Too much work in progress present obvious problems to agile teams, but there are also issues that go beyond efficiency or productivity. Too much WIP can be a symptom of a team struggling to prioritize, it can impede collaboration, and thwart a team’s ability to swarm on a task.
AI can’t replace human interaction or discussion, but several easy-to-use tools can help surface issues and synthesizing data that require a project team’s attention, discussion and action. This can be particularly helpful when it comes to making the most of agile retrospectives.
There are many concepts fundamental to Agile that could be applied n environments outside the realm of projects and project management. Concepts like iterating solutions and self-organizing teams are tools that can help make a better world.
When Agile was almost exclusively used as a software development delivery approach, there was heated debate about what was and wasn’t “pure” Agile. The conversation continues now, but why? Today Agile approaches serve many needs and stakeholder groups; agile purists need to recognize this reality.
By leveraging artificial intelligence, agile teams can gain greater insights into potential risks, make more informed decisions, and improve outcomes. From identification and forecasting to monitoring and contingency planning, here are four ways that using AI can make a positive difference in agile risk management.
An organization is looking into AI to streamline story point estimation for its agile work. The leadership believe that AI is going to simplify the process, increase productivity and improve accountability. But its agile teams are pushing back.
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No matter how much cats fight, there always seem to be plenty of kittens. - Abraham Lincoln |
