You Google a question your buyer asks all the time. Instead of ten blue links, you get an AI Overview with a tidy paragraph and a few citations. You try the same query in ChatGPT or Perplexity and watch another neat summary appear.
If your brand is lucky enough to be mentioned, it’s usually one line, stripped of style. But even then, the story isn’t yours anymore. The headline your managing editor spent hours crafting has been rewritten, all nuance flattened. Your once-differentiated point of view now reads like it came from a committee.
This is the new reality for marketers: Humans still read content, but increasingly, machines decide what they read first. The job now entails speaking to both audiences — human customers with distinct motivations and mercurial emotions, and robotic algorithms that extract, rewrite, and rank your ideas — without turning your content into boring slop.
The marketers who win in this new era will be the ones whose ideas survive translation. Here’s how to pair standout storytelling with extraction-ready structure.
The Two Audiences Problem
AI Overviews, ChatGPT Search, Perplexity, and voice assistants now read, compress, and represent your content, often before a human sees your page. They snip, condense, and reword, meaning fewer clicks, more paraphrase risk, and new rules for how credit and context travel.
This distribution shift has two practical implications for brands: serve humans with memorable narrative and serve machines with cleanly extractable facts.
Creating content for humans
We may be marketing in the age of machines, but people are still the ones who share (and ultimately buy from) your brand. Ipsos finds that even in marketing content, audiences have a strong preference for human-created content. So even if you’re using AI in your content marketing (which, let’s face it, in 2025 you should be), your message shouldn’t sound mechanical.
What moves people:
- Story arcs with specificity: real scenes, anecdotes, tension, stakes, resolution, and a clear POV that says something new (or says the obvious better).
- Line-level craft: vivid verbs, concrete examples, first-person takes, judicious humor, and sensory detail that earns attention.
- Useful originality: facts or information they can use today, e.g., frameworks, checklists, decision trees, before/after examples.
- Social proof with texture: quotes, screenshots, data points, and customer language that sounds like a person, not a press release.
The challenge:
- Attention math: As attention spans shrink, especially for text-based content, if the first 150 words don’t land, you’ve lost the scroll.
- Audience sophistication: Readers have seen a thousand AI-polished articles; they can spot recycled or over-optimized copy instantly. Every line needs a reason to exist.
- Trust deficit: Audiences crave authenticity; their spidey senses tingle when everything you publish sounds the same. Marketers must balance polish with personality and stay clear without sounding canned.
The takeaway for marketers: Algorithms can summarize information, but only humans can be moved by it. The best human-centric content earns attention by saying something that feels both familiar and fresh, useful and relatable. It draws readers in because it sounds like it was written by someone who understands them.
Even as generative AI reshapes how content is discovered and distributed, you can’t afford to forget these fundamentals.
Creating content for machines
AI engines and LLMs tokenize, extract, and rank. They don’t care how lyrical your prose is or how many hours your writing team struggled to find the exact right turn of phrase for that tagline. They want the claim, evidence, and context mapped to recognizable entities so they can answer a question confidently.
Machines tend to prioritize:
- Clarity & consistency: canonical terms, stable naming, unambiguous definitions, scannable H2s phrased like questions people ask.
- Structure & metadata: JSON-LD schema (Article, FAQ, Product, HowTo), bulleted summaries, glossaries, datelines, author credentials, organization details, canonical URLs.
- Credible citations: first-party data published on your site, outbound links to authoritative sources, methods sections for studies, and consistent cross-site alignment (docs, product pages, partner listings, and PR all using the same names and numbers).
The challenge:
- Zero-click reality: Assistants render answers inline; influence depends on how they summarize and cite you.
- Voice flattening: Witty lines or flowery language get lost in translation; only unambiguous phrasing gets reused.
- Attribution drift: The most parsable source often wins credit, even if they learned it from you.
The takeaway for marketers: Write with the model in mind. Label your answers, standardize your terms, and publish receipts. When writing for AI, clarity — not cleverness — is what earns citations. It’s also becoming clear that freshness is another factor that counts.
How Do Brands Create Content that Speaks to Both Humans and Machines?
To succeed in today’s search-and-summary landscape, you need a dual-pronged content strategy designed for both people and AI parsers. The art is in creating something that reads beautifully to humans while feeding machines the clean signals they need to understand and amplify your story.
Here are five moves to master both:
1. Lead with a scene; label with structure.
Start every piece with a hook that drops readers into a moment by opening with a question, conflict, or vivid visual. Then make sure your subheads, schema, and summaries clearly outline the main takeaways so machines can interpret them. Humans remember stories; machines remember scaffolding.
2. Make every claim quotable and parsable.
When you state an insight, back it with data, name sources explicitly, and phrase it cleanly enough for AI to lift. Think of it as writing for citation: a line that resonates with readers and a sentence that can stand on its own in an AI Overview.
3. Design visuals that speak in two languages.
For humans, visuals should tell a story complete with emotion and context. Machines need text alternatives, descriptive filenames, and clear captions. Whether it’s a chart or a product demo video, metadata is your friend.
4. Use video to teach twice — once to viewers, once to models.
In video or short-form content, open strong; the first three seconds are your headline. Speak keywords naturally in voiceovers, add captions with consistent terminology, and include a structured description when uploading. That helps algorithms surface you, and gives humans a reason to stick with your video until the end.
5. Keep your message stable across every touchpoint.
Machines learn from repetition and alignment. Humans learn from consistency and tone. Use the same product names, taglines, and phrasing everywhere, from blog copy to YouTube titles, so both audiences recognize and recall you.
Measuring Success in a Zero-Click Era
As AI summaries become the new first impressions, traditional traffic metrics no longer tell the whole story. A spike in visibility may not show up as a click, but it can still shape perception, recall, and buying behavior.
The new KPIs live at the intersection of influence and alignment:
- Share of summary: What percentage of AI answers use your phrasing, cite your brand, or reference your data?
- Assisted influence: Does AI visibility correlate with downstream impact, i.e., more branded searches, higher demo requests, stronger sales enablement conversations?
- Funnel impact: Measure the halo: influenced opportunities, demo-to-trial conversions, or ABM coverage lift tied to AI answer visibility.
- Recall tests: Prompt ChatGPT, Gemini, or Perplexity with category questions. Do they echo your terminology, your frameworks, your stats? That’s narrative imprint, not chance.
- Update velocity: How quickly and consistently can you update facts, numbers, and names across every owned channel? Alignment beats speed in a world of retrained models.
We’ve spent years optimizing for people and platforms. Now we’re optimizing for people and parsers. That doesn’t mean stripping the soul from your stories, but it does involve teaching machines how to carry them forward.
The marketers who can do both will own the next era of visibility.
Your stories deserve to be seen and cited. Discover how Contently’s platform helps brands build AI-ready content.
Frequently Asked Questions (FAQs):
What does it mean to create “machine-readable” content?
Machine-readable content is structured in a way that AI systems, search engines, and voice assistants can easily interpret and summarize. That means clear headers, consistent terminology, schema markup, and unambiguous claims so your ideas are easy to extract without losing their meaning.
Should marketers still care about SEO if AI Overviews and chatbots dominate search?
Yes, but SEO now means structuring for understanding, not just ranking for keywords. Schema, entity alignment, and first-party credibility matter more than ever. Traditional keyword tactics may fade, but semantic clarity and topical authority remain critical.
Does this shift change how we approach video and visual content?
Definitely. Treat every visual as both a story and a signal. Use descriptive titles, captions, and metadata so algorithms can understand the context, but still lead with human emotion and pacing that hooks a viewer in seconds.
