The easiest time to evaluate an AI vendor is before the tool becomes useful.
Once a team has built workflows around a product, moved data into it, granted permissions, trained employees, and embedded the output into operations, switching costs begin to shape the decision. At that point, due diligence is no longer a clean evaluation. It is an evaluation under dependency.
Start with the use case, not the feature list
Risk depends on what the tool will actually do. A writing assistant used on public marketing copy is not the same risk as an agent with access to internal documents, customer data, financial workflows, or production systems.
Define the intended use, the data involved, the degree of autonomy, the people affected, and the consequence of failure before comparing vendors. That gives the review a real risk boundary.
Know the supplier chain
Many AI products are layered systems. The company you buy from may rely on model providers, cloud services, subprocessors, plugins, data connectors, and external APIs. Vendor identity alone does not reveal the full dependency chain.
Ask what third parties materially participate in processing, storage, model inference, or support. Then determine whether changes in that chain trigger notice or reassessment.
Data use is a purchasing question
Before sensitive material enters a tool, the buyer should understand what data is collected, where it is stored, how long it persists, who can access it, how deletion works, and whether prompts or outputs can be used for training or service improvement.
Marketing language is not enough. Important claims should be tied to current contractual or technical evidence.
Permissions deserve their own review
An AI tool may be low-risk in isolation and high-risk once connected to email, cloud storage, source code, CRM records, financial systems, or publishing accounts. Review scopes, administrative privileges, token lifetime, revocation, least-privilege options, and what happens when an employee leaves.
Test the workflow, not only the model
A polished demo can hide failure modes that appear in real operating conditions. Test representative tasks, edge cases, unsupported inputs, error recovery, human-review points, and how the system communicates uncertainty.
Plan the exit before adoption
Good diligence includes an exit path: data export, deletion, credential revocation, workflow replacement, record retention, and ownership of generated or configured material.
The strongest vendor decision is not simply “approved.” It is approved with documented conditions, evidence, owners, review dates, and triggers that force reassessment when the vendor or use case changes.
The AI Tool Vendor Due Diligence Kit™ provides that operating structure, including risk tiers, vendor scorecards, data/privacy review, permissions, testing, contracting, monitoring, and controlled exit.
Related resources
Connect this topic to the larger system
Vendor selection should operate inside an AI-governance system that defines acceptable use, verification, escalation, access, and accountability. Continue with AI Governance & Verification for the broader framework, related tools, and supporting resources.