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 Scrum Master to Agile Coach: Embracing Systemic Change

In the early days of Agile adoption, the Scrum Master was the cornerstone of transformation — the facilitator, the servant leader, the protector of the process. The Scrum Master ensured teams lived the values of Agile, shielded them from disruption, and continuously improved how work got done.

But as organizations grow in scale and maturity, so must the roles that support them. Today, we’re seeing a natural evolution — from Scrum Master to Agile Coach, from team-level guidance to organizational enablement.

And that shift — when done with intention — changes everything.

The Evolution from Scrum Master to Agile Coach

When my team transitioned from Scrum Masters to Agile Coaches at my current company, I quickly realized the difference wasn’t just in scope — it was in perspective.

As Scrum Masters, our focus was at the team level: helping product teams deliver value predictably, facilitating ceremonies, tracking metrics like velocity and cycle time, and supporting the individuals doing the work. It was deeply rewarding, and it built the foundation for everything that came next.

But stepping into the role of Agile Coach expanded that view. We were now looking across teams — seeing how dependencies formed, how priorities were shaped, and how organizational structures either enabled or obstructed agility.

That wider lens helped reveal what I can only describe as “systemic friction” — the gaps, bottlenecks, and cultural barriers that no single team could solve on their own.

The work shifted from guiding a team to enabling the system.

The Embedded Coach Model

Our organization decided to pilot an Embedded Agile Coach model. Rather than keeping coaches separate as consultants or distant advisors, we placed them within the business units or programs they served.

This embedded approach bridged a crucial gap — coaches became part of the conversation where real decisions were made. We weren’t just teaching agility; we were helping shape the strategic and operational ecosystems where agility needed to thrive.

And that proximity changed the kind of value we could deliver.

We began spotting misalignments between strategy and execution — things you simply can’t see when you’re focused on a single sprint or release. We identified recurring systemic blockers that affected multiple teams, and we could trace them back to upstream processes, governance models, or unclear decision rights.

Because we were embedded, we had the context — and the trust — to address them.

Why Scrum Masters Still Matter

I want to be clear: this evolution doesn’t mean the Scrum Master role is obsolete. Far from it.

Scrum Masters are the heartbeat of team-level agility. They create safety, clarity, and flow for the teams delivering value every day. They’re the ones who turn theory into practice.

But as organizations scale, they need both perspectives — the Scrum Master’s focus and the Agile Coach’s altitude.

Think of it like this:

Scrum Masters optimize local performance — improving how teams plan, deliver, and learn.
Agile Coaches optimize global performance — improving how the system supports and amplifies those teams.

When those roles work together — with clear purpose and communication — the organization gains both speed and stability.

From Coaching Teams to Coaching Systems

As embedded coaches, we started paying attention not just to what teams were doing, but to why certain patterns persisted.

For example:

Were delays in delivery caused by unclear priorities or approval bottlenecks?
Were teams producing value, or just output?
Was leadership reinforcing Agile principles, or unintentionally rewarding old behaviors?

By gathering this data and observing across multiple teams, we could map the systemic causes of friction instead of just addressing the symptoms.

It’s a mindset shift from “fixing process” to understanding the ecosystem — and it’s what makes embedded coaching so powerful.

We also began to see how coaching could help influence culture at a strategic level. By bringing insights from teams into leadership conversations, we helped leaders connect business outcomes to the principles of flow, value, and experimentation.

In other words, we started closing the loop between strategy and agility.

Measuring Impact (and Learning Along the Way)

We’re still gathering data on the long-term impact of embedding coaches, but the early signals are strong.

We’ve seen more alignment between product and technology, improved visibility into value streams, and better conversations about tradeoffs at the portfolio level.

The shift also helped us identify capability gaps — areas where teams needed more support or where processes were unintentionally slowing delivery.

But most importantly, we’re beginning to think differently as an organization. Leaders are asking better questions. Teams are connecting their work to business outcomes. Agility isn’t just something we do — it’s something we’re becoming.

That’s real change.

AI and the Next Evolution of Coaching

AI is now emerging as the next enabler for embedded coaching.

Imagine being able to use AI tools to synthesize sprint data, identify emerging trends across teams, and visualize systemic blockers in real time. Tools like Jira and Confluence already offer AI-driven insights that highlight dependencies, sentiment, and flow metrics.

For an Agile Coach, that kind of visibility is gold.

