{"id":118682,"date":"2024-10-07T08:00:03","date_gmt":"2024-10-07T13:00:03","guid":{"rendered":"https:\/\/www.dacgroup.com\/insights\/blog\/none\/\/"},"modified":"2025-01-10T07:10:35","modified_gmt":"2025-01-10T12:10:35","slug":"the-future-of-seo-in-the-age-of-ai-search-engines","status":"publish","type":"post","link":"https:\/\/www.dacgroup.com\/en-ca\/insights\/blog\/search-optimization\/the-future-of-seo-in-the-age-of-ai-search-engines\/","title":{"rendered":"The future of SEO in the age of AI search engines"},"content":{"rendered":"\n

As artificial intelligence (AI) continues to revolutionize industries, the world of search engine optimization (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 optimizing content for keyword-based search algorithms but also on the underlying data sources that train these models. <\/p>\n\n\n\n

In this article, we\u2019ll explore how SEO is evolving in the context of AI search engines and outline a comprehensive strategy to stay ahead of this shift. <\/p>\n\n\n\n

1. Selecting the platforms that matter<\/strong> <\/h2>\n\n\n\n

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. <\/p>\n\n\n\n

The choice of platform will largely depend on current and anticipated user adoption. For instance, ChatGPT is integrated into various applications, including Microsoft\u2019s Bing, while Perplexity focuses on providing highly contextual, conversational search results. It\u2019s 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. <\/p>\n\n\n\n

2. Understanding user behavior on ai search platforms<\/strong> <\/h2>\n\n\n\n

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 personalized answers, and holding contextual conversations. <\/p>\n\n\n\n

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\u2019s 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. <\/p>\n\n\n\n

 3. Monitoring variations in outputs across platforms<\/strong> <\/h2>\n\n\n\n

Once you\u2019ve selected the platforms, the next step is monitoring how each AI search engine responds to similar queries. This isn\u2019t as straightforward as traditional SEO audits; AI engines can provide vastly different answers based on their training data and algorithmic priorities. <\/p>\n\n\n\n

For example, a query about \u201cbest smartphones in 2024\u201d may yield concise product lists on Google\u2019s 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. <\/p>\n\n\n\n

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. <\/p>\n\n\n

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\"\"<\/figure>\n<\/div>\n\n\n

Perplexity: \u201cDo I need snow tires with AWD\u201d
<\/p>\n\n\n\n

Google: \u201cDo I need snow tires with AWD\u201d<\/p>\n\n\n

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\"\"<\/figure>\n<\/div>\n\n\n

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 organize existing data, ChatGPT and other GenAI models are designed to create a response based on their understanding of the data. <\/p>\n\n\n\n

ChatGPT: \u201cDo I need snow tires with AWD\u201d<\/p>\n\n\n

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\"\"<\/figure>\n<\/div>\n\n\n

4. Investigating why these differences exist<\/strong> <\/h2>\n\n\n\n

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

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  • Training data:<\/strong> AI platforms train on different datasets. While Google may rely heavily on indexed web pages, platforms like ChatGPT might prioritize open datasets, Reddit discussions, or other community-driven sources. Each platform’s unique data influences how it interprets and presents information.\u00a0<\/li>\n\n\n\n
  • Algorithms and weighting:<\/strong> Different algorithms may prioritize different types of content\u2014one might emphasize recent, factual data while another leans into opinionated, long-form responses sourced from forums and social media.\u00a0<\/li>\n<\/ul>\n\n\n\n

    By investigating these factors, you can adjust your SEO strategy to address the strengths and weaknesses of each platform\u2019s data source. <\/p>\n

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