Artificial intelligence (AI) is transforming how we approach growth in Organic Search. It’s happening from both sides of the equation too: search engines and SEO practitioners & site publishers as well.

On the search engine side, features like Google’s AI Overviews are being implemented into results pages, changing the ways in which information is provided every time a user inputs their search. Generative AI-powered chat tools like ChatGPT are being used by an increasing number of users as a sort of supplementary search engine. Though the overall numbers are still marginal compared to traditional search, ChatGPT is expected to reach 1% market share by 2025 according to BrightEdge.

On the other side of the equation, AI-powered tools and features are being experimented with by SEO practitioners and services. There are a lot of potential applications, including automation of core tasks like keyword/topic variation and analyzing the content or HTML code on webpages. There are also more customer-facing applications, like actually generating on-page copy or images for websites, which are more experimental and come with their own potential upsides as well as downsides and risks.

In this article, we'll break down the practical ways you can use AI to supercharge your SEO efforts in 2024, cutting through the hype and focusing on uses that are ultimately going to provide quality output that search engines and, more importantly, your potential customers, are going to respond to.

Understanding AI’s Roles in SEO

AI is transforming how search engine rankings are determined. Advanced AI algorithms, like Google’s RankBrain and BERT, analyze search queries and web pages with greater sophistication than ever before. Instead of relying solely on traditional ranking factors like keyword density and backlinks, which have been around since the 00s, these newer elements of the Google search algorithm aim to replicate a human’s understanding of content quality, user engagement, and contextual relevance to surface the most valuable results. This shift means that SEO professionals must focus on creating high-quality, user-focused content that aligns with AI-driven ranking signals.

AI and semantic search

AI is also driving the evolution of semantic search, an effort by Google Search that’s been ongoing since the 2010s. Very broadly speaking, semantic search places greater emphasis on user intent rather than just keywords. Search engines analyze the relationships between words, topics, and search behaviors to understand what users are really looking for.

This has made tactics like keyword stuffing obsolete and reinforced the importance of natural language, topic clusters, and content depth – creating site content designed for humans as opposed to search engine spiders. As a result, businesses must optimize for broader search intent by providing comprehensive answers, structured data, and content that genuinely addresses user needs.

AI and search engines

Beyond influencing rankings and search behavior, AI is a fundamental part of the search engines themselves. Google, Bing, and other platforms use AI-powered models to continuously refine search algorithms, fight spam, and deliver personalized results. AI also powers tools like Google’s Multitask Unified Model (MUM), which can analyze text, images, and even videos to enhance search experiences.

Additionally, AI-driven automation tools assist in SEO tasks such as content optimization, keyword research, and technical audits, making it essential for marketers to integrate AI into their strategies to stay competitive.

Importance of Ethical AI in SEO

Consumer-facing AI applications like chatbots are still very much a developing technology. As a result, the need for responsible and ethical use of AI-driven tools is crucial.

There are a ton of compelling SEO uses out there, like the ability to enhance content creation and automate keyword research, but like with everything else it has to be deployed in ways that align with search engines’ guidelines and, ultimately, improve user experience. Ethical AI in SEO means anything that helps prioritize high-quality, relevant content rather than trying to manipulate rankings and doesn’t deliver genuine value to potential site visitors or customers.

AI can be misused for practices like automated content spam, keyword stuffing, and deceptive link-building schemes – in a best-case scenario these tactics will just be ignored by search engines (and users as well, most likely). Worse, clumsily deployed AI tools can result in inferior pages and sites that actively frustrate rather than delight your customers, and which your competitors will be able to outrank easily. For example, while Google does not penalize AI-generated page content, it does penalize content that’s low quality, duplicative, or irrelevant to your sector or target audience, regardless of whether the content was written by humans or a generative AI tool.

At this moment in time, think of AI more as a potential force multiplier to your own human SEO efforts. Outlined below are a few examples of areas where AI capabilities and tools can help out:

Practical Applications of AI in SEO

AI for keyword research

Advanced AI can analyze vast amounts of search data to suggest keyword ideas, assess search intent, and predict emerging trends. By understanding user behavior and preferences, AI helps SEO professionals move beyond basic keyword lists and create strategies that align with real audience needs. AI-driven keyword research tools can also refine targeting by analyzing competitors, search volume fluctuations, and seasonality, allowing businesses to optimize content with precision.

AI is particularly useful for discovering long-tail keywords and conducting semantic analysis. Traditional keyword research often focuses on high-volume search terms, but AI can identify less competitive, high-converting long-tail keywords that align with specific user queries.

For example, at BMG360 we may take the core keywords for your brand’s core product features or even individual SKUs. We’ll employ a large language model (LLM) to surface synonyms and semantic variations for each of those core keywords rather than pulling out a thesaurus and hoping for the best. Once that’s drafted, though, those outputs will be input into a proper SEO research tool to validate the terms against actual keyword data (confirm that people are searching for these terms, what their intent is, and throwing out any AI hallucinations) and surface longer-tail variations.

AI for content optimization and planning

By leveraging natural language processing (NLP) and machine learning, AI can evaluate factors like sentence complexity, keyword density, and topic coverage, providing recommendations to improve clarity and effectiveness.

AI can streamline content creation by generating detailed SEO outlines based on competitor research, search trends, and semantic relevance. This allows SEO professionals to structure page content for maximum impact while ensuring comprehensive topic coverage. Additionally, AI-driven content optimization tools can enhance existing content by suggesting keyword enhancements, internal linking opportunities, and metadata improvements.

One use case that’s easily doable with basic AI tools is analyzing competitors’ top-ranking pages for a specific topic your brand wants to target. By copying those pages’ copy into a tool you can check for any commonly recurring keyword phrases and keyword variations competitors are using in their headers and body copy as well as compare those pages’ layout and keyword usage against each other to get a better perspective to how to approach your own page.

AI for SEO performance analysis and reporting

Machine learning algorithms can analyze large amounts of website traffic, keyword rankings, user behavior, and backlink profiles to identify trends, and spot anomalies or outliers. This can potentially allow you to make informed decisions faster and optimize strategies based on real-time performance metrics.

Historical analysis is a core element of doing SEO in a strategic way. Since many aspects of how search engine algorithms rank pages are not made publicly available, refining your SEO efforts over time often depends on experimenting with site elements and then measuring shifts in core SEO KPIs like rank, inbound clicks, impressions, and average session duration over time. Retain what correlates with growth and iterate on or reverse changes that have no impact or negative impact. When managing a website that may have hundreds if not thousands of URLs, even a simple AI tool that can identify longitudinal trends in a large data set can help to do that type of analysis at scale.

At BMG360, we stay abreast of emergent marketing technologies, including AI, to help businesses avoid empty hype and stay ahead in an evolving digital landscape. Ready to supercharge your SEO? Contact us today to learn how we can elevate your search visibility and maximize your digital impact.

About the Author

This article was written by Mark Aspillera, Senior SEO Strategist at BMG360. Part SEO strategist, part client whisperer, Mark leads search strategy and client services for a tight roster of high-end national and international brands. From setting the big-picture SEO roadmap to running the day-to-day, he makes sure every tactic maps back to what actually moves the needle. If he's not deep in a rank report or a strategy doc, he's probably on a client call making complex things sound simple.

Mark also just published a new, complete Guide to Keyword Research in 2025. Get it free below.