It doesn’t replace the need for human intuition — it enhances it. AI handles the analysis, freeing us to focus on interpretation, conversation, and facilitation.

As we look ahead, the most effective coaches will be those who combine data-driven insight with human-centered coaching — using technology not to dictate change, but to guide it with empathy and precision.

The Mindset Shift

Ultimately, evolving from Scrum Master to Agile Coach — and embedding those coaches strategically — requires a mindset shift at every level.

It means moving from:

Team focus → System focus
Process enforcement → Value enablement
Local optimization → Global alignment
Short-term metrics → Long-term impact

It also means being comfortable with complexity — and recognizing that transformation is ongoing.

As an Agile Coach, I’ve learned that my role isn’t to have all the answers. It’s to help the system see itself more clearly, to make data visible, and to facilitate better decisions.

That’s where real agility begins.

The Future of the Embedded Coach

The future belongs to organizations that embed agility into their DNA, not just their processes.

And that’s what the Embedded Agile Coach represents — a bridge between strategy and execution, data and empathy, people and process.

In this role, we’re not enforcing frameworks; we’re cultivating environments where agility can thrive naturally. We’re connecting leaders to the realities of their teams, and teams to the purpose of their work.

That’s how transformation sticks.

Closing Thoughts

When I look back at my journey — from Scrum Master to Agile Coach — what stands out isn’t the title change, but the mindset evolution.

Moving from managing ceremonies to influencing systems has been both humbling and inspiring. Every day, I learn more about how small changes in structure and behavior can ripple across an organization.

And as I continue to watch, learn, and gather data, one truth remains clear:

Agility isn’t something we install — it’s something we grow.

The Embedded Agile Coach helps that growth take root — one conversation, one system insight, one mindset shift at a time.

That’s the future of agility. And it’s already here.

AI-Driven Collaboration: The Future of Hyper-Agility

If the last decade of Agile was about shortening delivery cycles, the next decade is about shortening the distance between decision and delivery.

Welcome to the era of Hyper-Agile Teams — where work happens in real time, collaboration is frictionless, and AI seamlessly bridges the gaps between tools, people, and ideas.

This evolution isn’t about speed for speed’s sake. It’s about responsiveness — the ability to align, adapt, and act the moment new information emerges. And as distributed and hybrid work becomes the norm, this responsiveness depends on something deeper than process: it depends on connection.

The Evolution from Agile to Hyper-Agile

Traditional Agile gave us structure — sprints, ceremonies, and backlogs that brought predictability to complex work. But in today’s distributed world, where teams span continents and time zones, that structure alone isn’t enough.

Modern teams can’t wait for the next sprint review to pivot. They need continuous awareness of priorities, dependencies, and customer feedback.

That’s where Hyper-Agile practices come in.

Hyper-Agile teams use real-time collaboration tools, integrated systems, and AI-assisted workflows to make decisions faster — and smarter. Instead of thinking in two-week cycles, they think in continuous flow, using real-time feedback to guide delivery.

Real-Time Collaboration in Practice

In my own experience leading Agile Coaches and Scrum Masters, my team operates like a Scrum team — complete with a backlog, sprints, and epics tied to strategic goals. But what makes it work at scale is how we collaborate in real time.

We use Lucidspark as our digital workspace for everything from value stream mapping and customer journey mapping to initial epic brainstorming. During a Zoom call, multiple people can contribute simultaneously — refining workflows, identifying bottlenecks, and aligning on next steps without losing momentum.

Once the session is over, that work doesn’t vanish into screenshots or notes. Lucidspark allows us to turn those ideas directly into Jira placeholders, embed links to Confluence, and maintain that original brainstorm as a source of truth.

It’s a living artifact — not documentation for its own sake, but context we can return to, refine, and build upon.

And that’s the key: real-time collaboration turns alignment into action.

Other tools like Miro, Mural, and Microsoft Loop offer similar capabilities. What matters isn’t which one you use — it’s how intentionally you use it. The technology is there to remove friction, not to create another layer of process.

The AI Factor: Collaboration Without Friction

AI is accelerating this shift from Agile to Hyper-Agile by eliminating barriers to flow and connection.

Imagine you’re in a virtual whiteboarding session, and AI automatically clusters ideas by theme, detects dependencies, and generates draft epics based on team input. That’s no longer futuristic — Lucid’s AI, Miro Assist, and Notion AI are already doing it.

