Your content has two audiences now. The smart ones write for both.

Quick answer: Marketing content is increasingly read first by AI systems (ChatGPT, Claude, Perplexity, Google's AI Overviews) before it reaches a human buyer. Dual-audience content is written for humans and structured for machines. It gives brands a sharper, more accurate AI representation, and tends to be clearer for human readers too. 

Almost every piece of marketing content produced in the last twenty years was written for the same kind of reader: a human one. That assumption is now half-right, and the half that's changed is the more interesting one for brands prepared to take advantage of it. 

A lot of your content is now read first, and sometimes only, by machines. Procurement AIs build vendor profiles from it, shopping assistants summarise it for consumers, research agents read it on behalf of buyers who never visit your site, and large language models absorb it as training data that shapes how they describe brands to thousands of future prospects. Some of these readers pass a version of the content to a human; others use it to make recommendations on the human's behalf. Either way, content strategy now has two audiences, and the brands designing for both show up more accurately when an AI describes them to a buyer. 

The scale of this shift is well documented. ChatGPT reached 900 million weekly active users in February 2026, while Google's Gemini app surpassed 750 million monthly active users in Q4 2025. According to HUMAN Security's 2026 State of AI Traffic report, AI-driven web traffic grew 187% between January and December 2025, with automated traffic now growing eight times faster than human traffic. Who actually reads your content has changed. 

What dual-audience content actually means 

The Connection Drift identifies this shift and names what it calls "dual-audience content infrastructure," and the framing is direct: 

"Your content must tell compelling stories for human buyers whilst providing structured data enabling AI accurate representation. When AI evaluates 'companies with proven delivery capability', your messages need to be correctly understood and evidenced for AI to put you forward." 

Two parts of that are worth holding onto. The first is "structured data enabling AI accurate representation." The machine reader needs more than prose. It needs the underlying facts, claims and connections in a form it can extract. The second is "correctly understood and evidenced." The bar isn't visibility, it's accuracy. Content that gets you found but mischaracterises what you do is a worse outcome than content the AI quietly skips. 

Why some brands get summarised as generic 

The report names the shift. What we see in practice is that the gap between brands that adapt and brands that don't comes down to two writing habits — both common in marketing content, both invisible to a human reader, and both responsible for AI summarising strong brands as generic. 

The first is implying more than is stated. A sentence like "we work with leading financial services brands" or "trusted by millions of customers" relies on a human reader filling in the context — what counts as leading, what counts as working with, what counts as trusted. An AI trying to summarise that claim will either ignore it or generalise it into something blander than what was meant. The human-centred writing instinct says "show, don't tell," but for a machine reader that instinct produces a flatter, less accurate brand representation, so tell and show. 

The second is separating the story from the evidence. A human reads a case study or a customer story and infers the pattern. An AI sees a story about one client or one customer — and unless the story is explicitly linked to a capability, a sector, a product category or an outcome type, the pattern doesn't travel. Brands with strong capabilities often have the evidence sitting there. Tagging it to the claim is what makes it usable. 

There's a body of external research that backs this up. A foundational study from Aggarwal et al. (Princeton, Georgia Tech, Allen Institute for AI, IIT Delhi) on generative engine optimisation found that adding citations, statistics and explicit quotations to content produced a 30–40% lift in how often AI systems cited that content in their responses, with citations the single highest-leverage tactic. Different research points the same way. The more explicit and evidenced a piece of content is, the better AI represents it. 

Where we'd start 

The Connection Drift names the shift. Here's how we'd answer the practical question of what content teams should do about it. 

1.  Build for both readers from the start. Don't optimise for AI after the fact. The brief, the structure and the writing all need to assume two audiences from the outset. 

2.  Make claims explicit. Where a human reader would fill in the context, give the machine reader the same context in plain text. The implicit becomes explicit; the elegant gets paired with the concrete. 

3.  Tag evidence to claims. Every capability or product claim should sit close to the proof — case study, customer story, named expert, verified review. The connection has to be on the page, not in the reader's head. 

This post draws on themes from our thought-starter, The Connection Drift: Navigating AI's disruption of customer acquisition. Download the full report to explore the three strategic shifts in more depth.