Three Approaches to Measuring and Managing AI ROI

After several years of AI experiments and pilot initiatives, a crucial question remains open for most companies: How much of a return — and what kinds of returns — are we getting from all of this AI investment? To many executives, AI ROI still often feels more like art than science: elusive, imprecise, and industry-dependent.

Surveys and benchmarks paint a confusing picture about current returns. Much of the guidance also remains focused on measuring inputs — encouraging organizations to invest, experiment, and build capabilities (“You should invest in …”) — rather than on outputs and how to assess impact (“Here’s how to measure results”). Today, few companies apply the same financial discipline to artificial intelligence as they would to a new factory or piece of machinery.

The authors’ interviews with more than 30 CEOs and senior leaders across various industries confirm that measuring AI ROI is anything but standard practice: Two companies making nearly identical investments may define success in entirely different ways. Yet companies that fail to identify an explicit approach to AI ROI — or that simply roll out generic AI tools and hope for productivity gains — rarely realize credible, lasting returns.  ROI measurement differs by the type of AI technology being used.  AI ROI also depends heavily on industry context.  Based on their interviews with executives, the authors identified three practical approaches to measure and manage AI ROI.

1. Function-focused approach.  Focus on one business function or a small number of functions or processes. Use tailored AI solutions and metrics.  Typical measures those can be used:  Function-specific KPIs, such as response time or error rates.  Potential pitfalls could be:  Siloed metrics and no shared view across the organization.  Organizations can start scaling metrics toward a companywide AI ROI playbook.
2. Coordinated approach.  Coordinate the deployment of broadly applicable AI tools and function-focused initiatives.  Possible metrics could be:  a mix of broad operational metrics and function-specific KPIs in selected high-impact AI initiatives.  Pitfalls organizations could face:  limited comparability and fragmented portfolio-level oversight. Organizations should apply consistent financial translation and measurement logic across all AI initiatives. 
3. Enterprise portfolio approach.  Engage in enterprisewide governance of the AI portfolio.  Organizations can use investment portfolio value, NPV/IRR, business case ROI.  Organizations should watch the risk of excessive bureaucracy that may constrain early-stage or exploratory initiatives.  They should use financial and strategic metrics. Allow early bets without full ROI measurement.