Do Keywords Still Impact SEO In The Era Of Google BERT AI?

Before we begin, there are more and more blogs out there discussing keywords and what they believe to be an overall lack of relevance nowadays.  All of us that went into a career working with SEO, did so on the foundational principals that good content, keywords, and backlinks are the backbone of SEO.  There are a bunch of other factors that are involved but overall, keywords keywords keywords.

With AI being a critical part of all searches now with Google and other search engines, things have changed significantly.  Keywords alone used to be the primary factor in determining the SERPs for any given query.  Now with AI, things have changed a bit.  So let’s begin with how BERT works, and then I will give you some ideas of how you need to tailor your content to better respond to a changing search engine.

How Google BERT AI Works

Google BERT (Bidirectional Encoder Representations from Transformers) is an advanced natural language processing (NLP) model designed to improve Google’s understanding of search queries. Unlike previous algorithms that processed words in a linear fashion, BERT reads text bidirectionally, meaning it analyzes words before and after a target word in a sentence to grasp the full context. This allows it to better interpret complex, conversational, and long-tail queries, especially those containing prepositions (e.g., “to,” “for,” “on”) that alter meaning. By improving contextual comprehension, BERT helps Google deliver more accurate and relevant search results, reducing misinterpretations that might have occurred with traditional keyword-matching algorithms.

How Google BERT AI Functions

At its core, BERT uses deep learning and transformer-based models to process natural language at a more sophisticated level. The transformer mechanism enables BERT to assign different levels of importance to each word in a sentence, allowing it to understand nuances, synonyms, and sentence structures. Unlike keyword-focused search methods that rely on exact-match phrases, BERT evaluates search queries in full context and compares them with indexed content to determine semantic relevance. This means that even if a webpage doesn’t contain an exact-match keyword, Google can still rank it highly if the content accurately answers the user’s intent. Because of this, traditional keyword stuffing is ineffective, and SEO strategies must now prioritize high-quality, naturally written content that aligns with user expectations.

Impact of Google BERT AI on Keywords in SEO

BERT has fundamentally changed how SEO professionals approach keyword optimization by prioritizing contextual relevance over rigid keyword targeting. Here are 15 key ways it impacts keyword usage in SEO:

1. Exact-match keywords are less critical – Google understands variations and synonyms.
2. Prepositions and stop words matter – Words like “for,” “to,” and “on” change query meaning.
3. Long-tail queries are better understood – More precise matching for conversational searches.
4. Search intent is prioritized – Google ranks results based on intent rather than just keyword density.
5. Better voice search optimization – BERT improves voice-based query interpretation.
6. Content must be natural and human-like – Keyword stuffing is penalized more effectively.
7. Semantic SEO is crucial – Using related words and phrases enhances ranking potential.
8. “People Also Ask” results are more relevant – Google refines its answer choices dynamically.
9. Stronger emphasis on user engagement metrics – Bounce rate, dwell time, and click-through rate impact rankings.
10. Poorly structured content is deprioritized – Unclear or cluttered articles rank lower.
11. Snippets and featured results improve – Well-structured, question-based content ranks higher.
12. Keyword research should focus on topics, not just phrases – Broader content clusters perform better.
13. Multi-language search has improved – Google can interpret meaning more accurately across languages.
14. Duplicate content is less effective – BERT prioritizes unique, well-researched material.
15. Local search queries are better understood – Google accurately processes region-specific phrases.

So as a whole, BERT uses a bidirectional contextual understanding to process any given query. Traditional search algorithms read text left to right (or right to left) and match keywords without full context. BERT, however, reads words in both directions at the same time and analyzes the entire sentence structure to extract true meaning.

For example:

Before BERT:

Search query:  “Can you get a prescription for someone at a pharmacy?”

Google might focus entirely on “prescription” + “pharmacy” and return general pharmacy information along with pharmacies near your location, based on your settings.

After BERT:

BERT understands that “for someone” changes the intent (the user wants to know about picking up a prescription for another person).  Google now serves relevant results that answer this exact intent (e.g., pharmacy policies for prescription pickup).

Transformer Based Model (Attention Mechanism)

BERT is based on a transformer-based model which uses an attention mechanism) to determine which words in a sentence matter most.  Therefore, if a sentence has ambiguous words, BERT looks at all of the surrounding words to determine their true meaning.  For example, if the search is for “How do you train a bass?“, BERT examines words before and after the words “train” and “bass” to determine if the user is searching for a bass fish or a bass instrument.  In this case, it would determine that you are not likely trying to train a fish, and therefore conclude that you are referring to training on a musical instrument.

