Our team at Agile Hive is currently conducting a survey with the dual purpose of gathering feedback on satisfaction with our current features, as well as gauging interest and feature-requests specific to AI integration with Agile Hive. We would love to include your thoughts as well. You can find our survey here.

AI and the Scaled Agile Framework (SAFe®)

Definition: Artificial Intelligence (AI) is a term used to describe a wide range of smart machines capable of performing tasks that typically required human intelligence. AI can be applied at all levels of SAFe to build intelligent customer solutions, automate value stream activities, and improve customer insights.

It is a technology that can revolutionize solutions developed by SAFe organizations and has the potential to dramatically influence the operational and business models of enterprises as well as increase individual and team productivity.

Courtesy: © Scaled Agile, Inc.

The Scaled Agile Framework (SAFe®) emphasizes alignment, collaboration, and delivery across multiple agile teams. As organizations scale agile practices, the complexity of managing dependencies, forecasting delivery, and ensuring transparency grows exponentially. This is where AI brings immediate value.

AI-driven tools are now able to analyze massive volumes of agile data—from team velocity to historical delivery patterns—to predict risks, identify bottlenecks, and suggest optimizations. For example, during PI (Planning Interval) Planning, AI can help anticipate which features are at risk based on past performance, team load, and cross-team dependencies. This helps release train engineers and product managers make data-informed adjustments early in the planning cycle.

Enhancing Flow with Intelligent Insights

Flow metrics—like flow time, flow efficiency, and flow load—are foundational in SAFe 6.0 to help organizations measure the speed and health of their value streams. AI takes these metrics a step further by spotting trends that humans might miss.

Imagine an AI system that alerts you when flow load increases beyond sustainable levels or when flow efficiency drops due to systemic blockers. It doesn’t just raise the flag—it can suggest actionable recommendations, such as reassigning capacity or breaking down oversized work items. This allows agile teams to continuously improve without waiting for retrospectives or quarterly reviews.

Smarter Product Management

AI also plays a critical role in product strategy. With access to market data, customer feedback, and usage analytics, AI can help product managers prioritize features that are likely to have the greatest impact. Natural language processing (NLP) can extract themes from customer reviews or support tickets, giving PMs real-time insight into user pain points.

Tools like AI-assisted roadmapping can also evaluate the feasibility of multiple product strategies against resource constraints and delivery timelines, giving product teams a competitive edge in making evidence-based decisions faster.

AI-Powered Project Forecasting and Risk Management

Project managers often deal with uncertainties around budgets, timelines, and scope changes. AI helps de-risk delivery by offering probabilistic forecasts rather than static estimates. Instead of a single deadline, teams can see a range of possible outcomes, complete with confidence intervals, based on real-time data and past performance.

Risk detection is also becoming more proactive. AI tools can monitor communication patterns, ticket statuses, and integration issues to flag early warning signs of project derailment—sometimes even before the team is aware.

At the 2025 SAFe® Summit in Sorrento, Italy, Dr. Natalia Kuzmina, the Head of Agile Practices at S&P Global Ratings, gave an engaging and insightful presentation on this topic, entitled “AI Enhanced SAFe® Framework“. The full recording is available to Scaled Agile’s SAFe Studio platform subscribers, however we wanted to call attention to a number of relevant points she addressed in her keynote.

First and foremost, even with the rapid changes and advancements in AI, at the end of the day, the core benefit of AI for SAFe practitioners should be the enhancement or extension of the practice of customer centricity. This includes everything from the deliberate development of user personas, empathy maps, competitive analysis, case studies, and success stories.

At the outset of your journey with AI in your SAFe environment, it’s crucial to complete a thorough assessment of your organization’s current SAFe implementation to identify the areas where implementing AI would add the greatest value. Before committing all resources to an AI initiative, conduct a pilot program(s) where the variables can best be controlled. This will help in the effort to analyze the effectiveness, as well as gather feedback for refinement efforts should your organization decide to roll this out in a large-scale effort.

And finally, Dr. Kuzima notes, only then, once an AI solution or host of solutions has been proven (or not), can you then decide to introduce it enterprise-wide. And certainly, when doing so, continue to monitor and document improvements and any areas of concern.

What’s Next?

AI is not replacing agile teams—it’s empowering them. By automating the mundane, surfacing actionable insights, and improving strategic clarity, AI allows agile organizations to respond faster to change and deliver more value with less friction. As AI capabilities mature, its integration into scaled agile and product/project management will only deepen—making it an indispensable tool for modern enterprises navigating complex digital landscapes.

Just as a reminder, our team at Agile Hive is currently conducting a survey with the dual purpose of gathering feedback on satisfaction with our current features, as well as gauging interest and feature requests specific to AI integration with Agile Hive. We would love to include your thoughts as well. You can find our survey here.

Related Resources

• Objectives and Key Results (OKRs) in the Scaled Agile Framework (SAFe®): A Guide to Strategic Alignment
• Understanding Flow Metrics in SAFe®: A Beginner’s Guide to Measuring and Improving Agile Performance
• Integrated Budgeting for SAFe® Practitioners
• Implementing WSJF in Jira: Unlocking Agile Prioritization