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SEO 43 min read June 3, 2020

How Artificial Intelligence Changes SEO: Asking Experts

How Artificial Intelligence Changes SEO: Asking Experts

Stacy Mine
Editor at Serpstat
Search engine algorithms are continually evolving and becoming more complex. Since their inception, search engines have walked their way from simple search tools to machines with sophisticated algorithms. All this directly affects the SEO sphere in two opposite directions. Raising sites to the top appears to be much more challenging, but at the same time, search results have become more high-quality. You will no longer get into the top using underhanded manipulation methods with the site.
In general, AI has fundamentally changed the approach to search engine optimization. In this article, we will discuss using it for our purposes and learn the opinion of experts on the topic of AI.

Our today's contributors:

VP of SEO and Content at G2
Head of SEO at Ringier AG
Managing Director at Dept Digital Marketing
Head of Research and Development at
Manager of Search and Findability at Red Hat
Onsite SEO Specialist for Oberlo at Shopify
Co-Founder at Director of at Wolfate Agency
Digital Marketing Manager at Brosix
Digital Marketer at Сognitive SEO and BrandMentions
SEO Consultant at L'Agence Web
Digital Marketing Manager at Uscreen
SEO Consultant at SUSO Digital
Freelance SEO and BI Consultant

What is artificial intelligence

Artificial intelligence (AI) is a field of development involved in the creation of intelligent technical and software systems that have capabilities previously associated with the human mind, that is: learning, creativity, understanding of the language, etc. AI allows computers to adapt to given parameters, learn from their own experience and perform various kinds of tasks.

Artificial intelligence technologies are divided into categories regarding their ability to imitate human abilities. Using these characteristics, all artificial intelligence systems, both existing and hypothetical, are divided into 3 types:
Artificial Narrow Intelligence which has a narrow range of abilities. These systems can only be trained to perform specific tasks. For example, it's Google's Rankbrain, Google Search, Apple's Siri, or Amazon's Alexa.
Artificial General Intelligence corresponds to human capabilities. It is universal, able to solve various problems and learn from its own experience.
Artificial Superintelligence, which surpasses human intellectual abilities.
The first (ANI) is the only type of artificial intelligence that humans have successfully implemented today. It is focused on achieving specific goals and is designed to perform individual tasks.

Why do you need machine learning

We want to automate specific processes using a computer, and over the past 50 years, we have already achieved tremendous results in this field. However, what is the difference between the traditional ways of programming machines and machine learning?

How the machine works is a specific algorithm. Let's analyze the concept:
An algorithm is a system of sequential operations that runs by defined rules and is designed to solve a problem.
It should be noted that this definition is not related to code or a specific programming language. An essential feature of the algorithm is that it is any sequence of steps following particular rules. However, the traditional way of programming is based on this general understanding of the algorithm.
The scheme is as follows:

We encounter a problem → analyze it → identify the rules → translate these rules into program code that allows you to automate the solution of the problem in the given conditions.

However, in some instances, the classic approach to automation fails. There are many problems in which we can't identify and write down the rules: for example, image recognition, human speech, processing and checking the quality of texts, or identifying complex patterns in data.
We communicate with people, understand writing, or perceive objects through sight. That is, our brain solves these "problems," but we cannot write down what rules it follows. For example, we quickly define a cat's concept in a variety of images, even if they are of different sizes, colors, shapes, or if it is a toy cat. A person understands that in front of him is a cat, even if he/she sees only its eye.

We are faced with a fundamental limitation of the solution to the problem when we cannot identify explicit rules that the algorithm should follow. And we need a new approach to automation, different from the classical one. This is where artificial intelligence comes.

AI bypasses the error of the classical approach. This allows the system to identify patterns and learn implicit rules (usually by analyzing thousands of examples) to analyze data (images, sound files, texts, etc.) according to specific schemes (as was the case with the cat).
Every day, the amount of information that a person must process increases exponentially. Therefore, machine learning (or AI) has become a necessity for humankind to automate routine work, save time and employees of organizations, and increase labor productivity.

