Everyone Has the Same AI Now. Your Strategy Has to Explain Why You Still Win

Two years ago a B2B marketing team could put “AI-powered” on a landing page and watch the demo requests climb. That window has closed. Your competitors have the same models, the same APIs and the same vendor decks you do. When every pitch opens with the same claim, the claim stops being a differentiator. It becomes table stakes.

This is the awkward truth sitting under most AI strategy conversations in go-to-market teams right now. The technology is no longer scarce. Which means a plan that amounts to “we picked a good model” is not a strategy at all. It is a procurement decision wearing a strategy’s clothes.

The model is a commodity. Act like it.

Frontier models are converging. Benchmarks that separated the leaders by a wide margin in 2023 now show gaps of a few points, and the gaps close within months of each release. Prices fall on a similar schedule. Whatever edge a team gets from choosing one lab over another evaporates by the next quarter.

Elsewhen, a London-based AI consultancy that works with enterprise product and growth teams, made this point directly in a recent piece arguing that the AI model is not your moat. The argument is that a frontier model arrives knowing everything about the world and nothing about your business, your customers or your pipeline. What it knows about those things is whatever you feed it. That feeding is where the advantage lives.

For marketers this reframes the question. Instead of “which AI should we use”, the question becomes “what do we have that nobody else can give a model”. The honest answer for many teams is uncomfortable: not much yet.

Where the advantage actually sits

Look at the AI deployments in B2B that have survived contact with a sales quarter rather than a slide deck, and three things show up repeatedly.

First, proprietary data. Not the CRM export everyone has, but the closed-loop record of what a buyer did after the content, the call or the trial. Win and loss notes that were actually written. Product usage tied to accounts. A model with that context writes a better outbound sequence than a model with ten times the parameters and none of it.

Second, distribution. Generative AI in business has made content cheap to produce, which has made attention the scarce resource. A team with an owned audience, a partner channel or a community that trusts it can put AI-assisted work in front of buyers. A team without one is publishing into a void that gets louder every week.

Third, workflow. The gains do not come from a chatbot sitting beside the existing process. They come from redesigning the process so that the model does the repetitive middle and people do the judgement at either end. An analysis in The Enterprise World argued that agentic AI in the enterprise has a management problem, not a technology problem, and the same holds inside marketing teams. The tooling is ready. The operating model usually is not.

What a real AI strategy looks like for a GTM team

A useful test: if you deleted the vendor names from your AI strategy document, would it still describe anything specific to your company? If not, you have a shopping list.

The teams doing this well tend to work through a short sequence. They start with the two or three workflows where volume is high and judgement is cheap, such as lead enrichment, first-draft personalisation and campaign reporting. They instrument those workflows so that the output can be measured against pipeline rather than against how impressive the demo looked. They then put the proprietary data in reach of the model, which usually means an unglamorous data engineering project rather than a new subscription.

This is the kind of work that Elsewhen’s AI strategy consulting practice frames as deciding which bets are worth making before deciding which tools to buy. The sequencing matters. Tooling chosen before the bets are clear tends to shape the bets, and rarely in a useful direction.

The boring work wins

There is a temptation, especially in marketing, to reach for the most visible application. An AI avatar on the homepage. A chatbot that answers pricing questions. These make for good launch posts and poor returns.

The better bets are duller. A piece in the Solihull Observer put it well when it argued that the real AI opportunity is hiding in the boring jobs: the admin, the reconciliation, the chasing. In a GTM context that means the CRM hygiene nobody does, the attribution model nobody trusts, the handoff between marketing and sales that drops a third of the leads. Fix those with AI and the pipeline moves. Fix the homepage and the screenshot moves.

What this means for the next planning cycle

The strategic question for 2027 budgets is not whether to use AI. Everyone will. It is what you will have in two years that your competitor, with access to the same models, will not. Proprietary data that compounds. A distribution channel that does not depend on an algorithm. A workflow that has been redesigned rather than decorated.

Teams that can answer that question are building something defensible. Teams that cannot are renting the same capability as everyone else and calling it a strategy. The models will keep getting better for all of them. Only one group is getting better at the same time.