By grounding AI evaluation in counterfactual logic, economic theory, and implementation realism, they steer organizations toward value creation...

Return on AI: A Decision Framework for Customers, Firms, and Society” by Pattabhiramaiah, Sridhar & Kanuri

by ChatGPT-4o

Introduction

The accelerating deployment of artificial intelligence (AI) technologies across sectors is outpacing firms’ ability to rigorously measure AI’s economic impact. In their groundbreaking paper Return on AI: A Decision Framework for Customers, Firms, and Society, Pattabhiramaiah, Sridhar, and Kanuri argue that traditional financial metrics fail to capture the full complexity, risks, and dynamic value of AI initiatives. To address this, they introduce the concept of RoAI—a multi-dimensional framework that integrates economic rigor, decision science, and causal logic to evaluate the true return on AI investments.

Rather than offering a simplistic return-on-investment (ROI) metric, the authors present RoAI as a governance and strategic planning tool, a “common calculus” to ensure responsible AI deployment amid hype cycles and scale-up pressures. In doing so, they illuminate not just how AI creates value—but also at what cost, under what assumptions, and for whom.

Key Contributions and Surprising Insights

1. Beyond Traditional ROI: The RoAI Equation

At its core, RoAI is a refined cost-benefit ratio, but one deeply embedded in causal inference, behavioral economics, and organizational science. The numerator includes not just revenue lift, cost savings, and risk reduction, but also:

• Operational synergies

• Temporal scale economies

• Network effects

• Option value

• Opportunity cost of not adopting AI

The denominator accounts for four often-underestimated categories: investment, change, operating, and learning costs.

Surprising insight: RoAI explicitly models “erosion factors” (𝜙ₜ) to account for the drop-off in expected benefits due to biases, under-adoption, governance failures, or misaligned incentives. This realism is rare in traditional ROI metrics and a valuable innovation.

2. Misattribution and Proxy Inflation

One of the most valuable and controversial warnings in the paper is the prevalence of proxy inflation—firms overestimating impact by substituting surface-level metrics (like click-through rates) for genuine economic outcomes (like conversion or customer lifetime value). This leads to scale-up decisions based on enthusiasm rather than defensible causal inference.

Safeguard proposed: Tie benefits to “cash-moving” metrics, not vanity metrics. Make assumptions and attribution models auditable and pre-register primary endpoints to avoid confirmation bias.

3. The Denominator Problem: Underestimated Costs

AI projects often underestimate change costs, governance costs, and long-tail operating expenses (e.g., cloud fees, model retraining, compliance). Small and mid-sized firms, in particular, encounter unexpected overruns, often exceeding original estimates by 30-50%.

Countermeasure: Break down costs explicitly and avoid treating governance, validation, and human-in-the-loop activities as one-off or optional. These should be recurring line items, especially for regulated sectors.

4. The Societal RoAI Ledger

A compelling and timely addition is the introduction of a Societal RoAI framework. This parallel analysis considers externalities on:

• Customers (e.g., fairness, transparency)

• Workers (e.g., displacement, augmentation)

• Communities (e.g., trust, cohesion)

• The environment (e.g., compute emissions)

Controversial takeaway: The authors argue that not every societal impact should be monetized—but they must be made visible and measured as indicators, thus pressuring firms to factor in non-financial responsibilities.

5. The Pilot-to-Scale Gap and Horizon Bias

The paper cautions against horizon error—a failure to choose appropriate time windows for evaluation. AI benefits compound over time due to learning effects and network dynamics, but costs are often frontloaded. If short-termism dominates, firms risk abandoning promising initiatives too early—or scaling flops prematurely.

Proposed discipline: Use Net Present Value (NPV) as a go/no-go filter and RoAI as a ranking tool only after NPV is positive. This prevents the “small denominator trap” where tiny investments appear deceptively lucrative.

6. Normalization Lenses: Tailoring RoAI to Objectives

A powerful innovation is objective-specific normalization:

• Cost relief → normalize by total cost

• Revenue growth → normalize by customer lifetime value or revenue at risk

• Resilience/compliance → normalize by invested capital

This provides a tailored view for different goals and avoids misleading apples-to-oranges comparisons across AI portfolios.

Recommendations for Stakeholders

For Firms:

• Adopt RoAI as a strategy tool, not just a reporting metric.

• Declare objectives and horizons ex ante, and select appropriate normalization lenses.

• Fund adoption, governance, and monitoring as core elements—not afterthoughts.

• Pilot initiatives where customer expectations and outcomes are measurable, such as fraud detection or personalization with A/B testing.

For AI Makers:

• Design systems that facilitate telemetry and observability, ensuring outputs can be measured causally.

• Support clients in estimating erosion factors, adoption curves, and monitoring requirements.

• Embed explainability and risk metrics by design to improve RoAI’s numerator (risk reduction) and denominator (lower governance cost).

For Regulators:

• Use Societal RoAI as a policy lever to evaluate net externalities across industries.

• Encourage or mandate externality disclosures, similar to ESG reporting, particularly around labor impacts and AI-driven misinformation.

• Reward firms with strong governance and measurement practices—these are leading indicators of responsible innovation.

Concluding Reflections and Outlook

This paper is a rare and necessary intellectual intervention in the era of AI exuberance. Pattabhiramaiah, Sridhar, and Kanuri provide a scientific, transparent, and ethically conscious alternative to the hype-fueled deployment of AI systems. By grounding AI evaluation in counterfactual logic, economic theory, and implementation realism, they steer organizations toward value creation that is verifiable, repeatable, and accountable.

From a geopolitical and societal perspective, the RoAI framework has the potential to raise the global bar for responsible AI deployment. It equips both Western firms and policymakers with a measurable framework to evaluate AI in markets where unregulated AI rollouts could produce social harm or extractive economic models.

For the United States, the RoAI framework could help correct the current tilt toward techno-solutionism by enforcing economic discipline and incentivizing transparent, socially aligned AI strategies. If adopted widely, this could even reduce regulatory friction between industry and government by creating a shared language for evaluating AI’s true impact.

But the future remains fragile. Without robust adoption of RoAI-like frameworks, firms may continue to scale unaccountable AI systems based on flawed assumptions, vanity metrics, or herd behavior. That would not only waste capital—it could undermine trust in the very systems that AI seeks to augment.

Final Verdict

Is the outlook positive? Only if RoAI or similar governance tools become institutionalized.

Is the paper valuable? Yes, profoundly so—for managers, investors, AI developers, and regulators alike.

Is the risk of inaction high? Absolutely. The opportunity to shape AI’s trajectory responsibly is closing. RoAI helps keep that window open—just a bit longer.