How artificial intelligence is used in search engine algorithms

We have already figured out a little what artificial intelligence is and why we need it. Now let's move on to SEO, as developments in the field of machine learning have not spared search engines and their ranking algorithms. As the number of documents on the Internet increases, search engines themselves develop, their ranking algorithms become more complicated.

Achievements of machine learning contribute to the development of artificial intelligence. The development of this area has been ongoing since 2003, but the first significant achievement was the Word2vec program.
Word2vec is a set of approaches for modeling and processing natural language words based on neural networks. The software was developed by Google researchers in 2013 and is used to analyze the semantics of natural languages.
Later, in 2015, RankBrain (part of the Hummingbird algorithm) was created at Google's Word2vec database.
RankBrain is an AI-based self-learning system that has allowed Google to speed up keyword research to provide users with the most relevant content per search query. RankBrain knows how to understand the meaning of the text, to find connections between words, learn words and phrases that it doesn't know, and tailor it correctly for the country and language of the request.
The icing on the cake of all this was the Google BERT algorithm, which was released in 2019.
BERT (Bidirectional Encoder Representations from Transformers) is also an NLP (Natural Language Processing) learning system based on a neural network. Unlike other models, BERT is designed to understand natural speech deeply.
In other words, BERT should let the machine understand the words in the sentence, given every detail of the context. Google uses BERT to understand user requests better and give them genuinely relevant answers.

Artificial Intelligence and SEO: expert opinion

Now we come to how all this affected SEO. To better understand this, I asked the experts to answer a few questions. Let's go!

How can Artificial Intelligence be used in SEO?

