Most enterprises are not capturing much value from AI in software dev to date (at least relative to the potential). The reason is that most are adding AI tools to their dev teams without changing the people and operating model aspects (i.e., limited changes to ways of working, team configurations, role definitions, stage gates, etc.). Many core aspects of software development haven’t changed in the past 10+ years, and that’s holding us back from moving to the new paradigm of software development! We will share examples of what makes the difference.

https://www.linkedin.com/in/martinharrysson
https://www.linkedin.com/in/natasha-maniar-945276107/

AI Engineer

McKinsey consultants argue that AI-powered development requires fundamental changes to agile workflows and team structures. Survey data from 300 enterprises shows top performers achieve 5-6x faster delivery by adopting AI-native workflows (spec-driven development, continuous planning), smaller pods (3-5 people vs 8-10), and consolidated roles where engineers orchestrate agents rather than write code directly. Key bottlenecks include work allocation challenges, manual code review overhead, and tech debt amplification. Success requires comprehensive change management: hands-on upskilling, new incentive structures, and measurement systems tracking outcomes beyond adoption metrics. Case studies demonstrate 51% increase in code mergers and 60x agent consumption when teams reorganize around AI capabilities.

Moving away from Agile: What's Next – Martin Harrysson & Natasha Maniar, McKinsey & Company