How do you define ROI from AI in your business — is it efficiency, revenue, customer experience, or competitive edge?
By Mahesh M. Thakur
In 2025, nearly every mid-to-large company is investing in AI. But what differentiates those who succeed from those who merely spend is how they define ROI. Is it cost savings through operational efficiency, rising revenues, better customer experience, or gaining a competitive edge? The answer shapes strategy, culture, measurement, and outcomes.
What current research shows
- A recent study by IBM’s Institute for Business Value found that enterprise-wide AI initiatives delivered an average ROI of 5.9%, often underwhelming expectations. (IBM)
- Another strong real-world case: Five9’s AI-elevated customer experience (CX) platform drove 212% return on investment and a net present value of USD 14.5 million over three years in contact centers that deployed AI at scale. (Five9)
- In the realm of customer experience, MiaRec reported that many companies are recovering thousands to millions of dollars in revenue lost to churn—as CX teams adopt AI-powered insights to predict churn risk, improve retention, and tie satisfaction (CSAT/NPS) directly into financial outcomes. (blog.miarec.com)
These findings suggest three things: first, ROI tends to manifest in multiple dimensions; second, outcomes often lag initial investment; and third, linking AI initiatives to core business metrics (not just technical metrics) is essential.
Efficiency vs. Revenue vs. Customer Experience vs. Competitive Edge
When executives talk about ROI from AI, they often focus on efficiency first—automation of repetitive tasks, reducing errors, streamlining processes, and freeing up leadership bandwidth. These are tangible, measurable benefits, often captured in cost reduction percentages, cycle-time improvements, and decreases in manual labor. But efficiency alone rarely transforms a company.
Others prioritize revenue growth, using AI to enable upselling, cross-selling, or creating entirely new products and services. For example, machine-learning-driven lead scoring can directly lift conversion rates, while AI-powered personalization can increase repeat purchases. These revenue gains often provide the clearest business case for investment.
Increasingly, leaders see customer experience (CX) as the decisive ROI lever. Faster resolutions, personalization at scale, and predictive churn analytics not only improve customer satisfaction scores but also reduce churn, driving longer-term revenue and loyalty. Companies like Five9 have demonstrated how AI-elevated CX platforms can unlock millions in net value by improving both the customer journey and bottom-line outcomes.
Finally, some CEOs define ROI in terms of competitive edge. These leaders are not just measuring today’s efficiency or revenue—they are using AI to outpace competitors, bring new offerings to market faster, and establish their organizations as innovation leaders. Competitive edge may be harder to measure in the short term, but over time it shows up in market share growth, successful launches, stronger stakeholder trust, and the creation of a culture that embraces experimentation and learning.
The most successful companies balance all four dimensions—efficiency, revenue, CX, and competitive edge—rather than chasing one in isolation.
What many miss
From my work advising leaders globally, including via my Advisory Page, I see two common mistakes:
- Overemphasizing efficiency early without linking it to strategic goals. Efficiency savings matter—but if they only improve internal metrics without meaningful revenue, market, or CX gains, they often fail to sustain funding.
- Ignoring the intangible or lagging benefits—like improved decision-making culture, stakeholder alignment, and stronger brand trust. These often show up in months or years, but contribute to a competitive edge if nurtured.
For example, a company may save 20% in support operations via chatbots (efficiency), but unless CSAT or retention improves, customers may still defect because the experience feels impersonal.
How to define AI ROI in a way that works
Here are five steps leadership teams should follow:
- Align with business KPIs from the start: Before deploying AI, decide whether revenue growth, customer satisfaction, or market position is your primary goal. Then build measurement frameworks around that.
- Use a “balanced scorecard” approach: Track a mix of metrics: efficiency metrics (cost, time), revenue metrics, CX measures, and competitive metrics. Do not rely on just one dimension.
- Choose both short-term and long-term ROI horizon: Some AI returns (e.g., revenue uplift, reduced error) happen quickly; others (culture, innovation, competitive positioning) accrue over years. Forecast both.
- Invest in change, culture, and adoption: AI tools alone don’t move the needle. The companies that win have leadership committed, teams aligned, a test + learn culture, and a clear advisory framework. (For more on advisory frameworks, see more in my Advisory Page
- Benchmark and iterate: Use baseline metrics before AI implementation. Compare with industry peers. Adjust as you go. Monitoring feedback, retention, customer experience, etc., will show course corrections.
Case Example
Consider one enterprise that leveraged its AI Advisory function to overhaul its sales funnel. They deployed AI lead scoring, which increased conversion rates by ~20% within six months; then paired that with an improved customer experience platform to reduce churn by 15%; over 12 months, their total revenue uplift exceeded efficiency gains. All this while maintaining cost discipline and managing total cost of ownership.
The strategic imperative
By defining ROI not just as cost savings but as efficiency, revenue, CX, and competitive edge, businesses position themselves not only to survive but to excel. As the market tightens, those who treat AI ROI holistically outperform those who chase narrow metrics.
For CEOs and C-Suite leaders focused on long-term impact, this means investing in frameworks, advisory guidance, culture, and continuous measurement. You can start today by revisiting your AI projects and asking: Which dimension am I optimizing for—and am I measuring it well?
Mahesh M. Thakur is a Top 0.1% Master Certified Coach, AI Advisor & CEO and C-Suite Coach. To explore how to move from AI to ROI across all four dimensions, visit my Home Page or learn how I work with boards and leadership via my Advisory page.
You can learn more by visiting these sources
- Measuring the ROI of AI and Data Training: A Productivity-First Approach, Data Society, March 2025. (Data Society)
- How AI Is Changing the ROI of Customer Service, Harvard Business Review, Jan 2025. (Harvard Business Review)
- Maximizing ROI with AI Lead Scoring: Case Studies & Success Stories, Superagi, June 2025. (SuperAGI)
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