In my own work, I use ChatGPT to help create epics and user stories from the rough ideas that come out of these workshops. I provide the context — what we want to achieve, our structure for acceptance criteria, and any constraints — and AI produces concise, actionable stories.

This not only saves time but gives teams a structured starting point for refinement. I’ve even piloted this approach with development and HR Technology teams, and it’s been a game changer.

AI isn’t doing the thinking for us — it’s giving us more space to think together. It handles the administrative overhead so we can focus on what matters: collaboration, creativity, and problem-solving.

Hyper-Agile isn’t just faster; it’s freer.

From Synchronous to Asynchronous and Back Again

One of the biggest myths about hyper-agility is that it means being “always on.” In reality, the best Hyper-Agile teams blend synchronous collaboration (real-time working sessions) with asynchronous follow-through.

AI tools make this balance possible. Meeting assistants can summarize discussions, highlight decisions, and draft next steps for review. Whiteboards and project boards sync across time zones so work keeps flowing even when people aren’t online at the same time.

This blend is critical for distributed teams — it ensures that real-time collaboration enhances flow without eroding focus.

When done right, it replaces chaos with clarity.

Cultural Shifts for Hyper-Agility

Technology may enable Hyper-Agile collaboration, but culture determines whether it thrives.

A truly Hyper-Agile team is one that:

Shares ownership — decisions aren’t top-down; they emerge from collective insight.
Values transparency — everyone can see what’s happening, why it’s happening, and how it connects to outcomes.
Embraces experimentation — ideas can evolve instantly, and failure is treated as feedback.
Builds trust through visibility — real-time tools make work observable without turning it into surveillance.

In this environment, leadership becomes facilitation. The role of an Agile Coach, Product Owner, or leader shifts from directing work to creating clarity and safety so collaboration can flourish.

And that cultural foundation — not the tools — is what transforms speed into sustainable delivery.

Measuring Outcomes, Not Activity

In a Hyper-Agile system, it’s tempting to equate speed with success. But true agility isn’t about how fast we move — it’s about how effectively we deliver value.

That’s why metrics must evolve alongside the technology.

Rather than tracking output (number of meetings, tasks completed, or documents produced), high-performing teams measure outcomes:

Did collaboration accelerate decision-making?
Did we reduce cycle time without increasing rework?
Are our customers experiencing better results?
Did our process enable innovation instead of bureaucracy?

When AI and collaboration tools are used intentionally, the answer to all of those questions is often yes — not because they replace humans, but because they empower humans to connect more deeply and act more decisively.

The Future of Hyper-Agility

As AI becomes more integrated into every collaboration tool, we’ll see the lines between ideation, delivery, and feedback blur even further.

We’re entering an age where your virtual workspace might proactively surface related documentation, auto-generate Jira issues, suggest backlog priorities, and even draft retrospectives based on sprint data.

But here’s the thing: the tools don’t make teams Hyper-Agile — the mindset does.

Hyper-Agile teams understand that communication is continuous, learning is collective, and speed is only valuable when it serves clarity.

The goal isn’t to do more; it’s to decide better and deliver smarter.

Bringing It All Together

The rise of Hyper-Agile teams represents a fundamental shift in how we think about collaboration. It’s not about more meetings or faster sprints — it’s about building connected systems of work where ideas can flow from thought to action without friction.

AI helps by automating the overhead. Tools like Lucidspark, Miro, and Microsoft Loop help by making those ideas visible and actionable. But people — their trust, creativity, and shared purpose — remain at the heart of it all.

So whether you’re mapping a customer journey, designing a value stream, or creating your next big epic, lean into what’s available to you. Use your tools fully. Build that digital whiteboard. Let AI handle the heavy lifting.

And remember — agility was never about moving faster than everyone else. It was about moving together, deliberately, and in the right direction.

That’s what makes a team truly Hyper-Agile.

References

Lucid, The Rise of AI-Powered Collaboration (2025)
Gartner, Future of Work: Real-Time Collaboration in the Age of AI (2024)
Atlassian, From Agile to Hyper-Agile: How AI is Changing Team Collaboration (2025)
DORA, Accelerate: State of DevOps Report (2024)

Spec-Driven Development Meets Agile: A Hybrid Approach

Agile has always prided itself on flexibility — responding to change over following a plan. But as organizations have matured, the pendulum has swung between two extremes: unstructured agility on one end and rigid governance on the other.

