Measuring the impact of AI on CX
As few as one in three companies are able to measure the ROI of AI in CX. Carlos Del Corral shares three approaches to help overcome the challenge
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Artificial intelligence is poised to revolutionize the customer experience. By providing customers with personalized insights tailored to their needs and behaviors, AI creates a feedback loop that boosts customer satisfaction. Businesses can measure the benefits of AI in CX, but questions remain about how to assess its return on investment (ROI) accurately.
We know there is huge potential for artificial intelligence (AI) in customer experience, and those who have embraced AI are already gaining a competitive advantage. Yet the primary obstacle hindering wider adoption is still the ability to measure ROI.
According to research carried out by Lumoa, an overwhelming 71 percent of respondents already use generative AI in their CX initiatives. But while companies are eager to incorporate AI to stay competitive, they often struggle to understand its financial impact and strategic benefits.
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The real challenge lies in quantifying the ROI from AI initiatives in CX. Integrating AI into CX strategies opens new avenues for innovation and engagement, but it also underscores the need for robust methods to evaluate its effectiveness and financial impact.
Our findings reveal that only 29 percent of CX professionals measure the return on how AI improves CX. This suggests many companies struggle to quantify the ROI of their AI projects, facing issues like measuring indirect benefits, early adoption stages or a lack of established ROI calculation methods. Some respondents viewed AI tools as "nice-to-haves" rather than game changers, which adds to the uncertainty around the tangible ROI of AI in CX.
Similar findings were uncovered during CX Network’s research into the Global State of CX in 2024. There, among the respondents who use generative AI, 39 percent said they had seen a positive impact on company profits, and 28 percent noted a positive impact on loyalty. The same research also found that among all respondents and all CX projects, 66 percent said the pressure to prove ROI is increasing and 63 percent said CX delivers benefits to their organization that go unmeasured.
These gaps present a significant opportunity for practitioners and AI decision makers to develop methods for assessing AI's impact. Doing so can guide decisions on expanding AI use and provide clear strategies to investors and stakeholders.
Based on our respondents' input, we've identified three key methods to help practitioners measure the ROI of AI in CX.
1. Quantitative KPIs
Some respondent companies use quantitative metrics to evaluate the efficiencies of AI in CX, such as hourly savings.
Reports ranged from 1 to 180 hours saved through AI, with one business citing a saving of 12,500 agent hours per week, translating to a 10 percent efficiency gain and 35 percent more productivity within the same hours.
Other metrics include cost per ticket, deflection rates, cycle time reduction and analytic dashboards to track performance.
2. Financial assessments
Many businesses calculate the ROI of CX through financial metrics like new business generation or revenue increases – the same can apply to AI.
For instance, one company anticipated a 20-25 percent reduction in contract volume post-AI implementation, saving 800 working hours per month.
ROI calculators and net present value (NPV) assessments, particularly over five-year periods, are becoming increasingly valuable for assessing the financial ROI of AI in CX.
3. Business and CX metrics
Analyzing AI's impact on overall business performance is crucial for measuring ROI. AI's role in boosting conversion rates, sales and opportunities for upselling and cross-selling can potentially increase revenue.
Assessing AI’s influence on customer satisfaction scores (CSat) and Net Promoter Scores (NPS) helps businesses determine whether AI positively impacts CX.
Many businesses still need to gain knowledge to implement and leverage AI effectively. As many as 43 percent of all respondents highlighted a lack of expertise as one of the greatest challenges they face when preparing and implementing AI.
This knowledge gap represents a significant opportunity for AI providers to educate businesses on the technology’s potential benefits, ease of integration with existing systems and ability to drive cultural shifts within organizations.
