Your Company Is Building an AI Elite. Are You In It?
The gap between AI super-users and everyone else isn’t about talent. It’s about who got invited to learn.
There’s a split happening inside your company right now.
You might not see it on the org chart.
It won’t show up in your next town hall.
But it’s there, and it’s growing fast.
On one side: the people who figured out AI early.
They’re saving hours a week, getting pulled into strategy meetings, and building workflows that make their managers look good.
On the other side: everyone else.
Still doing things the old way. Not because they’re bad at their jobs, but because nobody showed them a better one.
A new survey from Writer and Workplace Intelligence just put numbers on what I’ve been watching play out in my own company for months.
92% of C-suite executives say they’re actively cultivating a new class of “AI elite” employees.
These super-users are reportedly 5x more productive than their peers.
They’re 3x more likely to have received both a promotion and a raise in the past year.
They save nearly 9 hours a week compared to about 2 hours for everyone else.
And here’s where it gets uncomfortable:
60% of companies plan to lay off employees who can’t or won’t adopt AI.
77% say non-adopters won’t even be considered for promotions.
Read those numbers again. This isn’t a future prediction. This is now.
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I’m Watching This Happen in Real Time
I work in a mid-sized company. We’re not a tech giant. We don’t have an AI lab. But I can already see the split.
There’s a small group of us who started experimenting early.
We automated parts of our workflows.
We figured out which tools actually helped and which ones were just noise.
We started saving time.
And more importantly, we started thinking differently about our work.
And then there’s everyone else.
Good people. Smart people.
People who’ve been doing excellent work for years.
But they’re still writing emails from scratch.
Still manually pulling reports.
Still spending two hours on tasks that take me twenty minutes.
The gap isn’t about intelligence. It’s about exposure. Access.
And honestly, it’s about permission.
Permission to experiment.
Permission to fail at it.
Permission to spend a Tuesday afternoon learning a tool instead of clearing a backlog.
Most people never got that permission.
The Promotion You Didn’t Get (And Why)
Here’s what the survey doesn’t say, but I will: the AI elite isn’t a meritocracy. It’s a self-selecting club.
The people who became AI super-users tend to be the ones who already had more autonomy in their roles, more access to leadership, and more room to experiment. In many companies, that skews toward certain teams, certain seniority levels, and — let’s be honest — certain demographics.
When I look at who in my company is “AI fluent” versus who isn’t, the pattern isn’t about aptitude. It’s about opportunity.
The people closest to the decision-makers got early access.
The people in operational roles, the ones actually doing the work, that AI is supposed to improve, were the last to hear about it.
And now we’re going to lay people off for not adopting fast enough?
That’s not an AI strategy. That’s a failure of leadership disguised as one.
What Companies Get Wrong About AI Adoption
The Writer survey frames this as a competitive advantage story: companies with AI elites outperform those without. And sure, that’s probably true. But it misses the bigger picture.
If your AI adoption strategy creates a two-tier workforce with the anointed few and the expendable rest, you haven’t adopted AI. You’ve just found a new way to divide your company.
Real adoption isn’t about having 10 super-users.
It’s about having 500 people who are 20% better at their jobs.
It’s about the customer support rep who uses AI to draft faster responses.
The operations coordinator who automates a weekly report.
The HR manager who uses it to spot patterns in exit interviews.
That kind of adoption doesn’t happen because a CEO declares it from a stage. It happens when someone sits with a team and says:
“Here’s how this could help you specifically. Let’s try it together.”
That’s the work nobody wants to do. It’s slow. It’s unglamorous. It doesn’t show up in a quarterly earnings call.
But it’s the only version that actually works.
If You’re on the Wrong Side of This Split
If you’re reading this and feeling a knot in your stomach, here’s the thing: you’re not behind because you’re bad at this. You’re behind because the system didn’t make room for you. But you can make room for yourself.
Start with one task, not one tool.
Pick the most repetitive thing you did this week wether it’s the report you pulled, the email you rewrote for the third time, or the data you formatted by hand. Google how to do that one thing with AI. Not “learn AI.” Just fix that one annoying task.
Ask the AI person on your team.
Every company has one. They’re not gatekeeping. They’re usually desperate for someone to be curious. Ask them:
“Can you show me what you actually use day to day?”
That fifteen-minute conversation will teach you more than any course.
Stop waiting for official training.
Most companies are terrible at AI training. It’s either a generic webinar or a Slack channel full of links nobody clicks. Give yourself permission to spend 30 minutes a week experimenting.
Use your lunch break. Use a free tool. The goal isn’t mastery, it’s momentum.
Document what you learn.
Write down what worked and what didn’t. Share it with one colleague. This is how you go from “person who’s behind” to “person who’s figuring it out,” and that shift in perception matters more than you think.
If You’re a Manager Reading This
You have more power here than you realize. And more responsibility.
Audit who actually has access.
Not who theoretically could use AI tools, but who actually is. If it’s the same five people, you don’t have an adoption strategy. You have a clique.
Create protected time for learning.
Tell your team:
“Spend Friday afternoon trying one AI tool on one real task. No deliverable required.”
People won’t experiment if every hour has to produce output. Learning requires slack in the system.
Pair your AI people with your non-AI people.
Not in a “training session,” but in actual work. Put them on a project together. Let the knowledge transfer happen naturally through collaboration, not curriculum.
Measure adoption by breadth, not depth.
Ten people saving 9 hours a week is impressive. But 200 people saving 2 hours a week is 400 hours, and it means your whole team is moving, not just your stars.
The Real Question
I keep coming back to something I’ve observed in my own company: the people who are best at AI aren’t the ones who learned the most tools. They’re the ones who understood their own work well enough to know where AI could actually help.
That’s a human skill. That’s experience. That’s judgment.
And it can be taught, but only if you invest in it.
So here’s my question for every leader reading this:
Are you building an AI elite, or are you building an AI-capable workforce?
Because one of those creates a competitive advantage and the other creates a ticking time bomb.
The 60% of companies that are planning to lay off non-adopters?
They’ve chosen the bomb. They just don’t know it yet.
We're grateful to Anna Wojciechowska for sharing her work here. If you want clear-eyed writing about who really gets ahead at work and how to put yourself in that group, follow her. This story was originally posted on her publication Automate This.
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Anna's point is that the AI divide isn't about talent. It's about who got access, who got permission to experiment, and who got left doing things the old way.
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Read Next
The data cited in this article comes from the 2026 AI Adoption in the Enterprise survey by Writer and Workplace Intelligence, based on 2,400 knowledge workers across the US, UK, Ireland, Benelux, France, and Germany.
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"I keep coming back to something I’ve observed in my own company: the people who are best at AI aren’t the ones who learned the most tools. They’re the ones who understood their own work well enough to know where AI could actually help."
A big 'YES' to this. The way I learnt initially was to take a workflow I knew inside out and then try to do each step with AI. That showed me exactly what I could and couldn't trust AI to do. Obviously, that changes with new models, but I'm convinced it's the best way to learn.
Traditional corporate training can give you an overview of AI, but it doesn't make it relevant to your actual workflow.
The two-tier pattern Anna describes is a question of where companies stop on the adoption curve. Implementation is when the tool enters the company. Adoption is when it enters how people work. Full adoption is when it enters how the team thinks. Most companies measure dashboards and call it done at step one. The "AI elite" is what happens when a few people reach step three on their own initiative, while the rest are stuck at step one because nobody created the conditions for them to move. That is a leadership choice, not a talent gap.