Now, we’re seeing a convergence — a hybrid future where Spec-Driven Development (SDD) and Agile meet to balance clarity with creativity. It’s a model that honors Agile’s adaptability while embracing the discipline needed for large-scale systems, compliance-heavy industries, and multi-team coordination.

The challenge? Doing it without losing the essence of agility.

Why the Shift Is Happening

For years, Agile was the antidote to traditional waterfall methods that locked teams into fixed requirements. But as Agile scaled, new complexities emerged: multiple teams, shared dependencies, and overlapping product domains.

Suddenly, “just-in-time” requirements weren’t always enough. Teams needed stronger alignment, clearer boundaries, and predictable delivery models — especially when integrating with systems that demanded regulatory precision or safety validation.

That’s where Spec-Driven Development began to re-enter the conversation.

SDD emphasizes clear specifications, structured documentation, and traceable requirements. When applied thoughtfully, it doesn’t slow teams down — it creates shared understanding. The problem has never been the spec itself; it’s been treating the spec as static rather than living.

The hybrid model keeps the structure but invites agility into how that structure evolves.

Where Agile Still Wins

Agile remains unbeatable when it comes to adaptability, collaboration, and learning through iteration. It’s rooted in human feedback and fast loops — qualities that keep teams responsive to real customer needs.

In a purely spec-driven environment, teams risk overconfidence in the plan. In a purely Agile one, they can fall into chaos without shared direction. The hybrid model ensures we don’t sacrifice either.

It says: Define enough to align, but not so much that you eliminate discovery.

Agile wins when uncertainty is high — when you’re breaking new ground or solving novel problems. Specs win when the work is repeatable, regulated, or safety-critical. Blending the two allows leaders to allocate structure strategically, instead of applying one-size-fits-all methods.

How Hybrid Delivery Works in Practice

At its best, a hybrid delivery model builds a spec for stability and uses agility for innovation.

Here’s how it plays out across a product lifecycle:

Define the constants.
Establish what’s non-negotiable — the regulatory requirements, security constraints, and architectural foundations that create stability. These form your “spec-driven” backbone.
Iterate on the unknowns.
Use Agile principles to explore customer problems, prototype solutions, and validate assumptions quickly. The areas of uncertainty should remain flexible, open to learning and adaptation.
Keep the spec living.
Treat specifications like user stories — always evolving as you learn. A living spec creates traceability and adaptability.
Integrate feedback loops.
Every iteration should refine not just the product, but the spec itself. This turns documentation into a tool for discovery rather than a relic of planning.
Use AI to bridge the two worlds.
AI tools can analyze changes across systems, generate traceable documentation automatically, and surface where evolving code has drifted from the original design intent. This is where structure meets speed.

AI doesn’t just automate documentation — it enhances alignment. Imagine an intelligent assistant that flags when a user story’s acceptance criteria conflict with a system constraint, or one that keeps design specs synchronized with code repositories.

That’s not theoretical anymore. Tools from GitHub Copilot to Atlassian Intelligence are beginning to make this hybrid agility real.

The Cultural Bridge: Governance with Growth

Hybrid models require more than process adjustments — they demand cultural balance.

Traditional governance often feels like control. Agile governance, on the other hand, feels like enablement — creating safety to experiment within understood boundaries.

The best organizations understand that discipline is not the opposite of agility; it’s a prerequisite for scaling it responsibly.

In Agile culture, we want teams to experiment, but not in isolation. We want to minimize bureaucracy, but not at the cost of quality or compliance. Hybrid delivery achieves this by aligning everyone on outcomes while still giving teams autonomy in how they achieve them.

That alignment comes from shared language and mutual trust — not more meetings or heavier documentation.

When I’ve coached organizations through this transition, I’ve found that the biggest barrier isn’t process—it’s fear. Teams fear losing autonomy. Leaders fear losing visibility. The hybrid approach, done right, replaces both with confidence—a clear view of what matters and freedom to deliver it.

Rethinking Success: Outcomes Over Outputs

Hybrid delivery models can only succeed when success is defined by outcomes, not outputs.

This is where many companies get it wrong. They fall back into spec-driven metrics—how many documents completed, milestones hit, or hours logged. But none of those guarantee impact.

Instead, hybrid agility demands a focus on outcomes:

Did the product solve the intended customer problem?
Did it deliver measurable business value?
Did the team learn something that improves future delivery?

