Your prospects are asking ChatGPT which tools to buy before they ever open Google. When they do search, an AI Overview answers the question right there on the results page. The old SEO playbook still matters, but if that's all you're running, you're invisible to half the discovery layer. Here are 8 strategies the sharpest B2B teams are using to show up in both.

1. Optimize for AI Citation, Not Just Rankings

Generative Engine Optimization (GEO) is the practice of structuring content so AI platforms like ChatGPT, Perplexity, Claude, and Gemini can understand, extract, and cite it when generating responses. It operates on different principles than traditional SEO. AI models evaluate semantic relevance across passages, not just page-level keyword signals. The practical move is to write self-contained answer blocks under clear headings, include named experts and original data, and structure content so an LLM can pull a clean, quotable chunk without needing to parse through filler. One B2B SaaS case study showed a 187% increase in ChatGPT citations after restructuring content this way.

2. Build Topical Authority Through Content Networks

Keyword density has almost no correlation with high rankings anymore. What AI algorithms and Google both reward is topical authority: demonstrating that you genuinely understand a subject and how its concepts connect. The strategy is to build pillar pages with comprehensive guides, then link them to detailed subtopic pages that cover every angle. Clearscope's 2026 research calls this the "long long tail," where AI models evaluate not just individual pages but the network of associations surrounding a topic. A single well-optimized page won't cut it anymore. You need a coverage map.

3. Use Schema Markup to Feed the Machines

Structured data has gone from a nice-to-have to a hard requirement. Research from Whitehat SEO shows that JSON-LD schema markup increases AI Overview selection rates by 73%, yet only about 12% of websites currently use it. Implementing FAQPage, Article, HowTo, and Organization schemas gives both Google and AI systems a clean, machine-readable map of your content. For B2B companies, this also means making product specs, integrations, and pricing structured enough that an AI agent can parse them without scraping your HTML.

4. Publish Original Research and Proprietary Data

AI models are trained on consensus. If your content simply restates what everyone else is saying, it blends into the dataset and gets skipped. The content that earns citations is original: proprietary surveys, benchmark data, industry reports based on your own user data, or frameworks you've coined and explained clearly. This is where B2B companies have an unfair advantage. You're sitting on internal data that nobody else has. Packaging it into public-facing research creates something LLMs can't generate on their own, which makes you a primary source rather than a summary of someone else's work.

5. Treat Reddit, LinkedIn, and Forums as SEO Surfaces

Reddit is now among the top downstream destinations from Google Search, and AI models pull heavily from forum threads, LinkedIn posts, and community discussions when generating answers. The strategic play is to treat these platforms with the same rigor you'd apply to your blog. Have product experts answer technical questions on Reddit using your brand account. Publish practitioner insights on LinkedIn that mirror your site's editorial angle. These third-party mentions build the kind of distributed authority that both Google and AI systems use to assess credibility. If your brand only exists on your own domain, you're invisible to half the discovery layer.

6. Attach Real Humans to Your Content

Anonymous content is a trust killer for AI systems. LLMs and AI Overviews both weight content more heavily when it's attributed to a named expert with a verifiable digital footprint. That means author pages with bios, LinkedIn profiles linked to the byline, and credentials that match the subject matter. Google's E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness) have always mattered, but in 2026 they've become the primary filter AI systems use to decide what to cite. Investing in your team's personal brands pays direct SEO dividends.

7. Use AI for Content Production, Not Content Strategy

The 2026 research from TopRank Marketing and Ascend2 found that 93% of B2B marketers say research-based content is effective at driving engagement. But 72% of marketers fear AI will further homogenize content quality. The winning approach is to use AI tools for the production layer: drafting, repurposing, outlining, optimizing. But keep strategy, editorial judgment, and original thinking human-led. AI can help you publish faster. It can't help you publish something worth reading if the ideas aren't there to begin with.

8. Track AI Visibility as a Core Metric

If you're not measuring how often your brand gets cited by AI tools, you're flying blind on an increasingly important discovery channel. Tools like Otterly.AI, Peec AI, and BrightEdge's AI search tracking can monitor whether your brand appears in ChatGPT, Perplexity, and Gemini results for your target queries. This is still a developing measurement category, but the teams that start tracking it now will have a compounding data advantage. Add AI citation frequency to your existing SEO reporting alongside organic rankings and traffic. It's the only way to see the full picture.