This is what it takes to rank on AI searches: Out-the-box B2B marketing

Everyone in B2B marketing, it seems, is talking about AI search. It made major headlines in May when Google announced its AI Overviews (like snippets, but AI generated) and then rolled it back in the same month after the infamous, hilarious, “pizza glue” humiliation.

If you don’t know the pizza glue story and you relish online fails, do yourself a favour and lose yourself in an online rabbit hole about AI hallucinations. Or read this article by Forbes for the Google highlights, including using glue to make toppings stick to pizza and recommending eating one small rock per day for minerals and vitamins that are important for digestive health.

Despite the hiccups, AI search is picking up. Perplexity was on the market long before Google launched the AI overviews, and it is still, in my (and other more qualified researchers’) opinion, the best AI search tool on the market. Many of the articles referenced in my Perplexity searches ended up in my articles.

AI search is certainly not perfect, but with the debate of whether it will replace traditional search engines ongoing, it’s time for all forward-thinking marketers to look at what it takes to be referenced on AI search results. And whether it’s worth the effort.

Page jump TL;DR

1. What is traditional search?

• How traditional search engines rank content

2. What is AI?

• Large language models (LLMs) and their difference from search engines

3. What is AI search?

• How AI search uses natural language processing and machine learning to understand context and intent

4. How AI search works

• Contextual understanding, content discovery and data training

5. How to rank in AI searches

• Authority and expertise, structured data and SEO best practices, content depth, natural language focus, regular updates, multimedia engagement, and backlinks

6. How to optimise for AI search

• Topic clusters, semantic SEO, actionable content, voice search optimisation, and user experience improvements

7. Key differences between traditional SEO and AI search optimisation

• Search intent and contextual understanding, conversational queries and voice search, personalisation, user experience, entity recognition, and content synthesis across sources

8. What remains the same between traditional SEO and AI search?

• Quality content, technical SEO, and backlinks

9. Should AI search be part of your B2B marketing strategy?

• The value and effort of optimising for AI search

10. To ASO or not to ASO

• The importance of authority building and how it applies to both traditional and AI search

Let’s break it all down.

What is traditional search?

Traditional search engines, like Google, use algorithms to rank web pages based on relevance and authority. When a user types a query, search engines crawl and index billions of web pages, then rank them according to factors like keyword presence, backlinks, and content quality. The goal is to provide a list of results that best match the user’s search intent. SEO strategies focus on optimising content to meet these ranking factors, including using keywords, improving site structure, and earning high-quality backlinks.

What is traditional AI?

The AI we are all familiar with today is Large Language Models (LLMs) like GPT-4 (OpenAI), Llama (Meta AI), Claude (Anthropic) and Gemini—formerly Bard—(Google). They work differently to search by generating responses based on patterns in data they’ve been trained on. Instead of retrieving pre-existing documents, LLMs predict and generate text that fits the context of the query. They use statistical methods and deep learning to understand language structure, context, and meaning, synthesising answers from a broad understanding of topics. This allows them to respond conversationally and provide in-depth insights, even on complex topics, without simply pulling exact excerpts from web pages.  

What is AI search?

AI search refers to search technologies that leverage artificial intelligence, including machine learning, natural language processing (NLP), and semantic understanding, to deliver more accurate, contextually relevant, and personalised search results.

Unlike traditional keyword-based search engines, AI search engines aim to comprehend the intent behind queries, the relationships between concepts, and the overall context to provide superior results. AI search doesn’t just rank pages by keywords; it pulls together insights from multiple sources to give you a direct answer.

How AI search works

Understanding context

AI search tools understand the context of the query, focusing on intent, natural language patterns, and deeper relationships between concepts. For instance, a search for “how to improve B2B lead generation” might trigger responses that synthesise information about B2B marketing strategies, software tools, and case studies, even if those specific keywords aren’t always present.

Content discovery

AI search tools pull data from publicly available websites, articles, and databases. They rely on crawling data, similar to traditional search engines, but they prioritise trusted sources, favouring content that is clear, accurate, and regularly updated. Also similar to search engines.

Data training

AI search models are trained on vast amounts of data, including blogs, forums, news articles, whitepapers, and academic journals. As they learn, AI becomes highly skilled at finding the most useful content, whether it’s structured like a whitepaper or more conversational like a blog post.

