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Many AI models today are designed as generalists. But in customer experience, that approach often falls short.

In this episode of Deep Learning with PolyAI, Nikola Mrkšić sits down with Matt Henderson, VP of Research at PolyAI, to unpack why specialized AI models outperform general-purpose ones in real-world CX environments.

They explore the tradeoffs between speed, accuracy, and reasoning, why voice AI requires fundamentally different design decisions, and what makes PolyAI’s Raven 3.5 a step forward. The conversation offers a behind-the-scenes look at what it actually takes to build AI that works in production, not just in demos.

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In this episode of Deep Learning with PolyAI, Nikola Mrkšić sits down with Bill Staikos, Founder of Be Customer Led, to unpack why many CX leaders are still on the sidelines of the AI conversation — and what that means for the future of the function.

They explore how AI is reshaping the “interaction layer” of customer experience, why traditional CX metrics like NPS are no longer enough, and how leading organizations are starting to tie CX directly to revenue, cost, and risk.

The conversation challenges CX leaders to think bigger and make their function into an outcome-driven engine for business growth. In an AI-driven world, the leaders who understand that shift will have a much bigger role to play.

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Customer experience is often measured at the surface. But it’s built behind the scenes.

In this episode of Deep Learning with PolyAI, Michelle Schroeder sits down with Sam Stern, who leads service design at LinkedIn, to explore how employee experience shapes every customer interaction.

They discuss how LinkedIn is improving CX by fixing the systems employees rely on — from reducing “toggle tax” across tools to giving teams better data and real-time guidance with AI. The result is not just faster service, but more meaningful, higher-quality interactions when it matters most.

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In this episode of Deep Learning with PolyAI, Michelle Schroeder sits down with Nate Brown, Head of Education and Enablement at Metric Sherpa and cofounder of CX Accelerator, to discuss how AI is reshaping the way companies think about customer experience.

Nate shares a framework that’s gaining traction with CX leaders: make the quick parts quick, and make the slow parts meaningful. Automation should remove friction and resolve routine problems quickly, but the moments that matter most still require guidance, empathy, and human connection.

Together they explore how organizations can use AI to reduce effort for customers while still creating experiences that build loyalty, trust, and long-term value.

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In this episode of Deep Learning with PolyAI, Nikola Mrkšić sits down with Steven Fine, GM of Restaurants at PolyAI, to explore how technology has shaped the customer experience in restaurants over the past century.

From the first phone reservations to online booking platforms and now AI-powered voice agents, the conversation traces how hospitality has continuously adapted to meet changing customer expectations.

Nikola and Steven discuss why the phone is still one of the most important touchpoints in the restaurant journey, how missed calls translate into lost revenue, and how AI is helping restaurants capture demand while improving both staff and guest experience. Along the way, they explore no-shows, loyalty programs, large party bookings, and why voice AI is becoming a new revenue engine for the hospitality industry.

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In this episode of Deep Learning with PolyAI, Michelle Schroeder sits down with Sarah Wright, Vice President of Global Customer Service at ResMed, to talk about what it really takes to transform healthcare CX. Sarah describes the challenge as an Everest climb. Every step forward requires balancing innovation with responsibility, especially when patient health information is involved.

Unlike other industries, healthcare cannot experiment loosely. Data must be protected. Integrations must be intentional. The order of operations matters.

• In this conversation, they discuss:
  • Why protecting patient health information shapes every AI decision in healthcare
  • How fragmented systems make personalization difficult
  • What it takes to move from reactive break-fix service to predictive, personalized experiences
  • How “data stories” help build internal alignment for transformation
  • Why voice of customer and QA datasets are often the most powerful starting point
  • How AI should augment human moments rather than eliminate them
  • Why frontline teams become brand defenders and trust gatekeepers as automation scales
  • The importance of starting with baselines and defining what “good” actually looks like

Sarah also shares practical advice for leaders under pressure to modernize quickly: start with the friction that shows up in your data, focus on high-volume pain points, and avoid trying to fix experiences that patients already value.

Climbing healthcare’s AI Everest takes patience, humility, and strong change leadership. This episode offers a realistic look at what that journey involves.

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In this episode of Deep Learning with PolyAI, your host Nikola Mrkšić chats with Alex Brown to unpack what happens when AI takes over the administrative work that quietly defines modern healthcare. Instead of replacing doctors and clinicians, AI is reshaping everything around them: from documentation and scheduling to the workflows that consume hours of time.

The conversation explores why healthcare resisted automation longer than other industries, why AI’s fastest wins are operational rather than clinical, and how removing administrative friction could improve experiences — for both providers and patients.

Tune in to hear:

• Why healthcare’s biggest AI gains are operational, not clinical
• How admin-heavy workflows shape both provider burnout and patient experience
• Why “AI replacing doctors” misses the real opportunity
• What efficiency actually looks like in a healthcare context
• Where empathy still matters — and where automation clearly helps

If you’re curious about where AI is quietly reshaping healthcare today, tune in, and let us know what you think.

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In this episode of Deep Learning with PolyAI, Nikola Mrkšić sits down with Nathan Pearson, GM, Financial Services at PolyAi, to trace the evolution of personalized banking. From branch tellers and ATMs to digital channels and AI-driven experiences, they explore how banks have historically balanced scale, efficiency, and trust, and why personalization has always been central to customer relationships.

The conversation looks at why personalization has always mattered in financial services, how each new channel changed customer expectations, and what today’s AI moment has in common with past shifts. While technology keeps changing, the core challenge for banks hasn’t: delivering personal, trusted service at scale.

Together, they discuss:

• How early branch banking set the original standard for personalized service
• How online and mobile banking reshaped customer expectations around convenience
• Why “self-service” has never meant “impersonal” for customers
• How each new technology promised efficiency, but introduced new tradeoffs
• What banks can learn from history as they deploy AI across customer journeys

The big takeaway: while tools keep changing, the goal hasn’t. Customers still want banking experiences that feel personal, reliable, and easy — even when they’re powered by automation.

If you’re thinking about how AI should support (not replace) personalization for your business, be sure to tune in.

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