AI vendor marketing has a positioning problem. Features will not save it

Microsoft Copilot has spent the last year being folded deeper into the fabric of work. Salesforce Agentforce has been pushed hard as a new organising idea for enterprise automation. Google, OpenAI, Anthropic, Cohere and a long list of specialist vendors are all fighting to define what AI should mean inside the business, not just inside the browser.

That is the more interesting marketing problem.

The AI market is not short of capability. It is short of settled meaning. Buyers are being asked to compare models, agents, assistants, copilots, orchestration layers, infrastructure, embedded features and “AI-powered” everything. Some of it is genuinely transformative. Some of it is a familiar workflow wearing a brighter hat.

For AI vendor marketing, this creates a sharper challenge than simple differentiation. The job is not just to explain why one vendor is better than another. It is to help buyers understand what kind of thing they are buying in the first place.

That is a conversation we keep finding in the work The Rubicon Agency does with technology businesses. Product teams often have the capability story. Sales teams often have the use case story. Leadership often has the ambition story. The market, unfortunately, needs one coherent story it can remember, repeat and defend.

That is where AI vendor positioning has to do real commercial work.

SaaS usually gave marketers a tidy object to sell: the platform, the workflow, the dashboard, the subscription. AI is messier. The thing being sold may be an outcome, an embedded capability, a trained model, a reasoning interface, a data layer, a governance system or a new category of labour.

This is where a lot of AI vendor marketing starts to wobble. The language tries to sell the algorithm because the proposition has not yet decided what the buyer is actually buying.

Gartner’s 2025 analysis makes the same commercial problem visible from another angle: generative AI capabilities are becoming expected features across the technology stack, and traditional provider-enterprise value relationships are no longer enough to drive adoption.

In plainer terms, “we have AI” is not a position. Soon, it may barely be a credential.

The more useful question is whether the proposition is AI-enabled, AI-innovated or AI-created.

• AI-enabled propositions use AI to improve something buyers already understand.
• AI-innovated propositions change the workflow, economics or decision model.
• AI-created propositions exist because the underlying AI capability makes a new market behaviour possible.

Those distinctions matter because each one asks for a different story, a different proof model and a different buyer conversation.

As we argue in Marketing strategy guide for AI vendors: More than SaaS marketing with a shinier badge, the mistake is to treat AI as a shiny overlay on a familiar SaaS GTM model. It is not. It changes the sellable.

AI vendor positioning is the strategic definition of what the AI offer is, who it is for, why it matters and why buyers should trust it now. It must connect technical capability to commercial value, risk reduction, workflow impact and organisational adoption, rather than relying on model claims or feature lists.

There is a seductive simplicity to positioning around model performance. It gives the story a concrete centre: faster, smarter, cheaper, more accurate, more autonomous. The trouble is that model claims age badly. Benchmarks move. Releases land. Open-source alternatives improve. Incumbents bundle competing capabilities into tools buyers already use.

Cohere’s Joëlle Pineau put a sharper edge on this when she described the company’s position as “value, not magic” and said Cohere aims for AI that delivers business ROI rather than abstract superintelligence.

That is not just a research philosophy. It is a positioning choice.

Anthropic has made a similar move from another angle, with Dario Amodei talking about business needs such as coding, scientific work and intellectual tasks. OpenAI is also pushing harder into enterprise, with Sam Altman signalling enterprise as a major focus.

The pattern is clear. The market is moving from spectacle to use. From models to moments of work. From “look what it can do” to “look what it changes”.

That shift echoes what we often have to work through with AI and technology clients. The first version of the story is usually capability-led because capability is what the product team has built. The stronger version is value-led because value is what the buying group has to justify.

That difference sounds small. It is not.

AI product positioning is harder because the buyer is often evaluating a moving capability rather than a fixed software object. The product may improve, degrade, hallucinate, depend on data quality or require workflow redesign. That means positioning must explain value, limits, governance and adoption conditions, not just features.

The AI-enabled vendor has the easiest first story and the hardest differentiation problem. If AI improves an existing workflow, the buyer understands the use case quickly. But competitors can often make similar claims, and incumbents can fold similar features into existing contracts.

The AI-innovated vendor has more room to create distance. Here, AI changes how the work gets done. Think of tools that do not merely assist a human analyst, lawyer, developer or service agent, but reshape the process around them. The marketing has to explain the new workflow without making the buyer feel stupid for not already seeing it.

The AI-created vendor has the most exciting story and the biggest credibility burden. If the market behaviour did not really exist before, the proposition has to build its own mental model in the buyer’s head. That is expensive. Not just in media terms, but in attention, education and sales patience.

This is where proposition discipline matters. The commercial question is not “how do we describe the technology?” It is more awkward and more useful:

• What is the buyer really buying?
• What old behaviour are we asking them to abandon?
• What new belief must they accept before the proposition makes sense?
• What evidence reduces the perceived risk of acting now?
• What language will sales, product and leadership all be willing to use?

This is why The Rubicon Agency’s proposition development work is relevant to AI vendor marketing. The issue is not wordsmithing. It is deciding what truth the market can understand, remember and buy.

