The Expertise-Based Business (Updated for the AI era)

There’s plenty out there about how to become a recognized expert, how to build a business around your expertise, and how to monetize what you know. In this article, I want to go deeper into the whole concept of the expertise-based business.

Why?

Because a richer understanding of what this kind of business really is makes the possibilities clearer. It also surfaces practical ways to differentiate—ways that aren’t about acting louder on social media, posting more often, or claiming bigger credentials than the next person.

That differentiation matters. There’s never been a lack of competition for expertise-based businesses. Pretty much anyone can read a few books, spin up a website, and call themselves an expert. And now, with AI, it’s easier than ever to produce content that sounds like expertise.

So, getting to the root of what expertise really is—and what an expertise-based business is really doing—isn’t just an academic exercise. It’s critical to building a business that lasts.

How to define expertise (and why the usual definition is incomplete)

First things first: what do I mean by “expertise” and “expertise-based business?”

The typical definition of expertise is something like “a high degree of knowledge and skill in a particular field or discipline.” Expertise is usually gained through years of experience and/or study. And it’s through pointing to those years—plus the body of work, accolades, credentials, and accomplishments they’ve produced—that the expert establishes credibility.

That’s fine as far as it goes. But it contains a flaw that has helped fuel distrust of experts.

It’s rooted in the past.

The world—driven by what technology makes possible—changes too quickly for much of what we learned years ago to remain fully valid. AI has poured gasoline on that problem. In many fields, facts are cheap, answers are abundant, and yesterday’s “best practice” is today’s outdated shortcut.

Even when the underlying knowledge remains valid, the context almost always changes—tools, norms, constraints, economics, expectations, risks. So, the real question isn’t “What do you know?” It’s “Can you keep learning, keep updating your mental models, and keep applying what you know wisely in the real world?”

Real expertise isn’t static. It flows. It is always evolving. It deserves to be treated more like a verb than a noun.

Real expertise is more a verb than a noun.

By extension, to thrive and truly be effective, the guardian of expertise—the expert—must continually evolve and help others evolve. Another way to put this: she has to keep learning and help other people keep learning.

And when I say learning, I don’t just mean accumulating information or even building skills. I mean changes in judgment, attitudes, and behavior. Real experts are catalysts for learning that shows up in how people actually think and act.

This perspective leads directly to a better definition of an expertise-based business.

What is an expertise-based business?

The usual view of “building a business around your expertise” is that you—the expert—dispense pearls of wisdom or apply superior skills to help clients solve problems and capitalize on opportunities.

That view is incomplete, and in a lot of cases it’s actively harmful.

First, it implies the expert must know more than the client. That’s not only untrue; it can be counterproductive.

Yes, the expert will know more about some things. But the expert can’t know the nuances of a client’s situation better than the client does. In most cases, the expert can’t even know them as well without doing real discovery work. Developing understanding is an essential part of applying expertise productively, and it happens through collaboration with the client.

Second, the traditional view assumes the expert has answers. Worse, the right answers.

An expert should eventually arrive at answers (or more often: working theories and practical bets). But first and foremost, an expert needs to be able to ask good questions—and take clients through a process of discovering which questions matter, which assumptions are shaky, which constraints are real, and which paths are worth testing.

Most fundamentally, an expertise-based business is about process: the process of bringing expertise to bear in context, with the awareness to know when to draw conclusions and propose solutions.

An expert possesses knowledge, but more importantly is able to understand the limits of his own knowledge and facilitate processes that connect the right content—knowledge and skills—with a specific context.

This is very different from being a pundit or a critic—the kinds of people you see making confident pronouncements on TV or in op-eds. Those people are on the sidelines. Expertise-based businesses are “in the arena.” They are accountable to outcomes, trade-offs, and reality.

So, let’s bring it back around and offer a definition. This is still the cleanest way I know to say it:

An expertise-based business is a business engaged in continually developing expertise in a specific area and applying this expertise in context to create distinctive value for each individual client.

What AI changes (and what it doesn’t)

AI makes the definition above more relevant, not less.

It’s tempting to think AI “replaces experts.” What AI really replaces is a lot of what people used to mistake for expertise: quick answers, surface-level explanations, generic frameworks, and boilerplate content.

