I recently delivered a keynote to senior leaders of a Fortune 500 company. Right before I went on, two of their senior technology officers walked through the company's AI transformation. This is a company that's winning, capturing market share and investing millions of dollars to evolve how it serves customers.
After my talk, one of those technology leaders stopped me in the hallway. He wanted to know: "What do we need to do to go faster? We're ready from a technology perspective, but not evolving fast enough."
The constraint is human.
That's where change fatigue starts in most organizations right now. The technology runs on one timeline, and the people expected to use it run on another, often with no plan behind it at all. So my advice to him is the same advice I'd give any leader in the middle of an AI rollout. Bring human capability along with the technology in parallel, and accept that sometimes that means slowing down to speed up.
What Is Change Fatigue?
Change fatigue is the exhaustion and resistance that build up when people are asked to absorb change faster than anyone is helping them build the capability to handle it. On sales teams it sounds like "another initiative" or "the flavor of the quarter," and responding to that pushback is one of the essential change management skills for sales leaders.
Much of what's written about change fatigue in the workplace treats it as burnout. Burnout is a serious problem, and I'm not dismissing it. My concern is where that framing sends a leader. If organizational change fatigue is a personal stamina problem, the fix becomes self-care for each employee, and the system that produced the fatigue keeps running as it was.
What Causes Change Fatigue?
Change fatigue comes from asking people to adapt and iterate constantly without the support and development they need. They're learning the technology at the same time it's being deployed.
Change Is Arriving Faster Than People Can Absorb It
It's tempting to treat this stretch as something to get through, a busy season before things settle down. I don't expect them to settle. The pace of change isn't slowing, and what we're living through is a new way of working. We have to condition ourselves to work differently.
Gartner research published in Harvard Business Review in 2023 found that the average employee went through 10 planned enterprise changes in 2022, up from two in 2016. Over the same stretch, employees' willingness to support enterprise change fell from 74% to 43%. People were handed five times as much change in six years, and leaders are the ones who determine that volume.
AI's Learning Curve Never Flattens
AI makes this harder than earlier waves of technology. When I learned to play golf, there came a point where I knew how to play golf. AI isn't like that. It iterates so fast that the learning curve never flattens.
I say that with some humility, because my own team is living it. This is our first year on our own AI platform, and we're already on our 11th or 12th iteration. Some days it's a little exhausting, and I'm grateful for the people around me who are immersed in it, or I'd feel like we were falling behind.
Most AI Budgets Leave Out the People
Back to the hallway. I told him his presentation was fascinating and that I'd taken pages of notes. Then I reminded him that he's immersed in this technology 24 hours a day. Everyone else at that meeting had a department to lead and a P&L to deliver, so they're learning all of this on top of their day jobs.
Those are the people his AI rollout depends on. In its analysis of client spending on AI initiatives, Deloitte found that most organizations devote 93% of that spending to the technology side, from data to infrastructure, and only 7% to people-related work like training and change management.
The Bill Comes Due as Rework
Think about how many AI projects have been abandoned and how many companies are complaining about no ROI. This is why. It goes back to quality management. When the people side falls behind, the work gets redone, and from the inside, all of that restarting and redoing feels like change fatigue.
S&P Global Market Intelligence's 2025 survey of more than 1,000 respondents in North America and Europe found that the share of companies abandoning most of their AI initiatives jumped from 17% to 42% in a single year. The average organization scrapped 46% of its AI proofs of concept before they reached production.
How to Overcome Change Fatigue: Slow Down to Speed Up
Slowing down can sound counterintuitive for a leader who's under pressure to show returns on a big AI investment. Deloitte's research makes the case for it anyway. In a 2025 Deloitte survey of C-suite leaders at companies with more than 5,000 employees, just 16% reported they have fully designed roles, processes and operating models to integrate AI into work. Companies taking a tech-focused approach were 1.6 times more likely to report that their AI investments are not exceeding expectations.
My prescription for AI change management is that you have to run these things in parallel: human-centered change management alongside your technology transformation. Because most companies don't think about it that way, roughly 70% of these strategic transformations fail, according to McKinsey.
Prioritize Continuous Learning
You have to bring human capability alongside the technology transformation. Nearly half of employees in McKinsey's 2025 Superagency in the Workplace survey of 3,613 employees say they want more formal training and believe it's the best way to boost AI adoption. More than a fifth report that they've received minimal to no support.
Because AI keeps iterating, training can't be a launch event. It has to be built into the rhythm of the work, with time set aside for it, or people will keep picking it up on the side and falling further behind.
Mind the Adoption Gap
Every change has its early adopters and those who resist, and most people are muddling through the middle. The later someone starts, the further behind they fall, and every new iteration of the technology widens the distance between the people who are on board and everyone else.
Most people on your team are in that middle, carrying a full workload and trying to learn something new at the same time. They're the ones who experience the rollout as one more thing, and they're where a leader's attention does the most good.
Minding the gap starts with knowing where each person on your team is. Pace the next change to the people who have to live with it, and give the ones who are behind a way to catch up before you add more. Waiting it out only makes the gap bigger. Learning agility used to be an advantage for individuals. Leading through change now means building it into the whole organization, continuously, while the work keeps moving.
Model, Coach, Care
Satya Nadella built Microsoft's culture on model, coach, care. I come back to them often, because they're the most practical answer I know to the question that technology leader asked me.
Model means upholding the cultural standards and demonstrating the behavior you expect, including using the tools and technology yourself. Coach means taking responsibility for building the capability of the people around you. Care means knowing each person's capabilities and aspirations and investing in their growth.
Adoption improves when it starts at the senior leadership level. In BCG's AI at Work 2025 survey of more than 10,600 employees, the share of employees who feel positive about generative AI rises from 15% to 55% when they have strong leadership support. Only about a quarter of frontline employees say they receive that support.
It works every time. If we applied a little more of it across technology transformations, I think we'd see a lot more of them succeed for the long term.
Change Readiness Starts With You
Before change becomes organizational, commercial or cultural, it must first become personal. Before anything changes on the outside, something has to shift on the inside.
Face It Until You Make It walks through the process I use to understand my own relationship with change. Remember: You don’t have to feel fully ready to begin.
Frequently Asked Questions About Change Fatigue
Change fatigue overlaps with burnout, but the two call for different fixes. Burnout is usually addressed one person at a time. Organizational change fatigue is a leadership and sequencing problem, so it gets fixed in how leaders pace change and how visibly they model the behavior they expect.
Change fatigue lasts as long as change keeps outpacing people's ability to absorb it. AI keeps iterating, so waiting for things to settle only widens the gap between early adopters and everyone else. It eases when organizations build continuous learning into the work and pace each new change to the people who have to absorb it.
Many AI projects fail because the technology moves ahead of the people expected to use it. When the people side falls behind, the work gets redone: projects stall, get restarted or get abandoned before they deliver a return. It's a quality management problem, and inside the organization it shows up as change fatigue.
Manage change fatigue in a digital transformation by running human-centered change management in parallel with the technology from day one. Most transformation plans are built around the technology timeline. The people expected to adopt it need support and development on the same timeline, and leaders who model the change by using the tools themselves.
