Key takeaways
- Weight criteria before vendor demonstrations.
- Require evidence in your environment.
- Price the operating model and exit, not only the license.
Precommit the decision
Name the owner, users, workload, budget, data classification, integration boundary and failure tolerance. Assign weights before sales calls so a polished demo cannot silently redefine success.
Score six dimensions
Evaluate outcome evidence, security and governance, integration effort, reliability and operations, total economics, and portability or exit. Add stop conditions for controls that cannot be compensated by a high total score.
Run a paid, bounded trial
Use representative work, production-shaped identity controls and real reviewers. Preserve setup labor, interventions and rejected outputs. Free pilots can hide the cost that dominates a full rollout.
Negotiate evidence and exit
Put data handling, incident notice, model changes, audit access, export, deletion and termination support into the agreement. Revisit the score after the trial; do not treat procurement as proof of value.
Primary sources
Browse the publication-wide evidence index →
- NIST AI RMF: Generative Artificial Intelligence Profile (NIST AI 600-1)National Institute of Standards and Technology · Reviewed: Publication abstract, scope, citation and report metadata · Retrieved · Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
- OWASP Top 10 for Large Language Model ApplicationsOWASP GenAI Security Project
Limitations
This is an AccessAllGPT procurement framework, not a vendor ranking or legal opinion. Contract, privacy and regulatory review require qualified internal or external counsel.
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