Stop treating AI as a team member to “onboard.” Instead, give it just enough context for specific tasks, connect it to your existing artifacts, and create clear boundaries through team agreements. This lightweight, modular approach of contextual AI integration delivers immediate value without unrealistic expectations, letting AI enhance your team’s capabilities without pretending it’s human.
When you step into a new role as Scrum Master or agile coach for a team under pressure, you’re immediately confronted with a challenging reality: you need to understand the complex dynamics at play, but have limited time to process all the available information. This article explores how AI interview analysis can be a powerful sensemaking tool for agile practitioners who need to synthesize unstructured qualitative data quickly, particularly when joining a team mid-crisis.
In this experiment, OpenAI’s new stealthy LLM Optimus Alpha demonstrated exceptional performance in team data analysis, quickly identifying key patterns in complex agile metrics and synthesizing insights about technical debt, value creation, and team dynamics. The model provided a tailored Retrospective format based on real team data.
Its ability to analyze performance metrics and translate them into solid, actionable Retrospective designs represents a significant advancement for agile practitioners.
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.
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.)
Remember my article from six months ago about reinventing Hands-on Agile to counter the great agile reset? (Just kidding; of course, you don’t.) Back then, I reflected on how to adapt my business model best, given the massive changes in the marketplace for “Agile.” (See the link below.)
Well, it’s time for an honest update, and it is not pretty.
