Tactical steps for building a GEO-ready content strategy

Andrew Cross is CEO of Walker Sands. 

As generative AI tools reshape how B2B buyers find information, the traditional SEO playbook no longer guarantees visibility. Instead of scrolling through familiar blue links, decision-makers increasingly rely on AI-powered chatbots, copilots and summarization tools to surface answers in real time.

To stay visible, brands need to optimize not just for search engines but for the AI models interpreting and generating content on demand.

That’s where generative engine optimization, or GEO, enters the conversation — not as a replacement for SEO but as an evolution of it. While SEO focused primarily on keywords and rankings, GEO emphasizes the importance of trust, authority and machine-readable context.

So how can you optimize your digital content for inclusion and performance in GenAI-generated answers?

Audit current visibility and build an intentional content strategy

Start with an audit to measure baseline visibility using LLM responses for the brand on strengths, weaknesses and reputation in comparison to competitors in both trained and search-enhanced GenAI responses. Determine where your site is — and isn’t — cited, adding missing opportunities to a prioritized list.

During this process, also take note of any credibility gaps on your site that could be reinforced through case studies, customer testimonials, awards or other content enhancements. This analysis will help inform your editorial calendar and improve your chances of discoverability and citation.

Craft content that performs

Creating GenAI-friendly content goes beyond traditional SEO techniques. After building your content strategy, implement these best practices for writing and structuring your content so it’s more easily understood, cited and trusted by generative AI systems and human readers alike.

1. Structure content for machine readability
   AI models thrive on structure and clarity. As large language models expand their role in content discovery, schema markup — originally designed for search engines — is more important than ever.

By applying schema to elements like articles, FAQs, product information, author bios and organizational details, you’re giving AI systems the contextual signals they need to interpret and elevate your content accurately. In many ways, schema becomes the bridge between what you publish and what AI models understand.

2. Build author credibility
   Credibility is everything, and AI models are getting better at evaluating it. Google’s E-E-A-T framework — experience, expertise, authoritativeness, trustworthiness — already plays a role in search rankings, but those same signals increasingly inform how AI models decide which voices to amplify.

Robust author pages with professional bios, credentials, published works, media appearances and verified profiles send strong signals of expertise, making your subject matter experts more likely to be sourced in AI-generated responses.

3. Validate your claims
   AI models favor transparency. Content that includes clearly attributed facts, data points and original research is more likely to be surfaced in AI-generated answers.

Inline citations and backlinks to reputable third-party studies don’t just enhance credibility for human readers — they also signal trustworthiness to AI. As models weigh which content to surface, transparent sourcing becomes a differentiator.

4. Write for summarization, not just consumption
   While humans often engage with long-form content, AI typically scans for key insights it can extract quickly.

Structuring content with concise introductions that answer key questions upfront, question-based subheads that mirror common queries and consistent reinforcement of key themes helps ensure that both readers and machines can easily grasp the most important takeaways.

In this context, clarity outweighs cleverness. Remember the old inverted pyramid from journalism class? Essential information should never be buried deep in the copy.

5. Keep content fresh and model-relevant
   Generative AI models are trained on snapshots of the internet captured at specific points in time. If your content isn’t refreshed regularly, there’s a risk it won’t make it into newer model datasets — or worse, that it will become outdated in real-time AI interactions.
   Establishing a cadence for updating key pages, refreshing data points and expanding content with new examples helps ensure your brand stays visible and relevant as models evolve.

6. Recognize the shifting role of media
   In an AI-driven media landscape, niche outlets, blogs and owned content now carry more weight than ever before.

AI tools aggregate information from a wide range of sources, meaning that well-optimized owned content may influence AI-generated outputs alongside coverage from traditional top-tier publications.

This shift also opens the door for PR teams to explore partnerships with media companies whose content feeds directly into AI model training data — a strategic move that may become increasingly important.

The future of discoverability is hybrid

The rise of GEO reflects a larger shift: the growing convergence of PR, SEO and content marketing.

As search evolves, so must the teams responsible for driving visibility. Moving forward, successful brands will build content ecosystems that not only serve immediate business needs but position their expertise to be discovered across both human and machine touchpoints.

By adopting these tactical GEO steps, brands can future-proof discoverability, ensuring they’re findable whether a journalist is using an AI research tool, a B2B buyer is querying an AI assistant or a decision-maker is looking for trusted, authoritative expertise in an increasingly noisy digital landscape.