Build an AI ROI framework that proves business value

Let’s start with the traditional formula:

ROI = (realized benefit - total cost) / total cost

While the formula is straightforward, measuring AI ROI rarely is.

AI initiatives are shaped by more variables than most technology investments, including organizational size, industry requirements, business maturity, use cases, employee skill sets, governance policies and regulatory considerations, among others, affecting both costs and outcomes.

Organizations must also assess whether the information feeding AI is accessible, accurate, current and governed, and whether privacy, compliance, security and operational risks have been adequately addressed. These factors not only influence trust and accountability but can also affect the total cost of ownership (TCO) through data preparation, integrations, change management, training, monitoring and ongoing support.

It’s also important to note that many of the common platforms and tools that use AI, such as Microsoft, Salesforce, Workday and ServiceNow, provide metrics for their own systems. Yet these system-level metrics don’t reveal the overall value or ROI they deliver.² They show if the platform is being used but they don’t indicate whether employees are working more effectively. And even when systems are integrated, their analytics are often still siloed, limiting visibility into the bigger picture.

A perspective by Deloitte³ on AI ROI explains that organizations struggle to realize AI value because:

• Traditional assumptions break down

• Missing baselines create weak evidence

• Weak attribution obscures AI’s role (for example, control groups or A/B testing)

Because of this complexity, organizations need to view AI as a tool for accelerating business outcomes rather than a strategy on its own. Success depends on employee adoption, workflow transformation and leadership alignment in addition to technology.

Building an outcome-first AI ROI framework

Simply stated, start with the desired outcome. An AI value framework should use an outcome-first approach that aligns ROI metrics to specific business goals. Once organizations identify their challenges, opportunities and business outcomes, they can select the technologies that best support those objectives.

This creates a clear connection between AI investments and measurable AI business value, helping organizations make more informed decisions, build trust in the results and evaluate whether outcomes can be achieved in a governed, accountable and scalable way.

Here’s how:

1. Start with outcomes ― What business goal are you trying to improve?

2. Examine the workflow ― Where is friction slowing people down and is the information supporting the process accurate, accessible and ready for AI?

3. Establish a baseline ― What does success look like today and what risks could prevent desired outcomes?

4. Enable employees ― Can people use the technology effectively?

5. Scale proven results ― Expand what delivers measurable value.

6. Optimize continuously ― Refine and improve based on ongoing results.

According to a recent Gartner® report, “Nearly two thirds of digital workplace leaders say that their success is primarily evaluated by cost management or other activity-based metrics. Despite having an overabundance of available metrics and digital employee experience (DEX) being a strategic priority, legacy metrics fail to demonstrate business value or impact. Incorporating DEX scores and experience level agreements (XLAs) into success metrics enables organizations to effectively assess their impact. Those that do this, rate their maturity 58% higher on average.”⁴

That’s an important distinction because the organizations that measure experience and impact, not just cost and activity, see themselves as more mature.

Using an AI ROI framework that shows the difference between activity metrics and outcome metrics becomes clearer when viewed through the lens of business goals. Here are a few examples:

• Saving 30 minutes writing an article isn’t an impactful outcome, but publishing twice as much content with the same team size shows value.

• Saving three hours while drafting a proposal isn’t the outcome, but reducing proposal turnaround time by 40% is.

• AI-assisted customer interactions aren’t the outcome, but increasing first-contact resolution in customer service does.

Additionally, if the process becomes materially faster with the same quality, the value can be translated into avoided cost or higher throughput.

AI metrics that don’t prove value on their own:

• Number of prompts

• Active users

• Logins

• Time spent using AI

These metrics show activity, not AI business value.

Instead, measure:

• Revenue growth

• Cost reduction

• Throughput

• Cycle time

• Time-to-market

• Quality

• Customer satisfaction or retention

• Error rate improvement

• Risk reduction

• Time to proficiency

• Conversion rates or closed-won deals

• Governance and compliance outcomes

When organizations focus on business outcomes instead of activity, they can better understand which initiatives are creating value, where investments should be expanded and how AI is contributing to broader business objectives.

5 tactics to get the outcomes you want

To maximize impact, organizations need a clear approach for aligning investments, setting expectations and connecting adoption efforts to tangible results. The following tactics can help ensure AI initiatives drive measurable value across the business.

1. Align AI to existing business goals. Don’t create a separate measurement system for AI. Instead, evaluate AI’s impact against the objectives, KPIs and timelines you’re already tracking.

2. Establish a baseline through segmentation. Break down AI investments by team, user group, business challenge, and activity type. Understanding where you started is critical to accurately evaluating progress and success.

3. Translate time savings into business value. When someone reports “time saved,” keep asking, “So what?” until you can connect that improvement to a clear business outcome.

4. Track output relative to team size. Compare productivity over time against headcount. If output increases without adding headcount, AI is releasing capacity the business can redeploy.

5. Manage AI and labor costs together. View AI investments and headcount as part of one overall budget. This shifts the conversation from “How much should we spend on AI?” to “What’s the most efficient path to achieving our goals?”

Here’s how we can help

We’ve seen how organizations that create an AI ROI framework can successfully measure their business objectives. They also recognize that sustainable AI business value depends on information readiness, effective governance and a clear understanding of organizational risk. We help companies establish these foundations so they are better positioned to scale AI with confidence and demonstrate its impact.

Whether you’re looking for AI readiness, structure, clarity or the capabilities needed to turn AI potential into business value, we can help.

Learn more about our Consulting & Advisory Services, explore our approach to AI innovation or contact us today to start the conversation.