HBX Group sits in the middle of one of the most complex customer service chains in travel. A single issue can span a traveller, a travel agent, a global booking platform and a hotel on the other side of the world.

In this episode, we explore how HBX Group is leveraging artificial intelligence to transform its global customer service operations with Xavier Godoy Moya, Customer Experience & Automation Director, and Adam Tinsley, Global Customer Service Director.

We break down how HBX uses AI-driven classification to triage hundreds of thousands of monthly cases across voice, chat, email and ticketing systems. The team explains how generative models replaced traditional intent-matching systems, enabling accurate routing across more than 70 case types while supporting deterministic safeguards required at operational scale.

Adam and Xavier share results across several key use cases, including an AI classification system that accurately categorises customer inquiries into 70 different intents, automated handling of common requests such as booking confirmations and cancellations, and an innovative workflow that can automatically communicate with hotels on behalf of agents. Their real-time translation solution has enabled 24/7 service across 13 languages, dramatically expanding their global support capabilities.

We also discuss their approach to agent training, which includes an AI simulation system that lets new staff practice handling customer conversations at varying levels of difficulty before taking real calls.

The episode closes with measured business outcomes, including faster case resolution, growing automation rates, operational insights unlocked through AI-native reporting and the next wave of experimentation around sentiment-aware routing and more adaptive voice experiences.

Find out more about HBX Group: https://www.hbxgroup.com

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From IVRs to multimodal AI experiences, the hardest problems are rarely technical.

In this episode, we dive into the evolving landscape of conversational AI and enterprise automation with Anne Cantera, Senior Product Designer at Optimum. With extensive experience in transforming traditional IVR systems into conversational interfaces, Anne shares valuable insights on the practical implementation of AI in customer service environments.

We explore the concept of 'hybrid architecture' in AI implementation, discussing how combining rule-based automation with generative AI often provides the optimal solution for enterprise needs. Anne explains how her team strategically deploys generative AI in low-risk scenarios while maintaining deterministic approaches where precision is critical, particularly in their recently launched NLU routing system.

The conversation also covers the importance of customer-centric design in AI implementations, with Anne emphasising that the ultimate goal should be making experiences simpler for customers rather than focusing solely on containment metrics. We discuss how different industries approach AI adoption based on their risk profiles and how businesses are navigating the challenges of integrating new technologies with legacy systems.

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AI has moved from pilot projects to boardroom urgency. Companies feel the pressure to act, hire, and keep up. Many start with tools or chase a single unicorn hire. Chris Morrow, Founder of Digitalent, explains why this approach fails and why the real battle is for people who can drive transformation, culture change and long-term value.

In this episode, Chris draws on two decades of experience in tech recruitment and his work with VC-backed startups, global enterprises, and governments. He breaks down why AI hiring is unlike any previous talent cycle. Roles are ill-defined, expectations are inflated, and the best candidates command eye-watering compensation, especially in the US. He describes the AI talent market as an arms race, with startups, big tech and even nations competing for the same small pool of elite engineers and researchers.

The conversation goes beyond salaries and job titles. Chris shares what AI-ready companies do differently, why small proof-point projects matter more than grand 18-month programmes and how misuse of AI can kill belief internally before value is ever realised.

The episode also explores the global picture. The US surge in AI investment and talent density. The UK experiences brain drain and risk-averse capital. Australia’s early-stage market. Chris offers a blunt assessment of government readiness, education systems and why countries that fail to treat AI as a productivity and talent priority will fall behind.

This is the ultimate episode on the AI talent war, covering what companies should fix before hiring, which roles actually matter, how the best teams are built, and why the next decade will reward organisations that treat AI as a people-and-change challenge first and a technology problem second.

Check out Digitalent: https://digitalent.agency

Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrismorrow/

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Cars are becoming one of the most competitive frontiers in AI, and most people don’t realise how complex it really is.

