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!
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! Saving Changes...
I use AI to draft personas, customer journey maps, and user stories, which are then validated with stakeholders and SMEs. At the planning and delivery stages, AI helps to refine backlog items, identify dependencies, summarize meeting notes, and highlight delivery risks using project data. During retrospectives, AI helps to summarize feedback and suggest improvement ideas, while the team discusses and decides the final actions. AI helps a lot in project documentation, status reports, and presentation materials, making them more efficient. Once I get the AI-generated output, I review and validate it and also have a second pair of eyes from the team. Saving Changes...
Anil RahejaProject Manager| Tadweer GroupAbu Dhabi, United Arab Emirates
Strength of AI is in Analytics especially when the given datasets are large. Its best suited for customer feedback analysis, or market analysis Saving Changes...
The best with AI could arrange our idea even give us some info may we know but forgetting it to use during projects even helps on analysis and make it easy. Saving Changes...
In a federal agency, we operate under certain limitations, such as the handling of Personally Identifiable Information (PII). Frequently, we leverage AI to generate initial frameworks or templates, which we then populate with data according to our specific requirements. AI provides significant support by assessing risk factors, developing roadmaps, and reducing the manual workload for project managers—particularly when users provide clear and comprehensive context. Saving Changes...
Project Manager | Driving Clean Energy Innovations for a Sustainable Future| Canadian Nuclear Laboratories Ontario, Canada
I mainly use AI to help prepare for agile ceremonies. It helps me summarize information, identify gaps in user stories, and draft initial ideas before discussions. The real value isn't that AI saves time, it's that it allows the team to spend more time on collaboration and decision-making. For me, AI is less of a replacement and more of a thought partner. Saving Changes...
Gilberth Chiquillan HuayhuasIngeniero de Sistemas y Computo| Red Nacional de Ingenieros Digitales del PerúLIMA, LIM, Peru
Desde mi experiencia como instructor y Agile Coach en programas desarrollados para entidades públicas y organizaciones como la Contraloría General de la República, OEFA, MININTER y Electro Sur, considero que la IA aporta mayor valor en aquellas actividades ágiles donde existe mucha información dispersa y se necesita convertirla rápidamente en decisiones o acciones concretas. En retrospectivas, la IA puede organizar comentarios, identificar temas recurrentes y ayudar a transformar las lecciones aprendidas en experimentos de mejora. En el refinamiento del backlog, puede agrupar necesidades, proponer criterios de aceptación, detectar ambigüedades y relacionar requerimientos con riesgos o evidencias previas. En la planificación de sprints, puede apoyar la evaluación de escenarios, dependencias y restricciones, aunque el compromiso final siempre debe ser definido por el equipo. En el programa de Machine Learning & Deep Learning aplicado al control gubernamental para la Contraloría, trabajamos con el caso integrador CONTROL360+ Inteligente 2030. Una lección relevante fue que la IA y la analítica pueden ayudar a estructurar grandes volúmenes de información, descubrir patrones y generar alternativas, pero no deben sustituir el criterio del auditor, del especialista ni del responsable de la decisión. En capacitaciones para OEFA y MININTER, uno de los experimentos más útiles consistió en utilizar IA para generar una primera versión de contenidos, presentaciones o análisis; posteriormente, los participantes debían validar la información, ajustar el contexto institucional, proteger los datos y mejorar el resultado. El beneficio rápido no fue únicamente ahorrar tiempo, sino disponer de una base mejor organizada para iniciar la revisión humana. En Electro Sur, el desarrollo de un caso práctico con Product Backlog, Sprint Planning, Daily Scrum, Sprint Review y Retrospective confirmó que la calidad del resultado depende de contar con objetivos claros, criterios de aceptación y una validación permanente del equipo. La IA puede fortalecer todas estas actividades, pero no debe asumir la propiedad del producto ni de las decisiones. Mi principal aprendizaje es sencillo: La IA debe proponer, organizar y detectar patrones; el equipo debe interpretar, validar y decidir. Para obtener resultados rápidos recomiendo comenzar con un experimento pequeño, utilizar información autorizada, anonimizar datos sensibles, comparar el resultado con la experiencia del equipo y convertir el aprendizaje en una acción concreta para el siguiente ciclo. Saving Changes...
In Agile, AI can be leveraged as a supportive team member, contributing across multiple activities while complementing human collaboration. Its role can include:
- Story estimation: Assisting teams during sprint planning by providing contextual insights to refine story points.
- Critical decision support: Offering data driven perspectives to guide choices in high‑pressure situations.
- Impediment resolution: Suggesting solutions to blockers while final decisions remain with the team.
- Documentation: Streamlining the creation and maintenance of project artifacts.
- Team assistance: Supporting developers, Scrum Masters and managers with insights, and productivity boosts.
Across all these activities, AI serves as an assistant, providing insights and support while the final verifications, decisions and acknowledgements are made through human decision and collaboration. Saving Changes...
AI is broader term and there are multiple tools to be used. We still feel we are at the infancy as far as using AI is concerned. Current utilization includes scrum reviews and creating bots which help reduce the analysis work. Team is being encouraged to use multiple AI tools through smaller projects so that the understanding becomes clear. Once the team understands the process, the utilization follows. Saving Changes...
* I use it in the different stages: Discovery, Experimentation, Delivery, Reflection, and Scaling. Each stage can utilize different tools, always with the analysis and common sense of the human using it, and with responsibility to mitigate risks related to use, security, and privacy.
* It's important to always emphasize that AI is used as a support tool that generates benefits such as optimizing time, summaries, presentations, and trends, but it doesn't replace team discussions, stakeholder feedback, and the final decision will always rest with the Leader and/or Team responsible for the project. Saving Changes...
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