The adoption of AI is accelerating, yet many corporate AI projects still fall short of expectations. Our members have repeatedly raised this concern, and recent reports by MIT and McKinsey echo the same finding: too often, AI efforts fail to deliver the value organizations expect. This shared signal points to a clear need for a better way to evaluate AI solutions—one that balances potential benefits with the risks that come with them.
To meet that need, the members of the Data & Trusted AI Alliance created the AI Vendor Assessment Framework (VAF). The framework gives organizations a structured, practical approach for evaluating AI vendors during the procurement process—ultimately leading to the thoughtful and efficient implementation of AI.
Most AI frameworks focus only on risk. The VAF helps organizations weigh both risks and benefits, ensuring adoption decisions consider cost, impact, and ROI.
AI procurement often stalls in vague or overly technical discussions. The VAF provides plain-language questions and guidance that business, legal, and technical teams can all use.
Scattered and repetitive vendor questionnaires slow down procurement. The VAF creates a consistent set of expectations for vendors that streamlines evaluation and builds trust earlier in the process.
Without clear evaluation criteria, organizations struggle to compare vendors and justify procurement choices. The VAF provides both the evaluation questions and guidance on what answers should look like, ultimately helping leaders weigh both sides of the equation:
Can the organization manage the risks, or do they rise to a level the business cannot accept?
Do the benefits—whether efficiency gains, cost savings, or new capabilities—justify the investment?
These eight categories represent the areas that practitioners across legal, technical, and procurement teams identified as essential to evaluating AI vendors. Together, they cover the full range of risks and value drivers that determine whether an AI solution is ready for enterprise use.
Privacy & Data Protection – How the vendor manages personal data and safeguards user privacy.
Model Development & Explainability – How the system is built, tested, and explained to users.
Intellectual Property & Content Rights – How ownership, licensing, and content usage are handled.
Regulatory Compliance & Ethical Alignment – How the solution aligns with applicable laws and ethical standards.
Performance & Reliability – How the system performs in practice, including uptime, accuracy, and resilience.
Integration & Technical Risk – How easily the system integrates into existing workflows and infrastructure.
Vendor Stability & Support – How the vendor demonstrates financial health, operational maturity, and ongoing customer support.
Cost & Value Realization – How the vendor demonstrates ROI, efficiency gains, or other measurable business impact.
As AI technologies and regulations change, the Data & Trusted AI Alliance will update the framework with input from members and the broader community, ensuring it remains practical, relevant, and trusted.
Business and procurement leaders who evaluate and select AI vendors. You don’t need deep technical expertise to use it effectively. The framework also provides enough detail to support legal, compliance, and technical teams.
During the pre-contract phase—after you’ve narrowed your vendor list, completed initial demos or proofs of concept, and are ready for detailed due diligence before negotiating contracts.
No. The VAF complements specialized assessments. It highlights when deeper technical, legal, or compliance reviews may be necessary and helps ensure buyers ask the right questions early.
Most frameworks focus narrowly on risk. The VAF balances cost and benefit, helping organizations assess whether risks are manageable and whether benefits justify the investment. It is also written in plain language, making it accessible to non-technical buyers.
The Data & Trusted AI Alliance created the VAF with 26 member companies across 17 industries. Enterprise buyers, startups, legal teams, and technical experts shaped the framework to reflect real-world needs.
That does not automatically disqualify them. The key is whether they can explain their choices, provide evidence or safeguards, and show how their approach aligns with your business needs.
If a vendor’s responses feel too abstract, ask for demonstrations. For example:
Show how their system processes a test document with fake PII.
Provide a change log from recent model updates.
Share a redacted incident response report.
The framework focuses on generative AI, but its principles also apply to other AI systems. Many of the criteria—such as privacy, security, and compliance—remain relevant across different types of AI.
The VAF is not a certification, seal of approval, or regulatory checklist. It is a practical tool for buyers and vendors to structure conversations, surface risks, and assess value during procurement.
Yes. The VAF is a living framework. The Data & Trusted AI Alliance continues to refine it with input from members and the wider community, ensuring it stays practical, relevant, and aligned with evolving technologies and regulations.