How Artificial Intelligence is Revolutionising SEO: Understanding User Intent

This illustrates a massive shift in how consumers make purchasing decisions, increasingly relying on AI platforms.

AI is transforming Search Engine Optimisation (SEO), fundamentally changing how search engines interpret user queries and the formats in which they are asked. This is excellent for user experience, but it also opens new opportunities to gain visibility through AI functions such as AI overviews, voice searches, image queries, and longer-tail search phrases which are often clearer in intent and better for conversion.

Historically, search engines relied heavily on exact keyword matches combined with other metrics like domain authority to rank pages. The problem is that this can be manipulated, and the words people search when they're searching the problem, rather than the solution they need, will often get poor results as the algorithm misses the broader context behind the user's intent.

Nowadays, someone searching for, ‘best noise-cancelling headphones’ receives results tailored to wireless models with features like long battery life and comfort, even if they didn't explicitly ask for these. AI can even anticipate follow-up questions like, ‘are they good for remote work?’ or ‘do they block keyboard noise?’ This increases the chances of relevant products and services appearing, provided they address these issues.

Essentially, AI can predict what users want before they even know themselves, which is incredibly convenient. But how does this actually work and can we adapt our SEO strategy to leverage these new AI-driven opportunities? Absolutely, but let's first get a clear understanding of the mechanics involved.

In this post, we'll explore: 

• How semantic SEO works 
• How AI interprets user intent 
• A five-step process to integrate AI-friendly tactics into your content strategy

Semantic search isn't a new concept, it's been evolving for years. Take for an example a recruitment agent searching for candidates with specific qualifications such as a Project Manager certified in Prince 2. It would search for any CV with "Prince 2" and "Project Manager" included on it and create a list of any CVs that matched those criteria. Unfortunately, the results were overloaded with less relevant CVs i.e. assistants reporting to be a "project manager" or candidates still studying for "Prince 2".

Early semantic searches tried solving this by distinguishing between job titles and descriptions or completed versus ongoing certifications but these were cumbersome and required constant manual adjustment. Thankfully, today's semantic search has vastly improved through advances in Natural Language Processing (NLP) and AI, providing precise, contextually accurate results without the manual workload. 

Semantic search uses AI to analyse intent, context, and word relationships rather than just matching keywords. Google uses NLP tools like BERT (Bidirectional Encoder Representations from Transformers) and MUM (Multitask Unified Model) to understand the nuances of human language. Don’t worry, you don’t need to fully understand how they work, but you do need to understand the output. For example, if someone searches ‘best laptops for students’, AI knows they're probably seeking affordable, lightweight laptops with good battery life. The combination of ‘best laptops’ applied with that extra clue ‘for students’ allows it to anticipate likely products of interest.  

This shift means that purely keyword-driven strategies are no longer sufficient. Content must evolve into contextually rich narratives that naturally align with how AI processes queries. This approach benefits everyone: consumers find exactly what they're after, companies see better engagement, and search engines deliver more relevant results.

A key technical concept driving this advancement in semantic search is vector topic modelling. At its core, semantic search uses AI to convert words into mathematical representations known as vectors. Imagine each word positioned within a vast, multi-dimensional space, similar words cluster closely together, while unrelated terms are positioned further apart. For example, vectors for ‘gaming’, ‘graphics’ and ‘performance’ naturally cluster together, indicating contextual similarity. 

But why vectors? Representing words numerically lets the AI easily calculate relationships and spot context by measuring the distances between these vectors. So essentially, the closer the vectors are, the more related or relevant the AI assumes they are. It's akin to why images are powerful communication tools: visuals quickly convey meaning. Similarly, mapping words as vectors allows AI to rapidly understand context, intent, and meaning which is something traditional keyword matching isn’t capable of.

Understanding this changes how we approach writing content, shifting focus from just keywords to comprehensive, contextual, and intent-driven narratives that naturally align with how AI understands user queries. It’s our belief that advancement will make the online world a better, more convenient place by raising the standard of online content by rewarding clarity, relevance, and utility. For example, consider a user searching, ‘best apple varieties.’ Previously, this search might have mistakenly included tech-related Apple content. Now, AI recognises contextual hints like, ‘varieties’ and correctly shows results about fruit varieties.

