Software development has changed dramatically with the introduction of artificial intelligence, as AI impacts the way teams write code and deliver value to customers. Agile teams have begun to explore how best to use AI (as many as 30% of teams, according to the 17th Annual State of Agile report).
Ultimately, AI can help unleash innovative thinking that drives business agility as teams spend more time solving real problems and less time trying to interpret data. However, implementing any new kind of technology takes time, and many people are still figuring out how to use AI. So what does implementing AI look like in actual practice for Agile teams?
The best results often come from pinpointing opportunities to incorporate AI throughout your entire existing workflow. This blog post breaks down specific ways that teams can leverage AI to enhance agility, with practical examples and tips from experts who have spent decades leading teams and working in agile project management.
The benefits of using AI to enhance agility
“When we stop viewing AI as a threat and start viewing it as a collaborator, we can redesign our Agile workflows to be more inclusive and more creative,” Jessica Guistolise, senior evangelist at Lucid, says. “By acting as a ‘junior teammate’ that handles the first draft of the data crunching, AI gifts us the mental space to step back and look at the bigger picture. It invites us to solve novel problems rather than repetitive ones.”
Some of the benefits that Agile teams can gain from using AI include:
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Enhanced collaboration. AI can help reduce communication barriers by breaking down complex subjects into more understandable language, bridging knowledge gaps. It can also be used during conversations to identify friction or suggest ways to bring the discussion back on track.
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Automated routine tasks. Automating changes the game for Agile teams, as AI can automate tasks such as bug detection and regression testing, freeing time for developers to focus on more high-value tasks. Automation also minimizes costs and ensures faster issue resolution.
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Improved risk management. AI can be used to analyze real-time and historical data for proactively predicting and managing risk. AI-driven analytics help Agile teams identify challenges such as scope creep, resource shortages, and technical debt, leading to a better understanding of risk and smoother project execution.
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Streamlined meeting preparation and facilitation. AI helps people before and after their meetings, for example, by creating an agenda for a daily standup, or summarizing retrospectives to identify trends in team discussions. By using AI to summarize meeting documentation, people have more time for strategic decision-making and collaboration.
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Boosted innovative thinking. People can use AI as a brainstorming partner to think outside the box, asking questions and uncovering information they normally wouldn’t have thought of on their own. AI can also generate diverse prompts and solutions.
“From my experience working as a program manager, AI is most valuable when it amplifies the fundamentals of agility rather than trying to replace them. [For example], I use AI to surface the patterns that I may otherwise miss, like shifts in sentiment that signal friction. When I have that clarity upfront (and easily accessible), ceremonies stop feeling like status update meetings and start functioning as sharp, decision-making moments.”
—Kristin Dahlin, senior program manager I, Lucid
Considerations of using AI for Agile
It’s important to remember that AI should be implemented with intention. There are a few considerations that should be addressed before people experiment with AI or teams are asked to incorporate it within their workflows.
These considerations include data security and transparency about data usage. Leaders should also be aware of how introducing AI may change team dynamics or psychological safety. Continue to cultivate a space where it’s safe to fail and people can still practice and grow.
Also, remember not to lean too heavily on AI’s outputs. AI can highlight patterns, summarize blockers, or flag risks, but it can’t yet understand organizational nuances or the tradeoffs behind certain decisions. Teams should consider AI’s outputs as suggestions rather than taking them at face value, since AI is likely to have incomplete data, especially for certain types of work.
How to use AI for Agile
Now you’re familiar with the benefits of AI and how it can impact Agile teams, but how can you actually use AI in your daily tasks and meetings? Here are four ways to incorporate AI into your Agile workflow.
Streamline planning and prioritization
A major way AI can be used for Agile is during planning sessions and for prioritizing initiatives or backlog items. For example, Agile teams can use AI during brainstorming sessions to:
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Generate ideas and kick-start conversations
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Uncover unique solutions or strategies to problems
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Summarize and synthesize ideas
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Play devil’s advocate to challenge team decisions
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Come up with icebreaker questions or other activities to get the team started