The Knowledge Gap, AI Snake Oil, and The Real AI Opportunity

I was speaking to a business owner recently - smart, experienced, building something genuinely interesting - when he mentioned, almost in passing, that his web developer had told him his website would go offline if he stopped paying the monthly fee.

He had believed it. For two years.

I explained that he owns his domain. That the site sits on a server he can move. That none of what he had been told was true, and that the fee he had been paying was, at best, for services he had never clearly understood.

He was frustrated - and then curious. "How did I not know that?"

The answer is that he didn't know what he didn't know. And in that gap between what he knew and what he didn't, a business model had quietly set up shop.

This dynamic is as old as commerce. But right now, in 2026, it is playing out at extraordinary scale in the tech arena - supercharged by the growth we are seeing in AI - and the people who understand what is actually happening are sitting on one of the most significant professional opportunities in a generation.

The Gap Has Always Existed

There is nothing new about the space between those who understand how something works and those who don't. Every industry has it. It is why people pay three times what something is worth when they don't know the market. Why legal documents feel impenetrable until someone walks you through them. Why technology projects run over budget when the buyer can't evaluate what they're buying.

The gap is not sinister by nature. Most of the time it exists simply because knowledge is unevenly distributed - the good old information asymmetry principle from Economics 101 - and people with more of it can charge accordingly.

What makes AI different right now is the speed at which the gap is widening, and the size of the reward available to those who close it.

What the Data Actually Shows

PwC's 2025 Global AI Jobs Barometer - based on analysis of close to a billion job postings across six continents - found that workers with AI skills now command a 56% wage premium over peers in equivalent roles without those skills. That premium more than doubled in a single year, up from 25% in 2024. In the industries most exposed to AI, productivity growth has nearly quadrupled since 2022.

Meanwhile, DataCamp's 2026 State of Data and AI Literacy Report - drawing on 500+ enterprise leaders surveyed with YouGov - found that 88% of those leaders now rate AI literacy as essential for day-to-day work, equivalent in importance to basic writing ability. Yet 59% of the same leaders report a significant AI skills gap inside their organizations.

As Jonathan Cornelissen, CEO of DataCamp, said: "Companies are investing aggressively in AI tools without making the same investment in workforce capability. Make no mistake: that disconnect will limit the return on AI."

The gap between the expectation and the reality is not small. And it is sitting there, open, waiting.

What Fluency Actually Looks Like

AI fluency is not knowing how to use ChatGPT. It is not having taken a course, attended a webinar, or read the right newsletters. It is not the ability to talk about agents, retrieval-augmented generation, or the competitive landscape between frontier models.

Boston University's research on AI leadership frames it well: "The opportunity is to become the person who can lead AI-enabled change - who can guide strategy, evaluate use cases, manage risk, communicate across functions, and help the organization make better decisions in a world where AI is everywhere. That's the role that doesn't get automated. That's the role that gets promoted."

In my coaching work, I see the difference between AI-fluent leaders and AI-aware leaders clearly. The fluent ones have changed how they work. They use AI as a daily thinking partner - to stress-test their reasoning, synthesize complex information faster, pressure-check decisions, prepare for difficult conversations. The work they produce is sharper. The time they recover is real.

The aware ones know the landscape. They have the vocabulary. They have not yet changed anything about how they actually spend their days.

Both groups are smart. One of them has a fundamentally different relationship with the moment we are in.

The Opportunity Is in the Doing

I have a client - I'll call her Yara - who came to coaching eighteen months ago as a senior marketing leader feeling increasingly anxious about AI. Not technically afraid, but worried that the ground was shifting and she couldn't quite find her footing.

She started using the tools. Daily. On real work.

Within three months, she had restructured how her team prepared for quarterly reviews. She had cut her research-to-insight cycle in half. She was making better decisions faster and - perhaps most importantly - she had developed an intuitive calibration for where AI added genuine value and where human judgment was irreplaceable.

That calibration is the real asset. Because once you have it, you stop outsourcing your thinking about AI to other people. And that is where things get interesting.

The AI Snake Oil Phase Is Already Here

Every major technological shift attracts two types of actors: people building real capability, and people packaging perception. Right now, both are thriving.

Founders are being sold "AI-powered platforms" that are thin wrappers on top of existing APIs. Teams are paying five figures for "AI transformation programs" that result in a slide deck and no operational change. Agencies are rebranding basic automation as "intelligent systems" because the buyer doesn't have the fluency to challenge it.

