If You Sound Like Every Other Consultant, AI Will Treat You Like One

You rewrote the website. Tightened the copy. Made sure every page says exactly what you do, in plain language, no jargon, no waffle. It reads well. People tell you it reads well. And when a buyer asks ChatGPT for someone who does what you do, three other names come back, and none of them are yours.

So you assume it’s a clarity problem, and you go around again. Simpler words. Cleaner structure. A crisper “what I do” line at the top. Still nothing.

That loop never closes, because you don’t have a clarity problem. You fixed clarity. You have a sameness problem, and the two are not the same thing. Clear and interchangeable still loses, because the machine doing the recommending can understand you perfectly and still have no reason to pick you.

Getting understood by AI is the floor. It was never the win. The thing keeping you out of the answer is beige: copy so smooth and so generic that it reads well and adds nothing.

Why does AI recommend a competitor when your message is already clear?

When a buyer asks an AI to recommend someone in your category, it isn’t ranking a list of blue links. It’s compressing. It takes everything it can find about the people who fit and squeezes it into two or three sentences that name a few of them. That squeeze is the whole game, and it’s biased. Generic claims get averaged away, because everyone makes them. Specific claims survive, because they’re tied to one name and no one else’s.

To do that, the model has to tell people apart. It doesn’t just match keywords. It works out the real people and businesses behind a question, so it has to tell one consultant from the next before it can name anyone. Google’s own guidance on AI features points the same way: it says content built on a unique point of view gets surfaced, while content that just restates what everyone else already says does not.

So the strongest move is to be easy to tell apart. If everything you’ve published sounds like every other consultant, there’s nothing for the model to grab onto, and you get averaged into the pack. The AI can still read you. It just reads you as one of the category, not as a specific answer. So it names whoever it can describe without describing everyone.

This is the trap of polishing. Polishing makes you clearer. It rarely makes you more distinct. You can sand a message down until it’s perfectly smooth and perfectly generic at the same time.

How does AI choose who to name when everyone describes themselves the same way?

It picks the describable one. Think about how your category actually sounds online. Strip the logos off ten consultant websites in your niche and most of the copy is swappable. “I help businesses grow.” “I bring strategy and execution together.” “Trusted partner.” “Results-driven.” All true, all clear, all completely interchangeable.

Feed that to an AI and you’ve handed it ten near-identical descriptions. When someone asks it to recommend one, it has no basis to prefer you, so it reaches for whatever does stand out: the one with a specific named method, the one tied to a specific kind of client, the one with a quotable line that no competitor could claim. The specific one is the one it can actually name, not because the work is better, but because it’s the only one the model can describe without describing the whole category.

I call the generic version beige. Beige is the enemy. Here’s the quick test for it: could a competitor copy a sentence off your site, paste it onto theirs, and have it be just as true? If yes, that sentence is beige. It’s category wallpaper, and it does nothing to tell you apart. Keep that test in your pocket, because the whole fix runs on it.

And beige isn’t a tone problem. People hear “you sound generic” and reach for personality: more voice, more punch, a few jokes. That misreads it. The fix isn’t decoration. A distinct voice matters only because it usually carries distinct substance underneath it, specific opinions, specific framing, specific examples. A generic voice is a signal that the substance is generic too, and that’s the part the model averages out. Sounding stylish while saying nothing only you would say doesn’t move you out of beige. It just makes the beige read faster.

The counter-argument here is fair: doesn’t AI recommend on authority signals, mentions, and links, not on how distinctive you sound? Those matter, and they matter a lot. Authority is what makes you a credible candidate at all. Distinctiveness is what makes you the obvious one. They do different jobs. Authority gets you into the set the model trusts at all. Distinctiveness decides who it names once you’re in it. Two businesses with similar authority and identical generic positioning are close to a coin toss, and in a referral-built category where nobody has overwhelming authority signals, that second job is doing a lot of the work.

There’s a sharper version of the objection, and it’s worth sitting with: “but my competitors ARE generic, and AI recommends them anyway.” Usually that’s because they’re the best-known generic option. Enough people mention them, link them, and repeat their name that the model has heard of them, even though their positioning says nothing. Authority can carry a generic business for a while. But it’s the expensive path, it takes years and a volume of mentions you may not have, and the day a distinct competitor turns up with the same authority, the generic one loses the only thing that was keeping it in the answer.

