AI

The Future of Agility: Looking Ahead to 2026

Over the past several weeks, I’ve explored some of the biggest shifts shaping Agile in 2025 — from the return to basics to the rise of AI-driven agility, from platform engineering to value stream thinking, from hybrid development approaches to hyper-collaboration and evolving roles.

Each of these trends points toward a single, unmistakable truth:

Agility isn’t about frameworks anymore — it’s about mindsets, outcomes, and adaptability.

As we look toward 2026, I see the Agile world continuing to evolve in three key directions: simplification, augmentation, and integration.

Let’s take a closer look at where we’ve been — and where we’re headed.

1. Back to Basics — The Simplification Revolution

We started the series with what I still believe is the most critical conversation: getting back to Agile basics.

Somewhere along the way, many organizations overcomplicated agility with layered frameworks, rigid ceremonies, and too many tools chasing too little purpose. But the best teams are rediscovering that simplicity works.

In 2026, I hope to see even more organizations stripping away the unnecessary and focusing on what truly matters: clear goals, empowered teams, continuous feedback, and incremental delivery.

We’ll see more leaders asking:

  • “What value are we delivering this sprint?”
  • “What’s getting in our way?”
  • “How do we make it simpler?”

Those are the questions that keep agility human — and sustainable.

2. AI as a Co-Pilot, Not a Replacement

The second major theme of this year was AI-driven agility, and this trend will only accelerate in 2026.

We’ve moved beyond the novelty phase. AI isn’t just assisting developers or automating testing — it’s helping coaches, product managers, and entire teams make better decisions.

In my own work, I’ve used ChatGPT to generate epics and user stories from raw ideas, saving hours of prep time and giving my team a strong foundation for backlog refinement. I’ve also piloted this with development and HR tech teams — and the results were impressive.

In 2026, I expect this to become common practice. AI will be a collaborator in the agile process — helping us synthesize data, predict risks, and visualize flow — while humans focus on context, creativity, and connection.

The real opportunity isn’t in automation. It’s in augmentation — using AI to free us from the busywork so we can spend more time on meaningful work.

3. Platform Engineering and the Rise of Outcome-Driven Ops

Another trend reshaping Agile delivery is the evolution of DevOps into Platform Engineering.

In 2025, this shift began to take hold — dedicated platform teams building self-service environments that empower developers and accelerate flow. In 2026, I believe we’ll see this model become the norm for large enterprises.

The key difference is cultural: Platform Engineering isn’t just about infrastructure — it’s about creating leverage. It’s how organizations ensure teams can deliver independently without sacrificing governance or security.

The best platform teams measure success not by uptime or deployments, but by developer experience and time to value — the outcomes that matter most.

4. Value Stream Thinking — The True “Definition of Done”

In 2025, we started reframing “done” to mean value realized, not just code shipped.

That mindset shift — from output to outcome — is profound. It requires courage from leadership and patience from teams. It also demands systems that make value visible, from idea to delivery to customer impact.

In 2026, I believe more organizations will adopt Value Stream Management as a strategic discipline. We’ll see metrics evolve from velocity charts to value metrics — like cycle efficiency, customer satisfaction, and innovation throughput.

The companies that think beyond quarterly numbers will continue to lead. As Simon Sinek reminds us in The Infinite Game, the ones that play for long-term impact are the ones that truly change their industries.

5. The Hybrid Future of Development

The debate between Agile vs. Spec-Driven Development (SDD) is fading. In its place, we’re seeing hybrid models emerge — blending the structure of SDD with the flexibility of Agile.

In 2026, I expect this hybridization to accelerate, especially as AI helps automate specification creation, traceability, and documentation.

It’s not about choosing sides anymore. It’s about choosing what works — a theme that runs through every part of agility’s evolution.

6. Hyper-Agility and Real-Time Collaboration

Teams are becoming faster, more visual, and more connected.

In my teams, we use Lucidspark over Zoom to run real-time collaboration sessions — mapping value streams, visualizing customer journeys, and creating epics on the spot. Lucidspark integrates with Jira and Confluence, allowing us to maintain a single source of truth from ideation to delivery.

In 2026, expect to see more teams working this way — embracing asynchronous collaboration tools powered by AI, and creating seamless bridges between brainstorming and execution.

We’re finally closing the gap between thinking and doing.

