From Linear Change to Living Change
Why Our Change Models Must Evolve for an Adaptive Workforce
I think by now we’ve realized that our change models were built for a world that no longer exists.
They assumed change was episodic—something that you planned, announced, trained for, implemented, and then stabilized. Even if change was forced, it was still a singular event that assumed leaders could take stock of where they were, design a future state in advance, and move people toward it in orderly phases. And these change models assume resistance is the primary problem to overcome.
These days, change doesn’t arrive neatly packaged. It overlaps, compounds, accelerates, goes off the rails. It often comes from well outside our control—from technology shifts, policy changes, economic shocks, geopolitical events, changing social expectations.
Change is a constant in our operating environment. And now the models we use to navigate it have to change, too.
Let’s decode it. 🚀
From Linear Change to Living Change
Classic change models—from “unfreeze-change-refreeze” to multi-step transformation playbooks—were designed for a relatively stable world. They work best when change is complicated, with many moving parts, but ultimately knowable, predictable, and controllable with enough planning.
What they weren’t designed for is complexity.
Complexity is a state of having many parts and being difficult to understand or find an answer to. In complex systems:
cause and effect are only clear in hindsight and not predictable
outcomes emerge from a wide variety of interactions
small changes can have outsized effects
linear plans break under real-world conditions
As a complexity scientist, all the different things that can lead to new and emergent behaviors in a complex system fascinate me, but at the same time, they’re frustrating. Because while complexity models let you explain the system, it is ridiculously difficult to predict its behavior. Instead, that’s where probabilistic design comes in—you can narrow in on a range of probable outcomes rather than making linear predictions.
As we’ve spoken about before, probability is not intuitive and most people’s brains just don’t work that way. The only reason mine does is I’ve been studying complex systems and the mathematics behind them for fifteen years, and I get accused frequently of overcomplicating issues and playing 3D chess when I lay out the strategy.
Now that modern organizations are operating in these very complex environments, though, maybe it’s time for some 3D chess. Otherwise, we’re going to find ourselves swept away in a wave of digital transformation, AI adoption, workforce shifts, political shenanigans, and evolving customer expectations—stuff that doesn’t wait for us to finish reacting to one change before starting the next.
The result is something many leaders feel but struggle to name: change fatigue.
Change fatigue is often misdiagnosed as resistance, negativity, or a lack of resilience. I would characterize it as a predictable response to environments where:
change initiatives stack faster than people can absorb them
clarity is sacrificed for speed
activity is mistaken for progress
learning and recovery are underfunded or ignored
This is especially visible in organizations (like mine) that are large, complex institutions where people are asked to adapt continuously while still delivering mission-critical outcomes.
And that brings us to a crucial pivot. If change is constant, then managing individual transitions is no longer enough. We have to build organizational change capacity.
Want to read more about the things that limit your organizational change capacity? We discussed it in this article on the Speed Limits of Transformation:
So…What Has Changed About Change Models?
First off, change is continuous and not episodic. There is no “after” state where everything stabilizes and you can just relax into sustainment. Leaders must design their organizations and their strategies for constant movement.
Change must match the fast-moving ambiguous environment. What works in a stable, regulated environment will fall far behind in this one. You have to match your approach to the terrain.
Where we used to tout rollout plans, we need learning loops. Iteration, feedback, and adjustment outperform rigid implementation. Timelines matter but the decision points on them are agile and conditions based, and we look less at a series of steps and more at a collection of optional modules.
Essentially, we are moving from a traditional to an agile model.
Granted, I’m biased as a complexity scientist, but one of the most useful lenses for modern change is complexity theory and the idea of organizations as complex adaptive systems (I wrote an article for Procedia Computer Science on this a while back that might be useful as a research framework).
In this view:
organizations are networks of people interacting, adapting, and learning
change emerges from patterns of behavior, not just directives
leaders influence conditions more than outcomes
This reframes the role of leadership. Instead of trying to figure out how we get people to adopt the change that we are convinced will have a desired effect, we focus on the system itself—and what conditions make adaptation possible.
Instead of trying to model the perfect organization for a known future state, we model how to make the organization more flexible and allow it to pivot faster.
