When we began developing the AI Vendor Assessment Framework (VAF), we were responding to a few practical challenges: organizations were not getting enough transparency from AI vendors, the questions they asked only focused on risk (and not value), and every pocket of the organization suddenly wanted to procure 3rd party tools. One can see how this diminishes the potential of reaching ROI.

Most AI assessments have focused only on compliance checklists or abstract risks, without asking whether the solution would actually deliver results that could scale. The VAF emerged as a pragmatic tool for bringing both questions together.

The VAF points toward a future where everyone speaks the same language. Buyers, vendors, AI auditors, AI insurance organizations all then have a shared way of moving towards effective and thoughtful AI deployment at scale. By using a shared framework that holistically assesses risk and value, we move towards an era where asking the right questions early doesn’t slow adoption, but accelerates it by building trust on both sides of the buyer–vendor relationship.

For vendors, the framework provides clarity on what buyers care about, reducing wasted time and aligning conversations around shared expectations. For buyers, it democratizes the evaluation process, enabling business leaders—not just technical experts—to take an active role in shaping AI adoption.

Looking forward, the question is not only how the VAF helps today’s procurement processes, but how it can evolve into a foundation for broader accountability. Could the framework become a common language between enterprises and startups, enabling smaller innovators to compete on merit rather than scale? Could it serve as a bridge between fragmented regulations, helping companies align with diverse global standards while still moving quickly? Could it inspire other domains—like data partnerships, model transparency, or ecosystem governance—to adopt a similar balance of risk and value?

Our goal is for it to stay living, responsive, and community-driven. By putting it into practice, organizations will surface gaps, discover new needs, and generate feedback that can guide its next iterations.