Can AI do your SaaS positioning? What it still can't decide

Paste the product page into a model, ask for a positioning statement, and nine seconds later there's one on the screen. It names a buyer. It names a category. It names a benefit. It's cleaner than the paragraph currently sitting on the homepage, and it cost nothing.

The honest answer is that it did part of the job. AI writes positioning language well, better now than most first drafts produced in-house. What it can't do is make a positioning decision, and positioning is almost entirely decisions. Who we're willing to lose. What we're giving up in order to be sharp about something else. Which claim we'll still be defending in six months when a competitor calls it marketing.

Every one of those costs something. The model has nothing at stake.

So what comes back reads like positioning and functions like a summary. It's the category, wearing the product's name.

The average of everything already written

A language model returns the center of what it was trained on. Ask it about a category and it has read every vendor page, every review-site description, every launch announcement in that space, and it hands back the middle of the pile.

The middle of the pile is where everyone we compete with already lives.

This is measured, not a hunch. A group of researchers named the effect the artificial hivemind in a paper that won a best-paper award at NeurIPS in December 2025. They found two things happening at once. A single model keeps producing near-identical responses to open-ended prompts. And different models, built by different labs, produce strikingly similar outputs to each other.

Read the second one again. Different tools. Same answer.

The cognitive-science version is broader. In a 2026 paper on the homogenizing effect of language models, published in Trends in Cognitive Sciences, Sourati, Ziabari and Dehghani argue that these systems "reflect and reinforce dominant styles while marginalizing alternative voices and reasoning strategies," and that the convergence amplifies as more of us lean on the same handful of models.

Which means this isn't a prompting problem with a prompting fix. Better prompts narrow the output. They don't change what the machine is for. It's built to find the consensus, and positioning is the deliberate refusal of the consensus.

We've written before about why every homepage in a category ends up sounding the same. Generative tools didn't create that convergence. They just made it free.

And fluency is the part that gets us. A rough draft invites an argument. A finished-sounding one invites a signature.

Approving is cheap. Deciding is expensive. Most of us have signed off on a draft we'd have argued with if it had looked less finished.

Four decisions a model can't make for you

Not a scoring rubric. Four places where a real position has to say something, and where an averaged one goes quiet.

Who we're willing to lose

A position that excludes nobody selects nobody. The AI draft will say "for fast-growing teams." That isn't exclusion, it's a mood.

The real answer names the company that should go buy something else. A model almost never produces that, because in its training data almost nobody writes it down. Turning away revenue is a decision with a number attached, and only the people who own the number get to make it.

The trade-off we're committing to

Every position worth holding gives something up. Faster because less configurable. Cheaper because narrower. Slower to set up because it goes deeper once it's in.

A model optimizes for the sentence that offends no one, so what comes back is "powerful yet simple." Nobody has ever switched vendors because a product was powerful yet simple.

The claim that survives challenge

Try this. Imagine the sharpest competitor in the category reading our statement out loud on a call with our buyer, then asking them to make us prove it.

What survives? An averaged claim can't survive that, because it was never contested in the first place. It was assembled out of things everyone in the category already says, which is exactly why positioning that sounds right stops working the moment a real buyer applies pressure to it.

What gets left out

Positioning is mostly subtraction. Ten things are true about the product, and we say two.

A model has no reason to cut. Including things is the safest way to satisfy a prompt, so the draft carries four benefits, three audiences and two differentiators in one sentence. All of it accurate. None of it remembered.

What a hollow statement looks like up close

Here's the shape these tools produce. This one is invented, and we've all read forty like it.

For fast-growing e-commerce teams that need reliable transactional email, Relayline is a modern email infrastructure platform delivering dependable performance at scale, so teams can focus on growth instead of deliverability.

Run the four.

It's willing to lose nobody. "Fast-growing" excludes only companies in decline, and they don't self-identify.

It states no trade-off. Reliable, modern, and at scale, with no cost named anywhere.

Its central claim is "dependable performance at scale," which sits on every competitor's page in some arrangement. Nothing there can be contested.

And it leaves nothing unsaid. Four benefits made it in.

Now the version where four decisions actually got made.

We're for e-commerce teams sending under five million transactional emails a month who've had a deliverability drop nobody could explain. If you want a full marketing suite, we're the wrong call. We do one thing. Every send ships with the routing decision behind it, so when your inbox rate moves, you know why.

That second one is blunter, longer and less elegant. It's also the only one of the two a champion could repeat accurately to their boss three days later.

A model could have written it. It couldn't have chosen it. Every specific in there came from someone deciding. Five million. Deliverability. Not a marketing suite.

Buyers already discount the median

The skepticism has arrived faster than most teams have adjusted to. Most technology buyers now use AI somewhere in their research, and 94% of those who do fact-check what it tells them at least some of the time, according to TrustRadius's 2026 B2B Buying Disconnect report, published in July 2026 from a survey of 1,862 buyers.

The same reflex is now pointed at vendor copy. A buyer reading a claim that matches the four other tabs they have open doesn't sit there deciding whether a machine wrote it. They just stop counting it as evidence.

That's the shift worth sitting with. AI raised the floor on how good copy sounds, and in doing so it lowered the value of sounding good. We covered the trust side of this in what happens after AI content gets us found.

Every claim that could belong to five vendors now gets read as belonging to none.

Where the model earns its keep

Once the four decisions are made, it's genuinely good, and worth using without guilt.

Give it a decided claim and it'll produce twenty variants so you can hear which one survives being said out loud. Hand it your differentiator and ask it to argue the competitor's side, hard, until you find the seam. Feed it a page and ask what a buyer would still need to look up somewhere else. It compresses well, it drafts objection handling well, and it's tireless in a way people aren't at 5pm.

There's one more use that might be the best of them. Ask it for the most generic positioning statement it can produce for the category, then keep that on file as the thing you're not allowed to ship. It's very good at producing the average. That's precisely what makes the average easy to identify.

None of that is a small contribution. It's just downstream of the part that was hard.

Worth noting that the model reading your page is also increasingly the thing summarizing you to buyers before a human ever lands on your site. A page assembled from category-average language gives that summary nothing distinct to carry.

A test you can run before Monday

Take your closest competitor's product page. Same tool, same prompt, generate their positioning statement.

Put it beside yours.

If you could swap the two company names and both still read fine, the model didn't fail you. It reported, accurately, that the decision hasn't been made yet.

Then pick one of the four. Not all four, and not this quarter. One, this week, with the people who own the revenue number in the room. Who we're willing to lose is usually the one that settles the other three, and usually the one that takes the longest to say out loud.

Nine seconds to write the statement. The decision underneath it has been open for two years.

The writing was never the expensive part.

What to Do Next

If you've got an AI-drafted positioning statement that reads well, passes review, and goes quiet on all four decisions, you're not stuck on writing. You're stuck on the trade-offs nobody has been willing to name, and no draft of any quality closes that gap.

That's what a Bare Strategy positioning audit is for. We pull the four decisions out into the open, work them with the people who actually own the revenue number, and leave you with a position sharp enough to lose the wrong deals on purpose. If you want the diagnostic version first, start with how to audit your positioning for the AI era.

If that's where you are, start here. The first conversation is free.