Hamlet Batista
It can be used to perform many tasks that require perception like, for example, adding ALT text to images missing it. I wrote a tutorial that walks you over this. Another example is classify keywords and/or questions by the user intention. Kristin Tynski share an example. I also recently presented on automated quality content generation at the Search Engine Journal eSummit. These are a small number of examples of what is possible.
Carlos Castro
I think Artificial Intelligence is the core of both Text and Voice Search. Search Engines use sophisticated AI to predict which results will satisfy any given search.
Kevin Indig
The best use of AI in my mind is to detect outliers in CTR, heavily linked pages, traffic, etc. and changes from those factors.
Nikola Baldikov
There's a growing amount of SEO tools on the market that use AI to conduct content and keyword research and track social media mentions, among other things. These can help you identify opportunities for content and keep track of how they're ranking, two key components of SEO.
Andreea Sauciuc
Artificial Intelligence can be used in extracting insights from search data. It can work very well to understand what people search on Google and find out search intent with higher accuracy. Not to mention the benefits it can have on local search where people have an immediate and necessary need and desire, easy to convert.
Nikola Roza
When AI gets significantly smarter, we can use it to automate parts of marketing that are either tedious, or take a long time. For example, Grammarly is an AI-powered tool that can spot grammatical errors in a minute. Something that'd take you an hour of focused text analysis to do.
Another example is content writing. Writing first drafts is often the hardest part of article creation.
So if we could input data and links to a program, and then it spits the draft we can then make shine, that'd be a huge time saver.
Tobias Willmann
I see the big potential in structuring content. For example, group and link related articles. With thousands of articles, thats too much work for humans, but NLP can do it pretty well. Also detecting patterns and hidden content champions should be possible. With automated image or video entity and topic detection it's the same. It can support humans with a preselection of "to be recycled" - content and similar topics. I also see potential to keep users coming from search longer by offering them cool AI driven features like advanced search, bots... I do not really believe in a future in AI written content for SEO. It works now in many cases, but it's based on structured data in most cases and I don't really get the benefit of reading a running text if I cant get the information from a table or bullet points.
Edgar Suppes
Google is continuously working to improve the user experience and to provide the user with the best possible answer in the shortest possible time. The rich snippets have been with us for a longer time now. If you optimize your content for rich snippets, you may increase the visibility of your search results, which in turn can significantly increase the CTR.
Another interesting product is Google's "Discover". The product is developing into a good traffic supplier. AI optimizes the users news feeds on their smartphones by showing relevant news.
Max Dutru
AI can be used in a content strategy around topic discovery, keyword research and content optimization. These manual tasks can be processed by an AI-powered tool better and quicker.
Stephan Czysch
Let's face it: most websites fail as they lack, e.g., content strategies. So we shouldn't focus too much on trending topics such as AI or machine learning. There are definitely use cases for AI, but first things first: make up your mind on what you want to rank for. Check if your website is good enough and identify areas for improvement.
When you got the basics right, then you can have a look how and if topics like AI can add the remaining percentages to your website.
Amir Shahzeidi
AI is revolutionizing SEO. From search engines point-of-view advances in AI and ML means that search engines can better comprehend users' search queries and intent, to serve the best results possible. This is especially evident in the recent Google BERT update.
The use-cases of AI in SEO, unfortunately, have been very limited. So far AI has been more of a buzzword in our industry rather than real-world use cases for all businesses. However, this doesn't have to be the case, we can see more and more SEOs turning to ML and Python to better analyze, research, and understand big data.
AI can be a huge asset for SEOs and businesses, from finding keyword opportunities, automating competitive research , to better understanding your customers' journey, and creating relevant quality content that supports that journey.
Dan Taylor
Understanding how Google might potentially use AI as part of it's algorithms, and then trying to work with the theory rather than game is, for me, a crucial part to modern day SEO.
The underlying result of trying to optimizise for AI is that, as webmasters, we're trying to increase the number of topics and phrases covered within our content, and create more logical site structures that flow between said topics. Whether or not this is done under the guise of AI optimization (which John Mueller has been on record saying it can't directly be optimized for) or through a wild spate of "LSI keyword optimization" (again, not a thing), the end result will only benefit users.
The difference between those rewarded in organic search vs. those who don't, comes down to those working with the theory vs. those trying to game it.
John Ridd
Google uses machine learning to give the best search results to it users - understanding and unwinding this process is the key to informing a content strategy.
JP Sherman
Ask 5 people to define Artificial Intelligence and you'll get 6 answers. The challenge, when talking about AI, is coming up with a definition that everyone can get behind. This makes it incredibly difficult to start making predictions and nearly impossible to start talking about business critical actions based on AI. So, I'll do some disambiguation first. When it comes to search technology and SEO, I see Machine Learning (ML) as a learning system where human curation, review and refinement as critical to its learning abilities. Artificial Intelligence (AI) as a learning system that has the ability to remove most or all human review from the learning process and still get the results right most of the time. So, how can AI be used in SEO? I think on the search engine side, Google is using a highly advanced version of Machine Learning that continually removes parts of the human curation aspect. Therefore, right now, AI by search engines are primarily used for intent detection, disambiguation and the collection and use of non-textual signals for personalization and prediction. I have yet to see any SEO professional or software use AI for our primary jobs of improving tech, architecture, links, content or rankings. However, I am seeing some really interesting uses of ML to automate tasks like identifying content gaps to make content strategy more accurate. I've also seen some ML being used to assist content creators curate information across topics to make the content they product to be more authoritative.
Karyn Corrigan
Artificial Intelligence, I feel, is hugely instrumental for SEOs in understanding user intent, or why people reach a specific conclusion after searching through a keyword. AI can tell us if the intent behind a search term has changed, therefore prompting us to change how we speak about it to our potential customers, either through rewriting content, or changing the format or medium in which we communicate it.
Other ways AI is being used that fascinates me is through keyword research and schema markup. For instance, every month we hear about new tools that can cluster topics better, build a more conclusive keyword list, and give SEOs better data in general to do their job.
Charly Wargnier
Artificial Intelligence (though I prefer the term "Machine Learning" when referring to practices in the SEO field, Al being so vast in scope!) can help the SEO practitioner in many ways - such as:

  • Cut down and cluster an avalanche of operational signals to look for anomalies;
  • Forecast time series more accurately;
  • Detect user intent and find subsequent opportunities via clustering;
  • Auto-generate content at scale;
  • Auto-label your keywords, URLs etc.
I could go on, yet the thing to bear in mind is that ML can automate and speed-up many of your daily processes. Tasks that would usually take hours on spreadsheets may only take minutes with a well thought-out script!

