generative engine Optimisation (GEO) is no longer a theoretical exercise. New data confirms that AI chatbots are actively steering purchase decisions, replacing traditional search queries, and reshaping the visibility landscape for every brand we work with. Our team is already integrating structured AI visibility audits, citation strategies, and agent-readiness checks into client workflows — and the evidence below explains exactly why.

Key Takeaways

• 57.5% of AI-using consumers have been talked out of a purchase by a chatbot response, fundamentally altering the conversion funnel.
• 65% of AI users now substitute some product-related Google searches with chatbot queries, shifting where brand discovery happens.
• AI systems pull information from training data, live web crawls, and licensing partnerships — each channel demands a distinct optimisation approach.
• Structured AI visibility audits can now be conducted in six clear steps, giving agencies a repeatable framework for client reporting.
• AI agents are beginning to browse, compare, and recommend products autonomously, meaning site architecture and structured data matter more than ever.

AI Chatbots Are Actively Disrupting Purchase Decisions

The headline number is stark. A survey of 2,338 US consumers found that chatbots talked 57.5% of AI users out of buying a product they were considering, a finding detailed in Semrush’s consumer research on AI chatbot influence. Four in ten respondents also said they dislike chatbot ads, which complicates paid strategies within AI interfaces.

For our clients, this means brand sentiment inside AI-generated answers is now a direct commercial risk. We are prioritising reputation signals — reviews, authoritative mentions, and consistent product data — so that when a chatbot summarises a brand, the output supports rather than undermines the sale.

Traditional Search Queries Are Migrating to AI Platforms

The same dataset shows 65% of AI users have replaced some product-related Google searches with chatbot queries. This is not a marginal shift. It means a measurable slice of the research phase now happens in environments where traditional rankings are irrelevant and AI citation is everything.

We are responding by tracking AI share of voice alongside organic rankings. Pairing these metrics with GA4 conversion data in a single dashboard gives leadership teams a clear view of where visibility is growing or eroding, a reporting approach outlined in Semrush’s guide to creating an AI brand visibility report.

Understanding Where AI Sources Its Information

AI models do not rely on a single input. They draw from three primary channels:

• Training data — historical web content ingested during model training.
• Live web retrieval — real-time crawls triggered by user queries.
• Licensing partnerships — direct content agreements between publishers and AI providers.

Each channel requires a different tactic. We ensure client content is crawlable and well-structured for live retrieval, factually consistent across the web for training data accuracy, and clearly attributed for potential licensing value — all strategies explored in depth in Semrush’s breakdown of AI information sources.

Running a Repeatable AI Visibility Audit

Guesswork is out. We now follow a six-step audit framework to assess how each client brand appears across AI platforms. The process covers prompt testing, citation tracking, sentiment analysis, competitor benchmarking, and structured reporting — a workflow we have adopted from Semrush’s AI visibility audit methodology. Every audit ends with a prioritised action list that feeds directly into our content and technical SEO sprints.

Preparing Client Sites for Autonomous AI Agents

AI agents are a step beyond chatbots. They browse, compare, and recommend on behalf of users without those users ever visiting a website directly. Making a site readable, trustworthy, and easy to cite within these autonomous workflows is now a core technical requirement, as set out in Semrush’s guide to preparing sites for AI agents.

Our technical team is auditing schema markup, improving content clarity, and ensuring machine-readable pricing and product data are present on every key landing page. These are not future considerations — AI agents are already active in 2025.

GEO is now a measurable, auditable discipline. The brands that treat AI visibility with the same rigour as traditional SEO will hold the advantage as search behaviour continues to fragment across chatbots, AI agents, and generative interfaces throughout 2025 and beyond.

Frequently Asked Questions

What is Generative Engine Optimisation (GEO)?

GEO is the practice of optimising your brand’s visibility and accuracy within AI-generated responses from chatbots and AI Search tools. It focuses on earning citations, managing sentiment, and ensuring your content is structured for machine readability.

How do web designers prepare a site for AI agents?

Start by ensuring clean site architecture, comprehensive schema markup, and machine-readable product data. AI agents need to crawl, parse, and cite your content without human intervention, so clarity and structure are non-negotiable.

Why does AI chatbot sentiment matter for e-commerce brands?

Because over half of AI users have been dissuaded from a purchase by a chatbot response. If an AI tool summarises your brand negatively or inaccurately, it directly impacts revenue before a customer ever reaches your site.

What is an AI visibility audit and how often should it be done?

An AI visibility audit assesses how your brand appears in AI-generated answers across major platforms, covering citations, sentiment, and competitor positioning. We recommend running one quarterly to keep pace with model updates and shifting AI outputs.