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AI search is not just the future, it’s the now

We are in the midst of an AI revolution that’s changing the way users search online. AI search tools are becoming integral to user experience, offering conversational answers, personalised recommendations, and instant solutions. But how different is AI search compared with traditional search engines like Google?

What is AI search optimisation?

Traditional search engines rely on keywords and rankings, but AI-powered search engines are revolutionising the way users find information. Tools like ChatGPT, Google Gemini, and Microsoft Copilot are ‘large language models‘ and provide conversational, context-driven answers rather than lists of links.

AI Search Optimisation is about ensuring your business:

• Appears in AI-generated responses when users ask questions relevant to your products or services.
• Provides high-quality, structured content that AI algorithms can easily process and understand.
• Stays visible in a world where AI assistants and chatbots drive search behaviour.

The future of search is here, and it’s powered by artificial intelligence.

The generative AI market is exploding

According to Statista, the global market size of ‘Generative AI’ is forecast to continuously increase between 2024 and 2030 by a total 320 billion U.S. dollars (+887.41 percent). After the tenth consecutive increasing year, the market size is estimated to reach 356.05 billion and a new peak in 2030.

Statistics from Exploding Topics:

• The global AI market is valued at over $390 billion
• AI industry value is projected to increase by over 5x over the next 5 years
• The US AI market is forecast to reach $299.64 billion by 2026
• The AI market is expanding at a CAGR of 37.3% between 2022 to 2030
• By 2025, as many as 97 million people will work in the AI space
• 83% of companies claim that AI is a top priority in their business plans
• 48% of businesses use some form of AI to utilise big data effectively

The difference between Google and AI search

Google Algorithms (traditional search engines)

Google’s algorithms (e.g., PageRank, Hummingbird, RankBrain, BERT, etc.) power its traditional search engine. It uses a model that is pretty much unchanged since 1998 when it first appeared on the web.

• Crawling & Indexing: Google’s bots crawl websites, indexing their content into a massive database.
• Ranking: Algorithms assess relevance and authority based on signals like keywords, backlinks, structured data, and user behaviour.
• Search Results: Google delivers a ranked list of links (SERPs) based on the user’s query, optimised for relevance, intent, and engagement.

Key Features:

• Uses real-time data from the web.
• Relies heavily on ranking factors (e.g., E-E-A-T, backlinks, Core Web Vitals).
• Delivers links and summaries but does not synthesise content into a unified response.

AI Search (conversational search engines)

AI search engines like ChatGPT, Google Gemini, and Bing AI use advanced natural language processing (NLP) and machine learning to generate conversational responses rather than a list of links.

How AI Search Works:

• Training Data: AI tools are trained on large datasets, which can include publicly available web data, books, articles, and other text-based resources.
• Data Retrieval: Some AI tools integrate with live search engines (like Google or Bing) to pull updated information, while others rely on their own datasets.
• Content Synthesis: AI doesn’t just retrieve information; it synthesises content, summarising data into a coherent response tailored to the query.
• Customisation: AI can adapt its responses to be conversational, detailed, or action-oriented, depending on the user’s intent.

Key Features:

• Provides direct answers or summaries, not just links.
• Often integrates structured and unstructured data.
• May rely on live web access or pre-existing datasets.

Is AI a search assistant, or a search engine?

The answer is it can be both, and it depends on the AI tool in question.

Google Gemini (and Bing AI):

• These AI tools are integrated with live search engines like Google and Bing, allowing them to fetch up-to-date information from the web.
• They combine their pre-trained models with real-time web data to deliver more accurate and current responses.
• For example, Gemini can retrieve live Google search data for time-sensitive or location-based queries.

ChatGPT (OpenAI):

• The standard versions of ChatGPT (like GPT-3.5) are trained on a static dataset that includes information available up to a certain point (e.g., 2021 for older models).
• It does not have live web access unless integrated with plugins or third-party tools like ChatGPT Plus with Browse with Bing.
• When enabled with web access, ChatGPT can retrieve live data from search engines like Bing but primarily relies on its internal knowledge base.

Optimising for AI search assistants

An AI search assistant effectively conducts a search for us and delivers a summary of the results in a conversational style. This means for your business to be included in responses, then your website needs to be ranking on page 1 in the traditional results. Normal SEO principles apply here, and it’s important to target relevant keywords and get them ranking highly on both Google, Bing and any other relevant search engine.

Optimising for AI search

For your business to be recommended by AI search that doesn’t crawl the ranking results, then it needs to be prominent within its dataset.

AI models like ChatGPT work on a probabilistic basis, meaning they predict the most likely response based on patterns in their dataset.

Probability and artificial intelligence

1. Token Prediction:
   • AI models like ChatGPT generate responses by predicting the next word (or token) in a sequence.
   • The model is trained on vast datasets containing text from books, websites, articles, and more.
   • It uses statistical probabilities to determine which word (or token) is most likely to follow based on its training data.
2. Patterns, Not Facts:
   • The model doesn’t “know” facts in the way humans do. Instead, it identifies patterns in the data.
   • If a certain phrase, fact, or piece of information appears frequently in the dataset, the AI is more likely to include it in its response.
3. Recommendations Based on Probability:
   • If the AI has seen certain SEO agencies mentioned frequently in positive contexts in its training data, it might consider them highly probable candidates to mention in response to a question.
   • AI doesn’t evaluate or compare the merits of those agencies, it simply reflects patterns from the dataset.

For example, if you were to ask ChatGPT to recommend an SEO agency, then it would give you the most probable answer. If the dataset contains many references to a specific SEO agency (e.g., articles, reviews, or mentions), that agency may be recommended because it appears frequently and positively.

