AI: A New Thinking Partner in Agile Teams with Dan Neumann
Fri Sep 27 2024
This week, your host, Dan Neumann, discusses his perspective on the influence of Artificial Intelligence on Agile Teams. AI has created excitement and great expectations, undoubtedly changing how we perceive work and raising some concerns. In this episode, Dan dives deep into how Generative AI can impact Agile Teams’ work, describing AI’s use in this field and using valuable examples to describe several manners to incorporate AI to ease the work at different stages of an Agile process.
Key Takeaways
Generative AI, a new thinking partner to Agile Teams:
There are sensitivities around using the free AI models currently available.
AI could be considered a great partner in addition to Team Members.
The definition of done for each project cannot be delegated to AI, since the Team needs to determine the pros and cons, define the goals, and what it means to achieve them.
Miro AI can be used as a Retrospective partner to examine the retrospective data the Team has been collecting. It can also help provide different ways of facilitating Retrospectives.
AI is helpful to Delivery Teams in predicting releases.
Agile Teams can use the Monte Carlo Simulation to predict a Team’s velocity by looking at historical data to create a range of future possibilities.
Sprint planning could be simpler with the aid of AI.
An Agile Team can seek AI help to provide other work items that might support the original Sprint Goal, based on the product backlog.
How can AI assist in dealing with bottlenecks?
AI can help identify some bottleneck trends based on the existing delivery data.
AI as a tool for Product Owners and Quality Specialists to identify Acceptance Criteria:
AI can assist Product Owners and Quality Specialists in defying product backlog Item acceptance criteria.
To generate new acceptance criteria, test cases can be generated using an AI public tool or a technology ecosystem like Microsoft Copilot.
Using Microsoft Copilot, a Team can look at the sentiment in which you are engaging with your Teammates.
By searching the Team’s chat emails, AI can help you anticipate potential issues.
Ai can provide strategies to tackle a potential social challenge that might be reflected in the Team’s communication.
AI can use your historical information for risk management.
AI can help a Team identify risks and develop strategies to solve them or even when to accept those risks since the cost of mitigating them exceeds the Team’s capabilities.
Agile Teams can use AI for prioritization.
AI can explore big data, search for information on costs and benefits, and provide useful suggestions for prioritization.
Want to Learn More or Get in Touch?
Visit the website and catch up with all the episodes on AgileThought.com!
Email your thoughts or suggestions to Podcast@AgileThought.com or Tweet @AgileThought using #AgileThoughtPodcast!
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This week, your host, Dan Neumann, discusses his perspective on the influence of Artificial Intelligence on Agile Teams. AI has created excitement and great expectations, undoubtedly changing how we perceive work and raising some concerns. In this episode, Dan dives deep into how Generative AI can impact Agile Teams’ work, describing AI’s use in this field and using valuable examples to describe several manners to incorporate AI to ease the work at different stages of an Agile process. Key Takeaways Generative AI, a new thinking partner to Agile Teams: There are sensitivities around using the free AI models currently available. AI could be considered a great partner in addition to Team Members. The definition of done for each project cannot be delegated to AI, since the Team needs to determine the pros and cons, define the goals, and what it means to achieve them. Miro AI can be used as a Retrospective partner to examine the retrospective data the Team has been collecting. It can also help provide different ways of facilitating Retrospectives. AI is helpful to Delivery Teams in predicting releases. Agile Teams can use the Monte Carlo Simulation to predict a Team’s velocity by looking at historical data to create a range of future possibilities. Sprint planning could be simpler with the aid of AI. An Agile Team can seek AI help to provide other work items that might support the original Sprint Goal, based on the product backlog. How can AI assist in dealing with bottlenecks? AI can help identify some bottleneck trends based on the existing delivery data. AI as a tool for Product Owners and Quality Specialists to identify Acceptance Criteria: AI can assist Product Owners and Quality Specialists in defying product backlog Item acceptance criteria. To generate new acceptance criteria, test cases can be generated using an AI public tool or a technology ecosystem like Microsoft Copilot. Using Microsoft Copilot, a Team can look at the sentiment in which you are engaging with your Teammates. By searching the Team’s chat emails, AI can help you anticipate potential issues. Ai can provide strategies to tackle a potential social challenge that might be reflected in the Team’s communication. AI can use your historical information for risk management. AI can help a Team identify risks and develop strategies to solve them or even when to accept those risks since the cost of mitigating them exceeds the Team’s capabilities. Agile Teams can use AI for prioritization. AI can explore big data, search for information on costs and benefits, and provide useful suggestions for prioritization. Want to Learn More or Get in Touch? Visit the website and catch up with all the episodes on AgileThought.com! Email your thoughts or suggestions to Podcast@AgileThought.com or Tweet @AgileThought using #AgileThoughtPodcast!