The Myth:

Profitable companies are best positioned for AI transformation.

The reality: comfort is the enemy of adaptation.

Forrester's Predictions 2026: The Future of Work report introduced a term that should unsettle every profitable company reading this. They call them "coasters."

Coasters are disengaged workers who don't think their employer deserves their energy. Not loud quitters. Not actively sabotaging anything. Just people who have quietly decided to do the minimum. Forrester tracked this group at 27% in 2024, 25% in 2025, and expects it to rise to 28% in 2026.

Think about that. More than 1 in 4 of your employees is actively withholding discretionary effort.

The pattern behind it makes sense when you look at the data. Employees watched colleagues get laid off for AI that never materialized. They watched entry-level positions disappear, locking out the next generation of talent. They watched offshore arbitrage dressed up as innovation. And the companies that could most afford to invest in their people, the profitable ones, were the least likely to do it.

Forrester found that only 16% of individual workers had high AI readiness in 2025. That number is expected to reach just 25% by 2026. The reason: only 23% of AI decision-makers said their organizations offered prompt engineering training. Workers are teaching themselves through solo experimentation because their employers won't invest in them.

Here's the generational twist. Gen Z workers have the highest AI readiness at 22%, compared to just 6% for Baby Boomers. Yet companies are disproportionately eliminating entry-level positions, shutting out the people most capable of working with the technology. The Burning Glass Institute documented this pattern across industries.

55% of employers who laid off workers for AI now regret the decision. More than half. They bet on capabilities that didn't exist yet and discovered the hard way that you can't replace institutional knowledge with a chatbot.

Meanwhile, Mercer's Global Talent Trends 2026 report found that 97% of investors said funding decisions would be negatively impacted by firms that fail to upskill workers on AI. 77% are more likely to back companies committed to building their workforce alongside AI.

The profitable companies have the resources to invest in their people. Most of them aren't. And the market is starting to notice.

Why Comfort Kills Adaptation

Stephen Andriole nailed this in 2017. He argued that profitable companies are the least likely to transform successfully because they have the least incentive to change. How many successful companies, without market pressure, have truly rethought their business models? Very few.

Clayton Christensen spent decades studying this exact phenomenon. His insight wasn't that incumbents are stupid or lazy. It was that their business environment doesn't allow them to pursue new approaches when they first arise, because those approaches aren't profitable enough at first and because developing them takes resources away from the work that's currently paying the bills.

The focus on existing customers becomes locked into internal processes. It gets institutionalized. Even senior managers can't shift investment away from what's working toward something unproven. The profitable company doesn't fail because it doesn't see the change coming. It fails because everything in its structure is optimized to protect the current state.

Donella Meadows called these balancing feedback loops. Forces within a system that actively resist change because the current state is producing acceptable results. The company is profitable. The shareholders are happy. The processes work well enough. The system whispers: don't rock the boat.

Sangeet Paul Choudary identified this in his February 2026 HBR piece as "architectural self-preservation." Units of work define roles, expertise, and status inside organizations. Changing them redistributes influence, away from the people who manage approvals and toward the people who design new ways of organizing work. Leaders who sense resistance often call it cultural inertia. Choudary says it's the system protecting itself.

Vijay Govindarajan, the Dartmouth professor most people call VG, built a framework that names this imbalance precisely. His Three Box Solution says everything a company does falls into 3 boxes. Box 1: manage the present, run the core business at peak performance. Box 2: selectively forget the past, abandon the practices and attitudes that no longer serve you. Box 3: create the future, invest in experiments and new models.

Profitable companies pour almost everything into Box 1. And why wouldn't they? Box 1 is paying the bills. Box 1 is what the board measures. Box 1 is where careers get built.

Box 2 is where it gets hard. VG's insight is that what you need to forget is a future weakness, but it's embedded in your current strength. That's why forgetting is so difficult. The processes, the org chart, the tech stack, the way decisions get made. All of it was built for a business that worked. Letting go of any of it feels like sabotage when the numbers are still good.

Box 3 gets lip service. Companies announce AI initiatives, fund pilots, put it on the strategic plan. But without doing the Box 2 work first, the forgetting, the clearing out, Box 3 experiments get strangled by Box 1 rules. You can't build the future using the operating logic of the past. VG is clear about this: Box 2 and Box 3 are 2 sides of the same coin. You have to forget before you can create.

That protection is strongest when profits are good. When there's no immediate threat, when the quarterly numbers look solid, when the board isn't asking uncomfortable questions, the pressure to change evaporates. And the gap between where the company is and where it needs to be widens every quarter.

The Expectations Gap

Here's where profitable companies really get caught.

