Experience Exchange: Turning AI potential into CX results

AI can drive better customer experiences — but only if you know what you’re solving for.

In a wide-ranging Experience Exchange session, Tom Lewis, SVP at TTEC Digital, and Darryl Kelly, CEO of Aspect, unpacked the shift to relational CX, the role of deep learning, and the strategy needed to make AI work.

Relational interactions over transactional

Customer expectations are changing. The traditional, quick-hit service model is giving way to deeper, more complex interactions. 

As Kelly noted, “What we're seeing from customers is this relational type of conversation... it's turned from, 'Hey, I need this thing immediately' to, 'Hey, there's this complex multidimensional thing that I need this agent to resolve.'“

This shift underscores the need for businesses to move beyond efficiency metrics and focus on customer relationships. According to a study by PwC, 73% of consumers say a good experience is key to brand loyalty, yet 59% feel that companies have lost touch with the human element of CX. 

AI and automation can help streamline interactions, but brands must ensure these tools support — not replace — the deeper connections customers seek when trying to solve intricate issues.

AI's role in enhancing customer experience

AI is no longer just about automating routine inquiries. It's now a key driver of personalized, scalable experiences. “AI creates a really efficient experience at scale that's very personalized for the customer,” Kelly said. 

By handling low-complexity and even some high-complexity tasks, AI frees human agents to focus on emotionally charged, high-value interactions.

This approach isn't just theoretical — it’s delivering measurable results. Research from McKinsey shows that AI-driven personalization can increase revenue by 5% to 15% while improving efficiency by 10% to 30%. Businesses that strategically integrate AI into their CX strategy are reaping both customer satisfaction and operational benefits.

Balancing AI and human interaction

The challenge lies in ensuring AI doesn’t create disjointed customer journeys. “It should be one relationship that's centered around [the customer] and … the brand,” Kelly emphasized. A seamless CX strategy ensures AI augments human agents rather than creating friction.

This aligns with findings from Gartner, which predict that by 2026, 60% of large enterprises will use AI-driven personalization to orchestrate customer interactions. The key is designing AI systems that enhance — not interrupt — the customer journey.

The importance of specificity 

Kelly stressed that businesses must clearly define their AI objectives: “We need to get really, really clear on what we are hoping and expecting to come from AI.” 

Too often, companies implement AI without a clear problem statement, leading to inefficiencies and missed opportunities. Lewis recently touched on the growing gap between technology spending and actual results.

The lesson? AI, just like all CX technology, should be deployed with precision, addressing specific challenges rather than serving as a catch-all solution.

Deep learning in workforce management

AI isn't just reshaping the customer side of CX — it’s also revolutionizing the training and staffing of the workforce behind it. 

"Deep learning to me is everything,” says Kelly. “When you think about AlphaGo, AlphaZero, unsupervised learning environments, it's reflective of biology at scale." 

At Aspect, Kelly and team are leveraging neural networks to unlock patterns and improve workforce forecasting, productivity, and engagement. "We’re looking at individuals beyond ones and zeros,” he says They’re taking deep learning technology typically used to understand customers and turning it inward — fitting the right employees to the right tasks. Matching each employee’s skillset to the work best suited for them not only maximizes workforce efficiency but also fosters a deeper sense of fulfillment. And because these frontline staff are the conduit for every customer engagement, when staff are engaged, the customers notice — and brands benefit.

The result? More than just faster resolutions — it’s a deeper connection.

 “We want to make sure that when you call Delta or you call Nike, you’re not just talking to a person, you are talking to the person,” says Kelly. “The person who’s not only resolving your issue, but who is engaged in a relationship with you. So that, after you leave, it makes you go, ‘Wow, that was a moment.’”

Making AI work for CX

The conversation between Tom Lewis and Daryl Kelly highlighted a hard truth: success — and a return on investment — won’t be achieved by adopting AI for AI’s sake. 

AI is a powerful tool — but only when applied with purpose and precision.

Winning with AI (or any CX technology) takes strategy. Businesses need to define the problems they want AI to solve, align those goals with customer needs, and deploy specific technology that enhances both efficiency and personalization. 

It’s solely with this strategic approach that companies can expect to reach the outcomes — from higher customer satisfaction to more productive workforce — that AI promises.