What’s Covered?
This paper outlines a two-stage model evaluation framework to support strategic enterprise AI adoption. First, it filters models based on non-negotiable criteria—security, infrastructure compatibility, and legal compliance. Then it assigns Model Trust Scores by combining benchmark performance, operational metrics, and use-case relevance.
The Model Trust Score (MTS) helps companies assess which AI models are worth integrating. Unlike generic leaderboards, it considers context-specific needs, including governance risks. The method works in two steps:
- Non-Negotiables Filter: Does the model support secure deployment, meet data residency/legal requirements, and align with infrastructure needs?
- Contextual Scoring: Evaluates models across four dimensions—capability, safety, affordability, and latency—then synthesizes scores based on how relevant each benchmark is to a given use case.
Document Contents:
- Chapter 1: Problem definition—why generic benchmarks aren’t enough.
- Chapter 2: Non-negotiable requirements as a baseline (e.g., self-hosting, clean data, enterprise-friendly terms).
- Chapter 3: Multi-dimensional scoring system using 60+ benchmarks from providers and third parties like LiveBench, AILuminate, and LegalBench.
- Chapter 4: Deep relevance scoring—each benchmark is weighted for how much it says about a specific enterprise use case (5-point scale).
- Chapter 5: Industry-level analysis—95 use cases across 21 industries show major gaps in benchmark coverage, especially outside tech/legal.
- Chapter 6: Discussion of next steps—creating absolute trust certifications, more use-case-specific evaluations, and stronger integration between governance and technical assurance.
Models like Claude 3.5 Sonnet and OpenAI’s o3 mini are shown as top performers for general use cases. DeepSeek R1 stands out for cost-efficiency and strong raw performance but lacks public safety benchmarks—highlighting a broader issue with ecosystem coverage.
💡 Why it matters?
Enterprises are under pressure to adopt powerful AI tools but lack clarity on how to compare, trust, and govern models effectively. This framework translates abstract model performance into decision-ready insights, pushing the industry beyond “just pick the biggest model.” It also encourages responsible deployment by penalizing opaque or unevaluated models.
What’s Missing?
The methodology is comprehensive, but the framework still leans heavily on existing benchmarks, most of which are underdeveloped or irrelevant to many industries (e.g., pharma, education, logistics). It also assumes that safety can be accurately measured, yet current third-party safety benchmarks like AILuminate are out of date and lack model coverage.
There’s also no clear system for absolute suitability—everything is relative. While helpful for comparisons, enterprises still can’t answer, “Is this model safe enough?” without deeper thresholds or certifications. The paper suggests certification as a future step, but doesn’t yet bridge that gap.
Best For:
- AI governance teams selecting models across departments
- Risk and compliance teams comparing model deployment suitability
- Procurement leads evaluating tradeoffs between capability, cost, and legal risk
- Policy advisors designing certification or assurance schemes
Source Details:
Citation: Eisenberg, Ian. The Model Trust Score: The Framework for Strategic Enterprise AI Model Selection. Credo AI, March 4, 2025. https://www.credo.ai
Author Background:
Ian Eisenberg is Head of AI Governance Research at Credo AI, a company focused on enterprise AI risk and governance. The Credo team has contributed to ecosystem-wide safety initiatives including MLCommons’ AILuminate and supports evaluation innovation for enterprise needs. Eisenberg’s work integrates policy and technical insight, with a focus on aligning organizational risk with model performance across use cases. This report reflects the organization’s broader push for responsible adoption and real-time assurance in enterprise AI.