Do you use AI in your work? How?

Hamlet Batista
Artificial Intelligence can save a lot of time, but it requires human supervision.
We generally populate spreadsheets with the automated work and have a member of our team or client review and make any adjustments. For example, a common problem is mapping a large number of URLs between sites during a platform change. I explained how we use AI to do that.
We have someone manually review the mappings and make corrections. The AI still saves us more than 70% of the work.
Carlos Castro
Yes, I use a tool that analyzes the content of the top 10 ranking websites for any given query and based on their content, I know which words or sentences are being used that may help to improve my content's semantics.
Kevin Indig
I don't really use AI in my work. I'm starting to see a lot more solutions sprouting up but I think we're still very early on.
Nikola Baldikov
We certainly try to take advantage of AI advancements in Google around natural language and voice searches. We've recently begun a process of optimizing our content for voice search given the recent development in Google algorithms and the growing share of such searches. We also employ several tools that utilize AI to help us with SEO research, monitoring and evaluation.
Nikola Roza
Right now, I don't. I toyed with the idea of using AI content writing programs to create first drafts of my articles. But I quickly gave up because the quality takes a huge dive the longer the text is, and you end up with something that barely scores under "unreadable mess" level.
So I gave up on the idea as it's more work to rewrite what the machine gave me, than to write it myself.
Tobias Willmann
We work on some tag and NLP stuff and I use Google itself a lot, which should use AI too :)
Edgar Suppes
We mainly use AI based tools to collect content ideas.
Buzzsumo is very suitable for finding topics that are currently performing well.
It helps us to find out which topics are hot in our industry.
Stephan Czysch
Use cases vary from client to client, but in general we use AI, e.g., to cluster search queries into groups or to find patterns in web analytics data. So nothing super spectacular here, as we need to get the basics right in the first step and then constantly improve the website.
Amir Shahzeidi
I haven't used AI in my work. However, I have used Python and ML to analyze and digest big data. This includes categorizing search queries based on intent, auto-generating meta descriptions using text summarization algorithm, and identifying keywords opportunities.
Dan Taylor
We use a low-form of AI to perform mass categorization across some publishing websites; by using training sets to educate it that certain phrases such as "Manchester United" or "Troy Deeney" are suitable for a sports classification, so this can be run en masse.
We also use some grammar and content tools within the wider content optimization process, based on the theory that if the tool is working with data sets to make suggestions to improve content quality, it's good to have as a second phase to our own internal processes.
This still requires a form of human checking however, as AI isn't yet a substitute for logic.
John Ridd
The main use I have for AI as an SEO is recreating how search engines try to understand language so I can then create content to perfectly target my keyword research.
Natural Language Processing (NLP) is the process by which search engines use AI attempt to understand what a person wants when they type a phrase into Google and hit enter. SEO tools with semantic analysis capability reveal topics that make up the latent intent behind a search term. Search engines themselves use a similar process to understand content crawlers have discovered on a web page so they can assign appropriate search terms that can then be ranked.
So by using SEO tools with NLP functionality, I can effectively reverse engineer the process by which search engines break up content into constituent topics so I can then assign to the search terms I want to target.
JP Sherman
No. At Red Hat, for our site-search, we have several ML projects running to look at intent detection and personalization for better search results.
Karyn Corrigan
I enjoy using NLP and vision AI in my work on ensure that my content sends out the right message to both website visitors and search engines. Normally I create content naturally and then, before publishing, I will run it through an NLP tool for sentiment and entity analysis.
I also try to optimise our sites regularly with the most up-to-date schema markup to ensure search engines get the important information to helps us put our best foot forward in search.
Charly Wargnier
While dabbling with Javascript and R. I code mainly in Python. It is intuitive, versatile, and is a great pick for Machine Learning, as ubiquitous ML frameworks such as Sci-kit Learn, Tensorflow or PyTorch are all written in Python!
Currently, I use ML for regression problems, forecasting and text classification. As a server log aficionado, I'm also experimenting with ML for anomaly detection.
It's only recently that I've integrated Machine learning in my daily workflow. Needless to say I'm only scratching the surface of it - there is so much to learn and for me that is super exciting! :)

What AI-powered SEO tools do you know?

