Brick Digital Founder and CEO Lorenzo Luiso took to the road in October of this year, heading to the world’s largest Search marketing conference, BrightonSEO. Each year, the conference offers an inside look into the trends, opportunities and challenges within search, and this year was no exception. However, one topic of conversation stood tall among the rest, inescapable in nature and punctuated with a question mark: Now that AI is firmly grounded in our daily lives, what’s happening with the future of search?
In this article, Lorenzo explores this topic in detail, offering his expert insights and opinions on AI, search and what businesses need to take into consideration as we enter a new era of search.
Events like BrightonSEO have always been valuable, but in 2025 they feel particularly important. Search as a marketing channel is entering a new phase, not just in terms of algorithms, but in how people find information and make decisions online. This change in user behaviour is the development we as SEO experts need to stay close to in order to work towards the next iteration of SEO strategies.
For me as an agency leader, attending BrightonSEO isn’t just about going to talks. More than this, it’s a matter of understanding how the wider industry is approaching this new version of search, speaking with other leaders, and comparing what we’re seeing across our client bases. Those conversations offer useful insights into where search is heading, and what we as an agency need to focus on next to help our clients stay ahead.
The Direction of Search Is Becoming Clearer
One theme I noticed repeatedly was that search is fragmenting. We’re moving away from a world where Google alone defines how people search, and into one where AI-driven experiences, from SGE and ChatGPT to tools like Perplexity, are beginning to influence how people seek out information and shape buyer journeys.
This fragmentation means that “visibility” is becoming just as important as “ranking”. While ranking on Google will continue to matter, brands now need to consider how often they are cited or recommended across multiple search platforms. These AI environments are already part of the discovery and decision-making process, and even though it’s still early, they signal a shift that can’t be ignored.
Another message that came through strongly was the growing importance of technical excellence and structure. Content and user experience have dominated SEO conversations in recent years, but as search becomes more machine-led, success will increasingly depend on clarity, structure, and the underlying quality of data.
Tracking Visibility in Search Is Becoming More Complex
The impact of SEO can no longer be measured through rankings, clicks, and conversions alone. In the world of AI Search, “visibility” is becoming a new metric, but not one that can be tracked in the same way we track clicks.
With search fragmenting across Google, AI assistants and emerging LLM-driven tools, brands need a broader understanding of how visible they are across multiple systems. This requires more sophisticated tracking, analysis and interpretation.
Tracking visibility in this new era of search will increasingly include:
- Presence across systems: How often a brand is cited in AI environments, not just listed in search results.
- AI search bot analysis: Monitoring the frequency of AI search bots visiting your site via log file data.
- Cross-platform performance models: Analysing multiple data points together to understand commercial impact.
- Sentiment tracking: Reviewing not just how often a brand is mentioned, but how it is described by AI tools.
This more holistic view of visibility will become essential for understanding the true impact of SEO across a multi-system search landscape.
Content Structure Is Becoming a Competitive Advantage
The talks at BrightonSEO made it clear that content structure is becoming a major factor in whether brands are cited in AI Search. LLMs rely on clear structure to understand what a page is about and when it should be referenced.
AI tools look for signals in the code such as headings that outline what a section covers, supporting text that explains it, and schema that defines the purpose of the page. The more structured and consistent this is, the easier it becomes for these systems to understand the content, and the more likely they are to cite it.
This means brands need to rethink not only how content reads for users, but how it is built for machines. Code quality, schema usage, and content hierarchy are becoming more important as LLMs strip pages down to plain text or simplified HTML.
Competitive advantage will come from brands willing to review and restructure their content with both users and AI tools in mind, including:
- Cleaner content hierarchy: Headings and sections that reflect how LLMs read a page.
- Better code-level clarity: HTML and classes that accurately describe each element’s intent.
- Expanded schema usage: Defining what the page is, who created it, and what the key points are.
Brands that adapt early will make it easier for AI systems to understand, trust, and reference their content, and that is likely to become a meaningful differentiator.
The Future of AI Search Measurement Will Require Large-Scale Analysis
From our own testing (and from what was shared at BrightonSEO), it’s become apparent that meaningful AI Search measurement cannot rely on a mere handful of prompts. With more users turning to AI tools, and with the rise in conversions we’ve seen here at Brick Digital, brands need a more scalable way to understand their visibility.
The only reliable approach at this stage is large-scale prompt analysis, using hundreds or thousands of prompts across different categories. This provides a clearer picture of how brands are being cited, how they are compared to competitors, and what signals AI tools rely on.
A structured prompt-tracking model might include:
- Product or service visibility: Prompts around problems, products, or categories a brand solves.
- Brand perception: Prompts that reveal how AI tools describe the business and its reputation.
- Service-level insight: Prompts that highlight delivery times, customer service, or operational strengths.
This method also highlights a trend we’re seeing, where third-party sites are often cited far more than brand websites. While this may seem negative, it reveals which external sources AI systems trust, and where brands may benefit from building presence and earning citations.
Importantly, at Brick Digital we aren’t just analysing visibility metrics. We’re also reviewing the conversions that come through AI tools, assessing lead quality, and looking at what users say when they make contact. This helps close the loop, showing not only how people discovered a brand through AI Search, but what pain points they expressed and what prompts they may have used.
Large-scale analysis is becoming the next evolution of search tracking. As traditional data becomes harder to access, brands will need new metrics to understand how they are being represented across AI systems.
How the Industry Is Responding to AI Search
Some of the most valuable insights came from conversations with other agency owners and SEO leaders. After just one or two conversations, it’s not hard to see that everyone is grappling with the same challenge; AI Search is still a black box, and the industry is trying to understand it without claiming to have all the answers.
There’s a shared desire to test quickly, gather real data, and give clients practical advice on AI visibility. Most leaders agreed that the fundamentals of SEO remain at the forefront, along with strong technical foundations, clear content structure and a focus on user pain points.
There is still uncertainty around how to measure AI visibility and how to link it directly to commercial outcomes, but it was reassuring to see the industry aligned on the direction of travel and the need for adaptability.
What This Means for Brands Going Into 2026
The pace of change in AI Search is so fast that none of us can predict exactly what the landscape will look like in six or twelve months. Because of this, the most important thing for brands right now is preparation.
Search strategies need to become more adaptive, more holistic, and less tied to a single platform. That means looking beyond Google rankings and starting to understand how users discover information across multiple systems, from SGE and ChatGPT to Perplexity, Gemini, and other emerging tools.
Brands also need to prioritise the technical foundations that support AI visibility, such as clear structure, meaningful schema and content that AI tools can interpret easily. At the same time, companies should begin testing and tracking AI Search tools to understand which platforms their users rely on, and how those tools describe their products and services.
The key is not to wait for AI Search to “settle”. Instead, it’s a matter of building adaptability into your strategy now so you’re prepared for whatever comes next.
Staying Curious Is Key
If there’s one thing BrightonSEO reinforced, it’s that there are no definitive answers when it comes to AI Search, just as there have never been definitive answers in SEO. Search has always evolved, shaped by technology and user behaviour, and AI Search is simply the next phase of that evolution.
Rather than trying to predict exactly where things are heading, the focus needs to be on preparation, staying curious, testing often, and adapting quickly. The landscape will continue to shift, and user behaviour will shift with it. Our job at Brick is to stay close to what’s happening at the frontier so we can guide our clients confidently through the changes
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Lorenzo also attended BrightonSEO in 2024. To read his thoughts at the time (and see how things have developed over the course of 12 months), read his 2024 insights review.
