How to Position an AI Product When Everyone Sounds the Same

Open ten AI agent startups’ landing pages and you’ll read the same sentence ten times. Everyone is a platform, everyone leverages agents, everyone is intelligent, autonomous, and enterprise-ready. When the words converge like that, better technology stops being enough — because no buyer can tell your better technology apart from everyone else’s claims.

That’s the problem Angelina Yang put in front of Rob Kaminski, co-founder and managing partner of Fletch PMM — a consultancy that does positioning and homepage messaging for early-stage B2B startups and has run the exercise with more than 600 of them. “There are dozens of AI agent companies coming up right now,” she noted. “They all sound and read very similar — very similar words on the landing page, very similar claims, and same category jargon.” Then she handed Rob a real, pre-launch AI agent product — a multi-agent coordination layer with no homepage yet — and asked him to position it live.

Jargon isn’t positioning — it’s a signal you have no common ground

The first thing Rob does with a novel product is check whether its own language means anything to a buyer. In the pitch, phrases like “agent control plane” came up. His reaction is instructive: “That sounds so cool. But what is that? I have no idea.” The test isn’t whether the term is impressive — it’s whether the audience can decode it. “Does your audience know what that means? Do they know what they’d use it for? Do they know who the players are? And if the answer is no, it is a signal that we have not found that common ground.”

This is the specific trap of building on the frontier. “The future’s already here, it’s not evenly distributed,” Rob says. Your earliest customers are often “other tinkerers and innovators,” so you “start using all this crazy language that everyone starts to make up around a new space, only to find out when you go sell it to the mass market, everyone’s like, huh? I’ve never heard of these types of things before.”

Attach the unfamiliar to something familiar

The fix is to hook the new thing onto something the buyer already understands. Angelina got there with a principle she’d picked up from a talk on selling: “If you wanna sell something that’s unfamiliar, you have to attach it to something that’s more familiar to people.” Rob’s response: “Love that. It’s perfectly said.” He reframes it as the core move of positioning — “we’re looking for common ground.”

But he adds a sharp warning about how founders usually reach for familiarity: analogies. When Angelina built an elaborate “it’s an HR manager for your AI agents” metaphor, Rob challenged it. “Analogies can be dangerous because we are now speaking in abstractions.” The moment you talk about managing people, you invite the buyer to evaluate you as a people-management tool, and you drift away from the real job. Familiarity should clarify the actual job to be done, not replace it with a poetic stand-in.

Pick the one difference you can own

The temptation with a capable product is to say everything it does. Rob calls this the founder’s core mistake: “There’s like 10 different things it does that others don’t do… the thinking is if I could just get anyone to know all the things it does, they’ll see how valuable it is and they’ll buy.” Markets don’t work that way, because “the aim of a good positioning is memorability.”

For this product he mapped several distinct, ownable angles instead of blending them:

• Self-learning against the incumbent. Positioned in the agent-orchestration category buyers know, the wedge is a difference LangChain can’t easily claim: “Most of the agent orchestration frameworks like LangChain, you gotta hard code how they’re gonna think through applying which agent goes where. Not us. We continuously optimize the harness and the agents used for each task.”
• Token cost as the whole pitch. Anchored on a job buyers feel in their bill: “Reduce AI token cost by 70% without sacrificing quality.” Here the orchestration is the secret sauce, not the headline — you’re talking to a cost-conscious audience.
• The add-on play. Rather than compete with LangChain, sit beside it: “Keep your tool, but you do need this very separate specialized tool for this job” — a LangChain optimization add-on.

Crucially, you don’t merge these. “When we try and merge these, we then have to summarize,” Rob says, and you end up with the flavorless “we’re the best agent orchestration framework.” Instead, lead with the sharpest, most defensible one. He frames the choice as a bet on where the market is heading: “Skate where the puck’s going… where can we carve out our space in a way that’s defensible?”

Anticipate the AI objection

There’s one more thing AI products in particular have to handle. The more your pitch leans on autonomy, the more you trigger a trust reflex. “When we start to say the AI will do everything, then you start to get, well, wait, do I have any sense of control? Is it gonna be a black box? How will I know what it’s doing?” Rob says. That objection creates adoption friction, so a self-learning or fully autonomous claim has to come with an answer for control and visibility, or it works against you.

Standing out in a sea of identical AI landing pages, then, isn’t about finding fresher adjectives. It’s about locating the one job your buyer already recognizes, owning a single difference they can repeat, and refusing to blur it with everything else you could say.

FAQ

How do you position an AI product when competitors use the same language?

Stop competing on adjectives and anchor on a job the buyer already recognizes, then own one difference they can repeat. Identical landing-page language is a signal you haven’t found common ground with the buyer. Pick the sharpest, most defensible differentiator — like a capability an incumbent structurally can’t claim — and lead with it instead of listing every feature.

Why does jargon hurt an AI startup’s positioning?

Because terms like “agent control plane” impress other builders but mean nothing to mainstream buyers. When your audience can’t say what the term is, what they’d use it for, or who else offers it, that’s a signal you’ve found no common ground. Frontier language works with early tinkerers and then falls flat with the broader market.

Should I use an analogy to explain my AI product?

Carefully. Analogies create familiarity but “speak in abstractions” — an “HR manager for your agents” metaphor invites buyers to judge you as a people-management tool and drifts from the real job. Use familiarity to clarify the actual job to be done, not to replace it. The safest anchor is the concrete task the buyer already performs.

Can I lead my positioning with token cost savings?

Yes, if your buyer is cost-conscious. Rob positions one AI product entirely around “reduce AI token cost by 70% without sacrificing quality,” treating the underlying orchestration as the secret sauce rather than the headline. Leading with a felt problem — the AI bill — can be a stronger wedge than technical capabilities the buyer doesn’t yet know they need.

What is the “add-on” positioning strategy for AI tools?

It positions your product beside an established tool instead of against it: “keep your tool, but you also need this specialized tool for this job.” An AI product might launch as a LangChain optimization add-on. It reduces competitive friction and targets an existing user base, though it carries the risk that the core platform eventually builds the feature itself.

Why shouldn’t I list every feature my AI product has?

Because positioning aims for memorability, and every extra claim dilutes the others. Founders assume that showing all ten differentiators proves value, but buyers can’t retain a stacked message — “we save costs and we’re self-learning and we’re the best” collapses into nothing memorable. Lead with one ownable difference and layer the rest into later conversations.

How do I handle buyer distrust of autonomous AI agents?

Address control and visibility directly. When a pitch claims the AI does everything, buyers immediately ask whether it’s a black box and whether they’ll know what it’s doing. That objection creates adoption friction, so any self-learning or autonomy claim should be paired with an answer about how users retain oversight — otherwise the strength becomes a liability.

What does “skate where the puck’s going” mean for AI positioning?

It means positioning is a forward bet in an immature market, not a data lookup. You can’t fully see which differentiator will matter, so you choose the space you think you can defensibly own and push into it. Waiting to see what buyers care about means a competitor fills that space first while you’re still deciding.

What’s the difference between a secret sauce and a headline in positioning?

The headline is the one job or benefit you lead with; the secret sauce is the underlying mechanism that delivers it. An orchestration engine might be the secret sauce behind a “cut your token bill 70%” headline. Buyers choose based on the job you promise, not the machinery — so the mechanism supports the pitch rather than being the pitch.