Embracing AI in Agile
A Journey from Skepticism to Growth Mindset
Introduction
The use of AI in Agile methodologies is not just a passing trend; it's a paradigm shift that is reshaping the way teams work and innovate. Like any other technology or significant change, AI adoption follows a curve—from early skepticism to widespread acceptance.
As someone deeply invested in Agile coaching and organizational change, I see how embracing AI with a growth mindset can boost individual productivity and propel teams forward. My journey began at the Regional Scrum Gathering in Stockholm, where Henrik Kniberg's keynote sparked a newfound curiosity and determination to explore AI's potential in Agile.
The AI Adoption Curve: From Skepticism to Acceptance
Early Skepticism
In the early stages of AI adoption, it's natural to be skeptical. Questions like "Will AI replace human jobs?" and "Can AI really understand the nuances of our work?" are common. This phase is characterized by caution and a wait-and-see approach. However, staying in this mindset risks leaving your team behind as others forge ahead.
Exploration and Experimentation
The next phase involves exploration and experimentation. Teams begin to dip their toes into AI waters, trying out tools and seeing how they can integrate into existing workflows. This is where the growth mind becomes crucial.
Instead of viewing AI as a threat, see it as an opportunity to learn and grow. Embrace the idea that AI can augment human capabilities, making teams more efficient and innovative.
Integration and Optimization
As teams become more comfortable with AI, they integrate it into their daily practices. This phase is about optimization—finding the right balance between human and AI contributions. Tools like AI-assisted code generation, agents, and AI-driven retrospective insights have become indispensable.
The focus shifts from "Can AI do this?" to "How can AI help us do this better?"
Widespread Acceptance
Finally, AI becomes an accepted and integral part of the Agile toolkit. Teams that have embraced AI early on are now reaping the benefits, while those who lagged behind are playing catch-up. The growth mindset has paid off, as these teams are more efficient and adaptable to future changes.
What phase of the AI adoption curve is your team currently in?
Henrik Kniberg's Keynote: A Catalyst for Change
Attending Henrik Kniberg's keynote at the Regional Scrum Gathering in Stockholm was a turning point for me. Kniberg's insights on how AI is reshaping Agile practices were both inspiring and thought-provoking. He highlighted the potential for smaller, AI-enhanced teams, the evolving roles of developers, and the need for ethical considerations in AI decision-making. His talk sparked a curiosity that led me to deeply explore AI's impact on the 12 Agile Principles and Scrum frameworks (more on that in upcoming posts).
Agile AI synergy
Integrating AI tools into processes, teams, or development workflows creates value by automating repetitive tasks, surfacing data-driven insights, and enabling predictive decision-making.
However, their true potential is unlocked when adoption is structured through an Agile learning mindset—prioritizing iterative experimentation, collaboration, and adaptability.
Start with small, time-boxed pilots (e.g., one sprint) to test AI tools in specific workflows, such as automating user feedback analysis or backlog refinement.
Use daily syncs to share learnings and retrospectives to assess how AI impacts transparency, team ownership, and value delivery.
By treating AI as a hypothesis-generating partner rather than a solution-in-a-box, teams foster continuous learning:
AI identifies patterns in data, while humans contextualize insights and adapt strategies.
For example, during refinement sessions, an AI tool might flag a drop in user engagement, but the team collaboratively investigates root causes (e.g., UX issues vs. market shifts).
This Agile-AI synergy accelerates innovation while grounding decisions in empiricism—ensuring tools serve people, not the reverse.
Embracing the Growth Mindset
Adopting a learning mindset equals adopting a growth mindset.
Here are specific key points to consider you can take to your organization and team:
Continuous Learning:
View AI as an opportunity to learn and grow. Stay updated with the latest AI tools and trends, and encourage your team to do the same.
Experimentation:
Don't be afraid to experiment with AI tools. Start small, perhaps with AI-driven code reviews or retrospective insights, and gradually expand your use cases.
Collaboration:
Foster a culture of collaboration where team members share their experiences and insights about AI. This collective knowledge will help everyone adapt more quickly.
Ethical Considerations:
As AI becomes more integrated, it's crucial to address ethical concerns. Ensure transparency, fairness, and accountability in AI decision-making processes.
As with any tool or technology, collaboration is the key to successfully integrating AI into Agile practices. It's not about replacing human intelligence but about augmenting it. By working together, teams can leverage AI's strengths while mitigating weaknesses, creating a more efficient and innovative work environment.
Conclusion
The use of AI in Agile is not a question of "if" but "when." Embracing a growth mindset and exploring AI's potential can set your team apart in the ever-evolving tech landscape. From attending Henrik Kniberg's keynote to diving deep into AI's impact on Agile principles, my journey has been discovery and adaptation. Don't let skepticism hold you back—embrace the change and lead your team into the future of Agile with AI.
Call to Action: 👉 Connect with me on LinkedIn and share your experiences with AI in Agile teams. Let's learn and grow together!
In upcoming posts, I will explore AI as an enabler for agility, opportunities, and constraints!
Note: Images are AI-generated - this time with Mistral AI