7. The Embedded Agile Coach

Finally, we’ve seen the role of the Agile Coach transform.

As I shared in the last post, moving from Scrum Master to embedded coach changed how I viewed the system. Instead of coaching teams in isolation, we began to coach the organization itself — surfacing systemic blockers, aligning strategy to delivery, and enabling agility at scale.

This trend will deepen in 2026. Agile Coaches will become strategic partners, helping shape culture, leadership behaviors, and operating models. They’ll use data, empathy, and AI insights to guide decisions that stick.

The future of coaching isn’t about enforcing ceremonies — it’s about cultivating environments where agility can grow naturally.

So, What’s Next?

If 2025 was the year of rediscovery — of returning to values, rethinking roles, and rehumanizing agility — then 2026 will be the year of integration.

Agility won’t live in a corner of the org chart anymore. It will be embedded in leadership, technology, culture, and operations. AI will be a partner. Platform teams will be enablers. Coaches will be catalysts.

And simplicity — the value we started with — will remain the north star.

As we move into this next era, I’ll continue to ask the same guiding question that’s defined my journey so far:

“What actually works for us, right now, in our context?”

Because that’s the heart of agility — not dogma, not frameworks, but discovery.

Here’s to 2026 — the year we stop talking about doing Agile and start fully being Agile.

From DevOps to Platform Engineering: A Cultural Shift

When DevOps first entered the scene, it felt revolutionary — breaking down silos between development and operations, shortening delivery cycles, and empowering teams to own what they build. But like every great movement, it has evolved.

In 2025, we’re witnessing the rise of Platform Engineering — DevOps’ next act.

Where DevOps asked teams to “build it and run it,” platform engineering says, “Let’s build the system that makes it easier for everyone to build and run.”

It’s a subtle but powerful shift — from individuals owning pipelines to organizations owning the developer experience. And for Agile teams, that shift represents a huge opportunity to deliver faster, safer, and smarter.

From DevOps to Platform Engineering

DevOps was never meant to be a department. It was a mindset — a set of practices designed to improve collaboration, automation, and feedback loops between development and operations. But as organizations scaled, many discovered an unintended consequence: DevOps fatigue.

Developers were spending too much time managing infrastructure instead of focusing on delivering value. Toolchains became sprawling and inconsistent. And despite good intentions, velocity often stalled under the weight of complexity.

Platform engineering emerged as the natural evolution — creating dedicated teams that build and maintain internal platforms to support the rest of the organization. These platforms act as self-service ecosystems that abstract away repetitive, operational tasks, giving product teams the autonomy to focus on innovation.

Put simply, DevOps broke the wall. Platform engineering builds the bridge.

The Agile Connection

At its core, platform engineering aligns perfectly with Agile principles: empowerment, collaboration, and continuous improvement.

Instead of creating more hierarchy or process, platform teams function as enablers — reducing cognitive load for developers and removing barriers to flow.

In many Agile organizations, product teams rely on the platform team for tools, environments, and automation pipelines. That relationship only works when there’s a shared culture of trust and partnership.

If a platform team acts like a gatekeeper — dictating tools or enforcing standards without context — agility dies. But when the platform team acts as a service provider, co-creating with the teams they support, agility thrives.

That’s why the best platform teams treat their users (the developers and delivery teams) as customers. They use feedback loops, prioritize backlogs, and run retrospectives just like any other Agile team.

They’re not just building infrastructure — they’re delivering value streams.

How AI Is Accelerating Platform Engineering

Artificial Intelligence is playing an increasingly important role in how modern platforms operate.

AI-driven observability tools now predict system bottlenecks before they happen. Machine learning models optimize CI/CD pipelines by analyzing historical build data. Intelligent assistants help developers troubleshoot deployment issues or even write infrastructure-as-code configurations in real time.

And as someone who’s experimented with using AI to accelerate Agile delivery, I see enormous potential here.

Imagine platform teams using AI to automatically generate documentation for new pipelines, detect underused resources, or identify recurring incidents across teams.

Just as I use ChatGPT to create epics and user stories for my Agile coaching team, platform engineers can use similar tools to generate and refine infrastructure templates, draft runbooks, or even simulate changes before deployment.

It’s not about replacing engineering skill — it’s about amplifying it.

When done right, AI doesn’t remove human judgment; it enhances it. It enables teams to focus on strategy and outcomes instead of routine maintenance.

