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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.
Our 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. Analytical AI projects, which are typically based on established machine learning techniques like prediction and optimization, often produce more directly attributable financial returns but tend to be applied to targeted, well-defined use cases. Generative AI, in contrast, is broadly applicable, given its ability to perform a range of knowledge work tasks previously done by humans. A GenAI tool often creates improvements in speed, quality, or volume of work, requiring deliberate translation into financial impact. And some companies combine both analytical and generative AI solutions in a customized manner.
AI ROI also depends heavily on industry context. In the consumer goods sector, companies streamline their supply chains by using analytical AI, enhancing demand responsiveness. A B2B marketing agency using generative AI may focus instead on creative throughput and ideation, proposal win rates, or lead conversions — a different definition of “return.”
Three Pathways to Tangible AI ROI
Based on our interviews with executives, we identified three practical approaches to measure and manage AI ROI. These approaches reflect a range of AI maturity levels among companies, and varying strategic intents.
By comparing your organization’s current approach against this framework, you can identify where you are and what it will take to move forward. The overarching goal for leaders: to ensure the translation of AI activity into verifiable business results.
1. Function-focused approach
Who it serves: Companies trying to build credible proof points before scaling.
With this approach, you select one or a small number of business functions, such as customer service, marketing, production, or HR, as the starting point for focused AI tool deployment. In each function’s case, you build or acquire tailored AI solutions and equip people with rigorous, function-specific performance metrics. This means tracking outcomes such as shorter response times, fewer errors, improved quality, or reduced unit costs. For leaders, the logic is “If we can demonstrate credible ROI here, we can justify broader deployment elsewhere.”
Function-focused AI initiatives often deliver some of the most tangible ROI, especially when paired with deliberate workflow redesign. In customer service, organizations that deploy GenAI-driven agents and decision-support tools have reduced handling times and call volumes — often automating a high percentage of routine customer requests — and translated those gains into lower service costs and improved customer satisfaction.
For instance, Unilever redesigned its early-stage recruitment process around AI-based candidate assessment, reducing HR’s reliance on external recruiters while shortening time to hire and lowering recruitment costs. In other companies, finance units have experienced similar dynamics, where AI-based forecasting, pricing, or fraud detection systems embedded into core decision workflows have improved accuracy, reduced losses, and delivered measurable cost benefits.
The function-focused approach to AI ROI is particularly effective for building organizational confidence in AI investments. The plus side: By limiting scope and maintaining clear ownership, organizations can create credible proof points that are easier to measure, explain, and defend. The negative side: Because specific needs and contextual factors shape function-specific ROI, different success stories might be difficult to compare or aggregate as AI adoption expands.
Your next move: If you’ve already done several function-specific AI initiatives, it’s time to begin laying the groundwork for the next stage: coordination. As function-level proof points accumulate, leaders can gradually move toward