Handling Stop Words & Prepositions Correctly

In traditional SEO, stop words like “to”, “on”, and “for” were often ignored.  However, BERT recognizes that these small words change the intent significantly and must be considered in the search query.  For example, “Can you park on a hill without a handbrake?” With traditional search, Google might have returned general “how to park a car” results.  With BERT, the AI recognizes the critical importance of “without a handbrake” and will return safety related content.

Content Creation Moving Forward

BERT is designed to prioritize natural, user-focused content rather than keyword-stuffed or over-optimized pages.

Here’s what you should do: 

First and foremost, write your content for humans… and not just search engines.  Yes, it all goes back to WRITE GOOD CONTENT.  Focus on natural language and conversational content rather than trying to “game” the algorithm by intentionally keyword stuffing.  Structure content like a well written blog post, FAQ, or answer-based content rather than robotic sounding keyword repetitions.

So in using our “How do you train a bass?” example above,  here is a good example of writing content, along with a keyword stuffing bad example…

Good Example

“To train a bass fish, you need to condition it using a feeding schedule and environment cues. A bass can recognize feeding times when kept in a routine.”

Bad Example:

“Bass training tips: You can train a bass by using bass fish training methods. Training a bass requires good bass training techniques.”

Optimizing For Long-Tail & Conversational Queries 

Optimize your content for long tail and conversational queries.  Use natural, question based phrases that people type in search queries, or ones that are often used in Alexa or Siri voice queries.  This will also help you target “People Also Ask” questions in Google SERPs (since BERT helps answer these).  Include FAQ sections on pages to match search queries directly.

What Phrases Work Well For SEO Post-BERT:

“Can I pick up a prescription for someone else at CVS?”  or similarly, “”Best ways to rank higher on Google without ads?””

Here are some other tips:

• Use short sentences and simple words. 
• Break up content with headings (H2, H3), bullet points, and lists.
• Answer queries early in the content, then provide depth later in the page.
• Instead of repeating the same keyword, use synonyms and variations that naturally fit the topic. BERT recognizes these as related and will not penalize you for not using exact-match keywords.
• Focus on question-based content (Who, What, Where, When, Why, How)
• Use a conversational tone in articles and product descriptions.
• Structure answers in a direct, clear way (great for featured snippets).

15 Key Takeaways From The Article

• Keywords alone are no longer the backbone of SEO – With AI-driven search, context and intent now play a bigger role than simple keyword matching.

• BERT improves Google’s ability to understand natural language – It processes words bidirectionally, analyzing words before and after a given term for better context.

• Search intent is more important than exact-match keywords – Google now ranks content based on how well it satisfies the user’s query meaning, not just keyword presence.

• Long-tail queries and conversational searches are prioritized – BERT is designed to better understand complex and question-based searches, making natural content more valuable.

• Prepositions and stop words matter – Small words like “to,” “for,” “on” that were previously ignored are now essential for understanding query intent.

• Keyword stuffing is ineffective – Repeating a keyword multiple times without natural flow will not improve rankings and can harm user engagement.

• Semantic SEO is crucial – Google recognizes synonyms, related terms, and topic clusters, making broad, well-structured content more effective.

• Voice search is directly impacted by BERT – Since AI-driven search understands natural speech patterns, optimizing for voice queries is a must.

• Google’s “People Also Ask” results are more accurate – BERT refines follow-up questions dynamically, so structuring content to match these queries increases visibility.

• Content structure matters – Well-organized pages with H2/H3 headings, bullet points, and concise answers rank better due to improved readability and engagement.

• Featured snippets favor precise, well-written answers – Google prioritizes content that directly and clearly answers a search query, improving chances of ranking in position zero.

• Duplicate content is deprioritized – BERT favors unique, well-researched material, making it critical to focus on originality rather than reusing existing text.

• Local SEO is improved through contextual understanding – Google can now better interpret region-specific search terms and provide more accurate local results.

• Optimizing for long-tail and question-based queries boosts rankings – Queries like “How do I rank higher on Google without ads?” are more effective than generic keyword phrases.

• Writing for humans, not algorithms, is the best strategy – BERT rewards conversational, well-structured, and helpful content, so focus on user experience and readability over rigid SEO formulas. 

BERT demands an intent-driven, natural, and semantically structured approach to SEO. For your ecommerce and SEO expertise, that means creating thought leadership content, optimizing for AI-driven search, and focusing on UX metrics to stay ahead of the competition.