AI is excellent at producing plausible text and pulling together common knowledge. That creates two big problems for expertise-based businesses:

One, it floods the world with competent-sounding material. That makes it harder to be seen and trusted—especially through search. Google’s AI Overviews now provide AI-generated summaries directly in search results across many countries and languages, which changes click behavior and reduces the amount of traffic many sites used to rely on.

Two, it gives buyers the feeling of progress. People can ask an AI a question and get a decent answer in seconds, which can delay (or replace) the decision to pay a human. Sometimes that’s fine. Sometimes it’s a disaster—because the cost of being wrong doesn’t show up until later.

But AI also creates real upside for expertise-based businesses:

It speeds up research, drafting, analysis, and synthesis.

It can help you generate options faster and stress-test decisions earlier.

It lowers the cost of creating “first drafts” of many things—plans, outlines, emails, sales pages, learning designs—so you can spend more time on judgment and less time on busywork.

In other words, AI is a powerful tool for the “continually developing expertise” part of the definition. The danger is letting it become a substitute for the “applying in context” part.

In the foreseeable future, the winners are likely to be the expertise-based businesses that do two things at once:

1. use AI to increase throughput and quality of thinking, and
2. double down on the human elements AI can’t reliably provide: judgment, ethics, taste, context-sensitivity, and responsibility.

Expertise-based vs. expert-based

Given how much I’ve been talking about “the expert,” it’s worth asking whether there’s a difference between an “expertise-based” business and an “expert-based” business. You’ll see both terms used.

I still think “expertise-based” is the more accurate term.

In an expert-based business, the focus is on the person. That creates at least two problems:

First, every expert—no matter how experienced or famous—is human, biased, and limited.

Second, if the business depends on one person, what happens when that person steps back, burns out, or simply wants a different life? It’s not a great recipe for continuity.

In an expertise-based business, the focus is on the expertise. Expertise is malleable and evolving. It’s not tied to one person, and in many cases, it isn’t even fully “owned” by the business—because real expertise often emerges in collaboration with clients and in response to changing contexts.

AI pushes this distinction further. If your business is built mostly on personality and presence, AI can actually amplify you (by making content production easier), but it can also make you fragile (because imitation becomes cheap). If your business is built on evolving expertise and high-quality process, AI becomes a lever—something you use to deepen your work and deliver more value without pretending you’re omniscient.

What does an expertise-based business do?

Let’s wrap up by looking at the common ways expertise-based businesses deliver “distinctive value for each individual client.” Most people reading this will recognize these:

• Consulting
• Coaching
• Speaking
• Teaching/training

All of these can be done in-person, online, or in a hybrid way. They can be done in real-time, on demand, or asynchronously.

What matters is the level of engagement.

At the deepest level—and typically the highest-priced—engagement means direct, personal relationship. It means getting to know a client’s situation intimately and bringing expertise to bear in context.

At a more scalable level—often lower-priced per person—engagement means guiding people to create meaningful, personalized interaction with the content you provide. That can be as simple as reflection questions and activities in a course that force people to apply concepts to their own situation.

AI adds a third layer that’s worth naming explicitly: “guided co-production.” This is where you design an experience (a course, a cohort, a workshop, a consulting engagement) that deliberately incorporates AI as a tool, while you provide the structure, standards, prompts, and judgment. If you do it well, clients get faster progress and better outcomes. If you do it poorly, they get a slick pile of output and a false sense of certainty.

In the most successful expertise-based businesses, these different levels of engagement show up in a diversified portfolio of offerings that can be plotted on a Value Ramp.

That portfolio approach matters more now because AI is compressing margins at the low end. “Information products” that are basically packaged explanations are getting harder to sell at premium prices, unless they are tied to something AI can’t easily replicate: a strong point of view, a proprietary method, credible real-world examples, community, feedback, accountability, or access to you.

So, if you want a simple rule of thumb for navigating AI as an expertise-based business, here it is:

Use AI to make your work faster. Don’t let AI make your work generic.

If you’re doing that—continually developing your expertise, applying it in context, and building a portfolio that matches different engagement levels—you’re not just surviving the AI era. You’re positioned to do some of the best work of your career.

Jeff