The AI doesn’t independently assess quality or suitability but reflects the probability of the agency being relevant based on its training data. If “Agency X” appears frequently in blog posts, case studies, or articles related to SEO success, the AI might recommend it. Conversely, if “Agency Y” is less frequently or neutrally mentioned, it’s less likely to surface as a recommendation.

What does this mean?

Bias in Training Data:

• The AI’s dataset may reflect biases in the sources it was trained on. For example, well-marketed agencies with more online presence could dominate recommendations, even if smaller or newer agencies offer better services.
• Less-known or niche agencies are less likely to appear unless they have substantial online representation in the dataset.

Limited Context Awareness:

• AI doesn’t evaluate real-time factors like current client reviews, recent success stories, or specific needs of the user asking the question.
• The AI might suggest a generalised answer that doesn’t consider your specific goals, industry, or location.

Probable ≠ Best Fit:

• The recommendation reflects probability, not personalisation. While it may suggest a prominent or reputable agency, it doesn’t guarantee the agency is the best choice for your unique requirements.

To make your business the most probable answer in AI-generated recommendations like ChatGPT’s responses, you need to maximise your presence, relevance, and authority in the dataset the AI is trained on.

How to convince AI your website is the most ‘probable’ response

Build a strong online presence

AI models like ChatGPT are trained on a mix of publicly available information, such as blogs, websites, reviews, news articles, and forums. The more positive and authoritative mentions your business has across the web, the higher the probability it will appear in AI-generated recommendations.

• Optimise Your Website:
  • Publish high-quality, keyword-rich content about your services.
  • Use long-tail keywords and conversational phrasing that match voice and AI search queries.
  • Add FAQ pages, case studies, and expert blog posts answering common questions.
• Create a Blog or Knowledge Hub:
  • Publish content like “How to Choose the Best SEO Agency” or “Top Tips for Voice Search Optimisation”, positioning yourself as an expert.
  • Include mentions of your business naturally in helpful content.
• Engage in Digital PR:
  • Secure mentions in reputable industry publications.
  • Collaborate with journalists on platforms like HARO (Help a Reporter Out) to feature your agency in articles about SEO.
• Get Listed in Authoritative Directories:
  • Appear in trusted directories like Clutch, Yell, Trustpilot, and LinkedIn. AI often pulls information from such platforms when generating recommendations.

Increase mentions in authoritative sources

AI tools rely heavily on frequent mentions across trusted platforms to determine relevance.

• Create Evergreen Content:
  • Publish valuable resources like “Ultimate Guides and Statistics” that are likely to be cited frequently.
• Encourage Social Sharing:
  • Promote your content on social media platforms like LinkedIn and X.
  • Engage with influencers who can amplify your reach.
• Publish on Authoritative Platforms:
  • Write guest blogs or contribute to platforms like Medium, HubSpot, or Moz.

Engage in strategic partnerships

AI can pick up references from external sources. Collaborating with other reputable brands or influencers boosts your visibility in these datasets.

• Collaborate on Content:
  • Co-author articles, blogs, or studies with other well-known companies or industry experts.
• Secure Backlinks:
  • Focus on building backlinks from high-authority websites that AI is more likely to prioritise.
• Get Featured on Podcasts and Blogs:
  • Appear as a guest speaker or contributor on respected platforms.

As you can see, the tactics here are not much different to the tactics we would use to rank high on Google. The difference is that ‘reach’ is more important than specific keyword targeting. It’s not about gaining as many links as possible, but more about gaining as many mentions as possible. The more times your brand is mentioned across the web, the higher the likelihood it will be included in AI datasets, and this increases the probability of your brand getting recommended.

Key differences: Google Algorithms vs AI Search.

Feature	Google Algorithms	AI Search
Core Function	Retrieves a list of ranked links.	Provides synthesised, conversational answers.
Data Source	Crawled and indexed websites.	Pre-trained datasets, with or without live search integration.
Real-Time Updates	Always uses real-time data from the web.	Depends on the AI model (some access live data; others rely on static datasets).
Response Type	Links and summaries from the web.	Unified, natural-language responses tailored to the query.
Use of Structured Data	Leverages schema for rich snippets.	Uses structured data to improve context but delivers it conversationally.
Customisation	Limited to query refinement.	Highly conversational and can adapt tone, style, and depth.

AI search optimisation is the future of visibility

AI search has changed how users find information, offering conversational, context-driven responses rather than lists of links. Unlike traditional search engines like Google, AI tools such as ChatGPT, Google Gemini, and Bing AI rely on advanced natural language processing (NLP) and pre-trained datasets, often integrating live web data to deliver direct, personalised answers.

For businesses, this shift means adapting to a new search environment where appearing in AI-generated responses is key to success. To achieve this, it’s essential to:

• Build a strong online presence with positive mentions across reputable platforms.
• Focus on structured, conversational content that aligns with how users query AI tools.
• Leverage digital PR, partnerships, and authoritative citations to increase your visibility in AI datasets.

The tactics for AI search optimisation mirror traditional SEO strategies but emphasise reach over specific keywords, ensuring your brand is widely mentioned, credible, and authoritative. As AI becomes integral to search behaviour, optimising for AI search ensures your business remains relevant and discoverable in the rapidly evolving digital landscape.

The future of search is here, and it’s powered by artificial intelligence. Will your business be part of it?

Speak to us today

Don’t invest in any SEO work until you know exactly what you’re dealing with. AI search is a major part of how people are searching online and it’s only going to get bigger. Speak to us today and see how we can help your business optimise for voice search.

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