And why is this beneficial?
In recent blog posts, I have explored how artificial intelligence can support communication strategies and how organisations can incorporate AI into their wider marketing and communications activity. I have considered the role of AI within conversion strategies and channel management, as well as some of the practical and organisational considerations associated with AI adoption.
This article approaches the subject from a different perspective by considering how we create content that can be discovered, interpreted and potentially surfaced by Large Language Models (LLMs) when users are researching products, services, organisations and potential purchasing decisions.
The growth of generative AI is changing the way people access information. Traditional search remains an important part of the customer journey, but users can now ask increasingly complex questions of AI platforms and receive a synthesised response rather than a conventional list of search results. For marketers, this creates an additional consideration when developing content strategies: how visible is our organisation within the information sources that AI systems use to construct their responses?
Understanding the underlying search infrastructure
An important starting point is understanding that different AI platforms do not necessarily retrieve information from the web in the same way. Their underlying search, indexing and retrieval infrastructure can influence which websites, pages and sources are discovered and subsequently considered when generating an answer.
Claude, ChatGPT, Gemini and Perplexity each have different approaches to web retrieval and information synthesis, and these approaches continue to develop. Consequently, there is no single optimisation methodology that can guarantee visibility across every generative AI platform.
For marketers, the more useful consideration is to understand the information ecosystems that sit behind these platforms and how this relates to the behaviour of the audiences we are attempting to reach.
Claude and source retrieval
Claude, developed by Anthropic, has established a particularly strong position within professional and knowledge-intensive environments. Its web search capability uses external search infrastructure, including Brave Search, to retrieve current information when web access is available.
This creates an interesting distinction from AI platforms that are more closely integrated with the Google or Microsoft search ecosystems. It also demonstrates why a strategy designed solely around one search engine or one AI platform may not provide consistent visibility across the wider generative search environment.
From a content perspective, the requirement remains relatively fundamental: information needs to be clearly structured, relevant and sufficiently authoritative for retrieval systems to establish what the content represents and whether it contributes meaningfully to a particular query.
Claude's growing use within professional environments is also relevant for B2B organisations. Users may be researching a supplier, evaluating different approaches, interpreting technical information or preparing for a significant business decision. Content therefore needs to provide substantive information rather than simply incorporating a collection of relevant keywords.
ChatGPT and web retrieval
ChatGPT has become one of the most widely recognised generative AI platforms across both consumer and business environments, and its ability to access current information through web search creates an increasingly important relationship between generative AI and traditional search.
The nature of the search query itself is changing. A traditional search might involve entering a small number of keywords such as "B2B digital marketing consultant", whereas a user may now ask a substantially more detailed question that incorporates their organisation, objectives, sector and requirements.
This distinction is significant because the second type of query requires the system to interpret context and identify which sources are relevant to the overall requirement rather than simply matching individual keywords.
Consequently, organisations need to consider whether their content clearly communicates what they do, the markets and sectors in which they operate, the problems they solve, the expertise they possess and the evidence supporting their claims.
This information should not necessarily exist within one webpage. It can be distributed across the wider digital ecosystem, including websites, case studies, professional profiles, industry publications and other credible sources.
Gemini and the Google ecosystem
Gemini provides another important consideration because of its relationship with Google's wider search and information ecosystem. Google's extensive indexing infrastructure has historically provided the foundations for organic search visibility, and this makes the relationship between conventional SEO and generative AI particularly relevant.
For organisations seeking to increase organic visibility while also improving the likelihood that their information can contribute to AI-generated responses, many of the established principles of SEO remain important.
Technical website performance, information architecture, structured data where appropriate, internal linking, content quality, relevance, authority and user intent continue to provide the foundations for effective digital visibility.
Generative AI does not necessarily remove the importance of SEO. Instead, it increases the importance of ensuring that the information being indexed is sufficiently clear, useful and structured for both search engines and AI systems to interpret.
The technology is only one part of the strategy
Understanding the infrastructure behind different LLMs is useful, but it does not answer the most important strategic question, which is who the content is intended to influence.
Before considering whether content should be optimised for ChatGPT, Gemini, Claude or another platform, organisations need to understand how their customers research information and where they develop confidence in a potential supplier, product or service.
