Back in 2022, the “early” days of artificial intelligence (AI), many thought it was a fad. However, fast forward to 2025, and it’s completely changing the internet.
One area in which it’s improving is how we search online. And it’s improved it so much that Gartner suggests that search engine volume will drop by 25% by 2026.
This shift is being driven by AI search engines. These are tools that understand the context and intent of searches much deeper than traditional search.
As a user, an SEO, or an internet marketer, things are changing, and you get an understanding of exactly what and why.
Understanding Traditional Search Engines
Traditional search engines, like Google and Bing, have “static” algorithms. These are pre-defined algorithms based on specific metadata.
Two important metrics used in these search engines’ algorithms are keyword matching and link-based ranking.
- Keyword Matching: Search engines crawl their indexed database for web pages containing the exact words or phrases entered by the searcher.
- Link-Based Ranking: Search engines use a PageRank algorithm to determine the importance and relevance of a web page to supply the best results for the user.
Though great, this way of searching misses a lot of context. Due to traditional search engine algorithms being static, they struggle with context or intent behind a query, almost supplying a one-size-fits-all search result.
For example, a search for “bank” could yield a few different results. It could be a bank, as in a financial institution, or simply a riverbank.
Alongside this, it’s a lot less personal. Traditional search engines are generally pretty broad, requiring users to do further digging to retrieve the information they want.
Emergence of AI-Powered Search
Since popular AI models like ChatGPT (an AI chatbot) have become available to the public, the interest in AI search has increased dramatically.
The main technologies that control AI search are Machine Learning (ML), Natural Language Processing (NLP), and Semantic Search and Vector Embedding.
- Machine Learning: Enables AI-powered search engines to learn in real-time from user interactions and large datasets to evolve continuously.
- Natural Language Processing: Allows AI search engines to understand the nuances, context, and intent behind human language.
- Semantic Search and Vector Embedding: Let’s the engine match queries with conceptually related content, not just exact keywords.
Due to these core features, AI search can interpret user intent much deeper than traditional search engines. For example, distinguishing when a user is searching for an “Apple”, as in the fruit, and “Apple” as in the tech company.
Alongside this, they’re able to provide conversational-based results. Results that are easily understood by the searcher in natural language. Plus, it’s not static. It’s continuously learning, making the answer more relevant and personal to the user.
Key Differences Between AI and Traditional Search
Interaction Style
AI search engines are conversational interfaces, allowing for more natural interaction, just like chatting with a human.
Due to their NLP capabilities, they understand context-rich queries that can be applied throughout several conversational contexts.
For example, after asking an AI search “Best exercises for shoulder pain”, the users can then follow up with “How often should I do that”, and the engine will be able to understand both contexts and provide a relevant answer.
Traditional search engines, on the other hand, rely on keyword-based queries. In this scenario, the user needs to search for the exact thing they want. Each search is also treated independently.
Result Delivery
AI-powered search engines do an excellent job of delivering direct answers and summaries from multiple sources.
Sometimes, there isn’t a single piece of content that can answer a user’s search query. AI understands this. And therefore, instead of providing a random answer, it uses AI, NLP, and ML to analyse multiple datasets and merge them together to provide the best possible answer.
With traditional search engines, however, someone types in a search query and gets a list of websites to read. It doesn’t necessarily provide them with the answer. The search engine has provided them with websites that have the highest likelihood of answering the search.
For example, let’s say someone is looking for a specific question, like “How can I fix cache issues on my WordPress website with Hostinger hosting?”. The chances that there’s a piece of content related to this search are slim.
In this instance, search engines like Google will probably provide a result regarding cache issues on WordPress. AI search, though, will provide a summary of WordPress and Hostinger caching issues from multiple sources and merge them together.
Adaptability
Machine learning plays a huge part in AI search, as it allows the engine to continuously learn and adapt based on the past success of user interactions.
In real-time, these search engines are learning, without the need to be programmed or “told to do” something different. Therefore, after each interaction and each conversion, AI search will become more intelligent, predictive, and personalised.
Traditional search engines, however, operate under static, pre-defined algorithms. Therefore, they need to be updated or changed by developers to make them better. This means they adapt to new information, user behavior, and trends more slowly, resulting in a less dynamic experience.
Personalisation
AI search provides a much higher level of personalisation, as with its core technologies, it can analyse the user over time, learning more about them, their search history, response preferences, etc., resulting in a more user-centric output.
When it comes to traditional search engines, personalisation is limited to location and basic search history. Therefore, the results given are more generic, providing broader answers to queries.
Side-By-Side Comparison Between AI and Traditional Search
| Aspect | AI Search | Traditional Search |
| Interaction Style | Conversational, understands context and follow-up questions. | Keyword-based, each query treated independently. |
| Result Delivery | Provides direct answers and summaries by merging multiple sources. | Returns a list of websites; users must find the answer themselves. |
| Adaptability | Learns and adapts in real-time using machine learning. | Relies on static algorithms; updates require manual input from developers. |
| Personalisation | Highly personalised based on user behavior, history, and preferences. | Limited personalisation, mainly based on location and basic search history. |
Implications for Users and Content Creators
Search is becoming more responsive and advanced, changing how users search and how content creators get in front of their target audience.
Enhanced User Experience
The way we use search is changing forever, and for the better. Thanks to AI, ML, and NLP, users can get a more intuitive, conversational, and efficient search experience.
Mainly, this is down to the increased interpretation of user intent. This will deliver more direct and personalised search answers, reducing the time required for relevant and actionable information.
Alongside this, information is going to be easier to understand. Users will receive information on a conversational level, often tailored to their preferences, with previous successful AI search encounters.
Plus, beyond text, AI supports richer forms of interaction via voice, image, and video, further enhancing the accessibility and convenience of search.
Content Optimisation Changes
While traditional SEO remains important (for now), optimising to appear in AI responses is also essential.
Therefore, instead of only focusing on ‘standard’ SEO, such as on-page SEO, off-page SEO, and technical SEO, you’ll now need to consider developing content for AI understanding.
That means developing content that is well-organised, uses natural language, and provides in-depth explanations. This is the type of content that’ll appear more in the answers of AI search engines.
As a result, content creators need to shift from keyword density and push more towards clarity, relevance, and completeness of content.
Shift in SEO Strategies
Old SEO strategies relied heavily on keyword placement and strong backlink profiles. However, now, there’s a big focus on semantic relevance. Or, in other words, how well the content aligns with the intent and context of a search query.
Because of this, SEOs need to focus on optimising for entities, topics, and the relationship between concepts, allowing for AI search engines to easily understand their content and relevance.
As a result, there’s going to be a heavier emphasis on things such as structured data, answering related questions, and authoritative and complete information.
Conclusion
The impact of AI search is going to be something we remember for a lifetime. It’s going to completely change how we use the internet.
Old, traditional search worked well for a while. It provides us a list of relevant websites to our search queries, which we then have to click on, read, and decide whether they’re suitable for us.
Now, however, AI search skips a lot of this. It understands the intent of the search, summarising information from multiple sources to provide us with the best answer, reducing the time of searching by tenfold.
As we continue to use AI search, it’s going to get more powerful. With usage, it gets smarter from ML. Therefore, we can only expect this to get better and better as the years go by.
