Paper: arXiv 2511.21975

Authors: Hernan Huwyler

Abstract

Organizations investing in artificial intelligence face a fundamental challenge: traditional return on investment calculations fail to capture the dual nature of AI implementations, which simultaneously reduce certain operational risks while introducing novel exposures related to algorithmic malfunction, adversarial attacks, and regulatory liability. This research presents a comprehensive financial framework for quantifying AI project returns that explicitly integrates changes in organizational risk profiles. The methodology addresses a critical gap in current practice where investment decisions rely on optimistic benefit projections without accounting for the probabilistic costs of AI-specific threats including model drift, bias-related litigation, and compliance failures under emerging regulations such as the European Union Artificial Intelligence Act and ISO/IEC 42001. Drawing on established risk quantification methods, including annual loss expectancy calculations and Monte Carlo simulation techniques, this framework enables practitioners to compute net benefits that incorporate both productivity gains and the delta between pre-implementation and post-implementation risk exposures. The analysis demonstrates that accurate AI investment evaluation requires explicit modeling of control effectiveness, reserve requirements for algorithmic failures, and the ongoing operational costs of maintaining model performance. Practical implications include specific guidance for establishing governance structures, conducting phased validations, and integrating risk-adjusted metrics into capital allocation decisions, ultimately enabling evidence-based AI portfolio management that satisfies both fiduciary responsibilities and regulatory mandates.

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 2.5/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper presents a conceptual, risk-adjusted financial framework for evaluating AI investments considering ISO 42001 and regulatory exposures. While well-written and relevant to corporate finance and AI governance, it lacks rigorous mathematical formulations, concrete empirical backtesting, and datasets.

Research Flowchart

  flowchart TD
    A["Research Goal: Quantify AI ROI integrating ISO 42001 & Regulatory Risks"] --> B["Inputs: Productivity Gains, Model Drift, Bias Exposure & Compliance Costs"]
    B --> C["Methodology: Annual Loss Expectancy ALE & Risk Profile Modeling"]
    C --> D["Computational Process: Monte Carlo Simulations of Risk Deltas"]
    D --> E["Findings: Net Benefit Calculation incorporating Control Reserve Requirements"]
    E --> F["Outcomes: Risk-Adjusted AI Framework & Evidence-Based Governance"]

Cited References (8)

  1. How Finance Leaders Get ROI from AI (2025) — link
  2. Artificial Intelligence Act: Final Text (2024) — link
  3. ISO/IEC 42001:2023 Information Technology — Artificial Intelligence — Management System (2023)
  4. Artificial Intelligence Risk Management Framework ( AI RMF 1.0) (2023) — link
  5. Hidden Technical Debt in Machine Learning Systems (2015)
  6. How to Build a Successful Data Quality Strategy (2023)
  7. Machine Learning for Advanced Predictive Risk Modeling and AI Governance (2025) — link
  8. Quantitative Risk Assessment in R : An Open-Source Convolutional Framework for Modeling Uncertainty and Reserves (2025) — link

0 of 8 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.