AI Vendor Selection: The Executive Due-Diligence Guide

A polished demo can make almost any AI product look inevitable. The real test begins after the demo: Can the product improve a specific workflow, work with your data, survive security review, earn employee adoption, and remain economically attractive at scale?

I evaluate AI vendors as an operator, not as a technology collector. The goal is not to buy the most impressive model. The goal is to improve a business outcome without creating a dependency you cannot govern or escape.

This guide organizes the decision into six gates. If a vendor fails an early gate, stop. Do not let an exciting feature compensate for a weak business case.

Gate 1: Define the business problem before the product

Write a one-sentence problem statement before meeting vendors:

We need to reduce or improve [business metric] in [workflow] for [users], without increasing [material risk or cost].

If the team cannot complete that sentence, it is not ready to select a vendor.

Establish the current baseline using numbers you already track: cycle time, labor hours, error rate, conversion rate, backlog, customer response time, or cost per transaction. Then set a target and name the executive who owns it.

Ask these questions:

1. What exact workflow will change?
2. Which measurable result must improve?
3. Who owns the result after implementation?
4. What happens if we do nothing for twelve months?

The operator's test is simple: if the value story depends on vague language such as "transformation," "productivity," or "innovation," the work is not yet defined tightly enough.

Gate 2: Demand proof in your workflow

References and case studies are useful, but they are not substitutes for a controlled test using your people, your process, and representative data.

Ask the vendor to demonstrate the complete workflow, including the unglamorous parts: authentication, permissions, exception handling, human review, monitoring, and exporting the result. A demo that starts after clean data has magically appeared and ends before an employee must act on the output hides most implementation risk.

Require answers to these questions:

1. Can the vendor demonstrate our priority workflow end to end?
2. What customer result is most comparable to our baseline and operating model?
3. What portion of that result came from the software versus process redesign?
4. Can we run a time-boxed proof of value with written success and failure criteria?

Design the proof of value so it is allowed to fail. A good pilot produces a decision; a bad pilot produces a presentation. Define the stop condition, the maximum budget, the evaluation group, and the evidence required to proceed before work begins.

Gate 3: Verify security, privacy, and data rights

Security questionnaires should lead to evidence, not assurances. The appropriate controls depend on your industry, use case, data classification, and geography, so involve security, privacy, and legal leaders early.

Ask:

1. What data does the product collect, retain, and generate?
2. Is our data used to train shared or third-party models?
3. Where is data processed and stored, and what retention options exist?
4. Which employees, subcontractors, and subprocessors can access it?
5. What independent audit reports or certifications can the vendor provide?
6. How are access, encryption, deletion, incidents, and vulnerability management handled?

The contract should state who owns inputs, outputs, configurations, and any fine-tuned assets. It should also describe deletion, breach notification, audit cooperation, and what happens to your information when the relationship ends.

Use the NIST AI Risk Management Framework to structure AI-specific risk discussions and the NIST Cybersecurity Framework for the broader security program. Certifications can support your evaluation, but they do not transfer accountability from buyer to vendor.

Gate 4: Price the whole operating model

The subscription price is rarely the total cost. Model the full cost for at least three scenarios: pilot, expected production use, and a high-adoption case.

Include:

• License, seat, usage, model, storage, and support fees
• Integration, data preparation, and identity-management work
• Employee training, workflow redesign, and change management
• Human review, monitoring, quality assurance, and incident response
• Internal engineering or consulting required to maintain the system
• Switching and data-migration costs if the vendor relationship ends

Then ask:

1. What drives cost as usage grows?
2. Which implementation costs are excluded from the proposal?
3. How much human review is required at the target quality level?
4. What assumptions produce the vendor's ROI estimate?

Run your own value model. Use ranges instead of fake precision and separate hard-dollar savings from capacity that may or may not be converted into financial value.

Gate 5: Test adoption, control, and accountability

A technically capable product can still fail because it does not fit the way work gets done. Include frontline employees in evaluation and observe them using the tool without the vendor steering every click.

Ask:

1. Which decisions remain human-owned?
2. How can users inspect sources, correct outputs, and report problems?
3. What logs and performance data can administrators review?
4. How are model, prompt, policy, and feature changes communicated?
5. Can permissions and approval steps match our operating model?

For higher-impact systems, document the escalation path, fallback process, monitoring cadence, and person authorized to pause the system. The NIST Generative AI Profile is a useful companion when the product relies on generative AI.

Gate 6: Protect your ability to leave

Vendor viability matters, but no forecast can eliminate vendor risk. Design portability into the deal.

Ask:

1. Can we export our data, configurations, logs, and outputs in usable formats?
2. Which integrations or workflows would need to be rebuilt if we leave?
3. What termination assistance and deletion evidence will the vendor provide?
4. How do renewal pricing, product changes, acquisition, or shutdown affect us?

Avoid building a critical process around a feature that cannot be monitored, reproduced, or replaced. The more important the workflow, the more explicit your contingency plan should be.

How to score competing AI vendors

Score each question from 0 to 3:

• 0 — Unknown: no usable answer or evidence
• 1 — Weak: partial answer, material gaps, or promises without evidence
• 2 — Acceptable: meets the defined requirement with manageable limitations
• 3 — Strong: clear evidence, contractual support, and operational fit

Do not simply total all 27 scores. First identify non-negotiable requirements. A vendor that fails a critical security, data-rights, or exit requirement should not win because it has a better interface.

Weight the remaining sections for your use case. A low-risk internal drafting tool and an AI system influencing customer eligibility decisions should not use the same weighting or approval path.

The executive decision memo

Finish due diligence with a two-page decision memo containing:

1. The business problem and current baseline
2. The proposed workflow and accountable owner
3. Proof-of-value results against prewritten criteria
4. Total-cost and value ranges
5. Material risks, controls, and unresolved questions
6. Recommendation: proceed, revise, pause, or reject

This forces the decision back to business fundamentals. The right vendor is not the one with the best demo. It is the one that can improve a defined outcome, fit the way your company operates, and earn the right to scale.