I had the pleasure of speaking on a podcast just last week regarding AI and Product Development changes as well as other factors that will of course affect this industry. Specifically we focused on how it might affect developers and agile processes in general.
We can definitely see that artificial intelligence and machine learning are being increasingly integrated into agile workflows. AI tools are helping us automate tasks, predict project risks, and generate valuable insights that improve decision-making. This trend is enhancing the speed and efficiency of agile processes, particularly in large-scale projects or ones that require a deep degree of coordination across teams or third-parties. But more importantly, what can we leverage with these tools instead of fearing that they might take our jobs. I find these tools are helping me reduce keystrokes in my day to day life, but obviously require a lot of oversight in the early stages. Scrum is about empiricism, so I wanted to explore how these tools can help our teams using it for that purpose.
Incorporating these techniques into Scrum and project management enables more dynamic, data-driven decision-making which is at the heart of a Scrum process; empiricism. Empiricism in the context of Scrum refers to making decisions based on evidence, experience, and observation rather than on assumptions or predictions. In Scrum, this is implemented through the frequent inspection and adaptation of processes, allowing teams to make adjustments based on actual performance and outcomes rather than theoretical plans.
Risk Identification and Prediction
AI algorithms can analyze historical project data and current metrics to identify potential risks in real time. By applying machine learning models to past sprints, issues, and bottlenecks, AI can predict when and where delays or failures are likely to occur. AI can monitor velocity, team delivery metrics, and organizational metrics, forecasting which tasks are at risk of delays based on historical patterns and team availability. Scoring these risks based on their impact and likelihood, providing early warning systems that help us preemptively address challenges that otherwise would have been experienced rather than predicted.
Task Automation
Who doesn't like to automate repetitive tasks? Developers are notorious for doing this in their day to day work, why don't we harness it now for their work management? This can free up our team members to focus on more critical activities that require creativity, critical thinking, and decision-making. What Product Owners wouldn't love that as well?
How about automated backlog refinement? Can we use AI to prioritize backlog items based on business value, dependencies, and customer feedback, ensuring the most important work is done first? I think this is still a bad idea without the involvement of stakeholders, but we can still capture their feedback and integrate it into this process. We can also use the the tools can generate real-time reports on sprint progress, velocity, and team metrics, eliminating manual tracking and data collection tasks at all team levels; truly designing a pull mechanism to reporting for the organization.
AI can also suggest course corrections during sprints, such as reassigning people or modifying timelines based on real-time data. One of the more challenging skills is to help ensure the sprint is load-balanced according to skills and abilities and teaching those people how to collaborate well to maximize efficiency. AI can help us analyze that as well.
Decision-Making
Let's face it, computers are much better at processing large amounts of data and offer insights that would be challenging to uncover manually. It can see patterns much quicker and provide various models that we can decide the most valuable or statistically significant. Product Owners can use this for market trends, analytics of product usage, as well as prioritization health from the user base directly. As long as we have a great way to gather the data that feeds the model, we can fine tuen their decision analytics to help compete better in the market.
We can also enhance the feedback cycle by automatically analyzing and integrating user feedback into product iterations. By using natural language processing, we can analyze customer feedback and suggest changes to product features that better align with end-user needs.
Team Health
We can tap into our team's communication channels and analyze communication within Scrum teams to gauge morale and flag potential issues in team dynamics. By analyzing the tone in slack channels, email exchanges, even listening devices in our meetings, we can detect frustration or disengagement that could impact productivity. Naturally there are ethical concerns we would want to evaluate; particularly with psychological safety, but there can be ways to mitigate those issues while still providing our teams with valuable insights into their team health sprint by sprint.
As we stand on the edge of a new era in productivity, AI offers an unparalleled opportunity for teams to elevate their tools and deliver results faster and more efficiently by reducing keystrokes and analyzing data much faster. But like any powerful tool, the key lies in understanding its boundaries and embracing the learning curve.
Teams should not shy away from integrating AI; rather, they should lean into the possibilities, experimenting with different tools to uncover where they offer the most value. With strategic use and a growth mindset, AI can be a game-changer, automating repetitive tasks and augmenting human creativity. The future belongs to those who are bold enough to embrace AI's potential, adapt, and continuously learn / improve.