Agile in the age of AI - Henrik Kniberg

 https://hups-com.cdn.ampproject.org/c/s/hups.com/blog/agile-in-the-age-of-ai?hs_amp=true

Agile methodologies like Scrum are being impacted by the rise of AI. The traditional assumptions about team dynamics, roles, and development cycles are being challenged.

• Cross-Functional Teams: AI's vast knowledge and productivity acceleration are reshaping the need for cross-functional teams. Smaller teams and more teams with AI assistance may become the norm.
• Superteam: There is a possibility of super team, where these smaller teams will have a kind of standups to sync up, coordinate, and address dependencies and issues. Purpose and structure of these meetings will change from what they do now.
• Changing Developer Roles: With AI's capability to generate code, developers may shift to decision-making and oversight roles, with AI handling much of the coding work.
• Redefining Sprints: Agile sprints may become shorter or disappear as AI speeds up development cycles, making traditional timeboxing less relevant.
• Specialists in Agile Teams: Specialists may become roaming or shared resources, complementing AI capabilities within smaller teams.
• Evolution of Scrum Master Role: Scrum Masters may transition to coaches, guiding teams in effectively utilizing AI technologies.
• User Feedback Loop: AI-driven mock users could supplement real user feedback, allowing for more frequent and immediate input in Agile development.
• Additional Considerations: Various factors like 
• Product backlog prioritization: product backlog will need to be updated frequently. PO will focus more on strategic prioritization and stakeholder management. 
• Estimation methods: teams will need new ways to planning and forecasting. 
• Framework adaptations: Popular Agile frameworks like Scrum, Kanban, or SAFe might need to be adapted to accommodate the changes brought by AI. 
• Team dynamics: teams will require new ways to ensure human connection, creativity, and innovation in an AI-driven environment.
• Continuous learning will become even more cruicial as AI keeps taking up larger share of what it can contribute. Team members may need to focus on developing new skills, such as prompt engineering, AI model selection, and result evaluation.
• Ethical considerations need to be addressed in the AI-driven Agile landscape - biases, fairness and transparency. 

The Age of AI calls for a recalibration of Agile practices, with a focus on adapting to the new realities brought about by AI technologies.