Hamlet Batista
MarketMuse, RankSense, WordLift. I think SEMrush, Moz, Ahrefs, Serpstat and the other big players also use AI behind the scenes.
It should be on every SEO tool by now. The core technology is both free and open.
Carlos Castro
Yext is a very great tool that uses AI to monitor reviews, optimise for voice search, and many more useful solutions.
Kevin Indig
Oncrawl, Botify are great AI-powered tools we use in our work.
Nikola Roza
Kafkai, Ai Writer, Articoolo (AI content creation tools); MarketMuse (AI powered content optimization); CanIRank (tool that uses AI to determine whether you can rank for any given query).
Edgar Suppes
Buzzsumo, Google Analytics, Seach Console.
Max Dutru
Wordlift, Market Muse, HubSpot.
Amir Shahzeidi
None, as far as I know. Most tools that I currently use do leverage Machine Learning, however they're not AI-powered. I've tried several AI-powered SEO tools, and unfortunately most have been hit or miss, and none really provided substantial value.
Dan Taylor
There are a number of tools out there that work on combining performance data, competitor data, and then making optimization suggestions based on the small dataset within.
John Ridd
SurferSEO ,, IBM Watson.
JP Sherman
For SEO's who want to learn how search engines rank information, using the LTR (learn to rank) ML plugin for a search platform like SOLR is a fantastic way to start learning how to incorporate ML into your search, which will give actionable information to point your SEO efforts into good directions.
Karyn Corrigan
Google Cloud's AI & ML products are very interesting AI tools, and Bright Edge and Alli AI seem to be leading the way in the SEO sphere.
Charly Wargnier
There are a plethora of SEO tools leveraging ML out there, from Ryte's great Anomaly Detector and GetRedirects, to Quill (which cleverly use NLP models to deliver personalised stories). Also check out WordLift from my friend Andrea for some crazy Semantic wizardry!

And, while they're not SEO tools per-se, I'm fascinated by "Automated" ML solutions (Google AutoML,, Uber's Ludwig, Spacy.IO etc.). It's great to see how these tools are democratising ML in the organisation, allowing anyone to build quality custom models with limited coding expertise. Sometimes, removing the hassle of tasks you'd otherwise need to perform manually (hear hear hyperparameter tuning!) can be a godsend!

Last but not least, I've recently fallen in love with Streamlit. I think it is a game changer for web applications, even outside of ML!
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How did the BERT algorithm affect search results?

