AI's impact on Agile project management and Scrum mastery will go from "fun" to "totally game-changing" faster than you can imagine.
My team and I have spent years researching the intersection between artificial intelligence and software creation. So we had some interesting conversations with product managers, product owners and project managers, scrum masters, etc. Probably someone like you.
So I wanted to write about where AI is headed in Agile, Scrum, and project management.
Good AI is still very green. Not all of this technology is ready, but I'll stick my neck out and say it will be within the next six months.
TL;DR: Don't leave it until it's too late to explore how to integrate AI safely.
agile planning
Your development team is in the midst of a critical sprint when an unforeseen issue arises that disrupts the entire project timeline.
In tech, this hiccup can cost you dearly in time and resources. Plus, you have to figure out how to explain this to management and potential clients.
But what if artificial intelligence could help you predict and mitigate potential challenges before they occur?
Enter AI-driven predictive analytics.
By leveraging historical data and employing advanced machine learning algorithms, predictive AI solutions can analyze patterns, identify trends and predict potential obstacles in a project's path.
Let me give a few examples.
- Estimation: Human estimation is inherently flawed. We're just not born doing this. AI will enable realistic sprint planning, release planning, and better resource allocation.
- Risk: AI will be able to spot risks and bottlenecks more consistently and, on average, faster than humans. This means you can mitigate them before they cause problems.
- Prioritization: AI-driven analytics will enable efficient prioritization and adaptive re-prioritization of Product Backlog items. This process will be much less overhead when driven by AI, which will spot dependencies and automatically align everyone on what matters.
cooperation
The backbone of any successful agile team lies in collaboration and effective communication.
But getting everyone on the same page is a huge time sink.
Miscommunication (and its aftermath) is one of the most mentioned frustrations among PMs I talk to. This grows exponentially as (project and team) complexity increases.
Not to mention the hours engineers and PMs spend each day on Slack or Teams, fishing through old messages to find resources, or figure out what work was done in other areas of the project.
The time spent searching for information is necessary for most teams. But I think AI will turn "time spent" into "time wasted".
Let me illustrate with an example:
- No more trawling. AI will be able to understand everything that's happening on every project you're working on and surface important information from the tools you use like Jira, Slack, Teams, and GitHub.
- Omniscient AI. LLM is now good enough to allow you to ask any question about project progress, risks, etc. and give you concise, actionable answers.
- Fewer, better meetings. First, in the AI world, there is no need to spend time in meetings updating progress or summarizing data. Instead, meetings will be more strategic and creative. I don't know how many people in the software world wouldn't jump to this.
keep improve
Continuous improvement is inherent in the Agile methodology, the Agile Manifesto. It's all about improving the efficiency, productivity and effectiveness of the team through each sprint.
I think AI represents an opportunity to significantly shift—or “enhance” if you will—the way continuous improvement is done.
Let's see what this looks like for your team.
1. Quality: It is already possible to use artificial intelligence to support processes such as code review and deployment, and the development process itself has a wealth of tools available.
2. Performance Insights: AI is already available to help you understand your team's performance, identify patterns, and make data-driven decisions to improve your processes.
It will be better than humans at everything from high-level insights to highly granular and specific insights. Use them to identify areas for improvement. Plus, it's real-time with almost no time overhead, which speeds up the whole thing and means the agile planning process can be much more dynamic.
3. Resource Allocation: Make sure everyone is working on tasks that match their skills and strengths, and even their growth opportunities. It's a win-win. You can increase productivity and foster a more supportive culture.
what's next?
Let's put the hype aside for a moment. Now, embracing AI to overhaul traditional project management and Scrum practices is not strictly necessary. After all, most of the technology is very green, and many AI tools are in beta, or are still using old underlying models (like GPT-3, which is great, but won't change the world). Therefore, you may not lose a significant advantage over your competitors.
Yet this clock is ticking faster than any figurative time bomb I can remember. At least partial adoption of AI software development tools is no longer a luxury but a necessity, and it will take months rather than years. Safely adopting and integrating the right tools will be the biggest challenge for team members making decisions about tools for an agile cycle. Keeping up with advances in artificial intelligence is almost a full-time job.