Notes from the B2B Marketing Live UK event, 19 - 20-Nov-2025, ExCel, London
Following on from our recent work[1] considering the general subject of the potential impacts of artificial intelligence (AI) on web search, web traffic and trademarks, these notes from the recent B2B Marketing Live event present additional detail on current opinions relating to Generative Engine Optimisation ('GEO'); that is, the general suite of techniques which can be employed by brand owners with the aim of trying to ensure that their brand and website content are referenced as strongly as possible in results from AI-powered search.
Key points
The search landscape is fundamentally changing
The appearance of AI overviews (AIO) in search results has recently seen huge growth; AIO are now used by 1.5B users per month in over 200 countries, with anywhere between 18% and 47%[2] (estimates vary) of searches generating AIO, and increases of around 116% in the prevalence of AIO since May 2025[3]. Furthermore, AIO typically cover 70% of the SERP (search-engine results page) screen space. Complex, decision-related queries (such as those associated with B2B business) may also be more likely to generate AIO.
Accompanying these trends are the facts that, although website impressions (i.e. appearances in organic search results) are typically found to be up by brand owners, there have been significant decreases in click-through rates (CTR) to websites (down by between 32% and 35% for the highest-ranked results)[4,5], with the CTR for the top-ranked (organic) search-engine result having been found to have decreased from around 28% to 20%.
In addition, 'standalone' AI tools are also seeing large increases in usage, with many users increasingly not using 'classic' search tools at all, but instead being reliant on obtaining their answers 'in platform'. The most popular such tool, ChatGPT, now has 5.24B users per month, and 89% of B2B buyers use gen-AI as part of their purchase decision-making process. Search engines are also beginning to introduce embedded functionality such as Google's 'AI Mode', analogous to a gen-AI tool (though with the Google example currently only seeing 1-2% adoption).
Marketing success can no longer be judged (just) by click-through rates (CTR)
The evolutions in the search landscape are driving an increased acceptance by brand owners of 'zero-click' and reduced website traffic. Conversely, however, the extent of citation by AI tools is becoming an ever-more important metric, and is itself associated with factors such as brand visibility, authority, customer engagement, and trust.
Also of key importance is the fact that inbound brand referrals from generative AI ('gen-AI') tools or AIO tend to be more likely to convert to successful sales than click-throughs from classic search (due to an implicit trust by users of AI sources), with brands typically seeing sales growing more quickly than their observed increases in AI-driven traffic[6]. Overall, brands cited in AIO receive 35% more organic clicks and 91% more paid click than those not cited. Furthermore, customers searching asking complex (i.e. later decision-stage) business queries via LLMs are also likely to be associated with higher conversion rates, because of their inherent greater degree of 'pre-qualification', and overall it is noted that 61% of purchasers are influenced by AI responses. These observations are leading to the emergence of a dual objective for brands: (i) increasing AI traffic (i.e. GEO) and (ii) monetising more of this traffic (a relatively easier problem because of the greater inherent conversion rate from AI traffic).
Discoverability by humans and AI is key
It is becoming increasingly clear that brand and website content needs to be optimised for discoverability by both human searchers and AI systems (agents, LLMs, etc.). Whereas human users traditionally have preferences towards website clarity, usability and value proposition, AI systems tend to favour a different set of characteristics for those websites to be used more frequently as their trusted sources. These might typically include:
- Data being presented in a structured format - including mark-up, schemas, APIs, etc., plus an emphasis on plain-text content (and reduced usage of features such as Javascript rendering), use of semantic HTML, XML site-maps, technical signposting for crawlers (e.g. allowing access in the robots.txt file), etc.
- Indicators of trust - e.g. author mark-up, source references, authoritative in-links, etc. Trust and credibility for brands can be boosted through initiatives such as blogging, providing guest posts for external websites, and publishing content in other formats (such as on YouTube).
- Sites incorporating extensive volumes of original information - including comprehensive FAQ sections, etc. There is also some suggestions that "best" or "top" lists ('listicles') are favoured by LLMs in some cases. Brands can also increase the likelihood of being referenced in AI responses by including 'actionable IP' (brand-specific) content on their websites, particularly where this addresses key questions asked by buyers in their decision-making process and provides sales qualification information. The production of specific, textual content to address specific requirements for landing pages from pay-per-click (PPC) ads may also be beneficial.
Increasingly, brands are utilising segmented websites, incorporating both human-focused areas with extensive brand-heavy, visual and interactive ('conversion optimised') elements, and (often human-invisible) AI-favourable content, which is text-heavy and detail-, explanation- and answer-oriented. Traditional marketing techniques, such as offering downloadable information sheets (especially if 'gated' with a requirement for users to submit contact details) are becoming less popular, with users instead tending increasingly to source answers from (ungated) gen-AI tools.
Understanding how AI systems process queries is crucial - and highlights a continuing importance of classic SEO
When an AI tool is presented with a prompt or query, it typically splits these into smaller sub-queries for individual processing ('query fan-out'). Frequently, however, these tools will utilise classic search engines to source responses to thesensub-queries (e.g. Gemini uses Google, ChatGPT uses Bing), although some make use of proprietary bots. It is also noteworthy that Google remains the market leader for organic search by a significant margin (with AI engines still seeing a market share of less than 1%).
