How Google Uses AI to Rank Websites in 2025

Whаt Was Google’s RаnkBrаіn?

Google Rank Brain has been replaced with more modern AI systems

Google’s search algorithm has come a long way from simple keyword matching. Over the years, it has incorporated machine learning and artificial intelligence (AI) to better understand user intent and deliver the most relevant results.

One of the earliest AI-driven ranking components was RankBrain, introduced in 2015. At the time, RankBrain was a groundbreaking system designed to help Google process and interpret search queries more effectively. But search technology has evolved significantly since then, and today, Google uses a combination of AI models to rank web pages, making RankBrain just one piece of a much larger puzzle.

A Quick Recap: What Was RankBrain?

When Google first launched RankBrain, its primary goal was to interpret ambiguous or never-before-seen search queries. It helped Google understand the relationships between words and concepts, making it better at matching search intent rather than just looking for exact keyword matches.

Before RankBrain, Google’s ranking relied heavily on hand-coded algorithms and keyword-based matching. This meant that if someone searched for something phrased differently than usual, Google might not return the most relevant results. RankBrain helped bridge that gap by using machine learning to interpret queries and refine search rankings dynamically.

While RankBrain was a huge step forward, Google didn’t stop there. Since then, newer AI models have taken centre stage.

The Evolution of Google’s AI Search Systems

1. BERT (Bidirectional Encoder Representations from Transformers) – 2019

In 2019, Google introduced BERT, a deep learning algorithm that revolutionised how the search engine understands natural language processing (NLP).

Unlike RankBrain, which focused primarily on identifying search intent, BERT allowed Google to understand the context of words in a sentence. This meant that searches with complex phrasing or conversational language could be interpreted far more accurately.

For example, before BERT, Google might have misinterpreted a query like “Can you get medicine for someone at the pharmacy?” by focusing on individual keywords rather than the context of the sentence. After BERT, Google could better understand that the query was about whether it was permissible to pick up medicine on someone’s behalf.

2. MUM (Multitask Unified Model) – 2021

In 2021, Google introduced MUM, an even more advanced AI system designed to handle complex search queries and analyse content across multiple languages and media types (text, images, videos, etc.).

MUM differs from RankBrain and BERT because it doesn’t just understand search intent and context—it can generate answers and insights based on a vast range of sources. For example, if a user asks:

“What should I pack for a hiking trip in the Swiss Alps in October?”

MUM could gather and synthesise information from blogs, weather forecasts, and travel guides to provide a detailed, expert-level answer.

MUM’s ability to process images and videos is also a game-changer, making Google’s search results even more sophisticated.

How Google’s AI Systems Work Together in 2025

As of today, Google doesn’t rely on just one AI system to rank content. Instead, RankBrain, BERT, MUM, and other AI-driven models work together to refine search results and deliver the most relevant pages based on user intent.

Here’s how they function as part of a broader ranking system:

RankBrain still plays a role in understanding ambiguous queries, but it’s now just one piece of the puzzle.
BERT enhances Google’s ability to understand sentence structure and the relationship between words.
MUM processes complex, multi-layered queries, pulls insights from different content formats, and enhances multilingual search results.

These AI systems work alongside traditional ranking factors like backlinks, page speed, and content relevance to determine which websites deserve to appear at the top of search results.

What This Means for SEO in 2025

With Google’s increasing reliance on AI, SEO strategies must adapt to these changes. Here’s what businesses and website owners should focus on:

1. Prioritise User Intent

Google’s AI is designed to understand what users truly mean, not just what they type. To rank well, businesses should create content that directly answers searchers’ questions rather than just stuffing in keywords.

2. Write in Natural Language

With BERT and MUM at play, websites that provide well-written, natural, and conversational content will perform better. Content should read as if it were written for real people, not search engines.

3. Optimise for Voice Search & Conversational Queries

As AI-powered search grows, more users are searching with voice assistants like Google Assistant. This means businesses should optimise for long-tail, question-based queries (e.g., “What’s the best way to design a van decal for my business?”).

4. Use Multimedia Content

Since MUM can analyse videos, images, and podcasts, incorporating high-quality visual and multimedia content into your website can improve its search visibility.

5. Focus on High-Quality, Authoritative Content

Google’s AI models prioritise expertise, authority, and trustworthiness (E-A-T). Businesses should aim to create in-depth, well-researched content backed by real experience or professional knowledge.

Final Thoughts

RankBrain was just the beginning of Google’s AI revolution. Today, the search engine is powered by multiple AI-driven systems that continuously refine and improve search results.

For businesses, this means SEO is no longer just about keywords—it’s about understanding search intent, providing valuable content, and optimising for AI-powered search capabilities.

By staying ahead of these changes, businesses can ensure their websites remain visible, relevant, and competitive in an ever-evolving digital landscape.

(Updated March 2025)