Metrics That Matter: Measuring Outcomes, Not Outputs

One of the biggest traps in both Agile and DevOps has always been measuring the wrong things.

Counting deployments, tickets closed, or story points completed may look impressive, but they don’t tell you whether you’re actually delivering value. Platform engineering gives us a chance to rethink metrics in a way that’s truly outcome-driven.

Here are a few examples:

  • Developer Experience Metrics: How quickly can a new developer ship their first change? How easy is it to deploy safely? These measure friction, not just activity.
  • Flow Efficiency: How much time does work spend in progress vs. waiting? This reveals systemic bottlenecks that slow delivery.
  • Change Failure Rate: Are deployments reliable? Lowering this indicates platform maturity and resilience.
  • Lead Time for Changes: How long does it take from code commit to production? Faster, safer flow means happier teams and customers.
  • Value Stream Health: Are we improving how value moves through the system, not just how fast we push code?

As Gartner’s Agile Outlook 2025 report notes, “Outcome-based metrics are the single most accurate indicator of true agility — measuring whether value was realized, not just delivered.”

That’s exactly where platform engineering shines: it creates the conditions for better flow, higher reliability, and more sustainable delivery — not through more meetings or rules, but through thoughtful automation and intentional design.

Cultural Alignment: The Real Engine Behind the Platform

Technology alone doesn’t make a platform successful. Culture does.

Building a healthy relationship between platform and product teams requires the same principles we teach in Agile coaching: transparency, feedback, and shared ownership.

Here are a few cultural lessons I’ve seen separate thriving platform initiatives from struggling ones:

  1. Co-creation Over Command
    Platform teams succeed when they build with product teams, not for them. Invite developers into discovery sessions, gather user feedback, and treat platform improvements like customer-centric product enhancements.
  2. Empowerment Over Enforcement
    Instead of forcing adoption through mandates, platform teams can build irresistible products — ones that are so easy to use and so reliable that teams want to use them.
  3. Psychological Safety
    Just as Agile teams need psychological safety to experiment, platform engineers need it to innovate. When failures are treated as learning opportunities, platforms evolve faster.
  4. Shared Purpose
    Everyone — from platform engineers to product owners — should be aligned on one thing: delivering value to the customer. The platform isn’t successful when pipelines are faster; it’s successful when outcomes improve.

This alignment is what turns platform engineering from a tech initiative into an organizational capability.

Bringing Platform Thinking to Agile Coaching

Even outside of software engineering, the mindset behind platform engineering applies to Agile leadership.

As Agile coaches, we build platforms for people — frameworks, tools, and environments that help teams thrive. When we remove friction from processes, standardize what should be standardized, and free teams to innovate within safe boundaries, we’re doing platform engineering in a different form.

And, just like technical platforms, our success isn’t measured in how many ceremonies we run or templates we create. It’s measured in whether teams are learning faster, delivering value sooner, and growing more capable over time.

The Road Ahead

Platform engineering represents more than just a new technical discipline — it’s a cultural evolution. It extends the spirit of DevOps, strengthens Agile delivery, and creates a foundation where teams can move with confidence and autonomy.

As AI continues to mature, the best platform teams will be those that blend automation with empathy — using intelligent systems to reduce toil and elevate human problem-solving.

In that sense, platform engineering is really about the same thing Agile has always been about: building systems that serve people, not the other way around.

Because at the end of the day, great platforms don’t just accelerate delivery. They amplify culture.

And in 2025, that might be the most powerful outcome of all.

References:

  • Gartner, Agile Outlook 2025: The Age of Contextual Agility
  • McKinsey & Company, The Rise of Platform Engineering in Enterprise Delivery (2024)
  • Puppet, State of DevOps Report (2024)
  • GitHub, State of AI in Software Development (2024)

The Future of Agile: AI-Powered Tools for Success

Artificial Intelligence isn’t coming for Agile — it’s coming with it.

In 2025, AI hasn’t been just a buzzword hovering over product development; it’s becoming an active participant in how we plan, prioritize, and deliver work. From backlog refinement to sprint forecasting, intelligent systems are weaving themselves into the Agile fabric, helping teams make faster, more data-informed decisions.

But the real story here isn’t about replacing people with algorithms. It’s about how AI is evolving Agile leadership itself — augmenting human insight, amplifying creativity, and freeing up time for what really matters: coaching, connecting, and delivering value.

