AI Tech Vendor Evaluation
A structured template for evaluating AI vendors before you buy: technical capability, data practices, responsible-AI alignment, a 1–5 automation-level scale, demo-and-trial observations, a procurement roadmap, and a full question bank. Updated with an agentic-capability assessment.
A vendor demo shows the product on its best day. This template from MMA's Responsible AI Innovation Lab (RAIL) scores a vendor on technical competence, data practices, and responsible-AI standards before you sign, and because every vendor gets the same scorecard, the final numbers compare like for like.
What's inside
- A vendor profile, AI-competency, and responsible-AI practice review that asks for evidence behind each answer.
- A per-use-case automation scale (1–5) and expected-value scoring, so "we'll use AI" becomes a specific, measurable claim.
- A responsible-AI rubric scored 1–5 on fairness, transparency, privacy, accountability, and security.
- A demo-and-trial observation framework and a procurement roadmap with owners and dates.
- A categorized question bank you can send a vendor as-is.
- A 2026 agentic-capability assessment: guardrails, autonomy scope, logging, and integration control for tool-using, autonomous systems.
How to use it
Work the sections in order: business context first, so a score means something, then vendor profile and competency, then the 1–5 ratings, then the question bank you send the vendor directly. Delete any prompt you don't need for this vendor. This template is day four of the five-day sequence in a responsible AI framework for marketing teams.
Who it's for
Marketing, procurement, and IT teams evaluating an AI vendor they'll depend on. Fill in the editable Word version directly and you end up with an assessment you can circulate and compare.
Published by the Marketing + Media Alliance's AI Leadership Think Tank and Responsible AI Innovation Lab. Free to adopt and customize.