This shift connects directly to value stream thinking — looking beyond the completion of tasks to the flow of customer value through the system.

Agile and SDD can coexist beautifully when guided by shared purpose and metrics that matter.

Why Hybrid Is the Future

The reality is this: most organizations already operate in a hybrid world, even if they don’t call it that. They’ve combined elements of SAFe, Scrum, Kanban, and systems engineering.

What’s changing in 2025 is intentionality — leaders are starting to design hybrid models consciously, not accidentally.

We’re seeing it in regulated industries like healthcare and finance, where compliance requires traceability but innovation demands speed. We’re seeing it in global tech companies integrating AI into core products.

Hybrid agility gives teams the freedom to innovate responsibly. It acknowledges that agility without alignment leads to chaos, and alignment without agility leads to stagnation.

The future isn’t about picking sides between “spec” and “scrum.” It’s about creating a system where structure supports discovery—not suffocates it.

Bringing It All Together

In the early days of Agile, the manifesto asked us to value working software over comprehensive documentation. That principle still holds. But in a complex, interconnected world, some documentation isn’t just valuable — it’s vital.

The difference today is that our documentation can live, breathe, and evolve. It can become a conversation instead of a contract.

That’s what this hybrid future represents: a return to purpose-driven structure, where plans exist to guide learning, not restrict it.

When done right, it’s not Agile vs. Spec-Driven — it’s Agile and Spec-Driven, aligned around shared outcomes, supported by intelligent systems, and driven by teams who understand both the “why” and the “how.”

That’s not a compromise. That’s maturity.

References

Gartner, Agile Outlook 2025: Balancing Speed and Stability
Atlassian, Intelligent Collaboration and the Hybrid Future of Agile (2024)
DORA, Accelerate: State of DevOps Report (2024)
McKinsey Digital, Engineering Excellence and the Future of Hybrid Delivery (2024)

Redefining ‘Done’: Embracing Value Stream Thinking

For as long as Agile has been around, teams have measured progress by velocity, burndown, and sprint completion. We celebrate when work is “done.” But over the years, “done” has become one of those words that means everything—and nothing.

It’s time we redefine it.

In 2025, “done” isn’t about completing work. It’s about creating value—measurable, meaningful, and sustainable value that improves outcomes for customers, teams, and organizations.

That’s where Value Stream Thinking comes in.

The Evolution of “Done”

In the early days of Agile, the Definition of Done was simple and tactical: code committed, tested, deployed, documented. It gave teams clarity and accountability. But as organizations scaled, that definition became limited.

Teams were hitting their sprint goals, yet customers weren’t always happier. Projects were finishing on time, but outcomes weren’t improving. We were producing more, but not necessarily better.

I’ve worked in multiple companies where success was measured almost entirely by output—number of features shipped, tickets closed, or sprints completed. Those metrics may look good on dashboards, but they don’t tell you if you’re solving the right problems.

Value Stream Thinking challenges that. It forces us to zoom out from the backlog to the big picture—to focus on flow, impact, and purpose.

What Is Value Stream Thinking?

A value stream is the entire flow of work from idea to outcome—everything it takes to deliver value to a customer.

It’s not just development or delivery. It includes strategy, design, operations, feedback, and learning. Value stream thinking asks us to map that entire system, identify friction points, and optimize the flow of value across it.

Lean and DevOps communities have long embraced this concept, but its relevance to Agile has never been stronger.

When teams think in terms of value streams instead of functions or projects, they break down silos. They start asking questions like:

Where does work get stuck?
How long does it take for an idea to become customer value?
What steps actually add value—and which ones just create busywork?

Those questions don’t just improve delivery. They change the conversation from what are we building? to why are we building it?

Mindshift: From Output to Outcome

To truly adopt value stream thinking, we need a mindset change—and this is where many organizations stumble.

Too many still prioritize activity over impact. They’re driven by quarterly numbers, stakeholder demands, and delivery checkboxes. But optimizing for output creates a false sense of progress. You can ship 100 features that make no difference to your users.

Outcome-driven organizations measure success differently. They focus on customer satisfaction, reduced friction, increased retention, and business adaptability.

In my experience, the hardest part of this transition isn’t the tooling—it’s the thinking. You can’t transform your value streams if leadership still rewards teams for volume instead of value.

Those companies that look beyond quarterly metrics are the ones that change their industries for good.