The Rise of AI-Driven Agility

Across industries, AI is transforming Agile practices in three major ways:

  1. Predictive Analytics for Planning – Tools like Jira Advanced Roadmaps and Jellyfish are using machine learning to estimate delivery timelines, identify bottlenecks, and even flag stories likely to spill over. This allows leaders to move from reactive to proactive decision-making.
  2. AI Coding Assistants – GitHub Copilot, Amazon CodeWhisperer, and Tabnine are drastically accelerating development by suggesting code snippets, catching syntax errors, and maintaining standards in real time. Developers report productivity boosts of 20–55%, according to GitHub’s 2024 State of AI in Software Development report.
  3. Natural Language Automation – Tools like ChatGPT and Notion AI are enabling Agile practitioners to turn ideas into structured artifacts — from user stories to retrospectives — in minutes instead of hours.

The result? Less time spent documenting and more time spent delivering.

The Human Side of AI Integration

There’s a misconception that AI will make Agile obsolete — that automation will handle planning, estimation, and even retrospectives. But that thinking misses the point.

AI doesn’t replace the Agile mindset. It requires it.

Because agility isn’t about tools — it’s about learning, adapting, and applying feedback. AI is simply helping us do that faster, at scale.

The Scrum Master or Agile Coach of the future isn’t just a facilitator; they’re an AI translator — someone who can interpret data-driven insights, ask the right questions, and guide teams toward better decisions.

For example, when an AI-driven sprint forecast suggests the team can take on more work, the coach still has to ask: “Is this realistic given team morale, dependencies, or technical debt?” The insight is valuable, but it’s only meaningful when paired with human context.

That’s why the best leaders right now aren’t ignoring AI — they’re learning how to collaborate with it.

My Experience: Using ChatGPT as a Backlog Co-Pilot

As someone who leads a team of Agile Coaches and Scrum Masters, I’ve seen firsthand how AI can save time and unlock creativity — not by doing the thinking for us, but by giving us a head start.

Our internal team operates like a Scrum team. We maintain a backlog of epics and user stories, and we plan our work in two-week sprints, just like the product teams we support.

Here’s where AI comes in:

When we’re shaping our roadmap or preparing for backlog refinement, I use ChatGPT to help write epics and user stories. I provide context on what we want to accomplish, include a detailed template (description, acceptance criteria, benefits, dependencies), and add my raw notes or ideas.

Within seconds, I get clear, succinct, and well-structured epics and user stories — complete with acceptance criteria written in plain language that’s easy to discuss and refine.

This doesn’t replace our work. It accelerates it.

It saves me hours of writing and gives my team a consistent, high-quality starting point for backlog refinement. More importantly, it sparks better conversations. Instead of spending time wordsmithing in meetings, we spend it analyzing value and discussing tradeoffs.

I’ve since piloted this approach with a few software development teams and one HR Technology team. The results have been consistently positive: better-quality backlog items, faster planning sessions, and teams reporting that they feel more confident starting their sprints.

That’s the essence of AI-driven agility — using intelligent tools to remove friction so humans can focus on creativity, collaboration, and problem-solving.

How Agile Leaders Can Harness AI Responsibly

If you’re curious about bringing AI into your own Agile practice, here are a few lessons from my experience:

  1. Start Small, Learn Fast
    Don’t roll out an AI tool across the organization overnight. Start with one process or use case — like backlog writing, retrospective synthesis, or sprint forecasting — and inspect and adapt based on outcomes.
  2. Treat AI as a Pair, Not a Proxy
    AI tools are great at generating starting points but not at understanding context. Coaches and Scrum Masters should treat AI as a creative partner — one that suggests, not decides.
  3. Preserve Transparency
    When using AI-generated content (e.g., backlog items, sprint goals), make sure teams know it came from a tool. Transparency builds trust and prevents over-reliance.
  4. Balance Efficiency with Empathy
    AI may speed up tasks, but Agile’s heart is human. Use the time AI saves to invest more in people — coaching conversations, team health, and culture.
  5. Stay Curious, Not Defensive
    The pace of AI evolution can feel intimidating. But resisting it outright is like refusing to use version control in 2005. Curiosity and experimentation are the new competitive advantages.

