VUX World

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. Timestamps 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 Show notes Learn more about Intercom Connect with Fergal Reid on LinkedIn Follow Kane Simms on LinkedIn Article - The full stop problem: RAG’s biggest limitation Take our updated AI Maturity Assessment Subscribe to VUX World Subscribe to The AI Ultimatum Substack Hosted on Acast. See acast.com/privacy for more information.

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. Chapters 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 Show notes Find out more about DRUID AI Follow Joe Kim on LinkedIn Sign up for our webinar: How to run AI like your business depends on it Follow Kane Simms on LinkedIn Take our updated AI Maturity Assessment Subscribe to VUX World Subscribe to The AI Ultimatum Substack Hosted on Acast. See acast.com/privacy for more information.

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. Chapters 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 Show notes Check out Teneo Follow Anne Jenkins on LinkedIn Read Kane’s article - What agentic AI actually is: a deeply researched and definitive explanation Follow Kane Simms on LinkedIn Take our updated AI Maturity Assessment Subscribe to VUX World Subscribe 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. Timestamps 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 Show notes Follow Kane Simms on LinkedIn Discover VUX Consulting Take our updated AI Maturity Assessment Subscribe to VUX World Subscribe to The AI Ultimatum Substack Hosted on Acast. See acast.com/privacy for more information.