AI Is Everywhere, but ROI Is Rare: Why CX Is Leading the Way

Tatiana Poly at MiaRec explores why AI is driving measurable ROI in Customer Experience (CX) while many other business functions struggle to move beyond pilot projects, and what this reveals about how to achieve real, scalable impact with AI.

Over the last few years, Artificial Intelligence (AI) has shifted from a futuristic concept to a core part of business conversations in every boardroom.

Adoption has been staggering. According to McKinsey, 78% of companies now use AI in at least one function – up sharply from just 55% in 2023. Investments are equally eye-popping, with $33.9 billion pouring into private generative AI last year alone.

And yet, there’s a growing paradox: Despite soaring adoption rates, most companies are struggling to realize measurable returns from AI.

The Wall Street Journal recently highlighted this challenge, reporting that companies typically see less than 5% revenue growth and less than 10% cost savings from their AI initiatives.

Stanford researchers describe it as a “productivity paradox”-capabilities are advancing, but enterprise-level financial outcomes are lagging behind.

Why the Disconnect?

The reasons are complex but consistent across industries:

• Many AI projects are stuck at the pilot phase, never scaling across operations.
• Data quality issues prevent AI from generating reliable insights.
• Too often, AI is deployed without a direct link to meaningful business KPIs.

As a result, AI frequently produces interesting information, but not real business outcomes.

But in CX, the Story Is Different

Customer Experience (CX) has quietly emerged as one of the first domains where AI is delivering real, measurable ROI, and doing so at scale.

In CX environments, AI is being applied with sharp focus:

• Automatically measuring core metrics like CSAT, NPS, and NES across every interaction
• Predicting churn risk based on patterns in real conversations
• Prioritizing customer accounts by revenue at risk, not just satisfaction scores
• Tracking recovered revenue tied directly to AI-driven retention efforts

The results are undeniable.

Companies using this approach have recovered thousands to millions of dollars in churn-related revenue, not after years of experimentation, but during live, scaled deployments.

Why CX? And Why Now?

Several unique conditions make CX the ideal proving ground for AI-driven business value:

• Data at Scale: CX generates enormous volumes of structured customer interaction data daily.
• Clear Link to Revenue: Customer satisfaction and retention metrics are tightly coupled to growth and profitability.
• Operational Pressure: Contact centres and CX teams are under constant pressure to deliver more with leaner resources.

In CX, AI isn’t solving abstract problems, it’s answering urgent, bottom-line questions:

• Who is at risk of leaving?
• Why are they dissatisfied?
• How much revenue is at stake if we don’t act?
• What actions will make the biggest impact?

When AI is applied to these questions, the results become not just visible – but transformational.

Lessons for the Broader Enterprise

CX’s success with AI isn’t just a case study, it’s a blueprint.

To unlock real AI ROI across other parts of the business, companies need to move beyond experimentation and into outcome-driven deployment:

• Start with operational KPIs (not just “insights” or dashboards)
• Target clear financial outcomes (cost savings, revenue protection, growth)
• Focus on scale, integrating AI across the full customer or operational journey, not isolated use cases

This is the shift that will define the next era of AI adoption:

• From pilots to scaled impact
• From insights to financial outcomes
• From generalized tools to targeted business transformation

Reviewed by: Rachael Trickey