the product work in the intended environment. That work can include connecting systems, configuring access, training employees, evaluating outputs, changing workflows, and troubleshooting failures. Buyers should identify those requirements during diligence and estimate which teams will supply the labor. Every hour committed to implementation has an opportunity cost elsewhere in the organization.

Testing and quality assurance can consume substantial capacity when AI output affects campaign execution, measurement, or customer-facing work. The buyer needs people who know what an acceptable result looks like and can evaluate performance in the intended use case. Procurement should ask how much initial and recurring human review the proposed workflow requires. A demonstration can establish capability, but the production workload determines whether that capability is practical for the organization.

Training and adoption create another demand. Employees need to understand where the tool fits into their work, how to evaluate its output, and how to handle exceptions. Procurement should examine these requirements as part of the adoption plan. If training, workarounds, and human review absorb the capacity that automation was expected to release, the business case should include that cost.

A developing product can also require customers to participate in refinement. Staff may need to report bugs, explain edge cases, reproduce failures, provide feedback, and retest fixes. That exchange can be worthwhile when the expected business benefit warrants the effort, but the labor still belongs in the investment calculation. Procurement should establish how much product-development support the vendor expects from the customer and which specialists would provide it.

That burden can spread across scarce teams. Marketing operations may identify a workflow problem, analytics may validate the data, technical staff may diagnose an integration, and end users may test the correction. Buyers should map those dependencies before agreeing to a trial or implementation. A company with limited spare capacity can reject a promising tool when the adoption work exceeds what the organization can support at that time.

The same logic applies to a free trial. A test with no vendor fee can still require integration, data preparation, security review, and employee time. Buyers should define the maximum internal effort they will spend and the evidence required to justify a larger commitment. This prevents an evaluation from becoming an open-ended implementation project before the economic case is established.

Put data promises in the contract

Operational workload becomes visible during implementation, while data exposure requires diligence before sensitive information enters the system. Buyers should establish what information the vendor receives, where it is stored, how long it is retained, whether it is used for model training, whether shared or third-party models receive it, and what happens when the relationship ends. Legal, security, and technical teams can then assess that data path against the company’s requirements.

Statements about “your data” require precision because relevant rights can depend on the type of data, the contract, applicable law, and jurisdiction. Procurement teams should establish the rights and obligations for their specific data and use case. They need to know what the vendor may do, what the customer can require, and which contractual obligations continue after termination. Broad assurances should become terms that the responsible internal teams can evaluate.

Model training needs equally precise treatment. Buyers should establish whether submitted information is used to improve a customer-specific system, contributes to a shared model, or reaches a third-party model, along with the consent and contractual terms governing those uses. The vendor has a commercial interest in completing the transaction, so material representations about retention, deletion, storage, training, and data sharing should be checked against the agreement before deployment.

Product uncertainty makes this diligence more consequential. A buyer may decide that early access to a capability justifies uncertainty about performance, but the data obligations still need to be explicit throughout the relationship and after it ends. Where data handling materially affects the purchasing decision, procurement should resolve differences between sales representations and contractual language before the company supplies the relevant information.

Make the deal reflect product uncertainty

An early-stage vendor can be a rational choice when the expected opportunity justifies the uncertainty. A customer may gain early access to a useful capability, influence how the product addresses its use case, or negotiate commercial terms that fit the risk. It may also spend staff time on bugs, edge cases, and product refinement before learning whether the expected result will materialize. Procurement should price that uncertainty into the decision.

The deal structure should reflect the available evidence. Where a vendor has limited proof for the buyer’s use case, procurement should seek transparency about that limitation, practical opportunities to test the product where feasible, defined implementation expectations, and commitments proportionate to the evidence. Contract terms should address identifiable risks around data handling and delivery. Because the vendor benefits from closing the sale, procurement should test its characterization of product readiness through diligence and reflect material representations in enforceable terms.

Established products should face the same outcome test. Their operating history and relevant case studies can support diligence when the examples match the buyer’s intended use, but maturity by itself does not establish value for a specific organization. The decision still depends on expected business results, implementation demands, internal capacity, and contractual exposure. Product maturity is most useful for deciding how much uncertainty the buyer should accept and what evidence the deal should require.

Main highlights

• Define the business outcome first: Procurement teams should tie each AI purchase to a specific operational or commercial result. Use that outcome to judge vendor evidence, expected value, and implementation success.
• Match evidence to vendor maturity: Buyers evaluating established vendors should demand relevant customer results, while early-stage vendors require closer testing of product readiness, domain expertise, and implementation demands.
• Price the internal work of adoption: Procurement teams should include integration, training, testing, troubleshooting, human review, and employee feedback in the investment case. Free trials also consume staff capacity and need defined limits.
• Put data commitments in the contract: Legal, security, and technical teams should verify how vendors store, retain, share, train on, and delete company data. Material sales promises should appear in enforceable contractual terms.
• Structure the deal around uncertainty: Buyers taking on greater product risk should seek testing opportunities, clear implementation expectations, appropriate commitments, and contract protections that reflect the evidence available.