Product Manager, Learning| PMIDenver, Colorado, United States
Which agile activities—like retrospectives, backlog refinement, sprint planning, customer feedback analysis, or market analysis—have you found AI to support most effectively?
Have you tried a small experiment that delivered quick wins or learned a lesson that might help others in the community? Share your examples in the comments below!
Christina MartinProject Management| Mastec Wireless ServicesUnion Grove, WI, United States
Yes, I use AI in my agile work through tools like Copilot, Power BI, and other automated programs. They help me analyze data, create insights, automate repetitive tasks, and support better decision-making while I still review the results and make the final decisions. Saving Changes...
"In our operations, we are starting to use AI primarily for discovery and risk identification. We use AI to draft initial customer personas and journey maps, though we always ensure we validate these against our actual daily data. We're also exploring how AI can analyze our historical operational data to highlight flow risks or potential bottlenecks in our processes. We keep our approach grounded by ensuring human oversight is always the final step in deciding what action to take based on those AI insights."
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1 reply by Adriana Correa Guarín
Sep 08, 2026 3:09 PM
Adriana Correa Guarín
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Gracias por compartir tu experiencia. Me parece muy acertado el enfoque de utilizar la IA como una herramienta de apoyo para el descubrimiento y la identificación de riesgos, manteniendo siempre la validación con datos reales y el criterio humano. Considero que este equilibrio es fundamental para aprovechar el potencial de la IA sin perder de vista el contexto operativo y las particularidades del negocio. Además, el uso de información histórica para anticipar cuellos de botella y riesgos puede aportar un valor significativo en la toma de decisiones. Coincido plenamente en que la supervisión humana debe seguir siendo el paso final, ya que permite interpretar los hallazgos, priorizar acciones y garantizar que las decisiones estén alineadas con los objetivos de la organización. Como pregunta para el debate: ¿Qué desafíos han encontrado al validar las recomendaciones de la IA con datos reales y cómo han logrado generar confianza en los equipos para adoptar estas herramientas en sus procesos?
Saving Changes...
Donald LiyayiProduct Manager| Riverbank Solutions LtdNairobi, 30, Kenya
AI is not a replacement but a tool. What is critical is knowing how to use it effectively, with results that deliver value to your customers, team, and organization. While there are many tools and agents available to augment in task completion, the greatest challenge lies in securing team opt‑in and ensuring adherence to organizational AI policies—particularly around anonymizing data to safeguard company and client confidential information against breaches. I have found it to be very helpful in rapid prototyping.
I really like that link to the different frameworks. You're right about the importance of understanding how to use AI in the right way. But I think your point about responsible AI and human-in-the-loop is the most important one: without guardrails for consistency, trust, and ethical use, we risk undermining the very values agile is meant to uphold.
Exactly! It is humans being in the loop that gives the ethical framework. That is why humans still remain very important in the effectiveness of AI. Humans have to lay the foundation and give the rules to be followed by AI otherwise AI looses it's collaborative role, Saving Changes...
AI comes in very handy in customer feedback in my Organization, My Organization, a Government Development Agency deals with a lot of data from and about the needs of Stakeholders. AI is able to help identify projects and status of these projects and gaps that still need to be filled. Saving Changes...
John MahoneyPrincipal Consultant| High Expectations International LLCBay Saint Louis, Ms, United States
Sep 04, 2025 11:19 AM
Replying to Luis Branco
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Michael Brinn We've been exploring AI not just as a tool, but as a collaborative team member — one that supports cognition, learns with us, and even helps shape better team dynamics over time.
- Retrospectives + Emotional Tone Mapping
We’ve used AI to analyze the emotional tone of team feedback (anonymized and voluntary), identifying early signs of burnout, misalignment, or interpersonal tension.
This has become a powerful enabler of psychological safety, in line with Amy Edmondson’s work — fostering healthier retros and regenerative team rhythms.
- Backlog Refinement + Thematic Clustering
AI scans user feedback, historical stories, and technical debt to reveal emergent themes and weak signals we might miss.
It acts almost like a cognitive twin — not faster than humans, but broader in pattern recognition.
This mirrors the “externalize–combine” phases of SECI 2.0, accelerating actionable insight.
- Sprint Planning with Scenario Simulation
By blending historical velocity with dynamic constraints, AI helps us simulate realistic sprint outcomes. It’s like having a Cynefin-informed co-pilot — offering clarity when navigating complexity and reducing planning friction.
Quick Win
One of our best experiments: using AI to generate acceptance criteria based on historical stories and stakeholder patterns.
Result: 30% drop in story rework and better alignment between POs and developers — with minimal setup and zero disruption.
Key Insight
When AI is embraced as a learning partner, not just automation, it deepens reflection, foresight, and trust.
We now design processes where humans and AI co-evolve, building what we call augmented agility — a mindset that blends speed with sensemaking.
This shift aligns with regenerative leadership: we don’t use AI to replace judgment, but to amplify our ability to learn, adapt, and collaborate across evolving contexts.
Would love to hear how others are integrating AI into the team, not just into the tools — especially in ways that build capacity, connection, and clarity.
Since performance is always at the top on most every stakeholder's mind, identifying "Quick Wins" would be one of the highest values and lowest risks, Go after them and it will create a immediate value stream. Saving Changes...
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