AI and Automation

AI and Automation in Agile Development

How quickly could you launch an application if your software spotted bugs during your scrum team’s first time-box?

What if that software could troubleshoot those bugs – or offer design alternatives that your product owner could relay to stakeholders? 

That’s where artificial intelligence (AI) and automation come in.

Agile Development has significantly improved an organization’s ability to create quality software that continuously adapts to evolving consumer needs. Its iterative and collaborative nature means the creation and testing of products can happen simultaneously.

However, we live in an age where on-demand is coveted – even with the limits to how much mental, emotional, and physical output a human can sustain daily.  

That’s where artificial intelligence (AI) and automation come in. The combination of human direction and technological programming can unlock unmatched levels of efficiency and creativity – leading to market advantages regardless of the industry. 

Before understanding the benefits of AI and automation in Agile Development, let’s better understand each concept.

AI and Automation? 

AI refers to a machine’s ability to engage in cognitive behaviors that humans usually do. These include perception, reason, problem-solving, and creativity.

An example of primitive AI is the calculator on your phone, and more advanced models exist in the form of the commonly used Alexa, Siri, and the now-popular ChatGPT. 

Artificial intelligence assistant

Automation builds on this concept: programmers code a system to perform standardized, repetitive actions. That auto-generated thank you email or your weather app are examples of automation technology.  

Most of the time, AI and automation are to reduce human workload, which is why these inventions are becoming increasingly popular in Agile Development.


The Relationship Between AI, Automation, and Agile

It’s known that adopting agility in software development is essential to meeting time-to-market needs. We also understand that successful businesses keep loyal customers by satiating their appetite for having the next best thing now


It’s why Apple products are a household staple, and Jeff Bezos is a multi-billionaire. 


However, allowing AI and automation to collect and organize data may help Agile organizations stay ahead. Doing so can help improve agile planning and estimation and streamline continuous integration and delivery (CI/CD) while refining agile team collaboration and communication

Let’s examine how AI and automation can enhance these Agile practices. 


  • Improving Agile Planning and Estimation 

Whether it’s a system demo event,program implementation (PI), or iteration planning call, Agile teams rely on various feedback to inform the workflow of their next iteration. Stakeholders, co-workers, and end-users all share what they like and don’t like about new features during each phase of the software development cycle. Scrum development teams collect, analyze and apply this information to the next iteration. 

Agile traditionally encourages a minimalistic approach to software documentation. But updating products according to client demands means development teams need consistent access to the correct data at the right time, and this can be challenging for product owners to store and retrieve manually. 


Research suggests that AI, like natural language processing (NLP) technology, can help collect and store this information. It provides organizations with more accurate and organized feedback while allowing individuals to focus entirely on presentations during Agile ceremonies and events

  • Streamlining CI/CD optimization

CI is an ongoing process that involves developing and implementing new ideas that improve software. These suggestions are based on customer and stakeholder feedback. CD allows the development team to ensure their software is ready for production. The concept applies an iterative approach to testing, integrating, and releasing code changes. 

While automation is already a part of the testing process, machine learning – a form of AI that identifies and organizes data – coupled with data analysis and statistical models can help to streamline this further: by finding patterns in previous iterations that may help to predict future technical and human behavior. 

Doing so helps development teams stay ahead of the curve. It gives them insight into how to adapt their software to consumer needs – before users know them! With automated testing, people also put less time and effort into identifying and fixing potential future bugs. 

  • Refining Agile Team Collaboration and Communication

Automated code reviews, task management, and smart meet scheduling can save time when delegating tasks, tracking deliverables, and sharing feedback on coding standards.

Advancements in deep learning mean AI-powered project management can intelligently distribute tasks based on team members’ skills and availability. These advancements help to create a balanced workload and can be especially useful to product owners in assisting them to keep track of what team members are working on or when assignments are due.

We know that automated reminders and notifications keep everyone up to date on meetings. Imagine AI-powered scheduling assistants that simplify arranging calls by considering participants’ calendars and time zones – so you don’t have to. 

Regarding automated Code Review, AI-driven tools can analyze, then share feedback on code quality and adherence to coding standards. This speeds up the review process by allowing humans to focus on more complex aspects of development.

The Future of Agile Development with AI and Automation

Gartner’s research suggests AI will complete 80 percent of project management tasks through machine learning (ML) and NLP by 2030. It will leverage information from massive amounts of collected and stored data. 

Agile Development teams will need to consider

 risks, in addition to the human and financial cost of implementing AI and automation in its practices. 

However, the transition is necessary to stay ahead of the curve regarding software development in this increasingly digital world. 

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