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

Martin Harrysson and Natasha Maniar from McKinsey's Software X discuss the paradigm shift occurring in software development due to AI, focusing on the people and operating model aspects. They highlight a disconnect between the potential of AI and the reality of only 5-15% productivity improvements in most companies due to bottlenecks in collaboration, code review, and tech debt. They suggest moving towards AI-native workflows and roles, such as smaller, spec-driven teams and consolidated engineering roles. They share a client study where interventions like using agents for sprint story assignments and reorganizing squads by workflow led to significant increases in delivery speed and code mergers. The speakers emphasize the importance of change management, upskilling, and a robust measurement system to scale AI adoption effectively across large organizations.

Outlines

Part 1: Context, Challenges

Part 2: AI-Native Workflows, Roles

Part 3: Scaling, Change Management

Part 4: Future Outlook

Sign in to continue reading, translating and more.

Open full episode in Podwise

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

AI Engineer

Part 1: Context, Challenges

Introducing Software X: Leveraging AI for Software Development's People and Operating Model

AI's Paradigm Shift: From Individual Productivity to Scalable Team Impact

Bottlenecks in AI Implementation: Work Allocation and Code Review Challenges

Part 2: AI-Native Workflows, Roles

Rewiring the SDLC: AI-Native Workflows, Roles, and Upskilling for Top Performers

AI-Native Shifts: From Quarterly Planning to Spec-Driven Development and Smaller Pods

Team-Level Interventions: Impact on Agent Consumption, Delivery Speed, and Code Quality

Part 3: Scaling, Change Management

Scaling AI Across the Organization: Change Management and Key Interventions

Building a Robust Measurement System: Prioritizing Outcomes and Monitoring Progress

Part 4: Future Outlook

The Future of Software Development: Shorter Sprints, Smaller Teams, and Bold Ambition

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

AI Engineer

Part 1: Context, Challenges

00:20Introducing Software X: Leveraging AI for Software Development's People and Operating Model

Introducing Software X: Leveraging AI for Software Development's People and Operating Model

01:02AI's Paradigm Shift: From Individual Productivity to Scalable Team Impact

AI's Paradigm Shift: From Individual Productivity to Scalable Team Impact

04:04Bottlenecks in AI Implementation: Work Allocation and Code Review Challenges

Bottlenecks in AI Implementation: Work Allocation and Code Review Challenges

Part 2: AI-Native Workflows, Roles

07:11Rewiring the SDLC: AI-Native Workflows, Roles, and Upskilling for Top Performers

Rewiring the SDLC: AI-Native Workflows, Roles, and Upskilling for Top Performers

09:23AI-Native Shifts: From Quarterly Planning to Spec-Driven Development and Smaller Pods

AI-Native Shifts: From Quarterly Planning to Spec-Driven Development and Smaller Pods

12:28Team-Level Interventions: Impact on Agent Consumption, Delivery Speed, and Code Quality

Team-Level Interventions: Impact on Agent Consumption, Delivery Speed, and Code Quality

Part 3: Scaling, Change Management

15:12Scaling AI Across the Organization: Change Management and Key Interventions

Scaling AI Across the Organization: Change Management and Key Interventions

18:31Building a Robust Measurement System: Prioritizing Outcomes and Monitoring Progress

Building a Robust Measurement System: Prioritizing Outcomes and Monitoring Progress

Part 4: Future Outlook

20:34The Future of Software Development: Shorter Sprints, Smaller Teams, and Bold Ambition

The Future of Software Development: Shorter Sprints, Smaller Teams, and Bold Ambition