As such, traditional search-engine optimisation (SEO) techniques are still of relevance, and a high volume of branded web mentions is still found to be the factor with the highest degree of correlation with the extent of AI references[7]. Overall, "good digital marketing" - i.e. the aim of achieving an extensive online range of structured, contextual brand references which address EEAT (expertise / experience / authoritative / trustworthiness) criteria - still sits at the intersection of SEO and GEO.
Overall, however, having a brand presence across a wide range of channels and content types is key to being cited in AI responses. Key areas appear to be 'rich content' channels such as YouTube (particularly if titles, descriptions, etc. have been optimised for LLM readability) and user-generated-content or community channels, such as review sites. These types of insights can be drawn by analysing which sites are most frequently cited in AI responses, and this analysis consistently reveals that plaforms such as Reddit are favoured by LLMs. A diversification in platform focus for branded content is also particularly important in an era where many (particularly younger) users are becoming increasingly focused on 'social search' - i.e. utilising the native search functions in platforms such as TikTok, Pinterest, Reddit and Instagram in order to source answers to queries. Around one-quarter of users discover brands for the first time on social platforms, and many users utilise search across a range of platform types before making a final purchase decision. The aim for brand owners is therefore to optimise their own content across the same set(s) of platforms as are being used by their customer base, an initiative which is doubly beneficial given the increasing frequency with which classic engines such as Google are themselves also returning results from these types of platform. A key idea is the concept of the 'Day One' list, reflecting the fact that the most effective brand awareness for users is that generated by the results from the initial sets of searches carried out by them.
A key associated measurement factor when considering the likelihood of citation in AI responses in response to queries is 'share of model'. It may be informative for brands to track their presence and sentiment in LLMs by posing direct queries to the associated tools ('AI model reporting'), which can include specific questions relating to areas known to influence customer preference such as price, customer service, product features, ease of use, etc. A useful follow-up to this type of analysis can also be for brands to post their own mediative content online, to address any issues where identified.
However, branded content must be authentic and non-generic in order to build credibility (both with human users and with AI crawlers). Google also increasingly rewards material with 'personal' and 'expert' content. As such, online placement by brands of authored content can be beneficial, but should ideally also be accompanied by positive references from brand advocates, influencers and employees - noting that the set of employees of a company typically together have a following around twelve times the size of the company's official profile page.
AI responses themselves are becoming increasingly commercialisable
Some AI tools (including AIO / Google AI Mode) are starting to offer the option for paid-ad placement within them - in many cases, this is currently only available in the US, but is likely to expand in scope. Initial use-cases are likely to be focused towards e-commerce, as certain categories of products and services (such as pharmaceuticals and gambling) may be considered 'restricted verticals'. Increasingly, we are also likely to see more flexibility in targeting options for these ads (such as Google's 'Broad Match', 'AI Max' and 'Performance Max'), relating to the ways in which they are to be served up in response to particular query types (rather than being keyword-based).
Care must, however, be taken by brand owners with AI ad placement, as brand references in AI responses are less amenable to control over the context in which the brand is mentioned, which could be problematic if (for example) there are regulatory requirements regarding the way in which the brand must be promoted, or concerns about being referenced alongside competitors.
Additionally, some AI providers may be reluctant to offer paid boosting of brands, due to implications regarding trust, especially that of paying customers. OpenAI's Sam Altman recently stated that "ads on a Google search are dependent on Google doing badly; if [Google] were giving you the best answer, there’d be no reason ever to buy an ad above it"[8].
AI can also be leveraged to itself optimise brand content
Marketing success in the world of GEO is dependent on having brand content structured and presented in appropriate ways, with one estimate claiming that 90% of branded content will be synthetic by 2026. A final point to note is that AI can itself assist with many of these areas, including:
- Segmenting content by relevance to specific audience demographics - e.g. tailoring to local language (noting that 76% of B2B buyers prefer to purchase in their native language) or adapting websites or paid-ads to local audiences - though these types of initiative invariably also require an element of human QA. Increasingly, users expect content to be highly personalised, rather than being tailored to broader market segments.
- Offering capabilities for AI agents to carry out highly personalised tasks (such as brand audits, or ROI or pricing calculators).
- Optimising paid-media (pay-per click links / sponsored ads), through prediction of queries and keywordless targeting.
References
Event presentations
- 'Five tactics for driving more leads in an AI-powered global search landscape', C. McKenna and S. Oakford, Oban International
- 'AI traffic is money traffic', J. Kelleher, SpotDev
- 'Navigating the era of AI search', B. Wood, Hallam (hallam[.]agency)
- 'The impact of AI on B2B marketing and five expectations for 2026', G. Stolton, ROAST
- 'The future of organic search and SEO', J. Powley, Blue Array SEO
- 'Decoding the future: AI and the impact on B2B marketing', A. Moon, FutureEdge Academy
Other
[1] To be published as: 'AI's potential impact on web search, traffic and trademarks', Stobbs blog [link TBC]
[2] https://www.bcg.com/x/the-multiplier/the-future-of-discoverability
[3] https://ahrefs.com/blog/ai-overview-growth/
[4] https://ahrefs.com/blog/ai-overviews-reduce-clicks/
[5] https://ahrefs.com/blog/the-great-decoupling/
[6] N. Patel, NP Marketing (see e.g. https://www.instagram.com/p/DQz_jczEjlI/)
[7] https://www.semrush.com/blog/ai-mentions/
[8] https://www.techinasia.com/sam-altmans-lesson-google-trust-ads
This article was first published on 21 November 2025 at:
https://www.linkedin.com/pulse/ai-web-search-part-2-optimising-world-david-barnett-g7eme/
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