Real-World Impacts and Industry Trends

The fusion of AI and Agile isn’t theoretical anymore — it’s already shaping organizations:

  • Gartner’s 2025 Agile Outlook predicts that by 2026, 60% of Agile teams will use AI-assisted tools to plan and prioritize work.
  • McKinsey’s 2024 Global Developer Survey found that developers using AI-assisted coding tools complete tasks up to 30% faster, with fewer defects.
  • Forrester’s 2025 Digital Leadership Report highlights AI-assisted retrospectives as a key growth area, noting that teams using AI to summarize feedback see a 40% improvement in action-item follow-through.

In other words, AI is becoming another layer of agility — not replacing humans, but extending their reach.

The Coach’s Role in the AI Era

As AI takes on more of the mechanical tasks, the Agile Coach’s role becomes even more critical — not less.

We become sense-makers, connectors, and teachers. We help teams navigate complexity, interpret insights, and maintain psychological safety amid rapid change.

In many ways, AI frees us to double down on the human side of agility: empathy, collaboration, and systems thinking.

Coaches who embrace this shift will not only stay relevant — they’ll lead the charge into a more adaptive, data-informed, and human-centered future.

Final Thoughts

AI is changing the landscape of Agile delivery, but not its heart.

If anything, it’s reminding us that agility was never about rituals or rigid frameworks — it was about adaptability, learning, and improvement.

As I’ve seen firsthand, using AI doesn’t dilute agility; it enhances it. Whether you’re using ChatGPT to write epics or leveraging predictive analytics for sprint planning, the goal is the same: to make space for better thinking and faster feedback.

The challenge — and the opportunity — for all of us as Agile leaders is to stay intentional. To use these tools responsibly, transparently, and always in service of people and outcomes.

Because the best Agile teams in 2025 aren’t just faster. They’re smarter — human and machine, working side by side.

References:

  • GitHub, State of AI in Software Development (2024)
  • McKinsey & Company, Global Developer Survey 2024
  • Gartner, Agile Outlook 2025: The Age of Contextual Agility
  • Forrester, Digital Leadership Report 2025

LeSS Basics: Insights from My Recent Course with Angela Johnson

I just wrapped up a two-day LeSS (Large Scale Scrum) Basics course, and I have to start with this: if you can take any class from Angela Johnson, you should. Seriously.

Angela is a dynamic instructor with a real command of all things Agile. She speaks with confidence on nearly every topic, draws from rich real-world experience, and peppers her sessions with examples that actually make sense in day-to-day practice. On top of that, she’s funny, relatable, and real. In short, you’ll learn a lot, and you’ll enjoy the process.

The class was virtual, small (less than 10 people), and ran four hours each day. The intimate size made it interactive and engaging, which is essential when you’re discussing scaling frameworks — and trust me, there’s a lot to digest when it comes to LeSS.

What LeSS Brings to the Table

At its core, LeSS is about simplicity at scale. Unlike some scaling frameworks that can feel heavy and prescriptive, LeSS emphasizes a straightforward principle: organize teams around products, not components or functions.

Here’s why I like it:

  • Single Product, Single Backlog, Single Product Owner: LeSS centralizes priorities by ensuring there is one backlog per product and one Product Owner responsible for it. This eliminates a lot of the confusion and duplicated effort that can occur in multi-team environments. Everyone knows what matters most, and decisions flow from a single source of authority.
  • Empowered Product Owners: The framework assumes that the Product Owner is a true leader — someone empowered to say “yes” or “no” and make tough decisions. This clarity in decision-making keeps teams focused and aligned.
  • Feature Teams, Not Component Teams: Teams are expected to deliver end-to-end customer value, not just a slice of a system. This encourages cross-functional collaboration, reduces hand-offs, and ensures that every team owns the value they produce.

To me, this is elegant. It’s simple, clear, and scalable. The framework itself doesn’t try to prescribe every process or ceremony — it creates guardrails that allow organizations to grow without losing agility.

Real-World Considerations

Of course, as with any framework, there are practical realities to consider. In my current organization, for example, fully implementing LeSS would require a massive re-org. Our teams aren’t structured as feature teams, and roles and responsibilities aren’t aligned for a single-product approach.

That’s not a critique of LeSS — it’s just the reality of scaling frameworks in complex, existing environments. Re-orgs are possible, but they’re not trivial, and they’re not always feasible in the short term.

