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Why AI is the future of customer service with Fergal Reid, Chief AI Officer at Intercom

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Manage episode 522634070 series 2093893
Contenuto fornito da Kane Simms. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Kane Simms o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

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.

  continue reading

362 episodi

Artwork
iconCondividi
 
Manage episode 522634070 series 2093893
Contenuto fornito da Kane Simms. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Kane Simms o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

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.

  continue reading

362 episodi

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