SEO Gold Coast

Imagine search engine ranking algorithms that learn and adapt in real time, understanding user intent better than ever before.

It’s not just about keywords anymore, it’s about content context and it’s relevance to individual users.

AI can analyse vast amounts of data and is very good at predicting trends. AI is rapidly changing the way search is conducted and the way search engine results are communicated, the results may in fact be spoken out aloud to the user.

SEO companies like Hard Web can optimise content for search engines quickly and with incredible precision but in 2025 the search landscape is shifting beneath our feet.

Voice search is skyrocketing, and conversational queries are reshaping how we approach SEO.
We must change to reflect how people are searching.

But will people leave the search engine behind and just ask their AI?

illustration of bearded man with beanie holding a tablet

SEO in the age of AI

1. To what degree is AI changing the SEO landscape?

AI is significantly reshaping the SEO landscape. It’s not just a minor adjustment, but a fundamental shift in how search engines operate and how users interact with them. AI’s influence is seen in:

Search Engine Algorithms: Google and other search engines are heavily using AI to understand search queries and rank content. This means algorithms are becoming more sophisticated at understanding user intent and context, moving beyond simple keyword matching.
Content Creation: AI tools can now generate content, impacting how websites are populated and optimized. This raises questions about content quality and originality in SEO.
User Search Behavior: AI-driven features like Google’s AI Overviews are changing how users get information, potentially reducing clicks to traditional website links.

The consensus among SEO professionals is that AI is not just a passing trend but a core component of the future of search.

2. Will people still use search engines at the end of 2025?

Yes, search engines will remain highly relevant in 2025 and beyond. While AI is introducing new ways to find information, traditional search engines are adapting and integrating AI into their core functions.

Search engines like Google are still the primary way most people start their online journeys. Despite the rise of AI LLMs, chatbots and other interfaces, search engines provide a comprehensive way to explore the vast amount of information online.
AI is enhancing search engines, not replacing them. Features like AI Overviews are designed to improve the search experience, not to eliminate the need for search engines altogether.
Years of established behaviour have made search engines a deeply ingrained habit for information seeking. Changing this habit takes time, and search engines are evolving to stay relevant.

While the way people use search engines may evolve, their fundamental role as a starting point for online information discovery is expected to continue.

3. How much has search changed since the end of 2022 when ChatGPT was made available to the public?

Search has changed dramatically since the end of 2022, largely due to the public availability of advanced AI models like ChatGPT. This period marks a significant acceleration in the integration of AI into search.

Rapid AI Integration: Search engines have rapidly incorporated generative AI to enhance search results. Google, for example, has launched AI Overviews, demonstrating a major shift in how search results are presented.
Focus on Conversational Search: The rise of ChatGPT has emphasized conversational search. Search engines are now better at understanding and responding to complex, natural language queries, moving towards more conversational interactions.
Increased Content Production: The ease of AI content generation has led to an explosion of online content. This creates both opportunities and challenges for SEO, which requires focusing on high-quality, unique content.

The introduction of ChatGPT acted as a catalyst, accelerating the changes in search that were already underway with AI, leading to a noticeable shift in a relatively short time.

4. What are the main ways search engines have changed since AI was introduced to the general public?

The main changes in search due to AI can be summarized as:

AI-Powered SERP Features: The introduction of AI Overviews (also known as Search Generative Experience or SGE) is the most visible change. These overviews provide AI-generated summaries directly in the search results, aiming to give quick answers.
Emphasis on User Intent: AI algorithms are now much better at understanding the intent behind search queries, not just keywords. This means SEO needs to focus more on satisfying user needs comprehensively.
Personalization and Context: AI allows for more personalized search results, taking into account user history, location, and context. This makes SEO less about broad keywords and more about relevance to specific user segments.
Conversational Search: Voice search and natural language queries are becoming much more common and search engines are evolving to keep up with this rise.
Evolving Ranking Factors: While core SEO principles remain, specific ranking factors are evolving. Demonstrating Experience, Expertise, Authority, and Trustworthiness (E-E-A-T) is becoming even more important, as AI prioritizes reliable and high-quality sources.

These changes collectively mean SEO is becoming more nuanced, focused on user experience, and less about traditional keyword tactics.

5. In the age of AI, do I have to change the content on my website to keep my good rankings?

Yes, you most likely need to adapt your website content to maintain and improve your rankings in the age of AI.

Content Quality is Paramount: AI prioritizes high-quality, comprehensive, and trustworthy content. Thin, keyword-stuffed, or outdated content is less likely to perform well.
User-Centric Approach: Content needs to be even more user-focused, directly answering user questions and fulfilling their search intent in a thorough and engaging way.
E-E-A-T Optimization: Demonstrating Expertise, Experience, Authoritativeness, and Trustworthiness in your content is crucial for ranking, especially in AI-driven search.
Structured Data and Semantic SEO: Using structured data and focusing on semantic SEO (content that is easily understood by machines) can help search engines better understand and utilize your content in AI Overviews and other features.