This requires consideration of the complete information journey. Customers may use traditional search engines, AI platforms, LinkedIn, professional publications, industry websites, communities, peer recommendations or a combination of these channels, depending on the complexity and value of the purchase.
The answer will therefore vary significantly between sectors and audiences.
A consumer purchasing a relatively straightforward product will have a very different research journey from a procurement professional evaluating a professional services organisation, technology platform or complex B2B solution.
The content strategy needs to reflect this distinction.
From SEO to GEO
This is where Generative Engine Optimisation, or GEO, has become increasingly relevant.
GEO is generally used to describe the optimisation of content and digital information to improve the likelihood of being surfaced within generative AI responses. However, I believe there is a risk in treating GEO simply as the next version of keyword optimisation.
Traditional SEO has already developed considerably beyond the insertion of keywords into webpages. Effective organic visibility now incorporates technical performance, information architecture, content quality, authority, relevance, search intent and the wider reputation of an organisation and its digital presence.
The same principle applies to generative search.
An AI system needs to establish what a piece of information represents, whether it is relevant to the question being asked and, depending on the platform and query, whether the source provides sufficient value and credibility to contribute to the response.
This makes the quality, structure and context of content increasingly important.
Content needs context
A webpage stating that an organisation provides "digital marketing services" provides relatively little contextual information from which a search or AI system can establish expertise.
A more comprehensive digital presence might explain the organisation's sectors, customer types, areas of expertise, methodologies, technology, services, case studies, results and experience, supported by relevant evidence and external references.
This creates a considerably richer information environment and provides multiple signals through which both people and machines can understand the organisation.
The implication for content strategy is that individual pieces of content should not necessarily be considered in isolation. Their relationship with the wider information architecture of the organisation becomes increasingly important.
The importance of digital corroboration
An organisation may describe itself as a specialist in a particular field, but the strength of that claim can be influenced by the extent to which the wider digital footprint supports it.
If the statement only exists on the organisation's website, there is relatively little contextual evidence available elsewhere. If the same expertise is demonstrated through detailed case studies, professional profiles, industry contributions, publications, conference participation and credible third-party references, a much stronger picture of the organisation's expertise is created.
This does not mean publishing identical information across multiple platforms simply to increase volume. The objective should instead be to establish a consistent and credible digital footprint in which different sources provide complementary evidence.
This is particularly relevant to professional services and B2B organisations, where customers can undertake considerable research before making direct contact with a potential supplier.
LinkedIn as part of the information ecosystem
LinkedIn is therefore becoming increasingly relevant beyond its traditional role as a social media publishing channel.
People use LinkedIn to assess professional expertise, experience, networks, organisations, individuals and company activity. It can therefore form an important component of the wider information environment through which an organisation is understood.
For a B2B organisation, this could involve a technically strong website supported by detailed service pages, case studies, expert commentary, LinkedIn content, employee profiles, industry contributions and independent references.
The objective is not necessarily to ensure that every channel carries the same content. Instead, each channel can contribute a different element of the overall picture.
Perplexity and source-led research
Perplexity provides another interesting model because it is strongly focused on answering questions through web research and presenting sources alongside its responses.
This approach demonstrates the importance of considering content as part of a wider information network rather than assuming that one organisation needs to provide the complete answer to every possible question.
A useful piece of content can contribute evidence, explanation or expertise to an answer without necessarily attempting to provide a definitive response to the entire subject.
This creates an opportunity for organisations to develop a portfolio of genuinely useful information that addresses the questions customers are asking throughout the decision-making process.
Who is using these systems?
The adoption of LLMs is increasing, although usage remains uneven across demographics, markets, professions and business environments. The important consideration for marketers is therefore not simply the total number of people using generative AI, but the relevance of those users to a particular organisation and their position within the purchasing journey.
Different platforms are also developing different areas of strength and reputation. ChatGPT has achieved broad consumer and business adoption, Gemini benefits from its integration with Google's wider ecosystem, Claude has developed a particularly strong position within professional and knowledge-intensive use cases, and Perplexity has established a strong association with research and source-led answers.
These distinctions are relevant when considering where an organisation's customers may be seeking information and which digital environments should therefore be incorporated into its content strategy.
Should organisations optimise for every LLM?
I do not believe that creating a separate content strategy for every AI platform is necessarily the most effective approach.