Hamlet Batista
It helped search engines understand search queries and questions better. The best way to understand BERT is by writing some code. Make sure to check this tutorial I wrote on how to leverage it to classify intent.
Carlos Castro
BERT helped search engines to better understand search queries either with a text or voice search. This makes search results to be more accurate, especially in long-tail searches.
Kevin Indig
It improved Google's understanding of implicit and explicit language. It's a continuation of RankBrain, which means thin/bad content doesn't get as much attention anymore. That worked well in most areas but not in all. There are still verticals with spammy/low-quality content.
Nikola Baldikov
According to Google, this algorithm will affect up to 10% of results by using AI to better understand language in context. Common examples are how prepositions are used in a sentence, and how their meaning changes in different situations. This is something that speakers of a language easily understand, but has been difficult for previous algorithms to grasp. All of this means that searches using more natural, full-sentence type language will lead to more accurate results. This is particularly important for voice searches since they often use natural language.
Andreea Sauciuc
By looking at the data, after the BERT algorithm we've experienced a slight increase in rankings for the overall website, but nothing significant.
Nikola Roza
It boosted Google's understanding of relevancy. Google wants to rank the most relevant result, and not the one that has the most links. Google uses links as the necessary evil to gauge the "popularity" of any content asset, but BERT enables them to understand content at a deeper level so they don't need link data to make the call on who should rank and where.
Bottom line, BERT made it easier for weaker and smaller sites to beat the giants in their verticals. If their content is better and more relevant they have a good chance of ranking highly.
Tobias Willmann
I see it more as a continuous development. Google is getting better in detecting the users intent and search results differ more and more based on this intent. It feels like direct answers are getting better and show up more often.
Max Dutru
According to Google estimate, BERT algorithm affected roughly 10% of all U.S search queries. Informational and long-tail queries BERT algorithm impacted organic search results in rewarding pages with content that better match user intent as Google is getting better to understand natural language with NLP.
Stephan Czysch
In their blog post Google showed some brilliant examples on the effects of the update. Especially for search queries containing multiple words (aka long tail queries) search results weren't (and still aren't) perfect every time so improvements are more than welcome. Language is and will remain complex as words can e.g. have multiple meanings. Google and other search engines will continue to improve their algorithms - and so rankings will shift.
Amir Shahzeidi
BERT Algorithm update has been one of the biggest improvements in how search engines can recognize entities and answer questions in the recent history. With BERT came also a new shift to open-sourced technology allowing everyone to use this algorithm.
BERT has helped Google better understand the nuances and the context of conversational queries. Allowing the search engine to serve more relevant content, recognize entities, and make SERPs more interactive.
Dan Taylor
BERT was a step-forward in better language understanding, as we use single words to portray multiple meanings (homonyms) - such as the difference between bass and bass, bark and back, pitcher and pitcher... The list goes on.
By being able ot better understand these words, as well as the surrounding context (other words on the page, other content on the domain), Google can work to serve better results in niche queries, and potentially also use Chrome data for SERP personalization to use context to better serve results for ambiguous queries.
John Ridd
Based on our analysis, the BERT algorithm update gave the Google algorithm more of a focus on content quality with a change of user intent, and subsequently a move away from the importance of links (although we have seen a move back towards this in the core algorithm updates that followed the BERT update).
JP Sherman
BERT (Bidirectional Encoder Representations from Transformers) it's not just a cute name, it builds a bidirectional tranining aspect to language modeling. For NLP (natural language processing) the core effect is that it makes Q&A queries, inferences by search engines and other tasks more efficient by how it processes queries. This, in turn, makes search results more accurate, feel more personal and when applied to voice search, better understand the several ways a human can ask a single question.
Karyn Corrigan
BERT had, and still has, a positive effect on the SERPS as it tries to rank content based on a searcher's intent to provide the most relevant content to them first. Although Google mentioned that the update would only affect 10% of results, BERT is a bi-directional, contextual model of NLP meaning that results should reflect better the complete answer that a searcher needs from their search query, compared to the search results in the past.
Charly Wargnier
BERT was definitely a leap forward in how Google understands natural language. And while there's no such thing as optimising websites for it, focusing on creating good, useful, natural content has never been so vital.

If you wish to really understand how BERT works under the hood, get comfy and read this gem from Dawn Anderson. Dawn discusses the backstory and nuances of BERT's evolution, how the algorithm works to improve human language understanding for machines + what it means for SEOs. Time well spent!
What You Need To Know About Google BERT Update

What is your advice on optimizing your site considering AI in search engine algorithms?

