Search is changing in a way that is both subtle and significant.
Increasingly, our audiences are not navigating lists of links, but asking questions and receiving synthesised responses from tools such as ChatGPT, Google AI Overviews and Perplexity. In this environment, visibility is no longer defined solely by rankings or page position. It is defined by whether your content is selected, interpreted and trusted within an answer.
This shift is what sits behind generative engine optimisation (GEO): structuring content so it can be understood and referenced by AI systems. Unlike traditional search engine optimisation (SEO), which is built around driving traffic, GEO focuses on influence – ensuring that institutional expertise informs the answers users receive, irrespective of whether they click through to a website.
The pace of change should not be overstated, but it is material.
Research suggests that over 60% of users now interact with AI-generated responses on a regular basis, with many beginning their journeys in conversational tools rather than search engines. At the same time, “zero‑click” behaviour is becoming more established, with a significant proportion of queries resolved without a visit to a website. For the University of Leicester, where authority, trust and clarity are central, this has clear implications for how we approach digital content.
There is a genuine opportunity here. AI systems are designed to surface content that is clear, authoritative and well-structured. This aligns closely with the principles we already recognise as good practice: accurate information, consistent terminology and a clear sense of ownership. When content is designed with these qualities in mind, it is more likely to be cited within generated responses, increasing reach beyond traditional audiences.
However, there are also stark and real challenges.
The first is a loss of control. Content may be summarised, combined with other sources or presented without context. The second is that traditional signals of performance, such as page views and rankings, become less reliable measures of impact. Finally, the technical and editorial signals that influence inclusion – such as structured data, entity clarity and internal consistency – require a more deliberate and coordinated approach than many organisations are currently set up to deliver.
For the University Content Management (UCM) programme, this reinforces a set of practical principles:
- Structure over format: content must be modular, reusable and clearly defined, enabling it to be interpreted across different contexts
- Clarity over volume: concise, well-evidenced information is more valuable than lengthy, unfocused content
- Authority through attribution: content should demonstrate provenance, linking to recognised expertise and institutional credibility
- Consistency across the ecosystem: terminology, naming and relationships between content types must be coherent
Delivering against these principles is not solely a technical exercise. While structured content models, metadata and integrations are essential, they must be supported by governance, standards and shared ownership across the University. Editorial practice becomes as important as platform capability.
In practical terms, this means designing content types that can be reused across multiple channels, embedding quality controls such as accessibility and accuracy checks, and ensuring that our content is connected – to people, to services and to each other – in a way that builds a coherent digital presence.
If we approach GEO in this way, it becomes less about chasing a new form of optimisation and more about reinforcing good practice at scale. The outcome we are working towards is straightforward: content that is not only discoverable, but understandable – and capable of contributing meaningfully to the answers our audiences rely on.
