What questions should teams ask vendors about AI capabilities?

What should teams ask vendors about how the AI actually behaves?

Buyers get the most value when they move past “does it have AI?” and ask what the system does, under what conditions it does it, and where the vendor expects people to step in. That means probing training sources, model updates, deterministic logic, error handling, and the exact boundary between automation and human judgment.

Ask whether the product is a model wrapped around workflow rules, or a model that changes its outputs based on learned patterns. That distinction matters because a system built mostly on fixed logic has different testing, change-control, and failure characteristics than one that is learning from data. If the vendor cannot explain the behaviour clearly, treat the AI claim as unverified.

Ask what evidence the vendor can show for performance in conditions that resemble your environment, not just ideal demo inputs. The practical issue is whether the system stays useful when data is incomplete, prompts are ambiguous, or edge cases appear. A credible vendor should be able to explain known limitations, false-positive or false-negative behaviour, and how those limits are measured.

How should teams test vendor claims about data, training, and human oversight?

Teams should ask what data was used to train or tune the system, how recent it is, whether customer data is used for future training, and what contractual or technical boundaries exist around reuse. For AI-enabled products, data provenance and update behaviour affect accuracy, confidentiality, and whether the vendor’s assurances are actually sustainable over time.

Ask where humans review, correct, approve, or override the system. The most important due diligence question is not whether human oversight exists in theory, but whether it is real in the operating model. If humans only review outputs after the fact, the vendor is describing supervision, not control.

Ask how exceptions are handled when the system is uncertain or wrong. Good vendors can explain escalation paths, rollback behaviour, and whether the product safely degrades to a simpler mode. If the answer depends on manual cleanup after bad output, you are really evaluating recovery cost rather than AI quality.

Ask how changes are governed when the model, prompts, policies, or supporting rules are updated. A product can drift materially even when the branded feature name stays the same, so buyers should look for release notes, change logs, and a clear way to compare current behaviour against what was evaluated in procurement. NHIMG’s AI Security Platform Buyer’s Guide is useful here because it frames vendor evaluation around controls, PoC testing, and operational limits rather than marketing language.

What answers should make you cautious during procurement?

Be cautious when a vendor cannot separate model capability from product workflow, or when every limitation is described as “AI will improve over time.” That usually means the buyer is being asked to accept uncertainty without a control boundary. It also means you may not be buying a learning system at all, only a fixed rule set presented through AI language.

Be cautious if the vendor treats human review as optional but also claims high-stakes reliability. In practice, those claims only coexist when the system’s failure modes are low impact and well understood. If the AI touches decisions with operational, financial, or security consequences, you need a clear account of what happens when confidence is low or output is wrong.

Be cautious when the vendor cannot explain data retention, customer-data reuse, or how updates are validated before release. Those gaps can create hidden risk even when the core feature looks impressive in a demo. For buyers, the question is whether the AI is governable in production, not whether it is persuasive in a pitch.

Risk and Threat Considerations

Vendor claims about AI can hide weak controls, unclear data use, and overreliance on outputs that may be wrong, stale, or hard to challenge. The main risk is procurement of capability without accountability: teams may assume adaptive intelligence where the product is actually rule-driven, poorly bounded, or dependent on human cleanup.

Failure mechanism: The vendor obscures the boundary between learned behaviour, deterministic logic, and manual override, so the buyer cannot validate how the system behaves, what data it depends on, or when it fails safely.

Impact: That can lead to mis-scoped controls, unexpected errors in production, hidden data exposure, and a false sense of assurance that only becomes visible after the system is embedded in operations.

Practitioner Guidance

What to prioritise: Start with the questions that expose operational truth: what the system was trained on, what changes after deployment, and where human intervention is mandatory. Those answers tell you whether you are buying an AI capability, a workflow automation layer, or a marketing label.

What to verify: Ask for a concrete explanation of the product’s failure mode, the vendor’s update process, and the evidence used to validate performance in realistic scenarios. If the vendor cannot produce that without hand-waving, treat the risk as unresolved.

Common mistake: Teams often over-focus on model sophistication and under-focus on accountability. The better test is whether the vendor can show who is responsible when output is wrong, how the issue is detected, and what control prevents repeat failure.

Practitioner takeaway: The strongest procurement question is not “does it use AI?”, but “can we explain, test, govern, and override the behaviour we are buying?”

Related resources from NHI Mgmt Group

• What should security teams do about secrets hidden in SharePoint?
• How should security teams handle risks from AI browser extensions?
• How should security teams govern API keys used for generative AI access?
• How should security teams think about a compromised integration like Drift?