Will AI Replace the Scrum Team?

Many of us are skeptical about the overall impact of AI on our lives and future. That can only be unraveled as time passes by. With the right kind and number of checks and balances, AI is a friend.

The Past Provides Insight

Many years ago, when I was a software engineer, I worked on building an Integrated Development Environment (IDE) for Java. It was one of the most popular IDEs of the day, and it could take care of all your syntax errors. Prior to that, programmers were required to remember the exact syntax and type it in by hand! I have typed in hundreds of thousands of lines of code myself. As IDEs became popular, one common worry amongst the programmers was, “Is my job going away?” Well, we are all still thriving after 25+ years. So—no, the IDE did not take the job away but instead made the job easier so that we could do more in less time.

Recently, I was asked the same question on a panel on the topic of scaling agility. “Is my job going away?” This time the question was not about IDE, it was about AI. 

The good news is that AI is not going to take our scrum and agile jobs away. AI is going to make many jobs easier so that we can do more and different things. It will open avenues that we could not explore before due to constraints and limitations. It will change our jobs in a good way.

So, let’s look at how AI can benefit scrum teams. Right now, the smartest thing that an AI robot can do is pick a dinosaur from scattered toys when asked to pick an extinct animal. So, we are far from where AI can do non-mundane, human-like tasks. But regular data-driven tasks and activities can be handled very well by AI.

Scrum Masters and AI

How does a scrum master benefit the team?

A scrum master is a coach, facilitator, mentor, and trainer. A scrum master enacts scrum, removes impediments, and helps scrum teams become better at their game. A scrum master may also work on product-wide or corporate-wide scrum/agile adoption. The role is inherently human-centered and focused on culture, safety, space, communication, and flow.

Will AI replace scrum masters?

Far from a role replacer, today's AI will support scrum masters and their ability to guide their teams.

Some of the ways AI can help a scrum master include:

• Generating automated reports for senior management
• Scheduling meetings, sending reminders, generating automated meeting notes, and ensuring apt and inclusive communication
• Helping the scrum master coach team members to use language translators to overcome language barriers, thus improving overall communication

AI can analyze loads of historical data to determine patterns for risks, pitfalls, and bottlenecks. In the same way, it can also determine patterns for success and flow. This pattern identification will help scrum masters address overall problems rather than localized solutions. This will also help scrum masters remove impediments more proactively than reactively. AI can also help prioritize impediments.

AI can recommend the right set of training for scrum teams by analyzing the skill gap and needs of the product or feature being planned. AI can also track improvement action items from retrospectives and help determine team, product, or corporate improvement patterns, helping the entire organization become agile faster.

AI can therefore help scrum masters and agile coaches focus on the right things by taking care of repetitive, mundane tasks.

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Product Owners and AI

Product owners are responsible for:

• Gathering the requirements
• Working with customers and stakeholders 
• Analyzing the market and competition 
• Product economics
• Product planning
• Product backlog management 

The role is human-centric but also very analytical because the product owner analyzes a lot of information and data to maximize return on investment. AI can help a lot with the latter; however, the final decisions will have to be made by the product owners.

Ready to learn more? Check out the AI for Product Owners microcredential course.

Several ways AI can support product owners

Analysis

AI can gather and analyze feedback from social media, support tickets, and bugs, as well as the corporation’s product lifecycle and industry life cycle, and provide insights into customer behavior, market trends, product receptiveness, etc. Will it replace the direct interaction of product owners with the customers? Probably not. But it can make those conversations more productive and focused and lead to quicker decisions and better customer satisfaction.

AI-powered analysis will also provide insights into product backlog prioritization and roadmapping, leading to more successful products. The decisions will still be made by the product owner, but the data and information to make those decisions will be readily available. These predictive analytics can also be used to set overall product or corporate strategy.

Routine tasks

AI can automate other routine tasks, such as scheduling, follow-ups, survey question generation, providing insights from daily scrum, scheduling meetings and taking notes, coming up with collaboration activities and games, recommending values for product backlog items, generating operational reports, and updating dashboards.

Patterns and prioritization

AI can help product owners determine which technical debt items are part of a bigger pattern that needs to be addressed at the product level and help prioritize those over other tasks. It can also assist product owners in making better decisions on product backlog item priority.

