I read the Fast Company article about AI eating change management, noticed the trend in news about consultants scrambling to stay relevant, and thought, "Finally, people are starting to see what we've been saying all along". Process-driven, generic comms sending and checkbox-ticking change management won't cut it in a world where all that can be automated.
Real change management isn't about management at all. It's about changing minds.
The accepted story goes like this: humans are people, people resist change, resistance is to be avoided, so you need frameworks, plans, assessments and checklists to manage them. (I'm yawning already.)
The Fast Company article gets some things right. Yes, change management needs to be more adaptive, more experimental, and more focused on empowering leaders. But it's still dancing around the real issue. Even the most agile framework won't work if you're starting from the wrong premise about human nature.
Here's what the articles are missing: people don't resist change but they will react when it doesn't make sense. And once you see that difference, you can't unsee it. Everything shifts from gearing up to "manage resistance" to working out what would help people "make sense," in real time, together. I know which of those I'd rather do.
When you move away from the defensive position of managing resistance and towards helping people make sense of change, everything gets easier. The conversations are different. The skills required are different. The outcomes are better. The whole experience becomes something people actually want to engage with.
Last month, during one of our live sensemaking sessions, a director of a government agency finally shared what was really behind her concerns about their digital platform rollout. For weeks, leadership had labelled her "resistant" because she kept raising questions and slowing down decisions.
But when we created space for real dialogue, the truth emerged: "I'm not against the changes, we need them" she said. "but I'm worried we haven't considered the impact on my regional teams contextually, not just based on their roles on an org chart, but based on the reality that they're covering three areas with skeleton staff and unreliable services."
That same session, another leader revealed why they'd been pushing back on the training timeline. It wasn't because they disagreed with the sequence; it was because they were genuinely worried their teams wouldn't feel confident for go-live, and they couldn't bear the thought of setting them up to fail in front of the communities they serve.
Neither leader was resistant. They were reacting to change that didn't account for the real conditions their people were working in.
This is exactly why AI is exposing so much of traditional change management as performative theatre. When your premise is "manage resistance," AI just automates your bad assumptions. You get faster dashboards tracking the wrong metrics, shinier broadcasts that still don't land, and the same fundamental disconnect between what leaders think people need and what people actually experience.
What's interesting for me is that when your premise shifts to sensemaking, AI becomes something else entirely.
A few months ago, we worked with an aged care provider rolling out changes to meet new standards across 40 sites. The traditional approach would have been to create the communication plan, deploy the training, measure compliance, and, you guessed it, manage the inevitable "resistance."
Instead, we started by mapping what people were actually experiencing. Not what we thought they should experience, but what they actually were experiencing.
Turns out, the new care documentation requirements made perfect sense at head office but created impossible conflicts on the ground. Care workers found themselves torn between adhering to new compliance standards and delivering quality care within the constraints of existing shift structures. Site leaders understood the quality rationale but couldn't see how to implement the changes amidst high levels of burnout, employee turnover, and reduced time spent with residents.
Once we could see the real tensions, the solution became obvious. We didn't need better change management; we needed better implementation. Changes that actually worked in the real world of understaffed shifts and vulnerable residents.
The whole project shifted from "getting people to comply" to "making compliance make sense for care." It didn't stop the questions or the caution but that's ok - that's all part of sensemaking.
Here's what we've learned about AI in this work: it's incredible at pattern recognition, but only when you're feeding it the right patterns.
Better mapping: When you're actually tracking how people think and feel about change, not just whether they've completed training modules, AI can spot trends across thousands of data points that human consultants would miss. We can see which teams are genuinely building momentum versus which ones are just going through the motions.
Smarter adaptation: Real-time feedback means you can adjust course while change is happening, not six months later during the post-mortem. When AI tells us that clarity is dropping in one business area but momentum is building in another, we can intervene precisely where it matters.
Leadership at scale: The hardest part of change isn't creating the plan, it's helping leaders stay consistent under pressure. AI can help track decision patterns, flag when leadership shadows aren't matching leadership messages, and suggest course corrections before small inconsistencies become big credibility problems.
The Fast Company piece got some things right about what change management needs to become. But here's what makes those recommendations actually work when you add sensemaking:
Good advice: Move beyond linear phases to adaptive approaches.
What makes it work: Start with the system, not just the schedule.
How we do it: We use our trademarked Systems Thinking for Change frames (BCIP) to map the real interdependencies, hidden assumptions, and places where change might not make sense from the ground level before you build your timeline.
Good advice: Run experiments and close feedback loops.
What makes it work: Turn reactions into signals, not "resistance."
How we do it: Game Changers™ makes live reactions visible, on the mat, so leaders can tune pace, messaging, and support with precision, not guesswork.
Good advice: Empower leaders to embody the change.
What makes it work: Align what you say with what you do, especially under pressure.
How we do it: We do this through our Change Drivers education program, building leadership clarity and congruence so the message matches the behaviour when things get difficult.
Week 1: Map what's actually happening. Not what your project plan says should be happening—what's actually happening. Spend 90 minutes with your team listing the real interdependencies, the hidden assumptions about stakeholders, and the places where your change might not make sense from the ground level.
Week 2: Listen before you prescribe. Replace one broadcast communication with a two-way conversation. Ask three questions: What's working? What's not working? What would need to change for this to make sense? Then actually listen to the answers.
Week 3: Track what matters. Stop measuring compliance and start measuring clarity (do people understand why this matters?), capability (can they actually do the new thing?), and momentum (is energy building or draining?). These are the leading indicators. When they rise, adoption follows.
Week 4: Close the loop. Share what you heard and what you're changing because of it. Nothing builds trust like visible responsiveness to real feedback.
We're not retrofitting for this moment, we've been building for it.
The AI disruption isn't coming for human-centered change work. It's coming for change work that was never really about humans in the first place.
The future belongs to organisations that can sense and respond in real time, that can make change make sense as it's happening, and that can build clarity and momentum at the speed of business.
That's not a technology problem. That's a sensemaking problem. And sensemaking, it turns out, is deeply, irreplaceably human.
The question isn't whether AI will eat change management. The question is whether change management will finally start with the humans it's supposed to serve.
If your old playbooks aren't getting the results they used to, and you're looking for consultants who understand how to blend AI capabilities with genuinely human-centered change work, let's talk. We believe the future of change isn't about choosing between technology and people, it's about making them work together in ways that actually make sense.