Traditional technology procurement often happens in IT silos until final approval. This approach creates problems in the AI era because senior executives bring critical capabilities that technical teams may lack.

Business leaders understand economics intuitively. When a vendor claims to use Azure OpenAI services – which cost $100-200 per user monthly at scale – but charges $3,000 total for unlimited users, something's fundamentally wrong. The business model either isn't sustainable or the technology claims aren't accurate.
Senior executives also know how to conduct rigorous due diligence. Evaluating AI vendors requires the same scrutiny as acquisitions or strategic partnerships: verify claims, stress-test business models, identify red flags in presentations and pricing structures.

Finally, executives provide strategic skepticism. When technical teams get excited about innovation, senior leaders calculate downside scenarios and implementation risks. This balance between enthusiasm and caution is essential for successful technology investments.

Phase 1: Red Flag Screening (30-60 minutes)

Ask the same technology question three different ways. What AI models power your solution? Which specific algorithms? How does your AI architecture work? If answers shift between "Azure AI" and "ChatGPT" and "various AI options," stop immediately.
Calculate your transaction volume multiplied by AI processing costs, then compare to their total price. If their price is less than underlying AI costs alone, they're not using the technology they claim.
Request documentation immediately: system architecture diagrams, AI model specifications, security certifications, integration requirements. Don't accept promises to provide "after contract signing."
Observe who attends meetings. Real AI companies bring technical leadership to sales meetings. Questionable vendors send only sales teams.
Three or more red flags? Terminate evaluation.

Phase 2: Technical Validation (1-2 weeks)

Demand written documentation of data flow, AI integration points, model specifications, infrastructure, and security protocols. Work with your technical team or external consultant to verify claims.
Build an economic stress test. Calculate vendor's claimed AI costs plus infrastructure, development, support, and normal business margins. Compare this to their actual price. A gap exceeding 70% indicates high probability of misrepresentation.
For references, find customers on LinkedIn independently. Ask what problems they encountered. Real references discuss challenges overcome. Marketing references only praise.

Phase 3: Proof of Concept (2-4 weeks)

Use your data, not their demo data. Test in your workflows. Measure real performance. Include your team.
For AI-powered features, measure accuracy and count errors. Demand to see accuracy metrics from other customers. If they can't provide these, they don't track performance.
Test integration with existing systems. Can your team actually use it? What happens when it breaks?
If vendors resist meaningful proof of concept, they know their system won't perform.

Phase 4: Commercial Negotiation (1-2 / 2-3 weeks)

Only after technical validation discuss terms.
Contract protections must include performance guarantees with financial penalties, all costs documented with two-year price lock, data portability, no termination penalties, and clear retention limits.
Legitimate vendors accept reasonable protections. Questionable vendors claim their standard contract doesn't allow changes.

The Critical Questions Every Executive Should Ask

Technical questions:

1. Which specific AI models do you use? (Accept: "GPT-4," "Claude 3.5." Reject: "Industry-leading AI," "Proprietary algorithms.")
2. What training data powers your AI? (Accept: "10M anonymized transactions." Reject: "Proprietary data," "Various sources.")
3. What's your AI accuracy rate? (Accept: "94% accuracy, measured across 100K documents." Reject: "Very high accuracy," "We don't track that.")
4. Show me your system architecture diagram now. (Accept: Immediate diagram. Reject: "Proprietary," "After signing.")
5. What happens to my data? (Accept: Specific storage, encryption, access, deletion policies. Reject: Vague "industry standards.")

Economic questions:

1. What do your AI services cost you per transaction? (Accept: Transparent breakdown. Reject: "Confidential," "Bundled pricing.")
2. How is your pricing model sustainable? (Accept: Clear cost and margin explanation. Reject: Defensive, "Focused on market share.")
3. What's included versus additional cost? (Accept: Itemized list. Reject: "Everything's included" with asterisks.)