That said, even in organizations that can’t adopt LeSS wholesale, there’s a lot of value to be gained. Concepts like centralized backlogs, empowered product ownership, cross-functional feature teams, and continuous inspection/adaptation can be incrementally applied.

For me, this is exactly how I approach scaling frameworks in practice: take what works, experiment, measure the impact, and adapt along the way.

Key Takeaways from the Course

While I took a lot from the two-day session, a few points stood out the most:

  1. Simplicity is your friend. LeSS isn’t about adding more roles or ceremonies. It’s about streamlining decision-making and reducing complexity. That’s always been a theme I come back to in my work: over-engineered processes create more headaches than they solve.
  2. Alignment over activity. Having a single Product Owner and backlog ensures that all teams are pulling in the same direction. This eliminates the “siloed feature teams” problem and encourages collaboration across the organization.
  3. Inspect and adapt at every level. LeSS isn’t a rigid framework; it’s a mindset. The framework emphasizes continuous improvement, both for teams and the organization as a whole.
  4. Scaling is cultural, not just structural. You can implement processes, but the real transformation happens when leaders, teams, and stakeholders embrace agility as a guiding principle.
  5. Feature teams empower learning. Cross-functional teams delivering end-to-end value have far more opportunities to innovate and learn from real customer outcomes. This aligns perfectly with the outcome-driven mindset I advocate in my coaching work.

Putting It Into Practice

Even though my organization isn’t in a position to implement LeSS fully, I now have a new set of tools and ideas to apply to our hybrid approach.

For example, I can:

  • Experiment with centralized backlog thinking for product areas, even if we don’t have a single Product Owner yet.
  • Encourage feature-team-like behaviors — cross-skilling, shared ownership, and end-to-end delivery — within our existing teams.
  • Embed continuous inspection and adaptation into our current practices, using LeSS principles as a lens to evaluate impact.

Basically, I can integrate the essence of LeSS without overhauling the entire org. And that’s exactly what makes frameworks like this valuable: they give you a guiding philosophy, not a checklist.

Looking Ahead

I walked away from the two-day class inspired. Someday, I plan to take the three-day LeSS Practitioner course and get certified. But even before that, LeSS has earned a place in my Agile toolbox.

As with any framework, the key is pragmatism. You take the principles, experiment, gather data, and adapt based on what actually works in your environment. That’s always been my mantra, and LeSS reinforces it beautifully.

Final Thoughts

Two days, less than ten people, and a virtual classroom was all it took to make a lasting impression. Angela Johnson’s expertise and humor made the content accessible and memorable, and the principles of LeSS reminded me why simplicity, alignment, and focus on outcomes matter so much — especially at scale.

Even if your organization isn’t ready for full LeSS adoption, the course is worth it. It will expand your thinking, provide concrete strategies for improvement, and give you a framework to experiment thoughtfully with scaling Agile.

For me, LeSS is now part of the lens I use to inspect, adapt, and improve. I’ll apply what I can, gather the data, and keep learning — just like I always do.

Agility isn’t about adopting every framework; it’s about taking what works, testing it in your context, and continuously improving. LeSS reminded me that simplicity and focus at scale are not only possible — they’re essential.

Empowering Agile Teams with AI Tools

Smart Tools for Smarter Teams: AI and Agile Delivery

Everywhere you look, new AI-powered tools are promising to make Agile teams faster, smarter, and more productive. Sprint planning? Automated. Customer insights? Summarized. Standups? Written by bots.

The temptation is real: let the tools do the heavy lifting so people can “focus on the important stuff.” But here’s the catch: Agile has never been about tools. It’s about people, collaboration, and delivering value. If we’re not careful, smart tools can end up making teams less Agile — by replacing conversations with dashboards and judgment with algorithms.

At the same time, when used with intention, automation and AI can free teams from tedious overhead and give them more space for what really matters: learning, collaborating, and creating.

Here’s what that looks like in practice.

1. Automate the Mundane, Not the Meaningful

In my current team, we lean heavily on Jira automation. Simple rules automatically update statuses or populate custom fields so nothing slips through the cracks. This saves us from wasting time on manual upkeep and helps the team stay focused on actual delivery work.

We also use Jellyfish to pull all of our metrics into clean, visual dashboards. Instead of spending hours writing Jira queries or manually compiling reports, we can walk into a meeting with data already available — and spend our time talking about what the data means rather than fighting to collect it.