Simon Sinek describes this perfectly in The Infinite Game when he says,

“Finite players play to beat the people around them. Infinite players play to be better than themselves.”

Companies like Apple, Patagonia, and Costco didn’t win because they moved faster than competitors. They won because they focused on why they existed, who they served, and how they could improve lives—not just balance sheets.

Sinek’s Start With Why, Leaders Eat Last, and The Infinite Game are all essential reads for anyone leading Agile transformation today. He tells the stories of organizations that stopped measuring success by competition and started measuring it by contribution. That’s the essence of value stream thinking.

The Three Pillars of Value Stream Thinking

1. Visibility

You can’t improve what you can’t see. Value stream mapping provides a visual representation of how work flows—and where it doesn’t.

By identifying handoffs, bottlenecks, and redundancies, organizations gain a shared understanding of where time and value are lost.

But visibility isn’t just about data dashboards. It’s about transparency of intent. Everyone—from leadership to engineers—should understand how their work connects to business and customer outcomes.

When teams see how their contributions fit into the larger system, engagement skyrockets.

2. Flow

Flow isn’t just about moving faster. It’s about removing friction and waste so value moves smoothly from idea to delivery.

AI is becoming a valuable ally here. Intelligent observability and workflow tools can now analyze flow efficiency, predict bottlenecks, and recommend optimizations automatically.

For example, I use AI in my own Agile coaching practice to generate and refine epics and user stories for my team. That automation saves time and allows us to focus on what matters, not just how we structure it.

Platform and delivery teams can do the same—using AI to highlight inefficiencies or automate routine steps so humans can focus on creative problem-solving.

That’s the power of pairing flow with focus.

3. Feedback

Every value stream needs feedback loops that connect customer outcomes back to the teams delivering them.

That means looking beyond project retrospectives or sprint reviews—it means continuous measurement of real-world impact.

Are customers adopting the feature we built? Did it improve their experience? Did it align with our purpose?

When teams measure outcomes this way, they start designing with empathy and strategy, not just deadlines.

Why This Requires Cultural Alignment

Value stream thinking can’t thrive in a culture that prizes speed over substance.

It requires psychological safety to question the status quo. It requires leaders who prioritize long-term outcomes over short-term optics. And it requires shared accountability across departments—not “engineering vs. product,” but “we’re all part of the same flow.”

The best organizations I’ve seen practice value stream thinking not as a framework, but as a philosophy. They understand that agility isn’t about delivering faster; it’s about delivering better.

They empower teams to challenge wasteful processes. They reward learning, not just delivery. They understand that simplicity and purpose drive innovation far more than complex frameworks ever could.

The New Definition of Done

If “done” used to mean something is shipped, the new definition should be this:

“Done means we’ve delivered measurable value to the customer—and learned something that helps us deliver even more next time.”

That’s a subtle shift, but it’s everything. It turns Agile back into what it was always meant to be: a feedback-driven, purpose-centered way of working.

And when leaders embrace that mindset—when they stop chasing quarterly wins and start playing the infinite game—they don’t just improve their teams. They transform their industries.

Because in the end, output ends when the sprint ends. Outcome endures.

References

Simon Sinek, Start With Why (2009)
Simon Sinek, Leaders Eat Last (2014)
Simon Sinek, The Infinite Game (2019)
Gartner, Agile Outlook 2025: The Age of Contextual Agility
DevOps Research and Assessment (DORA), Accelerate State of DevOps Report 2024
McKinsey & Company, Value Stream Excellence in Digital Transformation (2024)

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:

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.
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.
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.
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:

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.
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.
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:

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.
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.
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.
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.
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

Back to Agile Basics: Why Simplicity Is the New Competitive Edge

Simple is Better

If you’ve followed my writing for any length of time, you’ve probably noticed that I tend to come back—again and again—to Agile basics. It’s not because I’m nostalgic for 2001 or trying to relive the early days of software delivery. It’s because somewhere along the way, we’ve drifted from the core of what made Agile powerful in the first place.

We’ve layered frameworks on top of frameworks. We’ve created certifications, roles, and processes that often do more to complicate delivery than to simplify it. And ironically, in our quest to scale agility, many organizations have ended up recreating the very bureaucracy Agile was designed to dismantle.

As we finish up 2025 and head into 2026, I’m convinced that the teams who will thrive in this new era are the ones who deliberately go back to basics—teams that rediscover simplicity, focus on delivering value, and adapt based on feedback rather than ritual.

