AI-powered search has moved from experiment to operating norm in UK B2B buying. A new study [email wall] of 175 UK business decision-makers, conducted by Norstat for Clarity Global, finds that AI tools now shape discovery, evaluation, and justification in most B2B purchase processes, with implications for marketing spend, channel mix, and internal coordination.
The most consequential finding is the depth of adoption. 79% of professionals report using AI daily or weekly in their work, with one third using it every day. 64% spend one to four hours each week using AI to make business decisions, and 80% devote at least an hour weekly to AI-ordained decision-making. The paper states AI has become embedded in the routine workflows of buyers and brand selection.
The AI chooses
Between 52% and 59% of B2B buyers now rely more on AI summaries, use traditional search less, visit fewer websites, read fewer long articles, and spend less time understanding raw information. 59% say they spend less time gathering knowledge and more time on assessing what an LLM produces for them. The report describes this as a compression of discovery: Buyers encounter fewer primary sources and more brokered, subjective summaries. The window in which a brand can influence perception has narrowed, therefore, as perceptions of a brand are filtered through an LLM lens.
At the top of the funnel, 87% of buyers use AI outlines to have an algorithm dictate what they should read. During shortlisting of potential choices, 65% rely on AI for vendor selection. In evaluation, 77% substitute AI for due diligence and technical assessment. 75% use AI to create or influence internal business case development. The pattern is consistent. AI does not sit at the margins of research, but creates reading lists, filters suppliers, makes technical comparisons, and forms internal justification.
What AI means for brand marketers
For marketing leaders, the operational implication is clear. Influence increasingly depends on how AI systems interpret and summarise a brand. The report argues that content not structured for AI summarisation risks exclusion from consideration by buyers. In practical terms, this shifts emphasis from volume of content to the practical and technical presentation of messages. Claims need to be direct, and messaging needs to withstand software interpretation and parsing.
The document introduces the concept of Generative Engine Optimisation, or GEO, and frames it as less mature and unpredictable compared to traditional SEO practices. The report characterises AI search as a “black box”, noting that model updates, training data, and answer logic remain opaque, as remains the case when reverse-engineering traditional search engine algorithms. The paper cautions against overreacting to trends, and to treat emerging practices as hypotheses. This reflects uncertainty as the evidence base for specific optimisation methods is still patchy.
The reliable third-party
Channel strategy emerges as a second effect. AI models draw on an unknown range of signals, combining owned content, search visibility, and social presence with third party validation from non-objective sources such as PR and commercially-driven market analysis. Integration in channels matters because AI systems aggregate claims. The report states that at the top of the funnel, AI answers draw less from owned content and more from third-party sources. At the bottom of the funnel, when buyers request recommendations like “best” products, algorithms prioritise information corroborated on the web, with third party sources, prioritised by opaque mechanisms, treated as more less subjective than content produced by vendors.
The spending implication is a change in reallocation. Technical SEO remains relevant, including site performance and structured data, because it’s assumed that AI models extract and interpret web content in the same way that traditional search algorithms have in the past. Content strategy remains central, particularly material that includes named experts, data referenced from third-parties, and impartial customer reviews. PR and analyst relations gain weight because they are considered authoritative. The report advises prioritising placements over link volume and focusing on quality, relevance, and fresh citations. For budget holders, this points to sustained investment in earned media and positioning by those deemed experts.
Measurement and guesswork
Measurement is presented as necessary, yet problematic. Standard analytics show activity on owned channels but do not reveal how a brand appears in AI-generated answers. AI responses are described as “wholly ephemeral,” as these tend to vary by prompt, model, time, and context. The report recommends companies “”look for a tool that uses automation to simplify AI search monitoring, along with real-time metrics” (but does not cite a specific solution), and to track results over time to assess whether AI reproduces an organisation’s claims, rather than just mentioning the brand.
A final operational theme from the report is internal alignment, arguing that inconsistent terminology in website, spokespeople, and sales materials can create mixed signals for AI systems, and by proxy, potential buyers. It recommends a process in which messaging is clarified, integrated into channels, measured in AI search outputs, and then refined monthly or quarterly.
The study is limited to 175 UK decision-makers, but provides evidence that AI is now a part of the B2B buying journey. For marketing decision-makers, the weight of evidence supports three priorities: treat AI search as a primary discovery channel, align messaging and proof to withstand the vagaries of buyers relying on LLMs to make their decisions, and re-allocate spend towards integrated content and third-party validation.
(Image source: “Supermarket shelves” by Frankie Fouganthin is licensed under CC BY-SA 4.0. )
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