A Packed Room!

Fresh off the heels of the fantastic New Orleans Global Scrum Gathering in May 2024, I wanted to revisit some key takeaways from my talk on the intersection of AI and Agile. In this blog post, I’ve combined some of my thoughts, the slides, and the videos from the original presentation. Enjoy!

Note: the videos below work best at full-screen!

The Title Page from My Presentation

The Big Questions About AI and Agile

Let’s jump right in to slide 4, “The Big Questions.”

The BIG Questions about AI and Agile

Here, we explored the potential of AI to empower Agile teams. We saw how AI and Agility can tackle repetitive tasks that used to consume hours, streamlining things like generating tasks, acceptance criteria, backlog refinement, and even test cases. This frees developers to focus on what they do best: building high-quality features, faster.

Now, the elephant in the room – “Is AI coming for my job?” While it’s true that AI will undoubtedly change the workforce, some roles significantly, it’s crucial to remember AI as a collaborator, not a competitor. The future of Scrum envisions AI as an augmentation tool, freeing developers from tedious tasks and allowing them to focus on creative problem-solving and building the right things.

Waterfall and the Law of Accelerating Returns

Moving on to slide 8, we delved into the idea that the same factors that led to the downfall of Waterfall methodology are now driving a major shift in how products are built – not just software products, but all products!

AI, Agile, and Sentient Sprinting

Sentient Sprinting: AI and Agility Supporting Humans

This brings us to the exciting concept of “Sentient Sprinting. Imagine sprints that not only guide the development process but also “do some of the legwork” for developers. Sentient Sprinting leverages AI to help Agile teams by automating tasks and suggesting optimal solutions, allowing developers to focus on critical thinking and building better, faster products.

Common Challenges

The Challenges

Slide 12 delved into the common challenges faced by Scrum teams, including lack of role effectiveness, poor sprint planning, and inefficient handling of backlog item dependencies.

Introducing SierraAgility and Spark!

Introducing Sierra Agility and Spark

Slide 13 introduced Sierra Agility (https://sierraagility.net/) and its built-in AI assistant, Spark. We then showcased a series of videos demonstrating Spark’s capabilities.

Simple Tasks – Defining a New Sprint

The first video highlighted how Spark streamlines sprint planning by recommending names, lengths, and capacity for upcoming sprints. The next video showcased how Spark provides daily insights to Product Owners and Scrum Masters, keeping them informed and proactive.

Simple Tasks – Getting Daily Advice

The “Game Changers”

Product Goal Improvement: We explored how AI and agile can come together to analyze data and user feedback to suggest improvements to product goals, ensuring they remain relevant and impactful.

Backlog Refinement: the next video introduced how Spark assists with backlog refinement by suggesting user stories, breaking down epics, and prioritizing tasks.

Acceptance Criteria Generation: This video demonstrated how Spark leverages user stories and product goals to automatically generate clear and concise acceptance criteria.

Test Case Generation: The final video showcased Spark’s ability to generate comprehensive test cases based on acceptance criteria, saving development teams valuable time.

The Future of AI and Scrum Teams

The future of AI and Agile is bright! We are currently exploring the potential for AI to enhance retrospectives, suggest solutions for user stories, improve estimation and dependency identification, and more.

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