Code 2025
Moving away from Agile: What's Next – Martin Harrysson & Natasha Maniar, McKinsey & Company
Martin Harrysson , Natasha Maniar
Overview
This talk argues that the rapid advancements in AI necessitate a fundamental shift in software development operating models, moving beyond traditional Agile methodologies. While AI tools offer significant individual productivity gains, realizing broader organizational value requires addressing bottlenecks in collaboration, code review, and work allocation. The presenters propose a transition to AI-native workflows and roles, emphasizing smaller teams, continuous planning, and spec-driven development to achieve substantial improvements in delivery speed and quality.
Who should watch
- AI Engineers and Developers
- Product Managers and Builders
- Engineering Leaders and Managers
- Those seeking to scale AI adoption beyond individual productivity gains
- Organizations struggling to see significant ROI from AI tools
Key takeaways
- Current AI adoption often results in only marginal productivity improvements (5-15%) due to unaddressed bottlenecks in collaboration, manual code review, and tech debt amplification.
- Effective AI integration requires moving from Agile to AI-native workflows, characterized by continuous planning and spec-driven development, rather than story-driven.
- AI-native roles involve smaller, consolidated pods (3-5 individuals) with full-stack fluency, where engineers act as orchestrators of AI agents.
- Top-performing organizations are seven times more likely to have AI-native workflows across the entire SDLC and six times more likely to have AI-native roles.
- Successful scaling involves comprehensive change management, including clear communication, incentivization, hands-on upskilling, and robust measurement systems focused on outcomes, not just adoption.
- Reorganizing teams by workflow (e.g., bug fixes vs. greenfield development) and leveraging agents for impact analysis can prevent rework and debugging time.
- Product Managers can iterate directly on specs with agents and observe real-time customer feedback, accelerating backlog prioritization.
- A holistic measurement system should track inputs (tool investment, upskilling), outputs (velocity, capacity, developer NPS, code quality), and economic outcomes (time to revenue, cost reduction).
Notable quotes
*The reality is that about 70% of the companies that we surveyed have not changed the roles at all.*
*The crux to actually scaling this is often about getting 20, 30, or even more things right at the same time.*
*Start now. This is a human change, and it takes some times, and it's a big change, and it's going to be a journey.*
Unofficial community note. Prefer the recording for nuance.