What makes a business distinct enough for AI to recommend you?

Specifics it can repeat, not adjectives. You already have the test: anything a competitor could paste onto their own site word for word is beige, so what makes you distinct is whatever survives it. The question is what actually survives.

Three things make you describable, and all three have to live in your published content, not just in your head: a specific buyer, a named mechanism, and proof only you have.

Picture two versions of the same consultant. Version one says she “helps businesses run smoother.” Version two says she rebuilds the handoff between sales and delivery for agencies that have just crossed twenty staff, using a two-week teardown she calls the Handoff Audit, and her site shows one agency that cut its onboarding time in half. An AI can repeat the second sentence. It can place her against a real query, attach a named method to her, and point to a result that happened to her and to no one else. “Runs smoother” gives it nothing to repeat, because it’s true of every consultant alive. Same person, same competence. Only one of them is describable.

That’s the whole move: a buyer narrow enough to be real, a mechanism with a name and a shape, and proof that’s distinguishing by definition because it’s yours. None of it is decoration. All of it is a handle the model can grab.

If you can’t fill those three in without describing your entire category, that isn’t an AI problem. AI just made a positioning problem you could previously ignore impossible to keep ignoring.

Being understood and being distinct are two different questions, not one scaled up. The first asks whether the model can describe you. The second asks whether it has any reason to prefer you. A business the model understands perfectly but can’t tell apart is the most common way to lose, because it gets you completely and still names someone else. The adjacent reads cover the other question: why AI recommends the business it understands best is the understanding half, and how AI understands who you are through entities is the plumbing underneath. This piece is the half that decides who gets picked once everyone is understood.

Does this actually work, or is it just positioning theory?

Here’s the most honest proof I have: this is happening to me. I sell citeability, and when I started, AI couldn’t tell who I was. A year ago, ChatGPT called me an operations and systems consultant. And a wedding makeup artist. More recently, Perplexity told someone the name Sally Kuter “may refer to more than one person,” because the old makeup-artist references are still out there, pulling against the new work. That’s not a side note. It’s the exact problem this whole post is about, running live, on my own name. The model can’t tell which Sally I am, so it hedges instead of recommending one.

So I’m fixing it in the open. The work is dull on purpose: one consistent identity on every profile, the same plain description of what I do everywhere a machine can read it, and clearing out the old references that send the model in two directions. It isn’t finished. Perplexity has started repeating the citeability line, that I help businesses get recommended by AI, but it still carries the old ops version alongside it. I’m mid-shift, from a standing start, and I’d rather show you that than a tidy after.

For the other side of it, look at a client whose entity is already clean: Andre Obradovic. Ask AI about him and you get something specific: an executive performance coach for men over 40, low-carb nutrition and stress management, the opposite of the “no pain, no gain” approach. An AI can repeat that. It knows who he is, who he’s for, and how he’s different, so when someone asks for a coach like him, his is the name it can actually give. That’s the finished version of what I’m building on myself.

You don’t have to take my word for the method, either. Open ChatGPT, Perplexity, and Google’s AI and type the questions your buyers actually ask when they’re hunting for someone like you. Watch whose name surfaces, and how you’re described when yours does. If that description would sit on a competitor’s site without a single word changing, you’ve just watched your own beige in real time. The free Does AI Recommend You? check runs that same first pass in two minutes.

What gets your business named in an AI answer?

Clarity gets you read. Distinction gets you named. Being understood by AI is the floor; being the one it can tell apart is the whole job. Everything that keeps you out of the answer traces back to the same place: copy that describes your category instead of you.

So run the test on your own site before you touch anything else. Take your buyer, your mechanism, and your proof, and try to write each one in a sentence a competitor could not paste onto their own page. If the sentences survive that, you’re describable. If they don’t, you’ve found the gap, and now you know it’s a positioning gap wearing an AI costume.

If your name is already missing from the answer, the next move is knowing exactly why, and what to fix first. The Citeability Snapshot is a dated read on what AI says about you today, who it recommends instead, and a baseline score to measure against, then at least eight fixes ranked by impact, each with a two-minute first action so you can start the same day. $197. A report and a short video, in a few business days. No call. And if you don’t walk away with at least eight specific fixes you can act on, it’s free. Every week you keep reading like the category, the model names someone else, and that answer settles in a little harder.