The Updated Change Model
Across sectors, updated change models and approaches increasingly emphasize:
Context-based decision-making. Frameworks like Cynefin help leaders distinguish between simple, complicated, complex, and chaotic environments and choose actions accordingly. In complexity, sensemaking needs to come before solutions because they will react differently in each environment.
Iterative, agile change. Change is treated as a series of experiments rather than a single rollout. Small pilots, fast feedback, and rapid learning replace big-bang implementations.
Operating model alignment. Change needs to be embedded in how work actually gets done through workflows, incentives, and information flows—not just something that comes from a top-down directive.
Capability building as infrastructure. Training, learning, and skill-development cannot be considered “supporting activities.” When interwoven into your processes, they allow change to happen at speed.
Of course, all of this work to create a more adaptive work design means you need a more adaptive workforce. We need creative, curious people who can operate well in more ambiguous environments, collaborate, break silos, make judgments, and recover quickly from setbacks.
That doesn’t mean you need to just find these people in the wild, though. You can train people with these skills and design a workforce full of adaptive talent! I’m looking forward to digging into how to do this in future work.
And you need to adapt your own processes to give people maximum clarity on intent, create the environment for experimentation, provide time and space to learn, and normalize iteration and non-linear progress.
Leadership Shift Required…
This is where change efforts these days often break down, and not necessarily through the fault of the leader. When leaders are asked to deliver transformation, they must be given permission to do these things:
Slow down when the signals are unclear and get clarity.
Acknowledge and coexist with uncertainty.
Adjust their direction publicly (yes, you can change your mind).
Protect learning and development time.
Manage cumulative change load.
If they’re allowed to do that, you have success. But more often than not, our leaders are rewarded for absolute certainty, speed, and confidence—whether or not they can actually achieve those in an environment that’s changing just as they are.
Instead of certainty of the environment, establish certainty of updates—when are you going to communicate updates, new information, and decisions to your team? If they can rely on routine communication, the ambiguous environment is easier to deal with.
Instead of speed, create an environment where it’s easier to backtrack when you get something wrong and enable experimental mapping of the terrain. You’ll get where you need to get faster that way than if you get to a farther point in a hurry and then realize it’s 100% in the wrong direction.
And instead of confidence…well, I take that back. We need that. We just need to divorce confidence from certainty. Leaders can still be confident in the skills they possess, in their team’s ability to get things done, and in the checkpoints they set to stop and evaluate progress.
Living in Living Change
Our change models are changing because the world changed. Simple enough.
Linear, rigid approaches struggle in complex, fluid, uncertain environments, and we are definitely in a complex, fluid, and uncertain environment now. And the people and the organizations that will thrive in those are not the ones with the best POAMs, but the ones who:
Treat change as a continuous condition.
Build resilience and flexibility into their systems (PIVOT!).
Design for learning and adaptation.
Invest in human capability as infrastructure.
And lead with both confidence and curiosity in the face of uncertainty.
Change isn’t going to slow down anytime soon, so our models, our mindsets, our work, and our workforce need to be able to keep up. That requires flexibility and adaptability, which we can create.
This is the work of modern leadership—and the heart of work that works.
This isn’t an exhaustive guide, but hopefully it gives you some ideas on how you can prepare your work and your workforce for continuous change.
1. Don’t Promise Stability.
Why it matters: When leaders promise that “things will settle down soon,” they unintentionally create distrust. People stop believing leadership timelines and conserve energy instead of engaging.
Leader actions:
Say out loud that change is ongoing and that adaptability is now part of the job
Replace “after this change” language with “as we continue to adapt”
Be honest about what is known, unknown, and still evolving
Signals you’re doing this well:
People ask more frequent questions instead of waiting for certainty
Teams plan in shorter horizons without anxiety
Fewer “is this the last change?” conversations
2. Match the Change Approach to the Context.
Why it matters: Not all change is the same. Treating complex change like a compliance rollout guarantees frustration—and that it’ll be wrong.
Leader actions:
Ask first: Is this simple, complicated, or complex?