Because here’s the truth: simplicity isn’t a step backward. It’s a competitive edge.

Remember Why Agile Worked in the First Place

Let’s go back to the Agile Manifesto for a moment. It’s twenty-four years old now, and yet its principles read like a breath of fresh air in an industry drowning in process.

“Simplicity—the art of maximizing the amount of work not done—is essential.”

That single line might be the most misunderstood (and most ignored) principle in modern Agile. When the manifesto was written, simplicity wasn’t a side note—it was a foundation. The early Agile movement wasn’t about ceremonies or roles or maturity models. It was about removing waste and increasing flow.

Agile worked because it was lean, empirical, and human-centered. Teams focused on delivering small slices of value, inspecting the results, and adapting quickly. They didn’t need an org chart of roles to do that; they needed clarity of purpose, empowered people, and fast feedback loops.

Over time, though, we started codifying and scaling that success. Frameworks like SAFe, LeSS (I like LeSS, BTW. Look for a post on that in the future.), and Scrum@Scale tried to make Agile more predictable at enterprise levels—which wasn’t inherently bad. But somewhere between “inspect and adapt” and “follow the framework,” the spirit of agility got lost.

Complexity Creep: How We Overcomplicated Agility

I’ve seen it firsthand across organizations of every size: teams struggling under the weight of processes that don’t serve them. Instead of agility being a mindset, it’s become a checklist.

Every meeting is prescribed.
Every artifact is templated.
Every role is defined down to the hour.

We’ve replaced thinking with following.

And ironically, that rigidity creates the very pain we’re trying to avoid:

Long feedback cycles.
Confusion about ownership.
Teams “doing Agile” instead of being Agile.

As Agile coach Barry Overeem once put it, “When a framework becomes the goal instead of the means, agility dies.”

This doesn’t mean frameworks are bad. Frameworks can be incredibly helpful starting points—they give structure, consistency, and a shared vocabulary. But they were never meant to be the destination. They’re scaffolding, not architecture.

The Simplicity Mindset

So what does it mean to return to simplicity? It’s not about stripping away everything and declaring “no process.” It’s about adopting a simplicity mindset—an intentional effort to reduce friction, focus on what matters, and make space for learning.

Here’s what that looks like in practice:

Start with your current reality.
Too often, teams adopt frameworks wholesale rather than examining what actually needs improvement. Start by asking, “What’s causing pain in our flow?” and “What’s blocking value delivery?” Then build from there—incrementally.
Keep ceremonies purposeful.
If a meeting doesn’t drive learning or decision-making, shorten it or eliminate it. The goal isn’t to follow Scrum to the letter; it’s to enable communication, transparency, and improvement.
Optimize for feedback, not process.
The faster you can learn from users, the faster you can adapt. Focus on shortening feedback loops—through demos, experiments, or even simple stakeholder check-ins.
Empower teams to adapt.
Give teams the freedom to tailor their own ways of working. Encourage them to experiment, simplify, and evolve their process based on evidence—not dogma.
Measure outcomes, not outputs.
Shift focus from how many story points you’ve completed to what value you’ve delivered. Are customers happier? Are users getting what they need faster?

When simplicity becomes the foundation, everything else gets easier. Predictability improves. Quality increases. Teams communicate more clearly. Leaders make better decisions because they’re closer to reality.

Why Simplicity Is a Strategic Advantage

In a world defined by uncertainty and AI-driven acceleration, complexity is the enemy of adaptability.

Organizations that simplify can move faster because their decision loops are shorter. They can pivot when markets change. They can innovate without being bogged down by process debt.

Research from McKinsey (2024) supports this: companies that streamline their delivery systems and reduce handoffs are 40% more likely to meet time-to-market goals and twice as likely to report high team morale. Similarly, Gartner’s 2025 Agile Outlook emphasizes “contextual agility” — tailoring practices to fit the environment rather than adopting a one-size-fits-all framework.

In other words, simplicity scales. Not because it’s less work, but because it focuses teams on what actually matters.

A Personal Observation

In my own experience coaching teams and leaders, the highest-performing ones aren’t those with the most mature framework or the most polished Jira dashboards. They’re the ones who understand their purpose, make decisions quickly, and keep communication open and honest.

