VUX World

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.Show notesFollow Anne on LinkedInFollow Kane Simms on LinkedInTake our updated AI Maturity AssessmentSubscribe to VUX WorldSubscribe to The AI Ultimatum Substack Hosted on Acast. See acast.com/privacy for more information.

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. Show notesCheck out Digitalent: https://digitalent.agencyConnect with Chris on LinkedIn: https://www.linkedin.com/in/chrismorrow/Discover UKAI: https://ukai.co/Follow Kane Simms on LinkedIn: https://www.linkedin.com/in/kanesimmsTake our updated AI Maturity Assessment: https://vuxworld.typeform.com/to/a26bf9Rr?utm_source=podcast&utm_medium=audio&utm_campaign=vuxconsulting25Subscribe to VUX World: https://vuxworld.typeform.com/to/Qlo5aaeWSubscribe to The AI Ultimatum Substack: https://open.substack.com/pub/kanesimms Hosted on Acast. See acast.com/privacy for more information.

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.Show notesDiscover more about Cerence AIConnect with Bala on LinkedInFollow Kane Simms on LinkedInTake our updated AI Maturity AssessmentSubscribe to VUX WorldSubscribe to The AI Ultimatum Substack Hosted on Acast. See acast.com/privacy for more information.

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.Timestamps00:00 - Intro02:31 - Welcome to Fergal Reid05:26 - How to train an NLU solution effectively?08:56 - What gen AI changed for Intercom10:57 - How would you describe Fin?14:30 - Fin’s performance increase17:18 - Intercom’s custom models22:14 - Large Language Models vs Small Language Models30:40 - RAG and 'the full stop problem'40:08 - Agentic AI capabilities at Intercom50:40 - Intercom’s approach to testing1:04:46 - About the most exciting things in the AI spaceShow notesLearn more about IntercomConnect with Fergal Reid on LinkedInFollow Kane Simms on LinkedInArticle - The full stop problem: RAG’s biggest limitationTake our updated AI Maturity AssessmentSubscribe to VUX WorldSubscribe to The AI Ultimatum Substack Hosted on Acast. See acast.com/privacy for more information.

Joe Kim, the dynamic new CEO of DRUID AI, dives into the transformative world of AI and its impact on business automation. With just over 70 days in his role, Joe reveals how DRUID strategically positioned itself ahead of the market and discusses the critical balance between generative and agentic AI. He highlights surprising customer preferences for AI over humans and explores how AI could serve as the next major platform for service distribution. Joe emphasizes the urgent need for businesses to educate themselves on AI to harness its full potential.

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.Chapters00:00 - Meet Anne Jenkins05:01 - AI is now a boardroom conversation06:48 – OpenAI’s agentic AI platform08:48 – What is an AI agent?21:38 – The hybrid AI model or end-to-end LLMs26:49 – AI in debt collection34:55 – How Teneo helps implement agentic AI42:39 – Context engineering47:19 – State management and LLMs55:40 – How Teneo designs AI experiences01:00:32 – Demo – teaching Teneo something new01:10:26 – How businesses should adopt AI01:16:44 – What sets Teneo apartShow notesCheck out TeneoFollow Anne Jenkins on LinkedInRead Kane’s article - What agentic AI actually is: a deeply researched and definitive explanationFollow Kane Simms on LinkedInTake our updated AI Maturity AssessmentSubscribe to VUX WorldSubscribe to The AI Ultimatum Substack Hosted on Acast. See acast.com/privacy for more information.

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.Timestamps00:00 – Intro01:34 – Meet Lloyds Banking Group03:18 – The unloved virtual assistant era05:30 – AI before ChatGPT06:50 – Stabilising the service and earning trust14:23 – From underloved to mission critical16:55 – Buy-in from stakeholders to scale AI capabilities18:41 – Defining new AI roles in banking20:51 – Scaling from 3 to 15 million conversations25:15 – How to handle a 5x increase in three years27:43 – Experiments with generative AI in banking30:24 – Using an LLM classifier31:07 – Improving tools within teams32:45 – Using generative AI for value and impact34:56 – Valuable design research sessions38:56 – Meeting customer expectations with generative AI46:04 – Maintaining consistency in probabilistic landscape49:30 – The right tool for the right job in conversational AI55:26 – How do you measure the impact of generative AI?56:13 – Responsible AI and proving business value1:01:10 – The next 12 months1:07:02 – Magic wand moments1:11:34 – If the team could start over again1:14:03 – Closing remarks from KaneShow notesFollow Kane Simms on LinkedInDiscover VUX ConsultingTake our updated AI Maturity AssessmentSubscribe to VUX WorldSubscribe to The AI Ultimatum Substack Hosted on Acast. See acast.com/privacy for more information.

In this episode, we dive into the art and science of context engineering with Martin Cross, President of Connect, a company that empowers enterprises to transform through the use of AI and automation.Martin shares how most businesses still approach AI with a technology-first mindset, deploying solutions before clearly defining the customer problem they’re solving. He explains why successful AI experiences begin with understanding the user journey, designing the right interaction, and then selecting the appropriate tools, not the other way around.We explore different layers of context - from session-level to journey-wide to CRM-integrated, and how they work together to transform generic interactions into deeply personalised, proactive experiences.We also unpack Connect’s acquisition of Elerian AI and why contact centre voice recordings represent an untapped goldmine of training data. Martin explains how fine-tuning models with real-world conversations, including dialects, accents, and customer intents, can dramatically improve AI accuracy and empathy.Additionally, we reflect on why the industry continues to repeat old mistakes and how businesses can design AI that truly understands and anticipates customer needs.Show notesFollow Martin Cross on LinkedInFind out more about ConnectFollow Kane Simms on LinkedInDiscover VUX ConsultingTake our updated AI Maturity AssessmentSubscribe to VUX WorldSubscribe to The AI Ultimatum Substack Hosted on Acast. See acast.com/privacy for more information.

Kylan Gibbs, Co-founder and CEO of Inworld, and Andrei Papancea, Co-founder and CEO of NLX, dive into the exciting evolution of voice AI technology. They explore the shift from basic assistants to lifelike voices that deliver personalized customer experiences. The duo details their collaboration to make high-quality text-to-speech accessible for developers. They discuss how brands can transition from mere voice to vibrant personalities, emphasizing the importance of user segmentation and customization. Their insights on measuring quality in voice interactions and the necessity for continuous experimentation highlight the future of voice AI.

Ian Jacobs, VP and Lead Analyst at Opus Research, brings a wealth of expertise in customer service and conversational tech. He dives into how AI is reshaping contact centers, balancing efficiency with human empathy. Intriguingly, he discusses instances where non-empathetic AI might actually serve customers better. The conversation expands to explore the future of work, human happiness, and the challenges of regulating rapidly advancing AI technology. Discover the complexities of navigating AI's role in modern business and society.