AI-powered search engines are now actively recommending local businesses by name, and the rules for earning those recommendations look nothing like traditional SEO. Our team has spent recent weeks stress-testing new strategies across client campaigns, and the latest wave of industry guidance confirms what we’re seeing on the ground: local businesses that fail to optimise for generative engine output will lose footfall to competitors who do. Here’s what we’re acting on right now.

Key Takeaways

• AI Search engines use structured local signals—reviews, citations, and entity data—to decide which business to name in a generated answer.
• A new generation of AI visibility tools lets agencies monitor brand mentions across ChatGPT, Perplexity, Google AI Overviews, and other platforms.
• Core SEO fundamentals still underpin AI search performance; generative engines pull from the same indexed content Google does.
• Content gap analysis is now essential for surfacing the exact questions AI models are answering—and ensuring your client’s site provides the best response.
• Enterprise-grade tooling is becoming necessary for agencies managing AI visibility at scale across multiple client portfolios.

Optimising Local Businesses for AI-Generated Recommendations

When a user asks ChatGPT or Google’s AI Overview “best plumber near me,” the model assembles its answer from structured data, review sentiment, and authoritative local content. We’ve started auditing every local client’s Google Business Profile, schema markup, and third-party citation consistency as a direct response to this shift, aligning our process with the practical framework for AI search optimisation for local businesses published by Semrush. The businesses that get named are the ones with clean entity data, strong review volume, and content that directly answers common service queries.

Tracking Brand Presence Across AI Search Engines

You can’t improve what you can’t measure. Traditional rank tracking doesn’t capture whether your brand appears in a ChatGPT response or a Perplexity citation. Our team is now evaluating dedicated monitoring platforms, guided by Semrush’s breakdown of the best AI visibility tools for 2026. These tools track:

• How often a brand is cited in AI-generated answers
• Which competitor brands appear instead
• Sentiment and accuracy of AI mentions

We’re rolling this into monthly client reporting so stakeholders can see exactly where they stand in the AI search landscape.

Core SEO Still Powers Generative Engine Results

There’s a temptation to treat GEO as an entirely separate discipline. It isn’t. Generative models pull from indexed web content, which means technical SEO, on-page optimisation, and high-quality backlinks remain foundational. We continue to build every client strategy on these pillars, consistent with the comprehensive SEO guide maintained by Semrush. Site speed, crawlability, internal linking, and structured data are non-negotiable. Without them, AI engines have nothing reliable to reference.

Using Content Gap Analysis to Win AI Answers

AI models favour content that directly and comprehensively answers a user’s question. If your site doesn’t cover a topic, the model will cite someone else’s. We now run quarterly content gap audits for every client, following the step-by-step content gap analysis methodology outlined by Semrush. This identifies the exact queries competitors rank for—and that AI models are pulling from—where our clients have no presence. We then produce targeted content designed to fill those gaps with clear, factual, well-structured answers.

Scaling AI Visibility Management Across Client Portfolios

For agencies managing dozens of accounts, standard tools hit their limits quickly. Workflow automation, cross-project reporting, and team-level permissions become critical. We’ve been comparing platform tiers to find the right fit, a process informed by Semrush’s own comparison of its standard and enterprise platforms. For most single-site businesses, the standard tier is sufficient. For our agency, enterprise-grade access gives us the scale and reporting depth our clients expect.

AI search is not replacing traditional SEO—it’s layering on top of it. The agencies and businesses that treat GEO as an extension of solid technical and content foundations will capture visibility that others forfeit. Our team is embedding these practices into every new and existing client engagement, starting now.

Frequently Asked Questions

What is generative engine optimisation (GEO) for local businesses?

GEO is the practice of optimising a local business’s online presence so that AI search engines—like ChatGPT, Perplexity, and Google AI Overviews—recommend it by name. It involves cleaning up structured data, building review signals, and creating content that directly answers common local queries.

How do web designers ensure a site appears in AI search results?

They build technically sound websites with clean schema markup, fast load times, and well-structured content that AI models can easily parse. Strong internal linking and comprehensive topic coverage also increase the likelihood of being cited in generated answers.

Why does content gap analysis matter for AI search visibility?

AI models assemble answers from the most relevant indexed content available. If your site doesn’t cover a topic your competitors do, the AI will cite them instead of you.

What tools can track whether my brand appears in AI-generated answers?

Dedicated AI visibility platforms now monitor brand mentions across ChatGPT, Perplexity, Google AI Overviews, and similar engines. These tools report citation frequency, competitor comparisons, and sentiment accuracy in real time.

How is GEO different from traditional SEO?

GEO builds on traditional SEO but focuses specifically on earning citations within AI-generated responses rather than just ranking in a list of blue links. It places extra emphasis on entity clarity, structured data, and direct-answer content formats.