A study of 1,702 AI citations found that structured data, semantic HTML, and metadata predict whether a page gets cited, more than the prose does.
A study of 3,861 people found that labeling content AI-generated lowers its perceived accuracy, but the wider reputational hit was smaller than feared.
AI models pay less attention to the middle of a long prompt. New research shows the bias is mechanical and fixable, and why context position is a real lever.
Emotional arousal drives sharing, and interesting content stops traveling fast. Here is what the evidence changes about producing content at volume.
Buyers judge price against a reference, not against value. Here is what fifty years of pricing research changes about how content presents an offer.
Marketing science says brands grow through penetration and memory, not perceived difference. Here is what that changes about producing content at volume.
Marketers predicting which ads sell score about 51 percent, close to chance. Here is what that means for reviewing AI-produced creative work.
Business purchases are group decisions, and the group changes size with the purchase. Here is what fifty years of research means for B2B content.
The quality difference between a brief with search data and one without is not subtle. The output reflects the difference directly.
Generative engine optimization does not replace SEO. It is an additional layer using many of the same inputs to produce a different kind of visibility.
AI citability is a property of content, not a separate ranking lever. It comes from specificity, authority and structure, the same things humans trust.
AI overviews do not pull from high-ranking pages at random. The signals overlap with search but the pattern differs, and your GSC data shows the gap.
Repurposing long-form content to social isn't shortening it. It's finding the most specific claim the piece makes and rebuilding that for the format.
Ghostwriting for executives with AI fails because the model has no voice context. What to load before the first draft — and how to run review faster.
AI compresses production time. An editorial calendar for an AI-assisted team looks different — more per person, same brief discipline, more attention on review.
Quality drift across an AI-assisted team is a process problem, not a personnel one. The fix is upstream — shared briefs, shared context, shared criteria.
Brand voice stays constant across social channels while format adapts. Teams that blur the distinction end up inconsistent on every channel they use.
AI social content fails the same way: generated from a topic, not from a source. Here's where AI earns its place in a social content workflow.
Social calendars built post-by-post collapse under pressure. The version that holds maps production to source material. Here's how to build it.
LinkedIn content that earns engagement comes from specific expertise. AI translates that expertise into post format. The perspective has to be yours.
Product marketing has stricter accuracy requirements and tighter hierarchy constraints than most AI content use cases. Here is how to handle both.
AI can't supply the perspective, experience, or authority that makes thought leadership worth reading. Here is what it can do, and how the process works.
AI can write individual emails. Keeping voice consistent across a sequence is a different problem. Here is the architecture that solves it.
B2B content built for a 6-month sales cycle has different requirements than self-serve or short-cycle content. Here is what changes and why.
In blind taste tests people preferred Pepsi. Knowing they drank Coke reversed it, and the reversal showed up in fMRI. Branding changed the experience.
We built an index of primary research on AI marketing, because too much guidance in this field cites no one and proves nothing. Here is what is in it.
The model that wrote your last draft knew nothing about your brand. It improvised from what you gave it, weighted by where in the prompt you put it.
Untrained reviewers tell AI text from human text at near-random accuracy, so reviewing drafts without a structured criteria set is not quality control.
The IPA Databank's 880 national campaigns show that over-investing in short-term activation degrades long-term efficiency. AI makes that easier to do.
A 1.3 billion parameter instruction-tuned model beat a 175 billion parameter base model with human raters. Rule format matters more than model choice.
The best model tested answered TruthfulQA correctly only 58% of the time, and larger models were less truthful than smaller ones. Scale is not the fix.
In six experiments with 4,600 participants, people detected AI text at near-random accuracy, using heuristics that turned out to be systematically wrong.
Concrete numerical data raised AI source visibility by up to 40% across five generative engines. Here is what the GEO research established for marketing.
Every new AI session starts from the same generic baseline. Brand context does not persist unless you build something to carry it. Why, and how to fix it.
A multi-agent stack is a coordinated set of AI systems, each handling one role, with human oversight at the handoffs. How to build one, and where risk lives.
The three terms get used interchangeably but solve different problems. The distinction changes how you diagnose AI quality issues and what you do next.
AI is genuinely good at some marketing tasks and not good at others. Most teams get this backwards — and the misalignment costs more time than it saves.
SEO and GEO share signals but differ where it counts. What changes when the reader is a model generating an answer rather than a human clicking a result.
The work that matters in marketing, strategy and creative direction and client judgment, is not delegatable. Here is where the human still owns it.
The marketing teams getting real results from AI aren't writing better prompts. They're giving the model better inputs before the session starts.
Generic AI copy is an input problem, not a model problem. What the model knows before it starts determines what it produces — and most teams start cold.
Generative engine optimization (GEO) is getting content cited by AI answer engines. The signals that drive citation differ from those that drive ranking.
The teams getting real value from AI are not writing cleverer prompts, they are feeding better context. Here is the difference, and why it compounds.