In today’s digital-first economy, customer experience (CX) is a business imperative  — albeit a costly one. Artificial intelligence (AI) promises to transform this landscape by reducing costs, improving satisfaction, and even driving incremental revenue.

But how do we know if AI investments are truly paying off?

The answer depends on how strategically AI is implemented. While AI can deliver measurable ROI across multiple CX dimensions, success hinges on aligning technology with customer needs, data maturity, and business goals.

The CX conundrum: Why AI alone isn’t enough

For many organizations, the contact center is both essential and expensive. Each year, the cost of operating a contact center rises, driven by increasing interaction volumes, higher labor costs, and growing customer expectations. Despite these pressures, businesses continue to invest in their contact centers. Why? Because the alternative – cutting costs – often leads to even greater losses.

Reducing headcount may lower expenses in the short term, but it can also increase wait times, a major cause of customer dissatisfaction. Similarly, slashing technology budgets can result in outdated systems that fail to meet modern customer expectations. The result is a widening gap between what customers want and what businesses can deliver.

The stakes for delivering exceptional customer experiences have never been higher. According to Salesforce (2025), 76% of CX leaders anticipate an increase in case volumes over the next year. Meanwhile, research from TCN (2023) shows that 73% of customers are willing to leave a brand after just one negative experience. The financial impact is equally significant. Poor customer experiences are estimated to cost businesses up to $3.7 trillion annually, as reported by Qualtrics (2024).

This is the customer experience conundrum: organizations can’t afford to cut corners on CX, but they also can’t afford to maintain the status quo. Despite its promise, AI alone hasn’t solved this problem. Many organizations struggle to realize value from AI due to three common pitfalls:

1. Capability-first, customer-second mindset.  When organizations choose AI projects based only on internal performance goals, they risk creating experiences that are misaligned with their customers’ preferred journeys.
2. Lack of data maturity. AI is only as good as the data feeding it. Many AI pilots fail because the organization hasn’t taken the necessary data governance steps to empower its preferred use cases.
3. Misidentifying problems to be solved. Not every use case will create equal value. Starting with the most readily available AI applications can lead to solutions that address non-existent problems.

To overcome these challenges, organizations must move beyond piecemeal AI adoption and toward integrated, insight-driven ecosystems that prioritize the right problems to be solved. The following case study shows how one company did just that.

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Case study: Revenue generation through personalization

While AI is often viewed as a cost-saving tool, its greatest potential may lie in revenue growth. AI-powered segmentation, predictive modeling, and personalized messaging can significantly boost acquisition, retention, and cross-sell efforts. These are the results one health insurance provider realized with the right AI strategy in place.

In the health insurance sector, providers seek to grow their customer base by increasing enrollments. However, for this insurance provider, legacy systems and siloed data repositories made it difficult to access the insights needed to inform acquisition and retention strategies. They lacked a unified view of the customer, limiting their ability to personalize outreach and optimize marketing efforts.

To solve these challenges, the organization began by integrating more than 20 disparate data sources to create a comprehensive, 360-degree view of its customers. This foundational step enabled the deployment of advanced analytics and marketing strategies. These included look-alike customer modeling to identify high-potential prospects, journey analytics to optimize customer touchpoints, and predictive lead scoring to prioritize outreach. Additionally, contextual marketing strategies such as propensity modeling, location analysis, and device usage analysis enabled highly personalized messaging.

The impact of these AI-enablement efforts was significant. The company achieved a 7% increase in new business growth, translating to $2 million in incremental annual revenue. It also unlocked $600,000 in operational cost savings within its contact center. Importantly, these gains were achieved without compromising customer satisfaction, demonstrating that revenue growth and customer experience improvements can go hand in hand.

Turn AI potential into measurable impact

AI holds immense potential for transforming customer experience, reducing operational costs, and unlocking new revenue streams. But realizing this potential requires more than just deploying the latest tools. It demands a strategic, data-driven approach that aligns AI capabilities with real customer needs and business objectives. When organizations invest in the right foundations, AI becomes a powerful enabler of both efficiency and growth.

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