How to rank in AI searches

To rank in AI searches, you need to build strong foundations that signal that your site is authoritative and trustworthy. These are the core elements that influence whether AI models recognise and prioritise your content:

Authority and expertise

AI search models prioritise trusted sources. To rank, your content needs to demonstrate authority on the subject. This means producing well-researched, insightful articles that prove your expertise, following the E-E-A-T model (Experience, Expertise, Authoritativeness, Trustworthiness).

Structured data and SEO best practices

Having structured data (such as schema markup) helps AI tools understand your content’s context and relevance. Product details, FAQs, and well-organised data enable AI to accurately pull key information.

Content depth and relevance

AI search favours comprehensive content that answers multiple aspects of a query. This is where in-depth guides, case studies, and whitepapers come in, as they demonstrate your ability to solve problems and offer complete solutions.

Natural language focus

Write in a conversational way that mimics how people ask questions. Long-tail queries and conversational phrases (e.g., “How can AI improve my B2B marketing strategy?”) align with the way users interact with AI tools.

Regular updates

Keeping your content fresh is crucial. AI models value updated, current information, so regularly revise and enhance your content to maintain relevance.

Engage with multimedia

Incorporating multimedia like videos, infographics, and interactive features boosts visibility. Engaging content helps AI models recognise that your content is valuable to users.

Backlinks and citations

AI models favour content linked by other reputable sites. Focus on earning high-quality backlinks to build credibility and authority.

How to optimise for AI search

Once you have a solid foundation in place, there are optimisation practices that should be introduced as part of your ongoing SEO or ASO (AI search optimisation) strategy:

Topic clusters

Organise content into topic clusters, where you structure related pieces around a central theme. This strengthens semantic relationships, helping AI better understand your expertise across interconnected topics.

Semantic SEO

Move beyond keywords by focusing on the broader concepts and relationships between topics. AI understands more than just exact matches, so make sure your content covers various perspectives on a subject to fully satisfy user intent.

Create actionable and authoritative content

AI search prefers content that offers actionable insights. Focus on providing clear solutions, backing up claims with data, and demonstrating expertise through examples and real-world applications.

Optimise for voice search

With the growing importance of voice search, ensure your content answers conversational, spoken queries. Focus on question-based formats (e.g., FAQs) that align with how users naturally ask questions via voice assistants.

Improve site speed and mobile friendliness

AI search models factor in user experience. Ensure your site loads quickly and is mobile-friendly to offer a seamless experience for both users and AI tools, which improves your chances of ranking.

What are the differences between traditional SEO and AI search optimisation?

If you’re reading all of the above, thinking, “This sounds exactly like SEO best practice”, you would be right. A lot of it is.

Many core SEO practices are still relevant for AI search, but the main differences revolve around how AI interprets intent, handles conversational language, uses personalisation, and evaluates engagement signals.

1. Search intent and contextual understanding

• Traditional SEO: Search engines typically match keywords in a query with the content that has been optimised for those specific terms. While algorithms have improved at understanding intent, it’s still somewhat keyword-centric.
• AI search: AI-powered search engines focus heavily on understanding the intent behind the query, not just matching keywords. Instead of looking for a specific word match, AI assesses how well your content answers broader questions and solves problems.

Tip: Focus on intent-based content rather than just keyword matching. Create content that answers specific questions, anticipates the broader needs of your audience and uses conversational language.

2. Conversational queries and voice search

• Traditional SEO: Optimising for written keywords and phrases is standard, where queries tend to be short and specific (e.g., “B2B marketing strategy”).
• AI search: With the rise of voice search (e.g., Siri, Google Assistant), more searches are now phrased as full, conversational questions (e.g., “What’s the best digital marketing strategy for a B2B company?”).

Tip: Optimise your content for how people naturally ask questions out loud. Create FAQs and conversational content that directly answers questions, such as how-to guides or step-by-step instructions.

3. Personalisation and predictive search

• Traditional SEO: Results are generally based on broad ranking factors like backlinks, relevance, and authority.
• AI search: AI tailors search results based on personal preferences, behaviour, and location. It remembers user interactions, providing more personalised results.

Tip: It’s harder to directly optimise for personalisation, but you can ensure your content remains highly relevant by focusing on audience segmentation and creating tailored content for different personas or industries within your target market.

4. User experience (UX) and engagement signals

• Traditional SEO: User experience is important for traditional SEO (e.g., bounce rates, page load speed), but it’s generally based on page performance.
• AI search: AI search engines are more sophisticated at evaluating how users interact with content over time. AI considers how well your content satisfies user needs by looking at engagement, such as time on page and interaction.

