Search Engine Optimisation has never been a static discipline. From the early days of keyword stuffing to the arrival of mobile-first indexing, the field has been shaped by constant shifts in how information is searched for and presented. Now, with the rapid rise of Generative AI, SEO faces another transformation. This time, it is not simply a matter of algorithms becoming more sophisticated but of the very nature of search itself being redefined. Let’s check it out:
What is Gen AI?
Generative AI refers to artificial intelligence systems that can create new content based on vast datasets. Instead of merely retrieving existing information, these tools produce text, images, audio and video that seem almost human-made. OpenAI’s ChatGPT, Google’s Gemini, and other large language models have moved beyond novelty and into the mainstream, influencing how users find and consume information. The change is profound: we are no longer only competing for space on a search results page but for attention in AI-driven, conversational responses.
This raises fundamental questions though. How do we optimise for systems that generate answers rather than merely list links? How do companies govern their SEO strategies when the search environment is now a hybrid of traditional ranking and AI summarisation? And how do we address the new ethical and operational challenges that come with it?
Redefining SEO in a Generative Context
In the Generative AI era, SEO is expanding beyond its traditional goal of ranking web pages. Search tools are becoming answer engines, offering users complete responses directly in the results, often without them clicking through to the source. This “zero-click” reality is not entirely new, Google’s featured snippets have been around for years, but AI models have amplified it by generating nuanced, conversational answers that may not link directly to their sources.
This means that visibility is no longer just about securing a top spot on a search results page but about becoming the trusted material AI models draw from when creating responses. The concept of “optimising for AI discovery” is emerging, where the goal is to have your brand or content influence the generated output, even if your page is not explicitly linked in the traditional sense.
At its core, SEO now requires a two-pronged approach. The first is traditional optimisation, which ensures that your site architecture, technical performance, and keyword relevance remain strong for standard search queries. The second is AI-oriented optimisation, which focuses on making content authoritative, unambiguous, and richly detailed so that it can be ingested, understood, and cited by AI systems.
Governance in the Age of AI Search
As the role of SEO evolves, so too must its governance within organisations. Companies that once viewed SEO as a tactical marketing function are now recognising it as a strategic discipline that intersects with brand reputation, content strategy, and even compliance.
Governance in this context means creating a structured approach to how SEO decisions are made, who is responsible for monitoring AI-driven search performance, and how to balance the needs of human readers with the requirements of machine interpretation. Larger enterprises are beginning to treat SEO data as part of their broader digital intelligence framework, integrating it into decision-making at the C-suite level.
This governance also extends to partnerships with AI platforms. Some companies are exploring direct relationships with AI providers to ensure their content is correctly attributed or licensed. Others are adopting internal content labelling systems that help AI models understand source credibility. As AI search experiences become more personalised, companies may need governance models that allow for agile testing and adaptation of SEO strategies, much like the experimentation frameworks already in place for paid advertising.
The New Challenges for SEO Professionals
The challenges brought by Generative AI are not simply technical; they are strategic, ethical, and even philosophical. One of the most pressing issues is attribution. If AI-generated answers synthesise knowledge from multiple sources, how is credit assigned? And if users no longer click through to a brand’s website, how do businesses measure the value of their content efforts?
Another challenge is data freshness. AI models, especially those not connected to live data streams, may rely on outdated training material. This creates the risk of brands being represented inaccurately in AI outputs. While traditional SEO has always had to contend with outdated information in search indexes, the problem is more acute here because the model’s knowledge base is less transparent.
There is also the matter of bias. Generative AI systems are shaped by the data they consume. If certain viewpoints, industries, or regions are underrepresented in that data, the generated outputs may reflect a skewed reality. This can affect everything from brand positioning to market competitiveness, especially for smaller players without the resources to flood the internet with authoritative content.
Finally, the rapid pace of change is itself a challenge. SEO professionals are accustomed to Google algorithm updates, but the iterative improvements in AI models can alter search behaviours overnight. Strategies that work today may become obsolete within months, if not weeks.
Strategies for Thriving in the Generative Era
To overcome these challenges, brands need to rethink their content creation and optimisation strategies. One of the most important steps is doubling down on authority. AI models are more likely to draw from sources that are widely cited, consistent, and authoritative in their field. This means publishing well-researched, long-form content that is backed by credible references and updated regularly.
Structured data becomes even more valuable in this context. By marking up content with schema, businesses help AI systems understand relationships between topics, entities, and attributes. This improves the chances that content will be interpreted accurately, even when summarised or paraphrased by a model.
Another key shift is in performance measurement. Traditional metrics like click-through rate and organic traffic still matter, but they no longer tell the full story. Brands may need to monitor “AI visibility”, how often their content is referenced in generated answers, even if indirectly. This could involve setting up brand tracking for AI outputs, similar to media monitoring in public relations.
It is also essential to diversify presence across platforms. While Google remains a major player, AI-driven search is not limited to one ecosystem. Tools like ChatGPT, Perplexity, and You.com are creating their own search experiences. Being discoverable across these systems requires a broader approach to content distribution, ensuring material is available in formats and repositories that AI models commonly draw from.
Ethical and Legal Considerations
Generative AI’s impact on SEO is not purely a technical challenge; it also raises ethical and legal questions. If your content is being used by an AI system without explicit permission or compensation, what recourse do you have? Current copyright frameworks are struggling to keep up with this reality, but some publishers are beginning to experiment with AI-specific licensing agreements.
There is also the question of truthfulness. AI-generated answers can be confidently wrong, presenting misinformation with the same tone as verified facts. Brands that are misrepresented in this way face a reputational risk, and there is currently no universal mechanism for correcting these errors across all AI platforms.
Governance frameworks should therefore include escalation processes for addressing inaccurate AI references, as well as proactive steps to minimise the risk of misrepresentation. This might involve building relationships with AI providers, regularly auditing AI search results for brand mentions, and engaging in public discourse about ethical AI practices.
The most successful brands in this new environment will be those that see AI not as a threat but as a partner, one that can amplify their reach if they understand how to speak its language. The key is to remain adaptable, invest in authoritative content, and keep a close watch on how AI systems are evolving.