Adapting content is not just about reacting to AI, but about aligning with the evolving expectations of both search engines and users in an AI-driven environment.

6. When should I change the content, is it urgent, should I change it now?

For many websites, adapting content for the AI era is becoming increasingly urgent. While there’s no single deadline to change, a proactive approach is highly recommended as Google has to reshuffle the rankings after the introduction of AI.

SEO in the age of AI is not a one-time fix but an ongoing process of adaptation and optimization. Regularly reviewing and updating content is becoming the new normal.
Prioritize Key Content: Start by focusing on your most important content – pages that drive the most traffic or are critical for conversions. Optimize these first.
Test and Iterate: Implement changes gradually, monitor performance, and iterate based on results. SEO is increasingly about continuous improvement and adaptation.
If your competitors are already adapting their content for AI, you risk falling behind if you delay.

The urgency to change your website depends on your specific situation and industry, but it’s generally advisable to start adapting your content strategy now rather than waiting.

7. In order to counter the rise of AI driven conversational search how exactly should I change my website’s content?

To adapt your content for AI-driven conversational search, focus on these strategies:

Answer Specific Questions: Think about the questions your target audience is asking and create content that directly and thoroughly answers those questions. Use a question-and-answer format where appropriate.
Long-Form, Comprehensive Content: AI often favors in-depth, comprehensive content that covers topics thoroughly. Aim for longer, more detailed articles and guides.
Natural Language and Conversational Tone: Write in a natural, conversational style, as if you were speaking to a user. Avoid overly technical jargon where possible.
Structured Content with Headings and Lists: Use headings, subheadings, bullet points, and numbered lists to structure your content logically and make it easy for both users and AI to understand.
Focus on Entities and Semantics: Think beyond keywords and focus on entities (people, places, things, concepts) and semantic relationships between them. Use a rich and varied vocabulary.
Optimize for Voice Search: Consider how users might ask questions verbally and optimise web content for natural language voice queries.

By focusing on creating high-quality, user-centric content that directly answers questions in a comprehensive and conversational way, you can better position your website for AI-driven search.

8. By what percentage are Google’s AI overviews reducing the number of clicks on paid Ads and organic website links?

It is still relatively early to have definitive, long-term statistics on the exact percentage reduction in clicks due to Google’s AI Overviews. However, early data and expert opinions suggest a noticeable impact.

Click Reduction: Some early reports and analyses indicate a potential decrease in clicks to traditional organic listings when AI Overviews are present, with estimates ranging from 20% to 60% in certain types of searches.
The impact likely varies significantly depending on the type of search query. Informational queries seeking quick answers may see a larger click reduction, while navigational or transactional queries might be less affected.
Google’s AI Overviews are still evolving, and Google is likely to make adjustments based on user feedback and data. The long-term impact may change as the feature matures.
SEO professionals and industry analysts are closely monitoring click-through rates and search traffic to understand the ongoing impact of AI Overviews.

While precise percentages are still emerging, it’s clear that AI Overviews have the potential to significantly alter click patterns in search results, requiring SEO strategies to adapt.

9. What is the most important thing I can do to make sure my website is seen as a source in Google’s AI overviews?

The most important thing to do to be featured as a source in Google’s AI Overviews is to build and demonstrate strong Expertise, Experience, Authoritativeness, and Trustworthiness (E-E-A-T).

Google’s AI algorithms prioritize content from sources that exhibit high E-E-A-T. This is the core principle for ranking in AI-driven search.
High-Quality, Original Content: Create original, well-researched, and expertly written content that provides unique value to users. Avoid thin, duplicated, or AI-generated content that lacks depth or authority.
Credibility Signals: Enhance your website’s credibility by:
- Author Bios and Expertise: Clearly showcase the expertise and experience of your content creators.
- Citations and References: Back up claims with credible sources and citations.
- About Us Page: Provide a detailed “About Us” page that builds trust and transparency.
- Reputation and Reviews: Positive online reviews and a strong brand reputation contribute to trustworthiness.
Technical SEO and User Experience: Ensure your website is technically sound, mobile-friendly, fast-loading, and provides a positive user experience. These are foundational SEO elements that also contribute to E-E-A-T.

Focusing on building E-E-A-T is not just about ranking in AI Overviews, but about creating a website that is valuable, reliable, and trusted by both users and search engines in the evolving AI-driven search landscape.

