Artificial Intelligence
Vendor Selection for AI Platforms: The Due Diligence Checklist Buyers Actually Need
AI-powered business software platforms vary wildly in maturity. Some vendors offer genuinely integrated intelligence with strong controls. Others add “AI” as a surface feature without governance, transparency, or operational reliability. For buyers, the challenge is separating real platform capability from marketing claims—especially when AI is embedded into CRM, ERP, analytics, finance automation, and support tools.
Search terms like “AI software vendor due diligence,” “enterprise AI platform checklist,” and “AI procurement evaluation” reflect buyer intent: decision-makers want a shortlist of questions that reveal the truth quickly.
Start With the Business: What Outcomes Are You Buying?
Before evaluating vendors, define your outcomes in business terms: reduce invoice processing time, improve forecast accuracy, lower churn, increase support resolution speed, or reduce compliance incidents. Then map those to AI workflows. This prevents feature shopping.
The 10 Due Diligence Questions That Matter Most
1) Where does the AI run and how is it controlled? Is AI embedded in the product? Is it calling third-party models? What guardrails exist?
2) What data is used and how is it protected? Ask about encryption, access controls, and data minimization practices. Clarify whether your data is used for training.
3) Can the AI explain why it produced an output? Explainability matters for trust. Look for evidence references, reasoning summaries, and traceability.
4) What’s the human oversight model? Can you require approvals for specific actions? Can you define thresholds and exception rules?
5) What monitoring and evaluation tools exist? Ask how the platform tracks accuracy, drift, errors, policy violations, and user feedback.
6) How does integration work? Does the vendor support APIs, connectors, and data exports? How does AI interact with your existing CRM/ERP stack?
7) What is the cost model? Is pricing per seat, per usage, per workflow, or per token? Can you set budgets and limits?
8) How frequently does the model change? Model updates can change behavior. Ask how updates are tested, documented, and controlled.
9) What security posture is in place? Review security documentation, incident response capabilities, authentication features, and audit trails.
10) What implementation support is included? AI success requires onboarding, training, and change management. Ask about enablement resources and timelines.
Red Flags That Indicate “AI Theater”
- Vague answers about data handling and training
- No ability to control approvals or define risk tiers
- No monitoring dashboard for AI quality and cost
- “It just works” claims without evidence or documentation
- AI outputs that cannot cite source data or context
Pilot Design: How to Test Vendors Fairly
Design a pilot that reflects real workflows, not demos. Include edge cases and messy data. Measure cycle time, error rate, user trust, and cost. Require audit logs and approvals for at least one high-risk workflow. A good pilot reveals operational maturity.
Bottom Line
Choosing AI-powered business software platforms is not just a feature decision—it’s a governance and operations decision. Use a due diligence checklist that tests data protection, transparency, controls, integration, monitoring, and cost predictability. Vendors that pass these tests deliver real platform value; those that don’t create risk and disappointment.
- Top Expert
Nathan Rowan
Program Research, Editor, Expert in ERP, Cloud, Financial Automation
