The New AI ROI Playbook: How Leading Companies Measure Success Beyond Cost Savings

AI investment is surging. Deloitte's 2025 AI Survey shows 91% of organizations plan to increase AI spending this year. But a troubling pattern is emerging: while companies pour billions into AI initiatives, most struggle to demonstrate tangible returns.

The problem isn't the technology—it's how organizations measure success. Traditional ROI frameworks, designed for discrete IT projects with predictable outcomes, are fundamentally inadequate for AI investments that transform how businesses operate.

To understand what separates successful AI investments from expensive experiments, Deloitte created a comprehensive AI ROI Performance Index—and the findings challenge conventional wisdom about measuring AI value.

The AI ROI Performance Index: A New Measurement Framework

Deloitte's index combines four key business metrics into a single score:

1. Direct financial return from AI initiatives

2. Revenue growth attributable to AI capabilities

3. Operational cost savings from AI automation

4. Speed at which these results were achieved

The top 20% of performers—AI ROI Leaders—demonstrate significantly different characteristics than their peers. Understanding these differences provides a roadmap for organizations seeking to capture real value from AI investments.

Six Practices That Define AI ROI Leaders

1. Rethink and Reimagine Business Models

AI ROI Leaders see AI as an opportunity to fundamentally transform their business models, not just improve efficiency. They're significantly more likely to define success in strategic terms: 50% cite 'creation of revenue growth opportunities' as their most critical AI win, and 43% point to 'business model reimagination.'

The contrast with average performers is stark. Most organizations measure AI success primarily through cost reduction—a necessary but insufficient metric that fails to capture transformative value.

2. Differentiate Investment Levels

AI ROI Leaders treat AI as a core organizational transformation and fund accordingly. A striking 95% allocate more than 10% of their technology budget to AI. Moreover, they're more likely to have significantly increased AI spending in the past 12 months and plan to do so again.

This isn't reckless spending—it's strategic investment in capabilities that compound over time. Organizations that underinvest in AI may save money in the short term but fall increasingly behind competitors building sustainable AI advantages.

3. Take a Human-Centered Approach

Workforce resistance remains a major barrier to AI adoption. AI ROI Leaders address this by positioning AI as a tool that augments human capabilities rather than replaces them. Some 84% believe agentic AI will enable employees to spend more time on strategic and creative tasks.

Successful implementation depends on deep organizational change management—addressing individual attitudes toward change and building a culture that supports acceptance and collaboration.

4. Elevate AI Ownership to the C-Suite

Among AI ROI Leaders, 62% report that AI is explicitly part of corporate strategy. Governance models are evolving, with 10% of respondents reporting the CEO as the primary owner of the AI agenda.

This C-suite ownership signals importance to the business and helps sustain investment through periods of uncertainty about ROI. When AI is treated as a strategic priority rather than a technology initiative, organizations are more likely to stay the course long enough to see results.

5. Measure ROI Differently

Here's a critical insight: 86% of AI ROI Leaders explicitly use different frameworks or timeframes for measuring generative AI versus agentic AI. They understand that a one-size-fits-all approach to ROI measurement fails to capture the diverse ways AI creates value.

Some AI initiatives deliver quick wins through automation and efficiency. Others require years of investment before transformative benefits emerge. Leading organizations develop nuanced measurement frameworks that account for these differences.

6. Mandate AI Fluency Across the Organization

AI ROI Leaders view AI fluency as a non-negotiable core competency. Among them, 40% mandate AI training for their workforce—not as optional professional development, but as a fundamental requirement.

This moves beyond voluntary education to embed AI understanding as a fundamental skill. Organizations that wait for organic AI adoption find themselves with pockets of expertise surrounded by resistance and confusion.

The Infrastructure Imperative

AI only scales on solid foundations. The survey reveals that 25% of organizations cite inadequate infrastructure and data as primary barriers to AI ROI. Design choices made today—interoperable architecture, robust data pipelines, and scalable compute resources—determine whether AI investments will compound or collapse.

Organizations that skip foundational investments to accelerate AI deployment often find themselves rebuilding from scratch when pilot projects fail to scale.

Redefining What Counts as Value

Perhaps the most important shift among AI ROI Leaders is philosophical. Traditional ROI models are too narrow for AI investments. Some 65% of survey respondents report that AI is now part of corporate strategy, recognizing that not all returns are immediate or financial.

This signals a shift: executives increasingly accept that returns may take years to materialize and that not all benefits can be captured in traditional financial terms. Innovation capacity, organizational resilience, competitive positioning—these intangible benefits often dwarf cost savings in long-term value creation.

The Bottom Line

AI ROI success is engineered, not improvised. It requires integrated data platforms, reskilled teams, scalable infrastructure, and strong governance. As adoption accelerates, the winners balance quick wins with bold ambition—redefining ROI beyond cost savings as a marker of innovation, resilience, and sustainable growth.

The question for every organization: Are you measuring AI success by yesterday's metrics, or building the measurement frameworks that capture tomorrow's value?