Three Approaches to Measuring and Managing AI ROI

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.”

Measuring and Managing AI ROI: Three Approaches

Companies often start with the function-focused approach and work up to the enterprise portfolio approach over time.

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.

As function-level proof points accumulate, leaders can gradually move toward a shared AI ROI playbook.

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 a shared AI ROI playbook with consistent definitions, financial logic, and data instrumentation standards. Start by standardizing metrics that can be transferred across functions and aligning financial assumptions across key use cases. As one CEO said, “We’re iterating toward a more structured model, linking AI impact to planning, budgeting, and playbook development; it’s an ongoing loop of learning.”

2. Coordinated approach

Who it serves: Companies trying to make AI ROI comparable across functions or units.

With this approach, you’re managing a growing set of AI initiatives across the organization. Concurrently with function-specific deployments, or even earlier, you’re also rolling out some general-purpose AI tools and shared AI capabilities that touch multiple teams and workflows. The defining challenge here is coordination: maintaining broad visibility into AI activity while selectively focusing on the metrics that have the most significant business and economic impact. Ideally, this approach facilitates shared learning, reuse of proven metrics and assumptions, and faster replication of successful AI use cases.

Organizations taking a coordinated approach often use shared AI platforms and capabilities to manage initiatives spanning multiple teams. At JPMorgan Chase, an internal AI platform called LLM Suite has been deployed to more than 200,000 employees across legal, research, client services, operations, and other functions. This gives people broad access to generative and analytical AI tools while requiring coordination mechanisms to ensure consistent value creation. At Amazon, the evolution of internal AI systems resembles an AI flywheel, whereby innovations — such as recommendations or robotics — that begin in isolated teams spread and are reused across the organization through shared machine-learning platforms and practices.

In both cases, value comes from coordinating how results are interpreted, compared, and scaled across the organization. At this stage, generative AI tools are often used both inside and across business functions, heightening the need for coordination. Analytical AI tools deliver results that are often easier to compare, via clearer links to operational and financial outcomes.

The logic and business motivation for coordination are straightforward: “We’ve invested in many AI initiatives, and we need a way to stay on top of them all.”

However, especially in larger organizations, coordination without clear standards can result in a patchwork of ROI methods, making it difficult to align priorities, compare outcomes, and decide what to scale.

Your next move: During this phase, it’s important to continue prioritizing and standardizing. Identify where deeper ROI instrumentation is warranted, and apply consistent financial logic across the full set of AI initiatives, regardless of whether they involve broad tools and capabilities or targeted deployments. Standardizing how results are translated into financial terms enables meaningful comparison and scaling across initiatives. As one CEO put it, emphasizing the need for a common baseline, “If an AI initiative claims to replace the work of four employees, I want to know who they are; otherwise, it’s not real savings.”

3. Enterprise portfolio approach

Who it serves: Companies that are ready to govern AI ROI at scale.

This stage represents the highest level of ROI maturity and is where you’re applying rigorous financial logic across the entire portfolio of AI initiatives. An AI initiative is treated like any other significant investment: It is governed through forums similar to those for capital projects and is evaluated with business cases, financial models, and portfolio metrics such as net present value and internal rate of return. This approach emphasizes funding projects that create measurable value as quickly as possible.

At Morgan Stanley, for example, AI initiatives are assessed through a structured evaluation framework that tests each use case against real-world criteria before deployment. This approach enables disciplined enterprise-level oversight and scaling of AI tools. In comparison, one equipment manufacturing company we studied applied strict financial discipline: Both analytical and generative AI initiatives were allowed to run for a limited trial period and were routinely terminated if they failed to demonstrate measurable value within six months. This ensured rigor but risked premature rejection of promising efforts.

A professional services firm pursued another option: It separated two kinds of AI initiatives — those that built mandatory foundations for generative AI adoption, where ROI was not enforced upfront; and targeted, tailored applications, where clear financial returns were required. Effective enterprise AI ROI management depends on deliberate and company-specific choices about timing, risk tolerance, and evaluation rigor.

At the portfolio level, both analytical and generative AI are evaluated as part of the investment mix, but often under different expectations. Analytical AI work fits naturally into traditional financial models, whereas generative AI initiatives may require staged evaluation and adapted governance. For example, milestone-based funding or phased ROI thresholds may be needed to save worthwhile initiatives from premature rejection when those projects have indirect benefits, delayed adoption, or value creation driven through learning.

Apply lighter ROI tracking to early-stage AI experiments and introduce more scrutiny as projects scale.

The enterprise portfolio approach to AI ROI offers clear benefits. You can compare AI initiatives side by side, compare them with other technology investments, track portfolio-level value creation, and make more confident decisions. As AI initiatives begin to reshape the operating model, however, initiative-level ROI comparisons become less informative; leaders should then rely more heavily on enterprise-level performance indicators to assess systemwide impact.

Your next move: If you choose to take an enterprise portfolio approach, it’s important to decide how strict you want to be. Fully enforced ROI can kill breakthrough AI bets too early if you overlook the value of new capabilities, learnings, and spillover benefits. The goal is to balance financial discipline with strategic patience: Apply lighter ROI tracking to early-stage AI experiments and introduce more rigorous scrutiny as projects scale. Consider creating a separate unit or governance track for more radical AI initiatives. As one executive told us, “You don’t need to measure everything from day one; start with clear KPIs for each area, then layer in more rigor as solutions mature.”

Getting AI ROI Right: Three Takeaways

Many organizations will move through all three approaches to AI ROI over time. Here are three parting takeaways from the executives we interviewed:

• Prioritize high-value, scalable AI use cases. ROI becomes most visible and meaningful when AI is applied to high-volume, high-leverage work. Whether through enterprisewide deployment or targeted use cases, even small productivity gains in large-scale activities can compound into significant value.
• Lead decisively. AI ROI doesn’t materialize by accident. The benefits come only when you provide direction, follow through, and rethink how work gets done.
• Remind yourself that your company and AI technology will keep evolving. To navigate ongoing changes, avoid both overengineering and under-measuring.

As your organization accumulates AI maturity, use the three approaches to track your progress and see your ROI grow.