How Product Owners Are Adapting Scrum for AI Product Development

Community blog post: Naren Chandraseharan is a seasoned product leader with over a decade of experience driving cross-functional teams across e-commerce, AI, and enterprise development. 

Artificial intelligence is reshaping the way products are built and delivered. For product owners, this brings both opportunities and challenges. Unlike traditional software, AI development introduces high levels of uncertainty, heavy reliance on data, and outcomes that can’t always be predicted at the start.

At first glance, this may seem at odds with scrum. But because scrum is built on adaptation and iteration, it can still serve us well. The challenge is not replacing scrum, but adjusting how we use it. This article explores how product owners and scrum masters are adapting practices to support the development of AI products, highlighting practical approaches that teams can put into play right away.

The new realities of AI work

Uncertainty is the norm.
AI features often begin with a hypothesis, not a clear requirement. For example, "We believe demographic signals can improve recommendations" is not the same as "Build a filter button." Product owners must get comfortable treating experiments as core backlog items.

Data is as important as features.
In AI products, data quality and availability can make or break success. It helps to think in terms of "data stories" alongside user stories. For example: "As a model, I need twelve months of clean interaction data so I can generate accurate recommendations."

Teams are more cross-functional.
An AI scrum team often includes data scientists, ML engineers, product experts, and domain specialists. "Done" might mean not only shipping code, but also reaching accuracy thresholds, validating data pipelines, and reviewing for fairness or bias.

Adapting scrum to AI development

Evolving the product backlog
A practical backlog for AI usually contains three streams of work:

• Discovery items: Research spikes to explore data, test feasibility, or reduce uncertainty.
• Development items: User-facing features, with acceptance criteria that include AI-specific thresholds.
• Infrastructure items: Data pipelines, training systems, and deployment foundations.

Product owners play a key role in balancing these three streams each sprint so that learning, delivery, and technical groundwork progress together.

Rethinking sprint planning
AI tasks are harder to estimate. Some experiments succeed quickly, while others fail despite best efforts. Teams can adapt by:

• Using confidence ranges instead of single-point estimates.
• Set sprint goals as testable ideas. For example: "If we add clickstream data, we expect recommendations to improve by about 10%."
• Define clear stop signals up front. This helps the team know when to cut losses and change direction if the work isn’t paying off.

Daily scrum with AI-specific updates
In addition to the standard questions, AI teams benefit from surfacing:

• Data quality issues ("Null values are rising in preference fields").
• Model performance changes ("Precision improved, but recall dropped").
• Ethical checks ("Bias review flagged a gender skew in recommendations").

This keeps the entire team aligned on factors unique to AI.

Making sprint reviews meaningful
A "click-through demo" is often not enough. For AI products, reviews should also include:

• Performance dashboards showing accuracy, precision/recall, or fairness metrics.
• Discussion of failure cases where the model struggles and why.
• Reflection on ethical considerations and trade-offs.

Being transparent about both successes and shortcomings builds stakeholder trust.

Managing stakeholder expectations

Since outcomes in this space are harder to predict, setting the right expectations is essential. Product owners can support this by:

• Using flexible roadmaps that show ranges and assumptions instead of fixed delivery dates.
• Holding regular check-ins to compare what was expected with what actually happened, so forecasting improves over time.
• Framing failed experiments as learning moments, not broken promises, to keep trust and momentum intact.

Key practices for product owners

• Start with the data: Spend the first sprints exploring, cleaning, and understanding the data before expecting strong model results.
• Roll out gradually: Release features step by step, with monitoring and rollback options built in from the start.
• Build data literacy: Learn the basics of model metrics so you can have meaningful conversations with technical teammates.
• Use the right tools: Experiment tracking, model monitoring, and data lineage tools aren’t nice-to-haves—they’re the foundation of sustainable AI work.
• Be transparent: Communicate with clear, plain language. Share what you know, what you don’t, and what you’re learning along the way.

The future of scrum in AI development

AI work challenges many of the assumptions product owners are used to in software development. Still, scrum is highly relevant. By rethinking the backlog, adjusting events, and broadening their own skills, product owners and scrum masters can guide teams through uncertainty with confidence.

In the end, successful AI products aren’t built by teams with only the "best" algorithms but they’re built by teams that also foster a culture of learning, openness, and steady adaptation. Scrum provides the framework. It’s up to us to shape how we use it.

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Interested in learning more about AI for scrum teams? Check out Scrum Alliance's microcredential courses, AI for Scrum Masters and AI for Product Owners.