Welcome to the AI Brief from the YPO Technology Network. I'm Stephen Forte. So, something I have been watching across every company I work with for the last six months, and I think it is starting to matter more than most CEOs realize. You have two workforces inside your company right now, not by department, not by seniority, not by tenure, by posture toward AI. One group has internalized these tools and is producing at a level that genuinely surprised me when I sit down with them. The other group is with the best of intentions doing the same job they were doing two years ago with the same outputs at the same pace. And the gap between those two groups is widening every quarter. By the end of this episode, I want to give you a clean read on what the adopters are actually doing differently. It is not what most people think, why this gap is going to widen faster than any technology gap in your career. and three concrete moves you can make starting this week, including one that nobody wants to talk about. Let's set the table. Let me give you the numbers because I am not making this up from anecdote. Writer, the enterprise AI platform, published their second annual AI adoption survey on April 7th. They identified a cohort they call super users, about 40% of employees in marketing, sales, HR, and customer support functions. Compared to their non-adopting peers, the super users are saving 4.5 times more time. They are five times more productive. And that number is confirmed by their managers, not self-reported. And they are three times more likely to be promoted with a raise. Read those numbers one more time. Same job title, same company, same tenure, 4.5 times the time saved, five times the output, three times the promotion rate. That is not a productivity bump. That is a different employee. Gallup's April workforce survey says 50% of American workers now use AI in their role, at least occasionally, up from 46% last quarter. 13% are frequent users. The frequent users report dramatic productivity gains. The occasional users report, well, the occasional users report what occasional users always report, mild improvement, and a vague sense that they should probably do more with it. And here's the part nobody loves talking about in polite company. Writers, same survey, 77% of executives say employees who refuse to become AI proficient will not be considered for promotion or leadership roles. 60% plan layoffs of employees who cannot or will not use AI. 92% of the C-suite are actively cultivating what the report calls AI elite employees. This is not coming. This is here. The conversation is already happening inside 92% of your peer companies. The only question is whether it is happening inside yours yet or whether it is going to happen at the next board meeting, whether you are ready or not. Here is the part most coverage gets wrong. The adopters are not using AI more. That is the symptom, not the cause. The adopters are thinking about their work differently. they have internalized a mental model that is genuinely different from how most knowledge workers have operated for the last 40 years. Let me try to describe it. When the adopter gets a problem they decompose it Not because someone trained them to because they have learned by using these tools that AI rewards decomposition They break the task into smaller logical pieces They figure out which pieces are reformatting, which are judgment, which are research, and which are decision. Then they iterate. They give the tool a first pass. They critique what came back. They rewrite the prompt. They check the output against what they know is true. They notice when the model is being too generous or too literal. They develop over time an intuition for what the model is going to get right and what it is going to get wrong. That is not a software skill. That is a thinking skill. It looks a lot like the skill a good consultant develops or a good editor or a good operations leader. The skill of seeing the structure of work clearly enough to delegate it precisely. The Microsoft Work Trend Index just published a finding I want every CEO to internalize. When managers actively use AI themselves, their employees show a 22-point lift in critical thinking about their work. 22 points. Not on AI use, on critical thinking. The fear that AI was going to make everyone dumber has it exactly backwards. The people who use AI well are getting sharper, faster, more analytical. The people who refuse to use it are stuck running the same mental patterns they were running in 2019. And in a world where the baseline analytical capability of the workforce is going up, standing still means falling behind. Now, I know what some of you are thinking. We have seen technology adoption cycles before. People always panic, then they adapt. I want to address that head on because I think it is the most dangerous frame in the room. In 1987, the economist Robert Solo looked around at the explosion of personal computers in offices and wrote a famous line. You can see the computer age everywhere, he said, except in the productivity statistics. It took roughly 15 years for the PC revolution to actually show up in measurable workforce productivity. From the early 80s to the late 90s, 15 years. That slow burn was a gift. People had time to migrate. The clerk, who was uncomfortable with WordPerfect in 1987, had a decade and a half to become competent. Whole generations got to retire before the technology made their original skill obsolete. The transition was painful, but it was paced. This cycle is not that. The adopters are pulling ahead measurably inside the same company in the span of a single quarter. The gap is not opening over 15 years. It is opening in months. I have a friend, a software engineer in his late 20s, who is technically very good. He uses cursor occasionally to spot check code. He treats it as a clever autocomplete. He is being measurably outpaced right now by 23-year-olds who design their entire workflow around AI from the first keystroke. He does not see it yet because the surface tasks still look the same, but the slope of his trajectory is flat. The slope of theirs is steep, and that is a software engineer. That is someone whose entire job is about producing precise written output and who works in a domain where AI is unambiguously good. Now picture the financial analyst doing manual Excel work all day, the procurement specialist living in vendor portals, the marketing manager building decks by hand, the HR business partner who still drafts every offer letter from a template. These are competent people, often very competent. They are not bad employees. They are skilled employees whose skills have stopped compounding Most of them do not know it yet Some of them will not believe it when they are told and a small percentage will hear it understand it and choose not to act on it anyway That last category is the one we have to talk about because every CEO listening to this has at least a handful of them So