Adapt your content strategy for traditional search and emerging AI experiences through audience insight, originality, and structured content.

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 just ranking.

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Building a process to create valuable content

The content strategy path I like to prescribe is as follows:

• Problem aware: Empathize with your audience by articulating their problem in a clear, differentiated way.
• Solution aware: Present your audience with objective, detailed, valuable options for solutions to their problem.
• Brand aware: Develop your brand’s association as a trusted solution provider.
• Product aware: Position your specific product or service as the ideal solution for the reader’s problem.

Once your research is conducted, you’ll have what you need to craft content and deploy it in multiple ways.

The linear workflow that persisted for years in traditional SEO, however, must evolve into a modular content engine – one where a single research output fuels multiple media types (articles, YouTube scripts, short-form video, LinkedIn posts, etc.), with platform-native variations all aligned to a central narrative theme.

Resources to use in content development

A few years ago, I would have started with well-known, well-established tools like Ahrefs and Semrush.

While those remain useful for benchmarking, they no longer represent how people discover or consume information as AI search transforms user behavior in real time.

AI search abstracts away keywords – users are asking multi-intent questions, and LLMs are generating synthesized answers.

SEO analysis is now, rather than the main starting point, one piece of the research pie.

It’s still important, but search optimization is now embedded throughout the content process.

The tools below have been important in the past, and my team still leans on them as part of a more holistic approach to content planning.

Qualitative interviews

Surveys are useful but can be expensive when you’re trying to reach audiences outside your CRM.

You can still get strong insights by engaging subject matter experts who share the same professional experiences, challenges, and responsibilities as your target audience.

Slack communities, live or virtual meet-ups, and memberships in organizations like the AMA or ANA can all offer on-the-ground perspectives that support your content mapping.

Audience analysis from AI systems

It’s critical to include intent analysis from AI tools and conversational search data.

Understanding how users phrase questions to AI systems can inform structure and tone.

Not all social media posts are created equal, but understanding your audience includes knowing where your audience likes to engage: X, Reddit, YouTube, TikTok, etc. (Not to mention that Reddit citations show up prominently in ChatGPT results.)

Utilize these platforms to gather real-time information on what your audience is discussing and to increase brand mentions, which will send strong signals to ChatGPT and similar tools.

Competitor analysis

Shift from tracking keyword overlap to evaluating content depth, originality, and entity coverage – where your brand’s expertise can fill gaps or improve on generic AI-summarized answers.

Adjust the KPIs to assess the impact of your content

For many years, SEO marketers focused on impressions and clicks, although more advanced practitioners also incorporated down-funnel metrics, such as leads, conversions, pipeline impact, and revenue.

Today, SEOs must expand their KPIs to include brand mentions in:

• AI summaries.
• Content-assisted conversions.
• Cross-channel engagement depth.

These are the new indicators of helpfulness and value.

Resist the urge to rest on your laurels

We’ve seen strong successes with AI search visibility that complement our traditional SEO results, but our understanding of best practices continues to evolve with each new round of aggregated data on AI search results and shifting user behavior.

In short, keep a parallel track of what has worked recently and where the trends are heading, since ChatGPT and its competitors are changing user behavior in real time – and with it, the shape of organic discovery across platforms.

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Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. Search Engine Land is owned by Semrush. Contributor was not asked to make any direct or indirect mentions of Semrush. The opinions they express are their own.

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About the Author

Adam Tanguay is Head of Growth at Jordan Digital Marketing, which he joined in Feb. 2019. Formerly Head of Marketing at Webflow and Head of Organic Growth at Weebly, Adam has developed successful growth programs with a mix of content strategy, copywriting, technical know-how, and analytics acumen across a range of organic channels.