Keywords

1 Introduction

Search Engine Optimization (SEO) stands as a cornerstone of the digital marketing world, playing a pivotal role in enhancing a business’s online visibility, thereby directly impacting its reach to the target audience [1, 2]. A strategic SEO approach provides deeper insights into search engine user behaviors, ensuring more informed business decisions and a robust digital presence [3]. As digital landscapes evolve, so do the methodologies and technologies underlying SEO [4]. This paradigm shift marks a transition from a keyword-centric SEO approach to one that prioritizes understanding the comprehensive context behind a user’s search [5]. In the vanguard of this evolution is AI. AI has changed both the user’s search experience and the strategies implemented by companies to optimize their content. With AI, search engines become more adaptive, focusing on user needs and delivering more personalized results [6].

This paper explores AI’s transformative impact on SEO in the digital age. We use the PRISMA framework for a systematic literature review (SLR) [7]. After detailing our methodology, we overview SEO techniques and AI's influence on them. We conclude by summarizing key findings and suggesting future research directions.

2 Methodology

Utilizing the PRISMA framework as our guiding methodology, we initiated a comprehensive search on the Scopus database. Our focus was primarily on the TITLE-ABS-KEY fields, leveraging a combination of keywords: “Artificial Intelligence” AND “Search Engine Optimization” OR “Search Engine Optimisation”. This foundational search produced a modest count of 33 articles. Recognizing the limited pool of results from this initial endeavor, we expanded our research parameters to encompass grey literature and employed the snowballing technique. This broader approach enabled us to identify a richer pool of 73 articles. A subsequent screening, centered on article titles and abstracts, refined our selection to 44 articles. Ultimately, we consolidated our findings regarding AI and SEO to a set of 28 articles, which became the backbone of our review. We have to mention that further references not related to the topic of AI were utilized to introduce each SEO technique.

3 Artificial Intelligence and SEO

Boyan and Freitag’s groundbreaking AI and SEO work introduced heuristics, notably a reinforcement learning-inspired approach [8]. Their system auto-combines these heuristics from user feedback, improving search engine rankings. Fast forwarding to 2011, Wang et al. presented a novel approach to search engine optimization rooted in the algorithm of BP neural networks [9]. Their method made the search engine smarter and more personalized, adjusting results based on individual user preferences. Yuniarthe explored the intricacies of AI's role in SEO, delineating its structure into evolutionary computation, fuzzy logic, and classifiers, along with statistical models [6]. He detailed an array of AI-driven SEO tools, including Polidoxa and the Fuzzy Inference System. Two further studies emphasized the potential of the Random Neural Network (RNN) in enhancing search capabilities [10, 11]. Their studies confirmed that the RNN model could adeptly predict user search queries and offered superior accuracy compared to traditional search algorithms. Further, an “Intelligent Internet Search Assistant” based on the RNN model showcased better performance relative to conventional search engines, particularly in gauging user preferences. Joglekar et al. introduced a tool aiming to rank search results exclusively on content quality, eschewing the traditional parameter of user click likelihood [12]. This tool leveraged the ‘term frequency-inverse document frequency’ weighting algorithm, coupled with subsequent processes like ‘singular value decomposition’ and ‘spherical K-means’ for optimal content display. In 2020, Horasan unveiled the potency of Latent Semantic Analysis (LSA) for keyword extraction in SEO [13]. Through LSA, the relationship between documents or sentences and the terms contained within were modeled, resulting in cost-effective strategies to target specific online audiences. Portier et al. in the same year, applied both filter and wrapper methods to select pivotal features from a vast dataset, and when combined with the Random Forest model, produced promising results in predicting Google's top search results [14]. In 2022, Yogesh et al. reinforced the fundamental pillars of SEO, asserting the primacy of keyword-centric web content and the significance of monitoring site traffic [15]. Another study illuminated the synergy between NLP and ML in amplifying SEO performance [16]. Their research hinted at a promising horizon where NLP and ML could revolutionize successful SEO outcomes.

The literature identifies various types and techniques of SEO, among which On-page and Off-page SEO hold paramount importance [17]. In the subsequent sections, we will delve into each SEO typology, elaborating on their distinct characteristics and significance. Additionally, we will explore the profound impact of AI on these specific SEO techniques, shedding light on the innovative ways AI enhances and transforms the traditional approaches to website optimization.

3.1 AI and On-Page SEO

On-page SEO enhances a website’s visibility by optimizing factors within its pages [18]. Initially, search engines relied on content, meta tags, and keyword density, leading sites to overstuff keywords [19]. Modern on-page SEO addresses both content and technical aspects, emphasizing the right balance of keyword relevance with user value. Elements like title tags, meta descriptions, URLs, and image optimization play vital roles. Image strategies focus on user experience and efficient loading, such as compressing images and using descriptive filenames [20]. Mobile SEO, a subset, ensures websites are user-friendly on mobiles, highlighting responsive design and easy navigation [21].

AI is becoming an integral part of on-page SEO, influencing both its mobile and non-mobile technical and content aspects [22]. AI tools can analyze website performance in real-time, auto-adjust for faster page loads, and test mobile-friendliness across devices. They can also auto-implement schema markup to aid search engines and optimize images by determining ideal compression levels and generating ALT tags [23]. For content SEO, AI, like OpenAI's GPT-4, assists writers in crafting high-quality, keyword-optimized content [24]. AI systems can assess content against competitors and recommend improvements, like keyword integration or topic expansion. Content optimization now focuses on meaning, user intent, and context rather than just keywords. Advancements like Google’s Knowledge Graph provide context-rich search results by understanding information relationships [25]. Similarly, Latent Semantic Indexing (LSI) evaluates the relationship between terms on a webpage, urging writers to emphasize comprehensive topic coverage over repetitive keyword usage [26]. The structural coherence of content has gained prominence with the rise of semantic SEO [27]. Topic clusters and pillar content interlink related articles, boosting contextual understanding and asserting a site’s authority. In today’s era, understanding user intent is vital. AI excels in semantic analysis, ensuring content aligns with keywords and the searcher’s intent [28].