Without that discipline, the market inherits the product team’s internal language. And nobody ever bought a platform because the internal taxonomy was impressively complicated.

Many AI vendors get their first motion from enlightened technologists, developers, data leaders or digitally mature operators. That audience can tolerate rough edges. They can infer value from architecture. They enjoy the possibility space. Some even like being early enough to suffer.

Bless them. Every category needs its pathfinders.

But early technical enthusiasm is not the same as organisational confidence. Once a vendor moves beyond proof-of-value, the story has to expand. The buyer group starts to include finance, procurement, risk, legal, security, operations and business owners whose tolerance for “trust us, it is clever” is refreshingly low.

This is where many AI stories cede the ground they worked so hard to win. The initial positioning lands with the friendly community, then fails to scale across the broader business.

We see this most clearly where a proposition has earned early interest but not yet earned enterprise readiness. The audience changes. The questions change. The proof burden changes. The language that once felt exciting can suddenly feel undercooked.

McKinsey’s 2025 State of AI survey shows the gap clearly. Nearly nine in ten respondents said their organisations regularly use AI, but most had not embedded it deeply enough to realise material enterprise-level benefits, with roughly a third beginning to scale AI programmes.

The vendor implication is uncomfortable. Adoption is not the same as value. Pilots are not the same as transformation. Curiosity is not the same as budget confidence.

AI vendors move from developer adoption to business buyer confidence by refining the story for risk, governance, value and organisational change. The early technical audience may buy possibility. The wider business needs proof that the proposition can scale, integrate, comply, improve work and justify budget beyond the friendly early community.

AI buyers are becoming harder to impress, and rightly so. Too many have seen a beautiful demo turn into a governance meeting with no obvious owner. Too many have watched a proof-of-concept die quietly because nobody could prove what changed.

Forrester’s 2025 buyer commentary points in the same direction: B2B buyers are shifting towards evidence-backed validation, with growing scepticism around AI-generated claims and a demand for measurable results and transparency. G2’s 2025 Buyer Behavior Report also shows that AI is now embedded in the buying conversation rather than sitting outside it.

For AI vendor marketing, proof cannot be treated as a late-stage sales asset. It has to sit inside the positioning system.

A credible AI value proposition should make several things clear:

• what the AI does
• where it works
• what it depends on
• where it should not be used
• how it is governed
• what changes for the user, team or business
• what evidence the buyer can expect before committing further

This is not about making the story defensive. It is about making the story believable.

That is why the companion article Why AI vendors need buyer-enablement content, not more thought leadership matters. AI buyers do not need more vaguely confident essays about the future. They need content that helps them assess readiness, risk, use cases, integration, procurement scrutiny and internal adoption.

The Rubicon Agency’s strategic content work often sits in this exact space: changing mindsets, influencing new models and helping buying groups make sense of complicated decisions. For AI vendors, that is not a content tactic. It is part of how the market learns to buy.

AI vendor language is currently doing too much and not enough at the same time.

It is doing too much when it tries to compress model architecture, automation, workflow redesign, business outcomes and category ambition into one heroic sentence. It is not doing enough when every vendor sounds like it sells the same intelligent assistant for every function in every sector.

Lexicon matters because AI products are less tangible than most SaaS products. A dashboard can be shown. A workflow can be mapped. A physical product can be photographed. An AI proposition often has to be understood through metaphor, use case, proof and boundary.

That is why careless language becomes a commercial risk.

“Agentic” can sound visionary to a technical buyer and reckless to a risk owner. “Autonomous” can signal productivity to one stakeholder and loss of control to another. “Co-pilot” may feel reassuring until the buyer asks who is legally flying the plane.

The answer is not to sand the story down until it says nothing. The answer is to build a controlled vocabulary that flexes by audience without changing the underlying proposition.

This is where the experience of The Rubicon Agency’s AI-powered virtual assistants work for AT&T and Five9 is useful. The task was not simply to describe an AI engine. It was to develop use cases, market differentiation, vertical application and sales materials that made the proposition understandable across business, employee and customer outcomes.

That is the level at which AI vendor positioning has to operate.

Not model first. Meaning first.

The next phase of AI vendor marketing will be less forgiving than the last one.

The market has enough excitement. It has enough demo clips. It has enough interchangeable claims about productivity, intelligence and automation. What it lacks is clarity about which AI propositions deserve trust, budget and organisational change.

The vendors that win will not necessarily be the ones with the loudest model claim. They will be the ones that can make the sellable feel real: specific enough for technical buyers, credible enough for risk owners, valuable enough for finance and simple enough for the wider business to repeat without mangling it.

That is the real positioning test. Can the market say back what you do, why it matters and why it is safe enough to matter now?

A third-party agency or expert view can help here, not as decoration, but as pressure. Someone needs to challenge the internal language, collaborate with the product and marketing teams, test the story against buyer reality or pick up the reins when the market is moving faster than the message.

If your AI story is growing faster than your buyers can understand it, book a call.

The Rubicon Agency