Similarly, AI can recognize user experience patterns across the product and then improve and personalize the experience based on those patterns. 

Risk management

Lastly, AI can help the product owner assess risks in developing a product or a feature and address them proactively. Risk management will become easier, and the number of risks will be reduced. The same can be done for dependency management between multiple scrum teams. AI can help identify, distribute, visualize, track, and manage those.

AI has the ability to help product owners deliver high-value products faster, minimize risk, increase customer satisfaction, improve user experience, and increase return on investment.

The Scrum Team and AI

From an overall agile/scrum team perspective, AI can help in many ways. 

The scrum team defined

A scrum team is self-managing, responsible for increment delivery, accountable, adheres to the definition of done (that is to say, the team maintains quality), estimates product backlog items, and collaborates. The role has technical, operational, and human aspects. AI can help a lot with the former.

AI can assist in facilitating scrum events (sprint planning, daily scrum, sprint review, and sprint retrospective) by:

• Scheduling the meetings
• Recording and organizing notes
• Updating product backlog items based on the conversations and sending those updates to the product owner for review
• Updating the sprint backlog on a daily basis based on team conversations 
• Generating automated reports and metrics for sprint and release tracking
• Providing historical velocity insights
• Providing historical improvement action items, insights, and analytics
• Providing estimate recommendations for product backlog items, although the final decision lies with the developers on the team

If you work on a software development or other technically focused team, AI can analyze the check-ins and:

• Perform code review
• Determine which functionality has been completed and automatically update tracking tools
• Perform code coverage for the new and existing functionality and determine which areas need more coverage
• Check for bugs and security vulnerabilities
• Automate testing tools to create test cases

In a little more advanced world in which technology evolves, these tasks may be possible:

• AI can create multiple solutions for things like A/B testing
• AI can suggest solutions for problems based on problem-solving patterns it has already learned
• AI will ensure that the definition of done (product quality) is not compromised by analyzing the completed work

AI will also help track dependencies, risks, and impediments. AI will therefore take away a lot of communication churn and time wasted on mundane tasks, thus freeing up the developers to work on smart, intelligent solutions.

Understanding AI (Artificial Intelligence)

 "AI" is broad. To understand how these emerging technologies fit into our careers—helping or hindering—it's informative to think about the different types of AI and what they are actually capable of.

Primarily, there are 4 types of AI:

Reactive Machines

Reactive Machines have no memory. They are built to solve simple problems that need massive data processing. A simple problem essentially means that the given input will always provide the same output. These systems don't have any memory and they simply do the assigned task, which usually is too big for the human brain to process.

Limited Memory Machines

The next type is machines with limited memory. It is designed to mirror how neurons work together in the human brain. It trains itself on new incoming data. It has limited memory that it uses to provide smarter answers. Examples of this level could be Natural Language Processing and Image Recognition.

Limited memory machines can look into historical data, learn and update algorithms, and become smarter. However, due to limited memory, they cannot store experiences. So, they cannot perform experience-based tasks like the human brain.

Theory of Mind

This is self-aware AI. It does not currently exist. If (or when) developed, it will give AI the ability to interact with the world, understand it at an emotional level, and react at an emotional level. It will also allow AI to make experience-based decisions.

Self-awareness

The final stage of AI is self-awareness, in which AI becomes consciously aware of its existence and how it fits into the world. This also does not currently exist.

From these four types of AIs, we are currently at the second level of limited memory. Success in building quantum computers may fast forward, achieving level three—Theory of Mind. However, currently, it seems a little further out. The first two levels, as we have seen earlier, are only helping agile and scrum teams become better and focus on the right things to deliver better products. 

The Future for Agile Teams

This is where we are today. In the future, can AI take away our jobs? The answer that resonates with past technological advancements is that it will change our jobs and make them easier so that we can focus more on what matters the most. Remember the first Agile Manifesto value: We value “Individuals and Interactions over Processes and Tools.” This will hold true even in the self-aware level of AI.

AI integration in scrum teams will lead to more effective and efficient teams, create a better flow in the system, improve decision-making, enhance product quality, and better meet customer demands. By leveraging AI tools and techniques, scrum teams can focus more on creativity and strategic planning, leading to a more dynamic and collaborative work environment.

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