EnterpriseVal: Quantifying the Efficacy, Reliabilit... | AI Research

EnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise
Many organizations are struggling to turn the impressive capabilities of generative AI into actual business results. While public benchmarks can tell us what a model is capable of in a general sense, they fail to answer the specific questions that matter to a business: Is this tool reliable, safe, and worth the cost when used with our specific data and internal controls? This paper introduces EnterpriseVal, an evaluation system designed to bridge the gap between general model performance and real-world enterprise deployment. It provides a structured, repeatable way to test AI workflows and decide whether they are ready to be scaled. The ai search story also surfaces in EU Regulators Demand Apple and Google..., adding another angle.

A Structured Approach to Evaluation

EnterpriseVal treats AI evaluation as a systems problem rather than just a model test. It requires a "frozen" configuration, meaning that every part of the process—including the model, prompts, retrieval methods, tools, and even the human oversight procedures—is locked in place during testing. The system categorizes use cases based on their autonomy level (how much the AI does on its own) and their consequence tier (the level of risk if the AI makes a mistake). These factors determine the "evaluation intensity," ensuring that high-stakes workflows undergo more rigorous testing than lower-risk tasks.

Measuring What Matters

The framework uses a comprehensive catalog of metrics grouped into six families: fidelity, utility, efficiency, reliability, assurance, and oversight. Instead of relying solely on automated scores, the system uses a grading protocol that combines blinded expert human judgment with calibrated AI-as-a-judge scoring. By using a statistical method called "prediction-powered inference," the system allows organizations to evaluate hundreds of outputs while only requiring a smaller, manageable number of expert human reviews. This ensures that the final metrics are both accurate and grounded in human expertise. The ai search story also surfaces in Google AI Releases TimesFM 3 for..., adding another angle.

Making the Decision to Scale

The ultimate goal of EnterpriseVal is to provide a clear, evidence-based decision for governance committees. The system uses a two-tier threshold gate—an executable algorithm that processes the collected metrics and their confidence intervals to produce a final recommendation: Reject, Conditional, or Scale. This process also includes a value-and-risk model that accounts for the "reviewer catch rate," which measures how effectively human oversight identifies and corrects AI errors. By making the human role a measurable part of the system, the framework provides a realistic view of safety and performance.

Pilot Results and Validation

The authors tested EnterpriseVal through a pilot program involving three workflows at a global bank. The results demonstrated the framework's practical utility: in a credit-memo drafting task, the best-performing model achieved an 88% citation precision and a 1.6% hallucination rate, both of which comfortably passed the pre-set safety gates. In a separate procedure transformation workflow, the system helped quantify a significant reduction in analyst effort, with refinement time dropping from an estimated 27.4 hours to 2.9 hours per document. These results highlight how the framework can move beyond abstract performance numbers to provide concrete evidence of business value. The same ai evaluation question is explored in ActMap, which adds a research perspective. as detailed in the full paper on Arxiv