Use rules and checklists for simple changes
Use expertise and planning for complicated changes
Use experimentation and sensemaking for complex changes
Resist the urge to force linear plans onto nonlinear problems
Signals you’re doing this well:
Fewer brittle plans that collapse on contact with reality
More adaptive responses when conditions shift
Leaders comfortable saying, “We’ll learn our way forward”
3. Run Change in Smaller Loops: Pilot, Learn, Adapt, Scale, Implement, Assess, and Do It Again.
Why it matters: Big-bang transformations overload systems and hide risk until it’s too late—and you often miss interfering factors that create new needed controls. Do it a bit at a time and you can rapidly adjust and iterate.
Leader actions:
Break initiatives into testable increments
Pilot with real users, not ideal conditions
Define learning goals, not just delivery milestones
Make course correction visible and acceptable
Checklist:
What are we testing?
What will tell us this is working?
What will we change if it’s not?
Signals you’re doing this well:
Fewer surprises late in the process
Faster course corrections
Teams more willing to experiment
4. Actively Manage Change Load.
Why it matters: People don’t experience change one initiative at a time. They experience the sum total of everything hitting them at once.
Leader actions:
Track concurrent changes impacting the same teams
Delay or sequence initiatives intentionally
Name tradeoffs explicitly: “If we do this now, we pause that”
Build recovery time after major pushes
Checklist:
How many changes is this team absorbing right now?
What can wait?
What support is needed?
Signals you’re doing this well:
Reduced burnout without reduced output
Fewer “last straw” moments
More honest feedback about capacity
5. Build Sensemaking Routines Into the Work.
Why it matters: People don’t need constant updates as much as they need your help interpreting what’s happening in context.
Leader actions:
Hold regular “what changed / what it means / what we’ll try” conversations
Encourage questions without immediate answers
Share patterns you’re seeing, not just decisions made
Invite multiple perspectives before locking in direction
Checklist:
What signals are we seeing?
What assumptions may no longer hold?
What’s our best next move next?
Signals you’re doing this well:
Better discussions and better decisions
Teams align faster after surprises
Less rumor, more shared understanding
6. Invest in Learning as Infrastructure.
Why it matters: Learning is a critical part of adaptation. Without it, you don’t anchor the change, so all you have is churn, not adaptation.
Leader actions:
Protect time for learning during change
Fund skill development tied to future needs
Reward learning behaviors, not just execution
Normalize “not knowing yet” as part of progress
Checklist:
What new skills does this change require?
Where will people practice safely?
Who is responsible for building capability?
Signals you’re doing this well:
Faster ramp-up during change
Increased confidence in new situations
Stronger internal mobility over time
7. Reward Real Adaptation.
Why it matters: If you only reward sticking to the plan, people will hide learning and avoid risk. You won’t get the power of multiple brains working on a complex problem if people just do what you tell them to.
Leader actions:
Recognize course correction positively when it’s good judgment
Promote people who learn and adapt effectively
Ask what someone learned, not just what they delivered
Make adaptability visible in performance conversations
Checklist:
Did this person improve the system?
Did they surface risks early?
Did they help others adapt?
Signals you’re doing this well:
More honest reporting
Faster problem-solving
Less fear-driven behavior
8. Model Curiosity and Adjustment as Leadership Strengths.
Why it matters: Leaders set the tone. If you never adjust publicly, no one else will. This is the big difference between real leaders and “do as I say, not as I do.”
Leader actions:
Say “I was wrong” when needed
Explain why you’re changing course
Ask questions you don’t already know the answer to
Demonstrate learning in real time
Checklist:
What did I learn this week?
What assumption changed?
What would I do differently next time?
Signals you’re doing this well:
Psychological safety increases
Teams surface issues earlier
Leadership credibility strengthens, not weakens
A Couple More Thoughts.
You cannot demand adaptability from people while running rigid systems, ask for resilience without designing time and resources for it, or manage modern change with outdated models. If you’re going to thrive with living change, you need to design your work as a living system, one that learns, adapts, and sustains human performance over time.
What do you think of the leader playbook? Is it useful for me to include resources like this with these articles? Would love to hear your thoughts!