Automation works best when it removes friction and drudgery. It should never replace the meaningful conversations that drive alignment, learning, and creativity.

2. Use AI to Spark Better Conversations

AI has also become a surprising partner in our backlog refinement process. We’ve started using it to help draft epics and user stories. Instead of spending time wordsmithing, we arrive with ready-made drafts that prompt the right questions:

  • Is this outcome clear enough?
  • What assumptions are we making?
  • What’s missing from this perspective?

It’s not about AI writing “perfect” stories — it’s about accelerating the conversation so Product Owners and teams can focus on refining value rather than formatting. For us, it’s saved our POs a huge amount of time and made refinement sessions far more engaging.

3. Keep the “Why” Front and Center

Even with all this automation, we constantly remind ourselves: tools exist to serve the team, not the other way around. Before adopting a new AI assistant or automation rule, we ask:

  • Does this help us deliver value faster?
  • Does it spark better team conversations?
  • Or is it just saving time for the sake of efficiency metrics?

That clarity keeps us grounded in Agile values instead of slipping into tool-driven habits.

4. Protect Psychological Safety

One caveat with automation and dashboards: data must never become a weapon. Tools like Jira and Jellyfish can reveal powerful insights, but if they’re used to police or rank individuals, psychological safety evaporates.

Our stance has been clear: dashboards exist to help the team improve, not to judge individuals. That framing keeps the tools aligned with learning and growth instead of fear and control.

Closing Thought

AI and automation aren’t going away. They will reshape how we work. But the smartest Agile leaders will remember: the goal isn’t to create smarter tools. It’s to create smarter teams.

When we let tools handle the repetitive tasks and free people to focus on creativity, collaboration, and customer value, we stay true to Agile’s heart. Tools serve the team. Not the other way around.

Agile & AI: From Genie-Level Hype to Practical Collaboration

AI has arrived in our workflows like an unpredictable genie — powerful, fascinating, and sometimes a little reckless. Kent Beck, one of the authors of the Agile Manifesto, recently described AI agents as “genies” that can grant wishes but rarely in the way you expect. His advice? Experiment boldly, but don’t forget the risk you’re taking.

That perspective resonates deeply with Agile practitioners. After all, agility is about embracing uncertainty, experimenting, and learning quickly. The challenge is figuring out how AI can serve our teams without letting the hype or fear distract from the real work: delivering value to people.

1. Build Safety Before Scale

Teams are often pressured to “adopt AI now.” But introducing AI without psychological safety creates resistance or misuse. Coaches and Scrum Masters can help by framing AI as an experiment, not a mandate. Encourage teams to:

  • Start with small, low-risk use cases.
  • Share what works — and what doesn’t — openly.
  • Normalize that AI will sometimes fail spectacularly (and that’s okay).

2. Use AI to Augment, Not Replace

Agile thrives on collaboration, creativity, and problem-solving — qualities AI can’t replicate. But it can amplify them. For example:

  • Product Owners can use AI to analyze customer feedback at scale.
  • Scrum Masters can generate retrospective prompts tailored to their team’s patterns.
  • Developers can get “draft” code snippets or test cases that spark discussion.

The key is treating AI as a sparring partner, not a decision-maker. Humans remain accountable for judgment, ethics, and empathy.

3. Focus on the “Why,” Not the Tool

It’s tempting to jump straight to prompts and plugins. But Agile leaders should remind teams: every tool is in service of a purpose. Before introducing AI into your workflow, ask:

  • What problem are we trying to solve?
  • How will this help us deliver faster feedback or higher value?
  • What will we measure to know it’s helping?

Without a clear “why,” AI risks becoming another shiny object that drains time instead of creating impact.

4. Coach for Resilience in Uncertainty

AI, like Agile, is about embracing the unknown. Coaches can use AI adoption as a teaching moment:

  • Practice adaptability when results aren’t perfect.
  • Encourage curiosity over judgment.
  • Help leaders resist the urge to over-control outcomes.

These mindsets don’t just make AI safer to adopt — they strengthen agility itself.

Closing Thought

AI will continue to evolve in ways we can’t predict — just like the genie that twists every wish. Our role as Agile leaders isn’t to tame the genie but to help teams use its power wisely, with intention and care. By grounding AI adoption in safety, purpose, and human collaboration, we keep agility at the center — and ensure that our teams remain the true source of innovation.