How Google Search Uses AI

Are your SEO efforts falling short, leaving you puzzled about what’s going wrong? Traditional SEO methods like focusing on keywords and backlinks are becoming less effective as Google’s AI systems evolve, fundamentally changing how search results are ranked.

This transformation is happening behind the scenes, making it harder to pinpoint why your content may not be performing as expected. To stay ahead, it’s crucial to understand how Google’s AI systems—such as RankBrain, neural matching, BERT, and MUM—are reshaping search.

By grasping these advancements, you’ll be better equipped to create content that aligns with Google’s AI-driven approach, increasing your chances of ranking higher.

Google’s AI Systems

Google has incorporated AI into its ranking processes since 2015, starting with RankBrain. By 2018, Ben Gomes, Google’s Senior Vice President of Learning and Education, declared AI the "next chapter of Search," predicting three key shifts:

1. From answers to journeys – Google will help users continue tasks from where they left off.
2. From queries to queryless discovery – Information will surface without needing a specific search query.
3. From text to visual discovery – Google will integrate more visual content into search.

Let’s explore these AI systems in detail:

RankBrain (2015)

RankBrain was Google’s first AI system, designed to "understand how words relate to concepts." It marked Google’s initial move toward interpreting content more like a human. For instance, when searching “What’s the color of the sky?”, RankBrain understands the relationship between "sky" and "color," enabling Google to provide relevant results even without exact keyword matches.

Neural Matching (2018)

Neural matching helps Google comprehend how "queries relate to pages" for more complex concepts. For example, if you search “tie my laces,” neural matching understands you’re referring to shoe laces and returns results on how to tie them.

BERT (2019)

BERT, or Bidirectional Encoder Representations from Transformers, was a breakthrough in AI search. It builds on RankBrain and neural matching by understanding how multiple words in a sentence relate to one another. BERT improves entity recognition, helping Google identify brands, individuals, and their expertise in specific topics.

In essence, BERT enables Google to interpret the nuances of human language, powering AI features like generative AI and AI overviews.

MUM (2021)

The Multitask Unified Model (MUM) is 1,000 times more powerful than BERT. While BERT focuses on understanding language, MUM generates language and can interpret text, images, and even video. For example, MUM can understand comparisons between two mountains or provide insights on the COVID-19 vaccine based on up-to-date, trustworthy sources.

MUM’s ability to quickly surface reliable information demonstrates the potential for Google to enhance search results more efficiently.

Harnessing AI for SEO: What You Need to Know

Just as generative AI can create content, Google’s AI can assess content quality and relevance in its ranking system. Think of it like a human vetting a source—Google’s AI can ask questions like:

• Is the author an expert in the subject matter?
• Are other experts discussing their expertise?
• Does the content align with industry standards?
• Are customers engaging with or sharing this content?

As Google shifts "from answers to journeys," it’s important to focus on how users search for information and engage with your brand. The future of SEO lies in understanding human behavior and crafting content that reflects the journeys users take when seeking knowledge.

Stop thinking of SEO in terms of keywords and signals—focus on creating content that resonates with how people search and interact with information.