Employees of all ages want to work for digitally mature companies. Companies with the cultural characteristics, the research mechanisms, and the opportunity to experiment. PwC's 2025 Global AI Jobs Barometer, analyzing close to a billion job postings from 6 continents, found that industries with the highest AI exposure saw 3x higher revenue-per-employee growth. Wages in AI-exposed roles carry a 56% premium, up from 25% the year before.

Those numbers don't describe companies replacing workers with AI. They describe companies that invested in their people alongside AI. The market is pricing in scarcity of humans who know how to work with technology, not surplus.

Customers expect experiences that match the best consumer apps they use every day. Partners expect integration that actually works. And your best employees, the ones who aren't coasters, are watching to see if you're serious about building the muscle for what comes next. If you're not, they'll find someone who is.

Deloitte's 2026 State of AI report found that the AI skills gap is now seen as the biggest barrier to integration. And the number 1 way companies adjusted their talent strategies? Education. Not restructuring. Not workflow redesign. Training. Which means the majority of companies are treating this as a knowledge problem when it's actually a leadership problem.

McKinsey found that organizations adopting a digitally ready setup can quadruple their 5-year revenue growth rate and nearly triple their total return to shareholders compared to those that don't. The data is not ambiguous. Companies that invest in the foundation outperform. Companies that coast on current profits fall behind.

The Two Traps

I see profitable companies fall into 2 traps over and over. In VG's language, both are Box 1 behaviors.

Trap 1: They use profits to justify inaction. "We're doing fine" becomes the reason not to change. The quarterly numbers provide cover. The board doesn't push. All energy stays in Box 1. Box 2 and Box 3 starve. And every quarter they don't invest in their foundation, the cost of catching up goes up. By the time the market forces their hand, the price of transformation is 10x what the price of staying current would have been.

Trap 2: They use profits to buy the wrong things. They write big checks for AI pilots, enterprise software, consultants with slide decks. It looks like Box 3 activity, creating the future. But none of it addresses the underlying issue: their processes aren't documented, their data is messy, their people aren't trained, and their organizational structure was built for a business that no longer exists. Without the Box 2 work, the forgetting and clearing out, those Box 3 investments get strangled by Box 1 operating logic.

Both traps lead to the same place. The company looks busy. The transformation roadmap has lots of initiatives on it. The executives can point to AI spending on the quarterly call. But the actual capacity to adapt, the muscle Meadows, Christensen, VG, and Choudary are all describing, hasn't been built.

What To Do Instead:

If you're profitable, that's great. Genuinely. Most companies I work with would trade their problems for yours. But profit is a position of strength, not a destination. Use it.

1. Fund the boring stuff with your good money. This is Box 2 work. Documenting your processes, cleaning your data, connecting your systems, training your people. It feels like maintenance. It's actually the most important investment you can make, because without it, nothing new you build will stick. Do it now, while you can afford to. The companies that wait until profits are under pressure will be trying to build the foundation while the building is on fire.

2. Invest in your people before investors start asking why you haven't. Mercer's data is clear: 97% of investors are watching for this. 77% favor companies building their workforce alongside AI. This is no longer a nice-to-have. It's becoming a signal investors use to evaluate your long-term viability.

3. Watch for your coasters. If 28% of your workforce is disengaged, that's not an HR problem. That's a leadership signal. Coasters are telling you something: they don't believe you're serious about what comes next. The fix isn't a motivational poster or a pizza party. It's demonstrating, through investment and action, that the company is building for the future.

4. Create space for experimentation. This is Box 3 work, and it only works if you've done the Box 2 clearing first. Your best people are already experimenting with AI tools on their own. Forrester's shadow AI data showed that workers in 90% of companies use personal AI tools for work, usually without approval. Instead of suppressing that, channel it. Give people permission to experiment. Watch what they build. The signals about what works are already inside your organization. You just have to listen.

5. Measure readiness, not adoption. The question isn't "how many AI tools have we deployed?" It's "can our organization adapt when the next wave hits?" Forrester's AIQ framework measures this at the individual level. McKinsey's Digital Quotient measures it at the organizational level. Pick a framework. Measure where you are. Build from there.

The Best Time to Fix the Roof

I have a saying: the best time to fix the roof is when the sun is shining.

If you're profitable, the sun is shining. That won't last forever. Markets shift. Competitors move. Customer expectations change. The companies that build the muscle for adaptation while they have the resources will be the ones that survive the next wave.

The companies that coast on current profits, that confuse "doing fine" with "being ready," will discover the hard way that comfort was never a strategy. It was a gap they couldn't see because the numbers were too good.

By the time the market forces your hand, the cost of catching up will be 10x what the cost of staying current would have been.

Putting your head in the sand because you're profitable isn't just foolish. It's expensive.