Tip: Create engaging content that encourages users to spend more time on the page. Use multimedia (videos, infographics), internal linking to related articles, and interactive elements.

5. Entity recognition and semantic SEO

• Traditional SEO: Optimising around keywords and topics is the foundation.
• AI search: AI search focuses heavily on entity recognition. It connects people, places, and concepts, understanding how they relate to each other. For example, it knows “Apple” could mean both the fruit and the company, based on context.

Tip: Focus on structured data and schema markup to help AI better understand the relationships between entities on your site. Topic clusters are also critical.

6. AI’s ability to synthesise content across sources

• Traditional SEO: The focus is on individual pages ranking for specific queries.
• AI search: AI can pull information from multiple sources to create a well-rounded response, even if the information is spread across different sites. This is especially important in AI-driven search assistants or knowledge graph results.

Tip: Ensure your content is well-researched and authoritative, as AI search will favour reliable sources. Consider linking to credible sources and providing thoroughly researched content that AI can use as part of its responses.

What remains the same between AI search and search?

• Quality content is still key: High-quality, authoritative content is essential.
• Technical SEO: Fast-loading, mobile-friendliness, and secure websites (HTTPS) are still critical.
• Backlinks: Building authoritative backlinks continues to demonstrate trustworthiness.

Should AI search be part of your B2B marketing strategy?

Seriously optimising for AI search is pretty involved, and the jury is still out on whether it’s actually worth the effort. We know that SEO works and gets results. In a 2023 study by SageFrog, SEO was the most profitable B2B marketing strategy, accounting for 34% of qualified leads.

Like AI, voice SEO—think Siri and Alexa—was predicted to destroy traditional search engines, with many forecasting the demise of Google. Yet, despite these bold predictions, Google remains dominant. AI search could follow a similar trajectory, with its true impact still unfolding.

When it comes to AI search, there is no data to tell us what can be gained. It’s currently all speculative. It’s easy to deduce that the more people use AI-powered search, the more visible your website and articles will be if they come up in those searches. But for now, Google holds 90.48% of the global search engine market share. And it’s AI overview feature has not been welcomed with much enthusiasm.

That being said, AI is not leaving our lives and the way we use it will only expand. It seems inevitable that AI search is coming and optimising now will be an advantage.

If nothing else, do this

If you’re already writing articles: 

Ensure your articles answer specific, high-intent questions that your audience might ask, and include structured data (like FAQ schema) to help AI search tools easily pull key information. Adding conversational language and updating older posts with new insights also boosts relevance.

If you’re already doing SEO

Focus on optimising for search intent rather than just keywords. Identify the actual problems your audience is trying to solve and adapt your content to address those needs in a more conversational and comprehensive way. Update metadata to reflect natural, longer queries.

If your digital marketing is sporadic

Regularly post simple, engaging content that answers common questions. You don’t need a massive overhaul—just start by scheduling a few consistent updates with a focus on evergreen content that addresses customer pain points.

If you’re doing all the B2B marketing things

Incorporate multimedia elements like videos or infographics into your content to increase engagement. AI search models value rich media that makes the content more dynamic and engaging. Also, regularly refresh your content to ensure it’s up-to-date and relevant.

To ASO, or not to ASO

Authority building for SEO, especially in a B2B context, relies heavily on your business being consistently mentioned and referenced across the web. These mentions help search engines, and AI models recognise your site as a trusted source. Awards, industry recognition, and high-quality backlinks from reputable sites signal credibility. Case studies, testimonials, and reviews add social proof, and publishing authoritative articles further positions you as a thought leader.

In essence, the more your brand is referenced by trusted sources, the more authoritative and relevant your site becomes in the eyes of search engines.

This goes for traditional search and AI search.

Remember, we’ve seen similar “industry-revolutionising” trends before. Voice SEO, fuelled by the rise of Siri and Alexa, was predicted to dismantle Google’s dominance. Yet, Google still holds over 90% of the global search market. AI search, while powerful, might follow the same pattern, evolving alongside rather than overtaking traditional search engines.

The best advice on how to optimise for AI? Optimise for traditional search and take what is easily applicable from the advice above to keep one step ahead of your competition.

For laserlike optimisation across the board, call The Lead Agency.