three moves, not as a checklist, as a sequence. The first, elevate the adopters now, today. Do not wait for HR. Do not wait for a comp cycle. The people producing four and five times their peers are giving you a signal. You do not need a formal performance review to see it. You can feel it in meetings. You can see it in their output. You probably already know who they are. Put them in broader scope, cross-functional work, role redesign. Let them rebuild the workflows that used to take three people and now take one. Promote them, but more importantly, give them territory. Most CEOs I talk to are still treating their best AI adopters like exceptional individual contributors. The move is to treat them like leaders because they are going to be the ones designing the next layer of how your company operates. The five times productivity gain compounds when you put them in roles where they can rewrite the work for the people around them. It stays linear if you just give them more of the same task. The second, and this is the move most companies are getting wrong, replace generalized AI training with workflow-specific one-on-one coaching. Generalized AI training does not work. The data is consistent on this. HR Dive published a piece in April titled, Plainly, Why AI Readiness Training Fails. Companies are spending real money on enterprise licenses, lunch and learn, certification programs, and AI literacy modules. Completion rates are mediocre. Behavior change is worse. The reason is simple. Generic training shows people what AI can do in the abstract. It does not show them what AI can do for their Tuesday morning. The thing that actually works is somebody sitting next to an employee, looking at the work they are already doing and showing them how AI changes that specific work, not here is how to use chat GPT. Instead, you spend three hours every Tuesday building this report. Here's how we get it down to 20 minutes. That conversation with that employee in front of their actual workload will convert more skeptics in one hour than a six-week generalized training program because the resistance you are running into is not laziness. It is not even fear, mostly. It is the very human assumption that nobody else understands their job well enough to help with it. AI shatters that assumption in about five minutes once it is actually applied to the work they do. Budget accordingly. This is not a learning and development line item. It is a coaching investment. One coach per 20 employees working through workflows one at a time. The payback period is measured in weeks. The third, and this is the one nobody wants on a podcast. You're going to have to phase some people out. Not many, but some. I want to be very careful here. I am not talking about the employees who are struggling to learn. The ones who are curious but slow. The ones who are scared but trying. Those employees just need a coach and a quarter or two of patience. I am talking about the people who have been shown repeatedly what these tools can do for their work. Who have had the coach sit next to them. Who have seen the demonstration. And who have decided for reasons of identity or pride or institutional inertia that they are not going to use it. You probably had this exact conversation in 2002 with the team member who refused to use email. Maybe in 1994 with the executive assistant who insisted on the typewriter The conversation is functionally the same The technology is foundational The refusal is real The choice belongs to the employee 60 of executives are already planning these layoffs, according to the writer's survey. I am not telling you to be ahead of that curve. I am telling you it is going to arrive at your door, whether you act on it or not. And the kind thing to do, the actually kind thing, not the comfortable thing, is to be honest with these employees early. Tell them what you are seeing. Give them a real opportunity to course correct. And then if they choose not to, treat them with dignity on the way out. Pretending the bifurcation does not exist is not kindness. It is just slower cruelty. One footnote, because I want to be clear about what I am saying and what I am not. I am not saying every employee needs to become a power user. They do not. The 40% super user cohort is the leading edge. Most of your workforce will land somewhere in the productive middle. Solid AI users who are meaningfully better at their jobs than they were two years ago without being super users. I'm also not saying AI fluency is a personality test. It is not. It is a skill. Most people can acquire it with the right coaching, the right tools, and a few months of practice. Some will love it. Some will tolerate it. Both groups are fine. The bifurcations between the curious, even the slowly curious, and the refusers. Not between the brilliant and the average. The friend of mine who is being outpaced is technically brilliant. He is just choosing not to internalize the new mental model. And he is not going to be brilliant for much longer if he keeps making that choice. This is not about intelligence. It is about willingness. The conversation in your peer companies and the 92% that are actively cultivating an AI elite is the conversation that is going to define the next two years of competitive advantage. The companies that elevate their adopters early will outproduce the ones that do not buy a lot. The companies that replace generic training with one-on-one workflow coaching will pull their middle cohort along with them. The companies that keep buying enterprise licenses and hoping for adoption will watch their tools sit unused and wonder why. And the companies that refuse to have the honest conversation about the small percentage of employees who will not adapt. Those companies will have the conversation. Anyway, 18 months from now, when the productivity gap has gotten wide enough that the CFO forces it, the kind move is the early move. The strategic move is the early move. The two are the same move. This is not about whether AI is the future. That argument is over. This is about whether your company is going to be the one that elevated the adopters, trained the curious, and was honest with the refusers, or the one that protected the resistors until it could not afford to anymore. The PC took 15 years to show up in the productivity statistics. We do not have 15 years. We have about 18 months before the gap inside your company gets wide enough to be irreversible without major restructuring. Walk the floor this week. Look at who is producing differently. Have a real conversation with one super user about how they work now. Have a real conversation with one refuser about what they think is going to happen. The data you collect on those two walks will tell you more about your company than any AI strategy deck. That is the YPO Tech Network AI Brief for Thursday, May 14th. I am Stephen Forte. If this was useful, send it to a fellow member. I will be back tomorrow with more. Until then, stay sharp.