When teams cut out what doesn’t serve them—extra layers of process, unnecessary approvals, redundant reports—they create the space for meaningful work.

And perhaps more importantly, they rediscover why they’re working that way in the first place.

That’s what simplicity gives us: clarity, focus, and alignment.

Getting Started: Your Simplicity Reset

If your team is feeling stuck, overwhelmed, or weighed down by ceremony, try this exercise:

List every meeting, report, or ritual you perform in a sprint.
For each, ask:
- What value does this add?
- What would happen if we removed or shortened it?
- Is there a simpler way to achieve the same goal?
Experiment with one simplification per sprint and inspect the results.

You don’t need to overhaul your process overnight. Small simplifications—done consistently—create compounding gains over time.

Final Thoughts

Agile has always been about people and outcomes over process and tools. Somewhere along the line, we inverted that.

But the good news is: we can find our way back.

If we start small, stay curious, and keep the simplicity mindset at the center of how we work, we can reduce friction, increase value, and build organizations that are genuinely adaptive—not just performatively Agile.

As we move into this next era of work—where AI, distributed teams, and constant change are the norm—the most resilient organizations won’t be the ones with the most elaborate systems.

They’ll be the ones that choose simplicity, clarity, and continuous learning.

Because in 2026, simple = sustainable.

References:

Beck et al., Manifesto for Agile Software Development (2001)
McKinsey & Company, The State of Agile Delivery in 2024
Gartner, Agile Outlook 2025: The Age of Contextual Agility
Overeem, Barry. Scrum.org Blog: “Revisiting the Agile Manifesto in 2024”

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:

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.
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.
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.
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.
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.

Agile Tooling Beyond Engineering—The Evolution of Jira and OthersAgile

For years, Agile tooling was almost synonymous with software teams. Jira, Confluence, Rally — these tools were built to track development work, manage sprints, and visualize backlogs. But today, organizations are pushing these tools far beyond IT, and it’s creating both opportunities and challenges for Agile coaches, Product Owners, and Scrum Masters.

The evolution is clear: tools like Jira, Rally, Asana, Monday.com, Trello, and VersionOne are no longer just for software teams. HR, marketing, finance, and operations are adopting these platforms to manage work, visualize dependencies, and increase transparency. The key question isn’t “Can we use a tool?” — it’s “How do we use these tools without losing the human-centered agility that makes them effective?”

1. Keep the Purpose Clear

Tools exist to serve teams, not dictate behavior. Automation, dashboards, and workflows should enhance collaboration and visibility, not become a cage of compliance.

In my experience, our teams use Jira automation extensively: updating statuses, populating custom fields, and ensuring nothing slips through the cracks. These automations free the team from repetitive work while preserving focus on value delivery. Jellyfish dashboards aggregate metrics so we don’t spend hours chasing data, allowing us to spend time analyzing trends and making decisions rather than compiling reports.

2. Avoid Turning Tools Into Command Centers

It’s tempting to treat dashboards and reports in Jira, Rally, or Asana as the source of truth for productivity: velocity, story points, or burn-down charts. But when tools are used to measure individuals instead of enabling conversations, teams lose trust and psychological safety.

The best tools are enablers: they allow teams to see the big picture, identify bottlenecks, and prioritize work — without turning every click into a judgment.

3. Support Cross-Functional Work

As tools spread beyond engineering, teams from HR, marketing, and operations benefit from the same practices that development teams rely on:

Visualizing work and dependencies (Trello boards, Monday.com dashboards)
Maintaining prioritized backlogs (VersionOne, Jira)
Using automation to reduce manual overhead (Jira, Asana rules, custom scripts)

I’ve coached HR and marketing teams to adopt these tools and prioritize work based on real outcomes — not just tasks. The result? Increased predictability, visibility, and the ability to push back when stakeholders request work that doesn’t align with priorities.

4. Remember That People Drive Agility

No tool — no matter how powerful — replaces human judgment, collaboration, or creativity. Tools should amplify Agile behaviors, not enforce them. As coaches and leaders, our job is to ensure tools remain a supporting cast rather than the star of the show.

Closing Thought

The evolution of Agile tools is exciting: Jira, Rally, Asana, Trello, Monday.com, VersionOne, AI assistants, and automation can transform workflows across the organization. But the human element must remain central. When we use tools intentionally to enhance collaboration, transparency, and learning, we create teams that are not only more efficient — they’re more empowered, resilient, and truly Agile.