The Future of SEO in the Age of AI Search Engines

As artificial intelligence (AI) continues to revolutioniSe industries, the world of search engine optimiSation (SEO) is poised for radical transformation. Traditional search engines like Google and Bing have long dominated the SEO landscape, but the emergence of AI-driven search engines such as ChatGPT, Perplexity, and others could reshape the way users find information. This new wave of AI search platforms necessitates a fresh approach to SEO strategies, focusing not just on optimiSing content for keyword-based search algorithms but also on the underlying data sources that train these models. 

In this article, we’ll explore how SEO is evolving in the context of AI search engines and outline a comprehensive strategy to stay ahead of this shift. 

1. Selecting the platforms that matter 

The first step in preparing for the future of SEO is identifying the AI-driven platforms that are gaining traction in the market. While ChatGPT and Perplexity are two of the most prominent AI models today, other contenders like Google’s Bard and Anthropic’s Claude are emerging as competitors. 

The choice of platform will largely depend on current and anticipated user adoption. For instance, ChatGPT is integrated into various applications, including Microsoft’s Bing, while Perplexity focuses on providing highly contextual, conversational search results. It’s essential to stay informed on trends that indicate which platforms are likely to see widespread adoption, as this will shape the focus of your SEO strategy. 

2. Understanding user behavior on ai search platforms 

Just as traditional SEO involves understanding how users phrase queries on Google or Bing, AI SEO demands understanding how users interact with conversational search engines. These platforms excel at interpreting natural language, providing personalised answers, and holding contextual conversations. 

Topical analysis can provide valuable insights into the types of questions users are asking and how they expect AI models to respond. Tools like Google’s Search Console and AI interaction logs can provide data on user patterns, showing what content users are being directed to, what they click on, and how they interact with search results. 

 3. Monitoring variations in outputs across platforms 

Once you’ve selected the platforms, the next step is monitoring how each AI search engine responds to similar queries. This isn’t as straightforward as traditional SEO audits; AI engines can provide vastly different answers based on their training data and algorithmic priorities. 

For example, a query about 'best smartphones in 2024' may yield concise product lists on Google’s Bard but more community-driven, nuanced discussions on ChatGPT. Monitoring these differences is critical for tailoring your content strategy to each platform. Tools that track AI-generated content across various platforms will be instrumental in assessing discrepancies. 

Performing a mid-funnel question-based query on Perplexity and Google SGE provide similar answers to the query. However, the way sources are displayed and the page laid out are different. Perplexity shows the sources at the top of the result, with the longer-form conversational answer below. Related videos and the ability to search videos, images, and generate images appear in the right rail. Google, on the other hand, shows the conversational response at the top of the page, with sources linked directly in the text and related pages (standard organic links) on the right. 

Perplexity: “Do I need snow tires with AWD”

Google: “Do I need snow tires with AWD”

ChatGPT displays a response to the question that is similar to the responses on Perplexity and Google, but without any source data. This is because ChatGPT is fundamentally not a search engine. Whereas Google and other search engines index and organise existing data, ChatGPT and other GenAI models are designed to create a response based on their understanding of the data. 

ChatGPT: “Do I need snow tires with AWD”

4. Investigating why these differences exist 

Understanding the “why” behind the differences in AI search results is a crucial next step. The variation can stem from several factors, including: 

• Training data: AI platforms train on different datasets. While Google may rely heavily on indexed web pages, platforms like ChatGPT might prioritise open datasets, Reddit discussions, or other community-driven sources. Each platform’s unique data influences how it interprets and presents information. 
• Algorithms and weighting: Different algorithms may prioritise different types of content - one might emphasise recent, factual data while another leans into opinionated, long-form responses sourced from forums and social media. 

By investigating these factors, you can adjust your SEO strategy to address the strengths and weaknesses of each platform’s data source. 

5. Designing experiments to influence source data 

Once you understand the data sources driving AI-generated search results, the next step is designing experiments to influence these sources. Traditional SEO often revolves around backlinks, keywords, and structured data, but AI SEO might require different tactics. 

For example: 

• Reddit influence: If a platform like ChatGPT is heavily influenced by Reddit discussions, optimising your presence on forums becomes a priority. Engaging authentically with communities, driving discussions, and sharing your content in relevant threads could improve your content’s visibility on these platforms. 
• Influencing AI models: Since some AI models are trained on open data sources, contributing to repositories or public discussions, such as academic publications or open-source projects, could be another way to influence the outcome of AI-generated responses. 

These experiments should be tailored to the specific data sources of each platform. 

6. Monitoring and reporting on experiment outcomes 

After implementing your experiments, it’s important to track the outcomes and measure the impact of your efforts. This can be achieved by analysing changes in how AI platforms rank or reference your content over time. For instance, if your goal is to influence Reddit-driven content, track how your posts are cited or how frequently your discussions are picked up by the AI models. 

Metrics to track include: 

• Increased visibility in AI-generated answers 
• Changes in the sentiment or context of how your content is presented 
• Shifts in user engagement with your AI-optimised content 

Regular reporting on these experiments will help fine-tune your strategy. 

7. Developing an AI-centric SEO strategy 

The final step is to consolidate your findings into a robust AI SEO strategy. This strategy should focus on continuous optimisation for each platform’s unique characteristics. For example, if ChatGPT’s responses are heavily influenced by social discourse, investing in community-building efforts may become more valuable than traditional on-page SEO. Alternatively, if Perplexity relies more on highly structured data, ensuring your website is well-optimised for schema.org and structured content might take precedence. 

As always, though, it remains critical that you optimise not for the search engine, but to ensure your content is valuable to users and technically sound so as to be findable. Google’s stated position on good content is still the correct framework for appearing in AI in search: that is, provide content that delivers E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness). 

Your AI SEO strategy should also include: 

• A plan to continually adapt as new AI platforms emerge and evolve 
• Ongoing monitoring of data sources to spot new opportunities for content influence 
• Regular updates to content based on AI trends and updates to the models 

The future of SEO will be shaped by AI-driven search engines, and businesses must be ready to adapt to these changes. By focusing on the platforms that matter, understanding user behaviour, and experimenting with how to influence the training data behind these AI systems, you can stay ahead of the curve. The key to success in AI SEO will be a willingness to embrace new strategies and continuously evolve as these platforms mature.