Generative AI is integrated as a supplement to human agile coaches
If you’re running an organization that depends on responsive, iterative product development, you know agile isn’t optional, it’s foundational. Yet executing agile well remains notoriously difficult. That’s where agile coaches have always played a role: coordinating teams, making decision-making processes more consistent, and reinforcing agile frameworks across the business.
Now, generative AI is stepping in to take over some of that load, but it’s not replacing anyone. It’s a powerful assistant, not a leader. Agile teams are using AI tools like ChatGPT to handle the lower-order work: summarizing long team discussions, generating retrospective prompts, and even helping to sketch out workshop frameworks. These are the support tasks that take time but don’t require deep human understanding. That’s where AI does well. And when it works, it frees your coaches to focus on the high-value functions: team alignment, conflict navigation, and strategic clarity. Those things still need a human who can manage nuance.
Joop Nilkuha, engineering manager at A.P. Moller-Maersk, uses AI tools this way. When her team faced an internal dispute spread across scattered chat threads, she handed it off to an AI to organize and summarize the arguments. What would’ve taken her half a day to sort through manually took 15 minutes using AI. She then used tools like Scrum Sage and Scrum Master Assistant to brainstorm solutions with her team. The key takeaway here? It worked fast, but she still had to validate the results. In her words, AI is “more often wrong than not” on fundamental topics if you don’t know what you’re looking for.
That’s a crucial point. If you’re thinking about replacing real coaching with AI, you’re skipping the part where human judgment decides what’s relevant. As Nathen Harvey, a developer advocate at Google Cloud, said: generative AI is like an eager intern, quick to give answers, not always right. If you’re building serious products, “quick but wrong” doesn’t cut it.
AI enhances entry-level Agile coaching and preparatory team discussions
Where AI really shines right now is in simple guidance and prep work. If you’ve onboarded junior team members recently, you know it’s an overhead burden, slowing velocity while they learn agile processes, backlog grooming, sprint planning, and the rest. Generative AI closes that ramp-up gap.
You can treat it like a fast-access coaching tool for the basics. Give it context, like an app that failed on Black Friday, and it’ll help you form the post-mortem questions that matter. This helps less experienced team members move past surface-level thinking quickly. They aren’t left wondering what questions to ask or what a sprint breakdown should include. It reduces noise and accelerates confidence.
Ilona Brannen, a leadership development consultant, says AI takes a team from novice to intermediate faster. Where it still falls short is deeper collaboration, cultural navigation, and interpersonal dynamics, the real heart of agile. That’s where human coaching lifts the ceiling.
Even more interesting: using AI before going into coaching makes the sessions better. People show up prepared, with a clearer idea of what they want to address. That lets human coaches get right into meaningful work rather than spending half the session setting the stage.
This is the signal for leadership. Piloting AI tools doesn’t mean removing coaches. It means getting more from them and elevating your people faster. The emotional front of team building can’t be automated, not without costing you the cohesion that agile depends on. But you can use AI to get to that human-centered work faster.
Expert oversight is key to mitigate the inaccuracies of AI in Agile practices
Most leaders exploring AI in their organizations know the promise, it’s fast, scalable, and accessible. But the limits are just as real. Generative AI tools still make basic mistakes, especially in areas that require context, standards, or judgment. Agile is one of those areas. While it’s tempting to offload coaching or facilitation to AI, the reality is that these systems lack the accuracy and discernment to run agile independently.
Agile relies on principles. Things like team accountability, iterative improvement, and customer feedback loops can’t be reduced to a formula or static checklist. If you ask an AI to explain Scrum values or diagnose issues in your team’s sprint cadence, it may give plausible but incorrect answers. This is why skilled oversight is critical. A team without that guidance risks being steered off course by tools that sound confident but don’t truly understand their context.
Joop Nilkuha at A.P. Moller-Maersk has tested these systems firsthand. Even when asking relatively simple questions about agile frameworks, the answers were often flawed. She emphasized the need for deep domain knowledge to curate what was useful and discard what wasn’t. Nathen Harvey from Google Cloud supports this view, recommending users treat AI not as a final authority, but as a contributor best suited for exploration, not decision-making.
