Across 2025, we have worked with clients who are discovering that strong Google visibility no longer guarantees visibility in AI platforms. At the same time, nearly half of B2B buyers now use generative AI tools during their vendor research, and 61 per cent prefer to research without speaking to a sales rep.
With AI systems increasingly shaping recommendations and early vendor shortlists, businesses that fail to appear in these touchpoints lose qualified prospects before they reach the website.
During recent audits, I have seen established brands with credible content and strong search rankings struggle to appear in AI Overviews and ChatGPT. When we analyse why, the pattern is consistent.
Generative engines evaluate businesses through very different technical criteria. They prioritise companies whose information can be extracted cleanly, verified quickly, and integrated confidently into customer-facing recommendations.
This is why we formalised our Generative Engine Optimisation services at Manning & Co. Your technical foundation now determines whether your business is even considered in the AI-driven conversations that influence future revenue – and we can help.
How do AI systems verify business credibility
AI systems rely on explicit technical signals. Structured data markup functions as a machine-readable business profile that communicates your expertise, leadership, and capabilities.
When a customer asks an AI platform for a provider within your category, the platform checks whether your information is clearly structured, whether your credentials are verifiable, and whether your content supports confident recommendations. Without these signals, even strong businesses are ignored.
This technical verification process connects directly to the citation authority metrics we explored when examining why brand visibility has become the most critical metric in today’s AI-driven world. AI systems need to understand not only what your business does but why it is credible enough to recommend.
Essential technical elements for AI citation success
Which structured data formats do AI systems actually use?
AI systems avoid making assumptions about meaning or context. They depend on explicit signals that define relationships, entities, and intent. Schema markup, particularly JSON-LD, remains the most reliable channel of communication between your site and generative engines.
The structured data formats that carry the most weight in 2025 include organisation schema to establish credibility and authorship, product schema to define attributes and usage, FAQ schema for direct question-and-answer clarity, and author markup to support credibility scoring. These elements link directly to the citation authority concepts that underpin brand visibility in an AI-driven environment.
Content architecture for AI synthesis
Content for AI requires a different type of structure. Generative engines respond well to BLUF (Bottom Line Up Front), where the key point comes first, followed by supporting detail. This works best for instructional or decision support content, though narrative pieces still benefit from gradual development.
Clear H2 and H3 headings create a content map that AI crawlers follow when identifying which sections answer specific questions. Tables and well-structured lists increase extractability and often improve citation likelihood.
Building citation ready content infrastructure
Citation readiness includes content quality and the technical signals that give AI systems confidence. Source attribution markup helps engines trace the origin of claims. Publication dates matter more in 2025 because recency has become a stronger weighting factor in AI models that continually retrain.
Internal links are another vital factor. Generative engines use internal linking patterns to understand topical clusters and the depth of authority. A coherent internal linking structure helps engines interpret your site as a connected ecosystem rather than a set of isolated pages.
Assessing your business AI readiness
A comprehensive audit reveals whether your current digital foundation supports AI-driven customer acquisition.
We often find that websites that validate technically still fail to articulate clear business value in formats that AI systems understand. This results in inconsistent or absent recommendations across platforms.
When we test client visibility across multiple AI models, it is common for one system to recommend the business while another ignores it entirely.
This usually signals a lack of strategic positioning rather than content quality problems. The measurement approaches we use build directly on the new metrics framework discussed in our analysis of AI era brand visibility, focusing on citation frequency and recommendation quality rather than traditional traffic metrics.
Building technical infrastructure for long-term market leadership
Strong technical foundations position businesses for long-term visibility as AI-driven research becomes mainstream. The three-pillar GEO framework continues to guide organisations that want to balance traditional digital presence with AI visibility and market authority.
Throughout 2025, we have seen rapid growth in industry-specific AI tools, each requiring different forms of structured information. Preparing for this now ensures visibility as customer behaviour continues to evolve.
This technical foundation work enables the authority-building strategies that drive sustained AI visibility and creates the infrastructure needed to support the conversational content ecosystems that perform well across AI platforms.
At Manning and Co., we have seen the competitive advantage that early GEO adoption creates. Companies that invest in AI-ready infrastructure achieve stronger visibility, more consistent recommendations, and a compounding advantage over slower-moving competitors. Contact us today to explore how Generative Engine Optimisation can strengthen your market position as AI-driven research continues to expand.
