EnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise
Abstract
Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically valuable tasks, yet most enterprise GenAI initiatives fail to show a measurable business effect and a large fraction of agentic projects are expected to be cancelled. We argue that this is substantially a measurement problem: public benchmarks answer "what can the model do?", whereas a deployment decision requires "is this workflow fit, reliable, safe and worth scaling - here, on our data, under our controls?". We present EnterpriseVal, a use-case-level evaluation system that closes this gap. It comprises (i) a formal specification of the use case and of the frozen socio-technical configuration under test, model, prompts, retrieval, tools, guardrails and human oversight, with an autonomy level and consequence tier that jointly set the required evaluation intensity; (ii) a metric catalogue spanning fidelity, utility, efficiency, reliability, assurance and oversight; (iii) a grading protocol that scales blinded expert judgement with calibrated LLM-as-judge scoring through prediction-powered inference; (iv) a two-tier threshold gate, stated as an executable algorithm, that maps metric vectors with confidence bounds to REJECT/CONDITIONAL/SCALE decisions; and (v) a value-and-risk model in which the reviewer catch rate is a measured parameter. We report a pilot across three workflows in a global bank. In credit-memo drafting, human-graded citation precision reached 88% and hallucination rate 1.6% for the best model against gates of 70% and 5%; in procedure transformation, analyst refinement effort fell from an estimated 27.4 to 2.9 hours per document. We separate established results, documented pilot evidence, the proposed system and open hypotheses, and specify the experiments required for full validation
Related
- Psychometric Tests: Quantifying the Consequences of Low Reliability and Improving Reliability Estimation
- On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study
- MOE-DGSCND: Multi-Objective Enterprise Green Supply Chain Network Design
- An Investment Analysis Research Enhanced Enterprise Development Algorithm for Bankruptcy Prediction
- A Three-Way Testing Framework for Quantifying Epistemic Calibration Uncertainty in SBI
BibTeX
@misc{ali2026enterpriseval,
title = {{EnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise}},
author = {Abbas Raza Ali and Muhammad Ajmal Siddiqui and Moona Zahid},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.21841},
eprint = {2609.21841},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.21841},
}
RIS
TY - UNPB
TI - EnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise
AU - Abbas Raza Ali
AU - Muhammad Ajmal Siddiqui
AU - Moona Zahid
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2609.21841
UR - https://arxiv.org/abs/2609.21841
AB - Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically valuable tasks, yet most enterprise GenAI initiatives fail to show a measurable business effect and a large fraction of agentic projects are expected to be cancelled. We argue that this is substantially a measurement problem: public benchmarks answer "what can the model do?", whereas a deployment decision requires "is this workflow fit, reliable, safe and worth scaling - here, on our data, under our controls?". We present EnterpriseVal, a use-case-level evaluation system that closes this gap. It comprises (i) a formal specification of the use case and of the frozen socio-technical configuration under test, model, prompts, retrieval, tools, guardrails and human oversight, with an autonomy level and consequence tier that jointly set the required evaluation intensity; (ii) a metric catalogue spanning fidelity, utility, efficiency, reliability, assurance and oversight; (iii) a grading protocol that scales blinded expert judgement with calibrated LLM-as-judge scoring through prediction-powered inference; (iv) a two-tier threshold gate, stated as an executable algorithm, that maps metric vectors with confidence bounds to REJECT/CONDITIONAL/SCALE decisions; and (v) a value-and-risk model in which the reviewer catch rate is a measured parameter. We report a pilot across three workflows in a global bank. In credit-memo drafting, human-graded citation precision reached 88% and hallucination rate 1.6% for the best model against gates of 70% and 5%; in procedure transformation, analyst refinement effort fell from an estimated 27.4 to 2.9 hours per document. We separate established results, documented pilot evidence, the proposed system and open hypotheses, and specify the experiments required for full validation
ER -