Doing so could recreate the channel-management challenge that marketers have experienced for many years, where increasing numbers of platforms require increasingly fragmented content strategies.
A more sustainable approach is to begin with the customer journey.
Organisations should understand the questions their customers are asking, where they seek information, which sources they trust and what information they require at different stages of the decision-making process.
Content can then be developed to address those requirements while ensuring that the underlying digital infrastructure allows the information to be discovered, interpreted and connected.
The technology therefore becomes part of the distribution and discovery strategy rather than becoming the strategy itself.
From content creation to information architecture
This shift towards AI-driven search is also changing the way I consider my approach to content strategy. For many years, organisations have focused heavily on content production, considering how many articles should be published, how frequently social channels should be updated, which keywords should be targeted and how content should be distributed across different channels. While these considerations remain relevant, the growth of AI search introduces a broader requirement: how effectively is the information itself structured, connected and understood?
It is no longer sufficient to consider whether an organisation is producing content; we also need to consider whether the wider digital ecosystem provides enough context for search engines and AI systems to establish the relationship between an organisation, its services, its people, its expertise and the evidence that supports its claims. This includes considering whether key concepts are explained consistently, whether related areas of expertise are appropriately connected across the website, whether case studies provide sufficient depth to demonstrate credibility, whether the website delivers genuinely useful information rather than predominantly promotional messaging, and whether expertise is supported by credible external sources.
The wider digital footprint also has an important role to play, particularly where information from websites, professional profiles, social channels, publications and third-party sources collectively contribute to how an organisation is understood.
This moves the discussion beyond content production and towards information architecture, digital credibility and discoverability. These are strategic considerations that determine not only what information an organisation publishes, but how effectively that information can be understood, connected and surfaced across the increasingly complex search and AI environment.
Creating a new content feedback loop
As the search environment develops, measurement will also need to evolve. Traditional measures such as organic search performance, referral traffic, website engagement and conversion remain important, but they provide only part of the picture when customers are increasingly using AI systems to research and evaluate organisations.
There is therefore a growing requirement to understand how an organisation is represented within AI-generated responses, including the questions for which it appears, the sources being used to support those responses, which competitors are being referenced and where gaps in the organisation's digital footprint may be limiting its visibility or credibility.
This creates an additional feedback loop within the wider digital marketing strategy:
Customer questions → Content → Search and AI discovery → Visibility → Website interaction → Conversion → Measurement → Content optimisation
The value of this approach is that it connects customer behaviour and content performance rather than treating content as a standalone activity. Insights from search behaviour, AI-generated responses, website engagement and conversion can be used to identify where further information is required, refine existing content and improve the overall digital experience.
As customer behaviour and search technology continue to develop, this feedback loop will become increasingly important in understanding not only whether content is being found, but whether it is contributing to how an organisation is understood and considered.
The objective should not be to manipulate an AI system into mentioning an organisation. It should be to create a clear, credible and connected digital presence that provides sufficient evidence for both search engines and AI systems to understand the organisation, its expertise and the value it provides to its customers.
Read more about Feedback loops.
The reason for creating the content still matters
This brings the discussion back to the original question: why do we want LLMs to use our content?
Visibility is clearly an important consideration. If a potential customer uses an AI system to research an organisation's products, services or expertise, being represented within the information used to formulate that response provides an additional opportunity to enter the customer's consideration set. However, the value extends beyond visibility alone.
Creating high-quality, structured and useful content requires an organisation to clearly articulate its expertise, the markets and customers it serves, the problems it solves and the evidence that demonstrates its capability. Over time, this creates a valuable organisational information asset that can support customers, employees, sales teams and marketing activity, as well as the search engines and AI systems through which people increasingly access information.
For this reason, I believe the objective extends beyond Generative Engine Optimisation. It is about creating an information environment in which an organisation can be accurately understood, with its expertise, proposition and credibility consistently represented across its digital presence.
Rather than focusing solely on how to make ChatGPT, Gemini, Claude or another LLM mention an organisation, the more strategic question is whether the organisation has provided sufficient high-quality information for these systems to understand what it does, why it is credible and how it could meet the needs of a potential customer.
This represents a more sustainable approach to content strategy because the objective is not simply to achieve visibility within an LLM-generated response, but to build the digital authority and clarity required for an organisation to be understood, evaluated and ultimately considered by the people using these systems.