For executives, this comes down to one thing: don’t cut experienced talent out of the loop. While AI can accelerate routine tasks and inject momentum into team discussions, without qualified people to moderate its output, you risk compromising your standards. If your teams lack the expertise to verify AI-generated advice, you don’t gain agility, you introduce unnecessary risk.
Increasing agile efficiency elevates the need for cross-organizational coordination
As development teams become more specialized and productivity speeds up through AI, new challenges emerge. One of the most significant is coordination. When execution becomes faster, maintaining alignment between teams becomes harder. Agile isn’t just about delivery; it’s about ensuring multiple streams of work remain synchronized, especially in complex organizations.
Henrik Kniberg, a lean and agile coach, sees this shift already happening. He predicts smaller units, human developers assisted by generative AI, delivering in shorter, more frequent sprint cycles. In that model, the typical two-week sprint might shrink to a single day. Planning and status meetings converge. Delivery rhythms speed up. But the demand for meaningful inter-team collaboration increases, not decreases.
This is a key issue for executives overseeing agile transformations. When delivery teams move faster, the organization must scale its ability to integrate outputs, handle handoffs, and manage shared goals. Agile coaches become more valuable in this scenario, not less, because they guard against fragmentation. They align sprint goals to business goals, ensure clarity across product verticals, and keep interpersonal trust strong in pace-driven environments.
If you reduce your coaching infrastructure while scaling AI across your teams, you risk faster misalignment, missed dependencies, and reduced product cohesion. That’s not cost reduction, it’s deferred cost, paid later through delays and rework. Investing in agile coaching under rapid-cycle AI-augmented delivery isn’t optional. It’s operationally necessary.
AI as a tool to free agile coaches for high-value interpersonal engagement
AI tools are changing the energy balance inside agile teams. In the past, agile coaches often had to spend a large portion of their time preparing retrospectives, aligning sprint goals, or pulling insights from team feedback. Now, generative AI can handle much of that groundwork, faster, and in many cases, with reliable structure. That shift is important. It means coaches can redirect their effort to what matters most: the human interactions that build trust, resolve conflict, and sharpen team performance.
This isn’t just about cost-saving. Shifting routine prep to AI opens up bandwidth for deeper engagement. Agile leadership isn’t built on checking boxes, it’s built on conversations. When coaches have more time to focus on coaching rather than setup, teams move faster, communicate better, and respond to change more effectively. That’s where AI delivers real leverage: not by replacing insight, but by enabling it to happen sooner.
Nathen Harvey from Google Cloud frames it clearly: AI isn’t the whole solution, it’s another tool. Use it to get ready faster, use it to generate options, but keep people in the loop. That loop is where insight happens. Ilona Brannen, a leadership development consultant, agrees. She explains that while AI helps with onboarding and task acceleration, it falls short when navigating the emotional elements of team leadership. In her view, removing the human touch strips away the connective tissue that makes teams function beyond process.
C-suite leaders should think of this dynamic as an operational shift. You’re not removing coaching from the system; you’re giving it better conditions to create value. Let AI handle the structured and repeatable. Let talented coaches step into the sessions already briefed and ready to focus on strategic alignment and cultural strength. That’s where you build durability, as a product organization, and as a leadership culture.
Key takeaways for leaders
- AI supplements Agile coaches: Generative AI can streamline prep work and surface insights quickly, but human coaches remain essential for resolving team dynamics, aligning strategy, and filtering out AI inaccuracies.
- Use AI to accelerate onboarding and team prep: Leaders should leverage AI to boost early-stage team productivity and reduce coaching bottlenecks by equipping junior staff with faster, contextual guidance ahead of human-led coaching sessions.
- Maintain expert oversight to reduce AI risks: AI-generated agile advice is often inaccurate or overly generic; organizations must ensure experienced coaches are actively validating outputs to avoid misaligned practices and workflow errors.
- Scale agile coaching with productivity gains: As AI accelerates delivery and teams become leaner, organizations will face more coordination complexity. Leaders should invest more, not less, in agile coaching to preserve alignment across fast-moving teams.
- Free coaches to focus on high-value engagement: Offloading routine tasks to AI allows agile coaches to spend more time driving collaboration, trust, and problem-solving. Prioritize tools that reduce prep time while enhancing human-led team development.