Vector modelling intuitively helps AI, but structured data explicitly communicates content details directly to search engines. Structured data standardises how information such as product details, job postings, events, and FAQs are presented, significantly boosting visibility in AI-powered search features. 

For example, implementing structured data for job listings ensures they appear prominently in Google's job search (Google for Jobs). Without structured data, job postings risk being buried in general results. But clearly defined data elements like job title, location, salary, company, and application deadlines mean AI confidently categorises listings, dramatically enhancing visibility and engagement.

Here we will discuss how to prepare to introduce AI to your website.

First things first, there is no point in having great content if search engines can’t index your page. That’s why taking a deep dive under the hood of your website to perform a thorough technical audit is essential. A website health check will audit everything from site speed and mobile friendliness to indexing issues or broken links on a page. Fixing these technical details makes sure search engines, AI and users alike have the smoothest possible experience with the content you already have.

Next, its essential to identify valuable primary keywords that accurately represent your offerings and delve into user intent - what your customers are actually asking search engines when they land on your site. For instance, if you are a business that provides certifications and educational resources for pension professionals, valuable terms might include, ‘how to become a certified pensions professional’ or ‘best pension trustee qualifications in the UK.’ These longer-tail phrases typically yield higher conversions due to their specificity. 

There's also growing importance in answering questions directly. AI summary features synthesise multiple sources to answer user queries instantly, reducing clicks on traditional results. Proactively including structured FAQs addressing common questions means pages will have greater visibility and higher engagement potential.

It’s as if answering questions or educating customers about your product first, increases engagement and conversion. Most hesitancy around a purchase is not knowing or believing it’s the right product for the searcher. Educating the customer about how your product or service works changes that and fixes any misconceptions or assumptions they might have formed to influence their lack of belief. As such, this approach should be considered as a core part of your strategy. 

Understanding why competitors outrank you and how to surpass them is crucial. This is achieved by systematically evaluating your existing content against competitor strategies to uncover missed opportunities, weaknesses, and areas for improvement.

By analysing competitor websites, the structure of their high-performing pages, and the types of content that drive the most traffic, you should start to get a feel for the kind of content that’s working for that search. It is possible to drill deeper by assessing on-page elements (title tags, headers, meta descriptions), semantic keyword usage, structured data implementation and internal linking strategies to identify: 

• Keyword gaps: terms competitors target but you aren’t
• Content opportunities: missing topics or FAQs 
• Engagement enhancements: interactive elements like calculator’s or authoritative links 

Once these gaps have been identified, key pages requiring optimisation, expansion, or entirely new content development, can be prioritised.

Having gathered all the research and insights, it’s time to put everything into a clear, structured plan. This is where a good content matrix comes into its own. Think of it as the architectural plan for your content. In reality, it’s a well-organised spreadsheet specifically designed to make content SEO execution straightforward and typically features:

• Optimised meta descriptions and title tags 
• Logical header structures (H1s, H2s, H3s) 
• Natural semantic keyword integration 
• Structured FAQ sections 
• Topic clusters and proposed internal link suggestions

This strategic blueprint ensures that your content, once added to your website, will be user-friendly, AI-friendly, and highly effective in driving engagement and rankings.

Finally, in order to enhance visibility and engagement, the following tactics can be implemented across your website: 

• Structured headings and semantic keywords to optimise readability
• AI-optimised metadata and internal links for better searchability
• Structured data (i.e. FAQ schema, how-to schema) for heightened searchability
• Integrated multimedia elements to encourage extended engagement

This strategic approach builds a cohesive, AI-optimised content environment which leads, over time, to the right audience reaching your site, encouraging higher conversion and improved website rankings.

By understanding how search engines interpret language, context, and intent, you can shape your content so that it speaks directly to what your audiences truly want. This isn’t guesswork; it’s a strategy powered by data, behaviour, and genuine relevance. 

Whilst it’s undoubtedly a challenge to keep abreast of SEO best practices in a rapidly changing digital ecosystem, getting some of these strategic tactics in place can help you keep on top.

Curious about where your content stands today? Let’s run a quick audit for you.