Measuring AI ROI: How to Build an AI Strategy That Captures Business Value

AI adoption is surging, but proving its real business value remains elusive.

Even as 82% of organizations consider AI essential, most leaders struggle to measure its true financial impact. In fact, 49% of CIOs cite demonstrating AI’s value as their top barrier, and 85% of large enterprises lack the tools to track ROI.

Capturing the ROI of AI isn’t just difficult. It requires an entirely new way of thinking.

# A New Lens for Measuring AI ROI: Trending and Realized Impact

Measuring AI ROI isn’t as simple as comparing costs to benefits. AI impacts are rarely immediate and often unfold over months — even years. To capture the full picture, leaders need a framework that reflects both early momentum and long-term results.

To measure AI impact effectively, we recommend breaking ROI into two measures across different time horizons. This allows teams to track both short-term progress and long-term financial value and align priorities across the business.

1. Trending ROI

These are early, progress-oriented indicators that suggest your AI initiative is delivering value, even if that value hasn’t yet shown up as revenue or cost savings. Think of this as the groundwork: benefits like improved employee productivity, faster time-to-value, or better customer engagement.

These impacts are often indirect but important, helping accelerate future growth or savings.

• Timeframe: Short to mid-term
• Nature: Indirect and enabling

2. Realized ROI

This is the quantifiable, results-oriented impact of your AI investment. Realized ROI includes clear financial outcomes such as reduced costs, increased revenue per customer, or higher conversion rates. Once AI systems are fully adopted and integrated, these measures are typically visible in the mid- to long-term.

Example outcomes include supply chain optimization, improved operational efficiency, or reduced regulatory fines through fewer errors.

• Timeframe: Mid- to long-term
• Nature: Direct, measurable business value

Next, let’s examine how this two-part ROI approach can help leaders articulate, measure, and report AI ROI progress with credibility and build organizational support as initiatives evolve.

# Why Measuring AI ROI Is So Difficult

Many organizations treat AI like any other business investment, expecting predictable, short-term results and linear returns. But AI doesn’t behave like a traditional system upgrade or efficiency play. Its experimental nature and delayed results make ROI calculations less straightforward.

While compute costs for AI models are dropping, maintenance expenses, such as platform licenses and developer support, remain high. Many enterprises expect AI to reduce costs, but may instead shift expenses elsewhere. For example, cutting 5% in process time but increasing long-term development costs by 10%.

The intricate nature of AI technologies makes it difficult to measure their direct impact on business outcomes, creating further challenges in quantifying ROI.

# Why AI Investments Require a Unique Approach

AI doesn’t fit neatly into traditional ROI models, and that’s where many organizations get stuck.

Traditional investments follow a clear, upfront expenditure model with predictable maintenance and depreciation costs. They rely on tangible assets like labor, materials, and infrastructure, and ROI is measured by direct financial gains like increased sales, reduced operational costs, or improved efficiency over a set timeframe.

AI investments operate differently. They involve compute power and licensing, data acquisition, developer time, cloud infrastructure, and continuous updates. AI costs extend beyond upfront investments to ongoing needs like compliance, talent, and system tuning. Early returns often align with trending ROI, which includes intangible gains like smarter decision-making or time saved. Realized ROI, like cost reduction, tends to appear over time and is often difficult to attribute directly without pilots or A/B testing. In most cases, it’s those early indicators that lay the foundation for measurable long-term value.

# What Makes AI ROI So Complex

• It impacts everything at once. AI influences multiple business areas simultaneously, making it hard to isolate its value.
• Returns aren’t always financial (at first). Many benefits, like faster onboarding or improved decision-making, appear in experience and efficiency before revenue. Plus, AI projects often focus on knowledge work, where measuring productivity in financial terms is inherently challenging.
• AI projects require long ramp-up periods and ongoing iterations. Unlike traditional IT investments, they must be continuously updated, retrained, and tuned, making ROI measurement more complex.
• ROI depends on adoption and requires change management. AI’s value depends on how well employees and customers adopt the system. If adoption is low, ROI is lower, even if the AI itself is effective.

Related Content: Center People and Processes in Your Next AI Implementation

Why It Matters

Leaders who expect AI to behave like a cost-cutting tool risk missing the bigger opportunity. Measuring AI’s value requires a longer lens — one that captures early signals and long-term outcomes.

Next, let’s dig into how to apply that lens to your own projects.

# Measuring ROI for AI: Fiscal Value and Beyond

To measure AI ROI effectively, organizations must define clear use cases and establish baseline KPIs.

# Define the Right Metrics at the Right Level

As Propeller Managing Director, Molly Lebowitz, puts it:

# Bridge Short-Term Signals to Long-Term Results

For leaders, the key is to connect short-term progress (Trending ROI) to long-term fiscal impact (Realized ROI).

For example, imagine your enterprise implements a customer support chatbot to handle Tier 0/1 support requests. At first, you may only see incremental improvements like faster response times or fewer escalations. As your user base grows, those efficiencies compound, translating into tangible savings, such as reduced headcount needs.

This is why choosing the right metrics is essential from the start.

