Speaker
Abstract
The book Team Topologies Second Edition (2025) demonstrates convincingly that organizing business and technology for fast flow of value via empowered teams produces outsized results for enterprises worldwide. As evidence from AI adoption spreads, it’s clear that organizations that already organize for bounded agency in humans are well-suited to adopting AI effectively and humanely.
The core principles from Team Topologies - organizing and empowering teams around independently-viable services, making cognitive load a key design principle, and making capabilities available via clear “vending machine” interfaces - translate superbly into the AI space by providing guardrails and heuristics for effective AI agency.
In this talk, Matthew Skelton - co-author of the groundbreaking book Team Topologies - shares deep insights about how organizations can find success with AI by using the patterns and principles from Team Topologies, based on experience with hundreds of organizations worldwide.
Topics
QCon London 2026 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
Building Engineering Teams Hosted by Wes Reisz Technical Principal @Thoughtworks, 16-Time QCon Chair, & Creator of The InfoQ PodcastFrom the same track
Tuesday 17 March
10:35 Churchill (Ground Fl.) Session organization Team Topologies as the 'Infrastructure for Agency' with AI Matthew Skelton CEO & Principal @Conflux, Co-Author of "Team Topologies", Leader in Modern Organizational Dynamics for Fast Flow The book Team Topologies Second Edition (2025) demonstrates convincingly that organizing business and technology for fast flow of value via empowered teams produces outsized results for enterprises worldwide. 11:45 Churchill (Ground Fl.) Session Blurring the Lines: Engineering & Data Teams in the Age of AI Lada Indra Head of Data Platform @Pleo, Previously Head of Data @Legend and Director API Platform BI & Data @Vonage Every senior engineer knows the feeling: a model makes a bad decision, a customer complains, and suddenly you're debugging a system that spans three teams, two pipelines, and a machine learning model nobody fully owns. Where do you even start? 13:35 Fleming (3rd Fl.) Session AI/ML The Ladder Is Missing Rungs: Engineering Progression When AI Ate the Middle Alasdair Allan Scientist, Author, Hacker, Maker, Journalist, CTO @Negroni Venture Studios, Interim CTO @Evaro Career progression in engineering has traditionally followed a predictable path: junior tasks teach fundamentals, mid-level work builds judgment, senior roles require synthesis across systems. 14:45 Whittle (3rd Fl.) Session AI Tools Rethinking Your Engineering Hiring Process & Signals for the AI Era Reece Nunn Software Engineering Manager @BBC AI has distorted the signals we rely on to hire engineers. CVs are increasingly tailored, screening can be rehearsed, tech tests can look “perfect,” and even system design and behavioural answers can be polished in ways that don’t reflect real on-the-job judgement. 15:55 Rutherford (4th Fl.) Unconference Unconference: Building Engineering Teams 17:05 Fleming (3rd Fl.) Session AI From Copilots to Orchestrators: A 12 Week Playbook for Training Engineering Teams Using AI Krys Flores Staff Software Engineer @Crunchyroll, Previously @Carta, @Lob, @Simple Habit, and @Nordstromrack.com|HauteLook Most engineering teams are stuck treating AI as autocomplete. Engineers have GitHub Copilot installed (or Claude or Cursor or whatever), they're generating snippets faster, but leaders can't connect usage to business outcomes—and developers are shipping code they don't fully understand.