In this episode, we’re joined by BalaKrishna Kolluru, Senior Director of AI at Cerence, the company powering conversational AI in over 500 million vehicles worldwide. With over 25 years of experience in machine learning and speech recognition, Bala brings deep insights into how generative AI is transforming the in-vehicle experience.

We explore how voice AI in vehicles has evolved from basic command-and-control systems to sophisticated conversational interfaces powered by hybrid cloud-edge architectures. Bala explains how Cerence's technology serves around 80 car manufacturers globally, and how their agentic AI approach enables seamless integration with external systems while maintaining functionality even without cloud connectivity.

We address the technical challenges of implementing voice AI in vehicles, from microphone placement to processing capabilities, and explain how Cerence collaborates closely with manufacturers to create systems that minimise cognitive load while keeping drivers connected and safe. We also discuss how generative AI has expanded the capabilities of in-car assistants, enabling more natural, contextual interactions that can anticipate user needs.

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What does it really take to build AI that can resolve customer support at scale reliably, safely, and with measurable business impact?

We explore how Intercom has evolved from a traditional customer support platform into an AI-first company, with its AI assistant, Fin, now resolving 65% of customer queries without human intervention. Intercom's Chief AI Officer, Fergal Reid, discusses the company's journey from natural language understanding (NLU) systems to their current retrieval augmented generation (RAG) approach, explaining how they've optimised every component of their AI pipeline with custom-built models.

The conversation covers Intercom's unique approach to AI product development, emphasising standardisation and continuous improvement rather than customisation for individual clients. Fergal explains their outcome-based pricing model, where clients pay for successful resolutions rather than conversations, and how this aligns incentives across the business.

We also discuss Intercom's approach to agentic AI, which enables their systems to perform complex, multi-step tasks, such as processing refunds, by integrating with various APIs. Fergal shares insights on testing methodologies, the balance between customisation and standardisation, and the challenges of building AI products in a rapidly evolving technological landscape.

Finally, Fergal shares what excites and honestly freaks him out a bit about where AI is heading next.

00:00 - Intro

02:31 - Welcome to Fergal Reid

05:26 - How to train an NLU solution effectively?

08:56 - What gen AI changed for Intercom

10:57 - How would you describe Fin?

14:30 - Fin’s performance increase

17:18 - Intercom’s custom models

22:14 - Large Language Models vs Small Language Models

30:40 - RAG and 'the full stop problem'

40:08 - Agentic AI capabilities at Intercom

50:40 - Intercom’s approach to testing

1:04:46 - About the most exciting things in the AI space

Learn more about Intercom

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Article - The full stop problem: RAG’s biggest limitation

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In this episode, Joe Kim, the new CEO of DRUID AI, unpacks what might be the biggest shift in technology since the internet.

We discuss the evolving landscape of artificial intelligence and its growing impact on businesses. Joe, who has been in his role for 70 days, brings fresh perspectives on how DRUID AI positioned itself as a workflow automation platform years before the market caught up, a bet that's now paying off as companies move beyond AI experimentation into real implementation.

The discussion covers the current state of the AI market, including the challenges of customer adoption, why some customers actively prefer interacting with AI over humans, and the importance of maintaining a balance between generative and agentic AI.

Joe also shares his vision of AI as the next major distribution platform. Just as Amazon has tried to make Alexa the operating system of our lives, OpenAI’s ChatGPT and similar platforms could become the primary gateway to accessing services in the future.

00:00 - Meet Joe Kim

00:33 - Celebrating 70 days at DRUID AI

01:35 - What attracted Joe to DRUID

03:06 - The real value of AI

06:43 - What Joe learnt about the market

11:19 - The AI God Box for enterprises

14:08 - The state of the AI market

20:29 - Why you should learn about AI as much as you can

23:47 - Generative AI adoption among enterprises

27:36 - The concept of agent washing

36:05 - Is AI bigger than the internet?

39:11 - ChatGPT as the new distribution platform

48:52 - Will chatbots replace websites and apps?

