Every week, new data highlights both the overlap and the divergence between effective organic search techniques across traditional SEO (Google SERPs) and GEO (ChatGPT, AI Overviews, Perplexity, etc.).
It’s a lot to absorb. One week, headlines say traditional SEO tactics work fine for ChatGPT.
The next, you’ll see reports that one platform is elevating Reddit while another is dialing it back.
Given how quickly this landscape shifts, I want to break down the approach, process, and resources my team is using to tackle content in 2026.
This goes far beyond a content calendar.
It’s about combining audience understanding, the interplay of organic platforms, and your brand’s perspective to build a content system that delivers real value.
The right approach for valuable content
The emphasis on quality and value in content is good for marketers.
The tenets of E-E-A-T remain central to our approach because they apply to AI search discoverability as much as to traditional SEO.
Producing strong content still depends on a rich understanding of your audience, good fundamental structures, and solid delivery methods – skills that always matter.
Start with your audience.
- Who are they?
- What do they need?
- What content will help them get there?
Approach content like any other product or service:
- Identify a need and address it.
- Understand the emotions involved.
- Show your credentials – including third-party brand mentions, which are a leading factor in AI search visibility.
Approach content like any other product or service:
- Find or understand a need and address it.
- Know the emotions (i.e., fear, uncertainty, urgency) in play.
- Show your credentials (in the form of authority, expressed in part by third-party brand mentions that are one of the leading factors of AI search visibility traction).
That said, content that has performed well in Google may not work as effectively for LLM search.
Instead of writing primarily for blue-link SERPs, we now focus on creating content that stands on its own as an authoritative, structured data source, with trust and originality as ranking signals.
That means prioritizing clarity, factual depth, and a consistent brand perspective that AI models can reliably quote.
In an age of mass AI content, original insights, data, and human perspective are key differentiators, so content systems should include a step for “original proof” – data, interviews, or commentary that make the material uniquely trustworthy.
We’re also thinking more about how content gets used in AI experiences, not just how it’s found.
Summaries, bullet points, and explainers that answer layered intent are increasingly valuable.
Incorporating schema, structured data, and a consistent brand voice improves how AI systems read and represent your content.
In short, the goal is to optimize for retrievability and credibility, not…
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