TL;DR: A Harvard Study of Procter & Gamble Shows the Way
Recent research shows AI isn’t just another tool—it’s a “cybernetic teammate” that enhances agile work. A Harvard Business School study of 776 professionals found individuals using AI matched the performance of human teams, broke down expertise silos, and experienced more positive emotions during work. For agile practitioners, the choice isn’t between humans or AI but between being AI-augmented or falling behind those who are. The cost of experimentation is low; the potential career advantage, on the other hand, is substantial. A reason to embrace generative AI in Agile?
Vibe coding — using natural language to generate code through AI — represents a significant evolution in software development. It accelerates feedback cycles and democratizes programming but raises concerns about maintainability, security, and technical debt.
Learn why success likely requires a balanced approach: using vibe coding for rapid prototyping while maintaining rigorous standards for production code, with developers evolving from writers to architects and reviewers or auditors.
TL; DR: Bridging Agile and AI with Proper Prompt Engineering
Agile teams have always sought ways to work smarter without compromising their principles. Many have begun experimenting with new technologies, frameworks, or practices to enhance their way of working. Still, they often struggle to get relevant, actionable results that address their specific challenges. Regarding generative AI, there is a better way for agile practitioners than reinventing the wheel team by team—the Agile Prompt Engineering Framework.
Learn why it solves the challenge: a structured approach to prompting AI models designed specifically for agile practitioners who want to leverage this technology as a powerful ally in their journey.
Effective product development requires both strategic alignment and healthy Product Backlog management. Misalignment leads to backlog bloat, trust erosion, and building the wrong products. By implementing proper alignment tools, separating discovery from delivery, and maintaining appropriate backlog size (3-6 sprints), teams can build products that truly matter. Success depends on trust, collaboration, risk navigation, and focusing on outcomes over outputs. Learn more about how to embrace the alignment-to-value pipeline and create your product operating model.
I have been interested in how artificial intelligence as an emerging technology may shape our work since the advent of ChatGPT; see my various articles on the topic. As you may imagine, when OpenAI’s Deep Research became available to me last week, I had to test-drive it.
I asked it to investigate how AI-driven approaches enable agile product teams to gain deeper customer insights and deliver more innovative solutions. The results were enlightening, and I’m excited to share both my experience with this research approach and the key insights that emerged. (Download the complete report here: AI in Agile Product Teams: Insights from Deep Research and What It Means for Your Practice.)