50:31 - The next 6–12 months for DRUID AI

56:59 - The biggest AI ultimatum facing businesses today

1:01:29 - AI replacing people is not growth

1:05:33 - The systems of record at enterprises and LLMs

1:08:46 - The future of AI automation

1:14:13 - LLMs could replace apps

Find out more about DRUID AI

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Everyone's slapping 'agentic' on their AI products, but what does that even mean? Anne Jenkins, Vice President of Solution Architecture at Teneo, helps us separate substance from buzzwords.

In this episode, Anne shares her expertise on agentic AI and the evolution of conversational AI technologies. We explore what truly makes an AI ‘agentic’ versus the marketing hype all over your LinkedIn feed and beyond. Anne explains why Teneo's hybrid approach, combining traditional NLU capabilities with large language models, creates more reliable, cost-effective, and latency-friendly solutions than pure LLM implementations.

We also discuss the challenges organisations face when adopting AI solutions, including the misconception that LLMs alone can solve all problems without proper conversation design or business rule integration. Anne shares valuable insights on how companies can prepare for AI adoption through education, competitive pilots, and architectural considerations.

We touch on many best practices when it comes to implementation, with Anne emphasising the importance of proper context engineering, state management, and dynamic prompting.

00:00 - Meet Anne Jenkins

05:01 - AI is now a boardroom conversation

06:48 – OpenAI’s agentic AI platform

08:48 – What is an AI agent?

21:38 – The hybrid AI model or end-to-end LLMs

26:49 – AI in debt collection

34:55 – How Teneo helps implement agentic AI

42:39 – Context engineering

47:19 – State management and LLMs

55:40 – How Teneo designs AI experiences

01:00:32 – Demo – teaching Teneo something new

01:10:26 – How businesses should adopt AI

01:16:44 – What sets Teneo apart

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Once switched off during busy periods, Lloyds Banking Group’s chatbot now handles more than 15 million conversations a year. Learn what it really takes to build AI solutions that customers trust with their money.

We sit down with Cainan Wright, Senior Product Owner, and John Young, AI Lead - Chatbots and Generative AI, from Lloyds Banking Group, to explore their remarkable nine-year journey in building one of the UK's most successful banking chatbots. From handling 3 million conversations in 2021 to over 15 million in 2025, they share the hard-earned lessons of transforming a rather "unloved" virtual assistant into a mission-critical service for almost 27 million customers.

We cover how the team stabilised their service by focusing on the basics, investing in design expertise, and securing organisational buy-in through demonstrating clear value.

Cainan and John also share valuable insights into their pragmatic approach to generative AI, explaining how they combine deterministic processes with generative language capabilities to deliver the best customer experience while maintaining brand consistency and ensuring regulatory compliance.

The team's journey offers important lessons about perseverance and the importance of striking a balance between innovation and responsibility when deploying AI in a highly regulated industry.

00:00 – Intro

01:34 – Meet Lloyds Banking Group

03:18 – The unloved virtual assistant era

05:30 – AI before ChatGPT

06:50 – Stabilising the service and earning trust

14:23 – From underloved to mission critical

16:55 – Buy-in from stakeholders to scale AI capabilities

18:41 – Defining new AI roles in banking

20:51 – Scaling from 3 to 15 million conversations

25:15 – How to handle a 5x increase in three years

27:43 – Experiments with generative AI in banking

30:24 – Using an LLM classifier

31:07 – Improving tools within teams

32:45 – Using generative AI for value and impact

34:56 – Valuable design research sessions

38:56 – Meeting customer expectations with generative AI

46:04 – Maintaining consistency in probabilistic landscape

49:30 – The right tool for the right job in conversational AI

55:26 – How do you measure the impact of generative AI?

56:13 – Responsible AI and proving business value

1:01:10 – The next 12 months

1:07:02 – Magic wand moments

1:11:34 – If the team could start over again

1:14:03 – Closing remarks from Kane

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