3.2 AI and Off-Page SEO

Off-page SEO focuses on third-party website actions, historically known as “link building.” It emphasizes the quantity and quality of backlinks [29]. Beyond link building, it aims to boost brand exposure and online reputation through methods like social media, content marketing, influencer outreach, and guest blogging [30]. The objective is to gain authority and credibility through external endorsements from trusted sources on various platforms. AI is enhancing off-page SEO by optimizing external online presence strategies. It can monitor and manage the online reputation of a website and its competitors [31]. Using sentiment analysis, AI algorithms can track brand mentions across the internet, distinguishing positive comments from negative ones [32].

3.3 AI and Local SEO

Local SEO has risen in prominence with the proliferation of mobile search and the increasing importance of location-based queries [33]. Local SEO focuses on visibility in Google's local pack, Maps, and Bing Places. Essential steps include optimizing a Google My Business listing, gathering positive reviews, creating local directory citations, and maintaining consistent name, address, and phone number (NAP) information online to boost search engine trust [34].

AI is reshaping Local SEO, enabling businesses to better target geographic audiences. By analyzing extensive local search data, AI tools identify regional patterns and preferences, allowing businesses to refine content and marketing for local demographics [35]. Through sentiment analysis, AI can also gauge customer reviews and feedback on online platforms, providing businesses with insights into their local reputation and areas of improvement [36].

3.4 AI and Voice Search

The rise of smartphones ushered in the mobile SEO era, with mobile searches overtaking desktop [37]. Voice search, popularized by assistants like Alexa and Siri, shifted focus from keyword-based to context-driven, natural language queries [38]. AI, including algorithms like BERT, is crucial in refining Voice Search SEO by understanding and responding accurately to conversational queries, capturing nuances and intents as users interact naturally with voice-activated devices [39]. Algorithms discern nuances in phrasing, precisely matching user intent with content. AI-driven analytics predict user questions from historical data and trends, optimizing real-time results [40]. AI enhances voice search by ensuring contextual relevance and a more intuitive experience (Fig. 1).

Fig. 1
A chart presents how A I and S E O integration influence the 4 pillars of modern S E O. The pillars are on-page, local, off-page, and voice. The components of local, off-page, and voice are mentioned. On-page includes content, technical, and mobile, with 2 to 4 elements of each.

AI Integration: influencing the four pillars of modern SEO

Full size image

AI has transformed SEO, but with challenges. The ever-changing AI algorithms require continuous adaptation by businesses. The “black-box” nature of many AI models introduces unpredictability, making consistent outcomes challenging for professionals [41]. As AI impacts online visibility, ethical concerns arise regarding digital equity, control, and potential manipulation [42]. It underscores the importance of merging AI in SEO with transparency, ethical practices, and inclusivity.

4 Discussion and Conclusion

The literature review explores the evolution of AI algorithms and their transformative impact on search engine optimization (SEO) techniques. Pioneering works such as Boyan and Freitag’s heuristics and Wang et al.’s neural network-based personalized search results have paved the way for AI-driven SEO advancements [8, 9]. In on-page SEO, the historical focus on keyword-rich content has shifted towards considering user value and technical aspects, with AI's integration revolutionizing content relevance and technical optimization [18, 22]. Content optimization now emphasizes semantic search, aided by Google’s Knowledge Graph and Latent Semantic Indexing, enabling comprehensive topic coverage [25, 26]. AI’s strength in semantic analysis ensures content alignment with user intent beyond keyword usage [28], promoting a more user-focused and contextually enriched approach to on-page SEO. For off-page SEO, AI plays a pivotal role in monitoring and managing online reputations through sentiment analysis, enabling businesses to respond effectively to customer feedback [31, 32]. This integration of AI enhances off-page SEO practices, empowering businesses to establish and strengthen their online presence beyond their website’s domain. In the context of Local SEO, AI-driven tools analyze local search data, enabling businesses to target specific geographic audiences more effectively [35]. Sentiment analysis further provides insights into local reputation and areas of improvement, contributing to a more data-driven and precise approach to engaging with local audiences [36]. AI’s presence in voice search SEO is essential as it enables accurate interpretation of conversational queries, matching user intent with relevant content through algorithms like BERT [38]. AI-driven predictive analytics ensure contextually relevant voice search responses, creating a more proactive and intuitive voice search experience [40].

Overall, AI’s integration into different types of SEO presents numerous advantages, transforming traditional practices and optimizing website optimization strategies. However, it also poses challenges in adapting to evolving AI algorithms and addressing ethical concerns related to data privacy and manipulation in the digital environment. By responsibly harnessing AI's capabilities, businesses can lead the digital transformation and achieve higher online visibility and user engagement in the competitive online landscape.

This study has limitations due to the reliance on proprietary search engine algorithms, making it challenging to understand universal AI-driven SEO applications. While AI offers automation, it also presents ethical issues around data privacy, biases, and user tracking. Future research should address AI’s ethical impact in SEO and its role in emerging technologies like Augmented Reality (AR) and Virtual Reality (VR). In conclusion, AI and SEO’s merging presents vast opportunities. Those who adapt and innovate within this framework will lead the digital transformation.