AI Glossary

Term	Explanation
AI Agent	An AI Agent is a program designed to perceive its environment and take actions to achieve specific goals. Think of it as an autonomous entity that can make decisions and interact with the world, whether it’s playing a game, answering questions, or controlling a system.
AI Models	AI models are the algorithms and statistical models that enable computers to learn and perform tasks intelligently. These models are created through training on large datasets and are the core of AI systems, allowing them to recognize patterns, make predictions, and generate content.
AI Training	AI Training is the process of teaching an AI model to perform a task by feeding it large amounts of data. During training, the model learns to identify patterns and relationships in the data, which it then uses to make decisions or predictions on new, unseen data.
Artificial General Intelligence (AGI)	AGI is a hypothetical type of AI that possesses human-level cognitive abilities. Unlike current AI, which is specialized for specific tasks, AGI would be able to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to or exceeding human capabilities. AGI is still a theoretical goal, not yet a reality.
Artificial Intelligence (AI)	Artificial Intelligence is a broad field of computer science focused on creating machines capable of performing tasks that typically require human intelligence. These tasks include learning, problem-solving, decision-making, language understanding, and visual perception.
Chain of Thought	Chain of Thought is a technique used to improve the reasoning abilities of AI models, especially large language models. It involves prompting the AI to explain its reasoning process step-by-step when answering a question. This method helps the AI to break down complex problems and arrive at more accurate and logical conclusions, mimicking human-like thought processes.
Chatbot	A Chatbot is an AI program designed to simulate conversation with human users, especially over the internet. Chatbots can answer questions, provide information, or perform tasks based on user input, often used in customer service, information retrieval, and for conversational interfaces.
Conversational AI	Conversational AI refers to technologies that enable machines to understand, process, and respond to human language in a way that mimics natural conversation. This includes chatbots, voice assistants, and other systems designed for interactive, conversational experiences.
Deep Learning	Deep Learning is a subfield of machine learning that uses artificial neural networks with many layers (deep neural networks) to analyse data. Deep learning excels at complex tasks like image and speech recognition, natural language processing, and other areas where intricate pattern detection is required.
Generative AI	Generative AI is a type of artificial intelligence that focuses on creating new content, such as text, images, audio, and video. Unlike AI that only analyzes or acts on existing data, generative AI can produce original outputs, making it useful for creative tasks, content creation, and design.
Generative Pre-trained Transformer (GPT)	GPT is a specific type of generative AI model known for its ability to generate human-like text. “Pre-trained” means it has been trained on a vast amount of text data, and “Transformer” refers to the neural network architecture it uses. GPT models are used in applications like chatbots, content creation, and language translation.
Hallucination (AI)	In the context of AI, particularly with large language models, hallucination refers to instances where the AI generates outputs that are factually incorrect, nonsensical, or not based on the input data. It’s like the AI is “making things up” confidently, even when wrong. This is a key challenge in AI development, especially for applications requiring high accuracy and reliability.
Large Language Model (LLM)	A Large Language Model is an AI model trained on extremely large datasets of text. These models are designed to understand and generate human language for a wide range of tasks, such as translation, summarization, text generation, and question answering. Examples include GPT models, and they are the driving force behind many recent AI advancements in natural language processing and generative AI.
Machine Learning (ML)	Machine Learning is a field of AI that focuses on enabling computers to learn from data without being explicitly programmed. Instead of following fixed rules, ML algorithms improve their performance on a specific task over time as they are exposed to more data.
Natural Language	Natural Language refers to the way humans communicate, both in spoken and written forms. It encompasses the languages people naturally use, like English, Spanish, or Mandarin, with all their complexities, nuances, and irregularities.
Natural Language Processing (NLP)	Natural Language Processing is a branch of AI that deals with the interaction between computers and human language. NLP aims to enable computers to understand, interpret, generate, and manipulate natural human language, bridging the gap between human communication and computer understanding.
Prompt Engineering	Prompt Engineering is the process of designing and refining input prompts for AI models, especially large language models, to achieve desired outputs. Effective prompts are crucial for guiding AI models to generate relevant, accurate, and high-quality responses. It involves understanding how models interpret language and crafting prompts that elicit the best possible performance for specific tasks.
Reinforcement Learning	Reinforcement Learning is a type of machine learning where an AI agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions and learns to optimize its behaviour to maximize rewards over time. This approach is often used in training AI for games, robotics, and autonomous systems.
Supervised Learning	Supervised Learning is a machine learning approach where the AI model is trained on a labelled dataset, meaning the data includes both inputs and the desired outputs. The model learns to map inputs to outputs based on this labelled data, enabling it to make predictions or classifications on new, unlabelled data.
Token	In the context of AI language models, a Token is the smallest unit of text that the model processes. Tokens can be words, parts of words, or even single characters. Language models break down text into tokens to understand and generate language. The way text is tokenized can affect how efficiently and accurately a model processes information.
Training	Training, in the context of AI and machine learning, is the process of teaching a model to perform a task by exposing it to a dataset. During training, the model adjusts its internal parameters (like weights in neural networks) to improve its performance on the task. The goal of training is to create a model that can generalize its learning to new, unseen data.
Weights	In AI models, particularly neural networks, weights are numerical parameters that determine the strength of connections between nodes in the network. During training, these weights are adjusted to minimize errors and improve the model’s accuracy. Weights are crucial for the model’s ability to learn patterns and make predictions; they essentially store the learned information within the network.