Hamlet Batista
Focus primarily on identifying important questions and providing the best answers to them. If you can do that, you will make the search engines (and users) lives a lot easier.
Carlos Castro
I don't think there is a formula or a checklist to optimise a site considering AI. I think the best advice would be to write for users, search engines will understand content and queries like a normal person would, so a well-written text that focuses on addressing the user's needs is what we all should do and should have been doing for a long time.
Kevin Indig
There is not much you can do except for creating more human-like content. Overoptimization and shallow/fluffy content doesn't work as well anymore. Instead, you need actual experts to write the content.
Nikola Baldikov
The best advice continues to be to focus on providing quality content that's aligned to your audience's needs. To understand those needs, I would advise using an AI tool to conduct research. In that way you can learn about what people are interested in, what questions they have and what type of content they're looking for. Once you have this it's a matter of producing high quality content that is focused first and foremost on helping people. Google is getting better every day at rewarding just such content.
Andreea Sauciuc
My best advice on optimizing the site considering AI in search engine algorithms would be focusing on search intent. Natural language, expressions and human-write content should work for the best.
Nikola Roza
Since we don't know the exact use of A.I in the algorithm (and even if we did know it right now, it wouldn't really matter because the program's always learning and evolving); I think our best bet is to use the AI software we have available to streamline our processes and make life easier for us.
So use AI for what it's worth but never forget that it'll probably never be able to beat human login, intuition, and common sense. So going forward, it's content; and it's links. And it's also the smart use of AI where possible, to get an early advantage over those that don't.
Tobias Willmann
AI will enable the search engine to evaluate more like real users, so if you optimize for users thats a good first step. It's getting more and more impossible to transfer fancy SEO strategies and best practises from one project to another. Of course basics like crawlability are important for everyone, but I became more careful with additional thinks not recommended by Google itself. It's super important to test.
Edgar Suppes
Google is constantly evolving, but the goal for the search engine is and remains the same: it wants to provide the best possible user experience in the shortest possible time.
We always write our content with this goal in mind. We optimize our texts largely to answer questions such as "When will shoe XY be released" or "Where can I buy shoe XY". We always try to answer these questions in our content.
Trying to answer users' questions in our content has always been our goal in SEO and that seems what Google rewards in the long run.
Max Dutru
Always create relevant content for humans and not for robots. Don't mislead users as search engines promote pages that match keyword intent and answer specific questions. The best way to optimize your site is to provide authentic, helpful information that reads naturally.
Stephan Czysch
Focus on users and their needs! What do they expect to find? What's the best approach to present it? We aren't optimizing for search engines and their algorithm – they aren't using your website and they won't convert. So simply focus on your target group, their needs and constantly improve your website.
Amir Shahzeidi
Create quality content for your target users, deliver the best user-experience and leverage structured data.
Let's face it, we can't really optimize content for BERT or any other algorithm. As SEOs and content creators, we need to understand that the primary focus of AI in search engine algorithms, is to A) Better understand the context of each query and B) Serve the most relevant, quality piece of content. Make this your north star and you'll be golden.
Dan Taylor
Work with the theory, and don't try to game it. AI isn't TF*IDF, LSI, Keyword co-currence... AI is aimed at better understanding and matching content documents to user searches, so if you want to really optimize for AI, you need to be writing highly expert, authoritative, and trust-worthy (EAT) content that covers a users initial base query, subsequent queries, and potential stacked queries.
John Ridd
When writing content for a give web page or article think in terms of topics and categories - the days of solely counting individual keywords is over.
That said, it's important to remember search engines can't read like you and I, as far as AI has come in the last 5 years it is not close to fully understanding language as we do. So by knowing the limitations of the AI search engines use and what measurable elements they use to assign relevance (topics, entities and keywords) for a given search you have all the information you need to create targeted content.
JP Sherman
Take your time to understand ML & AI, but remember it's Google's job to understand you, your job is to build great websites with authoritative content that seres your customers. If and when you do use ML, use it to understand and automate tasks to shorten the time between discovery and execution.
Karyn Corrigan
In 2020, because of AI, SEO is all about searcher intent and really understanding what searcher's want when they click the search button. If you focus on answering the questions and concerns your customers have, in a format that suits their requirements, you are on the right path in terms of AI. Using expensive tools to tell you the right keywords to use is not as important as understanding your searcher and their needs.
Charly Wargnier
While over the past half-decade, improvements in computing performance hugely benefited the AI field (Google achieving quantum supremacy is now real!), the basic rules to perform well in organic Search haven't changed:

  • Build strong technical foundations. Design lighting-fast websites. Stick to the crawlability rules to ensure content is discovered often and well.
  • Oh, and did I mention to write for users? ;)


Based on the answers of experts, we can conclude that the topic of the use of artificial intelligence in SEO is quite controversial, and there are many diverse opinions on this subject.

However, it is pointless to deny that the influence of technologies based on neural networks in the field of optimization is tremendous and grows every year, as new search engine algorithms and tools that experts can use are released.
Thanks to all the experts for sharing their opinion with our readers! ❤️
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