# Define the Right Metrics

Select a mix of Process Measures (focused on how work is done) and Output Measures (focused on what results), tied to Trending and Realized ROI:

Process Measures (Often Trending ROI)

• Customer Satisfaction: Improved retention, lifetime value, upsell rates, and higher satisfaction through personalized interactions
• Employee Productivity gains: Reduced task time or strategic redeployment of hours
• Time-to-Value Efficiency: Faster product launches or service delivery
• Intangible Benefits: Brand reputation, employee satisfaction, innovation signals

Output Measures (Typically Realized ROI)

• Revenue Growth: Increased sales, new markets, AI-powered features
• Cost Savings: Automation, streamlining operations, labor efficiencies
• Performance Metrics: Higher productivity, fewer errors
• Risk Mitigation: Reduced regulatory risk, fraud prevention savings

Once metrics are in place, organizations also need strong oversight structures to track results effectively.

# Build a Governance Program to Strengthen Your AI Strategy

For better oversight, create a dedicated AI ROI governance process. Build an intake system that captures all AI initiatives and categorizes expected returns, splitting Trending ROI from Realized ROI.

Encourage leaders to estimate costs and benefits upfront and track actuals over time. Reviewing estimates quarterly can help surface lessons learned, assess portfolio value, and refine ROI expectations for future projects.

For detailed governance strategies, see AI Governance: How an AI Council Could Make or Break Your Strategy.

# Estimating and Calculating AI ROI

# Start with a Clear Hypothesis

To accurately estimate ROI, start with a hypothesis: What specific outcomes do you expect AI to drive? These might include cost savings, revenue growth, improved efficiency, or reduced risk. From there, identify relevant metrics using the Process and Output measures above and determine a simple, realistic way to track each one.

# Use Baselines and Benchmarks

Establish internal baselines to understand your current performance, then set expectations for improvement. Where possible, use external benchmarks, especially in the first year, to help validate your assumptions. And remember: ROI should evolve alongside your business goal. Build in regular review cycles and feedback loops to stay aligned.

Watch for common blockers: Many AI projects can take 12-24 months to deliver ROI. Delays in data quality, computing resources, talent, or regulatory requirements can all slow things down. Be realistic about the path to value.

# Step-By-Step ROI Calculation

Here’s a four-step process to guide your AI ROI estimates:

1. Identify the Investments: List all upfront and ongoing costs, including licensing, training, infrastructure, developer time, system maintenance, compliance, data readiness, and even tech debt.
2. Define the Expected Benefits: Estimate financial and operational gains using the Process and Output measures outlined earlier.
3. Calculate Net Benefits: Net Benefit = Total Benefits – Total Investments
4. Calculate ROI Percentage: ROI (%) = (Net Benefit / Total Investment) x 100

In addition to ROI percentage, some organizations also track Payback Period — the time it takes to recover their AI investment — to better understand when value will be realized.

Example: AI-Powered Recruiting Tool

Let’s say a mid-sized enterprise implements an AI-powered recruiting tool to automate screening, improve candidate matching, and streamline scheduling.

• Total Investments: $240,000/year
  Including licensing, setup, infrastructure, training, maintenance, and project management.
• Annual Financial Benefits: $350,000
  Comes from reduced time-to-hire, lower recruiting costs, improved recruiter productivity, and higher-quality hires.
• Net Annual Benefit = $110,000
• Annual ROI = 46%
• Payback Period = 8.2 months

Over time, this kind of initiative compounds value — fewer hires are needed, faster placement, better candidate outcomes — even if the returns aren’t immediate.

# Download the AI ROI Calculation Template

Want to plug in your own numbers? Download our AI ROI Calculation Template to walk through the same steps we use with clients. Estimate costs, map expected benefits, and calculate ROI with clarity.

# Best Practices For Sustaining AI ROI

# 1. Prioritize Strategic Investments

AI projects deliver the strongest returns when they align closely with core business objectives. Focus on initiatives that tie directly to revenue growth, cost savings, or competitive advantage, and validate minimum viable products (MVPs) before scaling. Embedded AI features from existing platforms can often provide faster time-to-value than custom builds

# 2. Manage AI Cost Strategically

Controlling AI costs isn’t just about upfront budgeting; it’s about building sustainability. Rationalize your tech stacks early, map infrastructure needs carefully, and choose build vs. buy strategies intentionally. And always plan for ongoing maintenance and tuning costs, which can erode ROI if overlooked.

# 3. Measure Returns and Scale Intelligently

Effective ROI oversight requires discipline:

• Establish an intake and governance process to estimate ROI upfront.
• Conduct regular AI tech audits to assess efficiency and identify areas for improvement.
• Set clear benchmarks for when AI solutions should scale, not before they show measurable returns.

# Conclusion: Turning AI ROI into a Strategic Advantage

Measuring and maximizing AI ROI requires more than good intentions. It demands structure, discipline, and a different mindset: one that balances early signals of progress with long-term business outcomes.

Unlike traditional investments, AI projects unfold over time. They require iterative improvements, strong long-term adoption strategies, and the patience to track both realized and trending ROI metrics. Leaders who take a disciplined, transparent approach to ROI can build momentum early and demonstrate meaningful impact that earns lasting support.

The path to sustainable AI success starts with clarity: defining goals, measuring what matters, and connecting progress to real results.

Next Steps

Ready to make AI ROI measurable and meaningful for your organization? Connect with Propeller’s AI strategy experts to build a tailored roadmap that turns innovation into measurable impact.

Special thanks to Propeller Alum Billy Marks for his help with developing the concepts for this piece.