Are your Change Agents indifferent to Generative AI?

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Even though it’s not perfect, Generative AI is a very disruptive technology, opening new frontiers in all fields, including business agility. And it is constantly improving. Have your Change Agents started to explore the potential of Generative AI for driving change initiatives? If so, what are some specific ways they envision using Generative AI to move change forward? If not, what do you plan to do to encourage explorations?

Practical AI applications are on the rise

Generative AI is taking the world by storm. While tech enthusiasts have readily adopted this technology, change agents who prioritize strategy, value streams, and organizational development over solely focusing on the technology itself often miss the bigger picture, ironically. This phenomenon is reminiscent of the internet’s arrival, but with the crucial distinction that today’s technologies are evolving at a much quicker exponential rate. If your change-agents are indifferent to the biggest change we are experiencing right now in the realm of ways of working, that could be a concern.

In recent months, since ChatGPT has become accessible, I have conducted a workshop and had numerous informal discussions with agile coaches, scrum masters, POS, PMOs, and project managers from different organizations on how to use LLMs to improve their results and ways of working. I’ve observed that those who are not directly involved in data science, or software development often show a hesitancy to experiment with LLMs, preferring to wait for them to mature and become widely adopted. We can interpret this situation through the lens of Rogers’ Curve on the diffusion of innovation.

Here’s how, as of January 2024,  the Lean-Agile coaching community is evolving in terms of experimenting or adopting generative AI in their day-to-day activities:

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Below, I present a non-exhaustive list of AI applications, already observable in the market, in the field of agile coaching:


  • Include ChatGPT as a team member by providing it with transcriptions of meetings and asking for analysis and recommendations.
  • Utilize it as a sparring partner and challenger in the decision-making process.
  • Create your own GPT using existing APIs. It might seem daunting, but it’s about utilizing these APIs and feeding them your documentation to create an automated version of your worldview. Or of your user manuals 😊.
  • Prioritize tasks based on varying perspectives and scenarios.

Early Adopters/ Early Majority
(For simplicity, I group early adopters and early majority into a single category.)

  • Use it to assist in writing reports and preparing presentations.
  • Create summaries and minutes from audio files, videos, or manual drafts
  • Analyze and Forecast data
  • Software development

Late Majority

  • Check your documents for errors and typos
  • Support for translations
  • Learning new stuff


  • It’s not good enough for us 

So, what?

From a practical standpoint, you can:

  • Lower barriers to entry. Fortunately, high-quality and free beginner training is readily available, such as those offered by Google and
  • Spark curiosity and share success stories: Highlight practical use cases of LLMs across various industries and domains, showcasing how they can solve real-world problems and enhance workflows.
  • Webinars and Workshops. Host interactive sessions with LLM experts to answer questions, address concerns, and provide guidance to newcomers.
  • Create Communities of Practices: create ad-hoc communities where participants can ask questions, share experiences, and learn from each other, creating a supportive environment for exploration

The more you interact with generative AI, the more you will be able to find ways to extract value, for yourself and for your organization. Create your own patterns and find your path, otherwise someone else will do it for you.