Proof questions:

1. Can we test with our data for 30 days? (Accept: Yes. Reject: "After contract," "Demo is sufficient.")
2. Can I talk to your CTO this week? (Accept: Here's their calendar. Reject: "Not necessary," "They're busy.")
3. What are your recent failed implementations? (Accept: Honest challenges discussion. Reject: "We've never failed.")
4. Give me 10 customer references, I'll choose 3. (Accept: Unfiltered list. Reject: "These 3 are our best.")
5. What security certifications do you have? Show documentation now.
6. If I remove AI features, what functionality remains?
7. What's your customer retention rate and why do customers leave?
8. What percentage of your customers achieve projected ROI?

Red Flags That Should Stop Evaluation Immediately

Certain patterns warrant immediate termination of evaluation.
Technology story changes under questioning. First it's "Azure AI," then "ChatGPT," then "various AI technologies." Real companies don't get confused about their own core technology.

No technical team present. When only sales people attend meetings and every technical question gets "I'll follow up on that," the vendor is hiding problems. Real AI companies have technical founders eager to explain their work.

No documentation exists. Requests for architecture diagrams or security protocols get answered with "we'll send after you sign." This means they don't have real technology to document.

Pricing makes no mathematical sense. They claim to use enterprise AI services costing $5,000+ monthly but charge $3,000 total. They're either losing money or lying about technology.

Security certifications missing. When SOC 2 or ISO 27001 are perpetually "in progress," they're not ready for enterprise deployment. Legitimate companies already have these.

All references from personal network. Same ethnic community, same city, obvious friends. Legitimate companies have diverse customers across industries and geographies.

Urgent pressure for decisions. "This discount expires Friday" or "we have other companies interested" means they want to close before you do proper diligence.

Punitive exit strategy. No data portability, expensive termination fees, unclear what happens to your data. The vendor is planning to trap you.

Timeline and Team Structure

Evaluation should span 10-12 weeks: Week 1-2 for screening, Week 3-4 for validation, Week 5-8 for proof of concept, Week 9-10 for decision analysis, Week 11-12 for negotiation. Pressure for faster decisions is itself a red flag.

Your assessment team should include the senior decision maker holding final authority, technical leadership handling validation, operations managing business validation, internal audit conducting due diligence, and legal protecting through contract review. The senior executive should chair this committee.

The Real Cost of Getting It Wrong

Direct costs include $36,000+ in annual fees, $50,000-200,000 in implementation, $20,000-50,000 in training, and $30,000-100,000 in integration.

Indirect costs: 6-12 months of productivity loss, opportunity cost of delayed solutions, competitive disadvantage, damaged morale, and reputation risk.

Total potential loss: $200,000-500,000+ per failed implementation.
Investing 10-12 weeks in due diligence to avoid a $300,000 mistake delivers 20-30x ROI.

When to Walk Away vs. When to Negotiate

Walk away when three or more critical red flags are present, vendor won't provide basic documentation, economics don't support claims, reference checks reveal problems, security is inadequate, or vendor resists reasonable protections.

Negotiate when technology is validated but pricing needs adjustment, minor gaps are addressable, references are strong but terms need improvement, or cultural fit is good but timeline needs extension.
The framework: Can identified risks be contractually mitigated with financial penalties? If yes, negotiate. If no, walk away.

Conclusion

The most successful AI implementations treat technology procurement with the same rigor as acquisitions or strategic partnerships. You wouldn't acquire a company without thorough due diligence. Don't buy transformative technology with less rigor.

The AI revolution is real and valuable. But the path from AI hype to AI value requires executives to apply rigorous, skeptical, evidence-based evaluation to technology decisions.
Your role as a leader isn't to slow down innovation. It's to ensure innovation investments deliver real returns and don't become expensive mistakes.

About the Author
Jamshid Muslimov is an Advisory board member of Allgorithm.au, He is senior business executive with 20+ years of experience across global consulting firms (KPMG, PwC, Moore Global) and multinational corporations. He specializes in building control frameworks, business transformation, digital strategy, and financial due diligence. Based in the UAE, he advises organizations on transformation strategy.