This is not a moral argument. When demand outpaces understanding, the middle fills with noise - it's a natural market dynamic. The problem is that most buyers cannot yet tell the difference between real value and snake oil. And that knowledge gap can be expensive.

In the last six months alone, across my coaching and advisory work, I have seen leaders approving AI budgets without a clear use case tied to business outcomes, teams implementing tools that nobody meaningfully uses after the initial rollout, and founders deferring decisions because "we'll wait and see how AI plays out." All of these are symptoms of the same issue: low decision-making confidence in a high-noise environment. When confidence is low, people either overpay or do nothing. Neither is a good strategy.

A Simple Filter

You do not need to become technical to avoid being misled. When evaluating anything labeled "AI," pressure-test it against three questions:

What specific problem does this solve? If the answer is vague, the value is vague. 

What changes in our workflow if we implement this? If nothing changes, nothing improves. 

Where is human judgment still required? If the answer is "nowhere," it is almost certainly wrong.

This alone eliminates a surprising amount of noise.

Where This Lands for You

You do not need to become an AI expert. What you do need is to upgrade how you think, decide, and operate in a world where AI is already part of the system whether you opted in or not.

Part of that is personal - building enough hands-on familiarity that you can tell good work from good-sounding work. Part of it is who you surround yourself with. If the technical side genuinely is not where you want to spend your energy, find someone you trust who lives there. Get second opinions. Get multiple quotes. Talk to people who are actually building things, not just advising on them. And use AI itself to get smarter about AI - ask it to explain what a particular tool does, help you evaluate a proposal, or tell you what questions you should be asking the consultant sitting across from you.

Major organizations - including the hyperscalers who build and sell AI platforms themselves - are now requiring their workforce to actively use and understand these tools. It is showing up in performance reviews.

But there is a version of this that misses the point entirely.

If your team is using AI to write emails that still have "(insert name)" and "(insert problem)" in them, that is not AI adoption. That is copy-paste with extra steps. I see it constantly - on LinkedIn, in my inbox, Nobody is fooled. And more importantly, nothing of value was created. Logging that you used an AI tool this quarter is not the same as having genuinely changed how you work.

What actually matters is the leader who uses AI to pressure-check their reasoning before a difficult board conversation. The founder who synthesizes six months of customer feedback in an afternoon. The manager who prepares for a hard conversation more thoroughly because they had something to think against.

Being AI native is not outsourcing your thinking. It is thinking better because of the tools available to you. Augmentation over automation.

Frequently Asked Questions

Do I need to learn how to code to stay relevant? No. What you need is decision literacy, not technical depth. You should be able to evaluate use cases, understand trade-offs, and ask the right questions. Most senior leaders who go too deep technically end up missing the strategic layer entirely.

How much time should I realistically spend on AI? Less than you think, but more consistently than you probably are. Fifteen to twenty minutes a day applied to real work compounds fast. Fluency builds through repetition in context, not through courses.

What are the highest-impact ways to start? Start where friction already exists in your workflow - preparing for meetings, synthesizing information, drafting and refining communication, stress-testing decisions. If AI is not saving you time or improving quality in these areas, you are not using it well yet.

How do I know if an AI investment is worth it? Tie it to one of three outcomes: revenue growth, cost reduction, or decision quality. If it does not clearly move one of those, question it. Define success upfront - otherwise everything looks like progress.

What mistakes are leaders making right now? Three patterns: delegating AI entirely to junior teams without staying involved, buying tools before clarifying use cases, and confusing activity with impact. The leaders getting this right stay close enough to understand what is changing without getting lost in the weeds.

Is this a temporary hype cycle or a real shift? Both. There is hype. There is noise. There is also a structural shift in how work gets done. The mistake is treating it as entirely one or the other.

Final Thought

The gap we started with is not going away. If anything, it is widening. The difference now is that the cost of staying on the wrong side of it is increasing, and the upside of closing it is becoming more visible by the quarter. You do not need to rush. But you do need to engage.

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Merve Hokamp is the founder of Leadrise Coaching & Consulting, a global executive coaching and leadership development firm working with leaders, teams, and organizations. She has coached leaders across 37 nationalities, holds an MBA from INSEAD, and spent 11 years in senior roles at Google before founding Leadrise.

If you are navigating a career transition, a restructuring, or simply trying to figure out what leadership means for you in this moment, let's talk.