2026 Guide to AI Buyer Due Diligence
Evaluating AI vendors requires a structured approach that goes beyond feature comparison. This comprehensive framework covers the critical dimensions of due diligence—from pricing transparency to data governance to contractual protections—that informed buyers should assess before committing to AI tools.
The Due Diligence Imperative
AI procurement differs from traditional software buying in several important ways. AI systems often process sensitive data, make consequential decisions, and create dependencies that are difficult to unwind. Thorough due diligence protects organizations from risks that may not be apparent during sales conversations.
This guide provides a systematic framework for evaluating AI vendors across the dimensions that matter most to enterprise buyers.
1. Pricing Transparency Assessment
Understanding the true cost of AI tools requires looking beyond headline pricing:
Pricing Model Clarity
- Is pricing publicly available or only disclosed during sales?
- What is the pricing unit (seat, usage, resolution, API call)?
- Are there multiple pricing dimensions that compound?
- How are overages calculated and billed?
Total Cost Modeling
- What are the implementation and onboarding costs?
- Are there required professional services?
- What training costs should be anticipated?
- Are there integration costs for required systems?
Contract Terms
- What is the minimum commitment period?
- Are there auto-renewal clauses?
- What are the price escalation terms at renewal?
- What are the termination conditions and penalties?
2. Data Governance Evaluation
Data handling practices are critical for AI tools that process business information:
Data Processing
- Where is data processed geographically?
- Is data encrypted in transit and at rest?
- Who has access to customer data within the vendor organization?
- Are there data isolation guarantees between customers?
Model Training
- Is customer data used to train or improve AI models?
- Can customers opt out of data use for training?
- How is training data anonymized or aggregated?
- What controls exist over model outputs?
Data Retention
- What is the data retention policy?
- Can customers request data deletion?
- What happens to data after contract termination?
- Are there data portability options?
3. Security Posture Review
Security assessment should be proportionate to the sensitivity of data processed:
Certifications and Audits
- What security certifications does the vendor hold (SOC 2, ISO 27001)?
- Are audit reports available for review?
- How recent are the certifications?
- What is the scope of the certifications?
Security Controls
- What authentication options are supported (SSO, MFA)?
- What access control granularity is available?
- Are there audit logging capabilities?
- What is the vulnerability management process?
Incident Response
- What is the security incident notification policy?
- What is the expected notification timeline?
- Has the vendor experienced security incidents?
- What remediation was provided?
4. Vendor Stability Analysis
Assessing vendor viability helps avoid disruption from acquisitions or failures:
Financial Health
- What is the funding history and current runway?
- Is the company profitable or on a path to profitability?
- What is the customer concentration risk?
- Are there signs of financial distress (layoffs, pivots)?
Market Position
- What is the competitive landscape?
- Is the vendor a leader, challenger, or niche player?
- What is the customer retention rate?
- Are there notable customer references?
Product Direction
- What is the product roadmap?
- How frequently are updates released?
- Is there a history of delivering on roadmap commitments?
- How is customer feedback incorporated?
5. AI-Specific Considerations
AI tools require additional evaluation dimensions:
Model Transparency
- What AI models power the product?
- Are models proprietary or based on third-party providers?
- How are model updates communicated?
- What explainability features are available?
Performance and Reliability
- What accuracy or performance metrics are available?
- How is performance measured and reported?
- What are the SLA commitments?
- How are edge cases and failures handled?
Bias and Fairness
- What bias testing has been conducted?
- Are there fairness metrics available?
- How are bias issues identified and addressed?
- What human oversight mechanisms exist?
6. Contractual Protections
Legal review should address AI-specific contractual considerations:
Liability and Indemnification
- What liability caps exist?
- What indemnification is provided for AI-related claims?
- Are there carve-outs for gross negligence or willful misconduct?
- How are IP infringement claims handled?
Service Level Agreements
- What uptime commitments are made?
- What are the remedies for SLA breaches?
- How is performance measured?
- Are there exclusions that limit SLA applicability?
Exit Rights
- What are the termination rights?
- Is there a transition assistance period?
- What data export capabilities exist?
- Are there any post-termination restrictions?
Due Diligence Checklist
Use this checklist to ensure comprehensive evaluation:
| Category | Key Documents | Questions to Ask |
|---|---|---|
| Pricing | Quote, order form, pricing schedule | What is the all-in cost? |
| Data | DPA, privacy policy, security whitepaper | How is our data handled? |
| Security | SOC 2 report, security questionnaire | What controls protect us? |
| Stability | Company overview, customer references | Will you be here in 3 years? |
| Legal | MSA, SLA, acceptable use policy | What are our rights? |
Apply This Framework
Use the Scanner to research vendors and access structured data on pricing, deployment, and risk factors for each product.