Google’s incredible dominance in search may be decreasing in the age of generative AI and innovative competitors. In response to these threats, Google is making a prodigious effort to retain its dominance. As a leader in generative AI technology, it’s incorporating AI features into its various products. Google has also continuously improved the relevance and end-user experience for search results.
Nonetheless, the emergence of OpenAI’s ChatGPT, its imitators, and its competitors has created an arms race where the players are all trying to leapfrog each other with better, faster, cheaper, generative AI-based software products. For the first time in years, Microsoft sees an opportunity to take a chunk of Google’s search business.
Here’s what advances to look for as you seek to improve your search experience and results. The same technology advances can improve customer experience on your company’s website, and the employee enterprise experience.
Recent advances
Generative AI
The explosive adoption of OpenAI’s ChatGPT caused search engines to offer their own versions. Microsoft implemented ChatGPT in Bing, and Google introduced Bard, its generative AI engine.
The colossal advance is that generative AI provides immediate answers rather than the result sets that search engines produce. Generative AI typically responds with appropriate prose. That’s well beyond what search engines can do. By contrast, search engines expect end-users to:
- Read the web pages in the result set and find the answer they sought. That task can be frustrating and time-consuming.
- Refine the search, often multiple times, and read more web pages returned by these result sets.
- Write whatever paragraph, page or report they need based on the answers.
Generative AI simplifies and shortens this research and writing process by crafting the answer directly. Generative AI can also respond to follow-up questions while retaining the context of previous questions in a dialogue. Search engines are not capable of taking context into account.
Advanced search engines
Advanced search engines are a significant step beyond using keywords, phrases and related criteria. Higher-level features make searching more convenient and more effective in every way. Examples include:
- Natural language processing – The search engine processes questions or entire sentences, rather than just keywords, stated in English or other languages.
- Query intent detection – The search engine tries to understand the user’s motivation behind the search query.
- Auto suggest – It’s more than the auto-complete we’ve all experienced: The search engine suggests multiple, hopefully more relevant or targeted queries derived from the initial search query.
- Multi-media results – The search engine returns text, image and video results.
Video search
TikTok, YouTube, Vimeo and other video websites offer search engines where the result set consists entirely of relevant videos rather than the traditional list of related web pages.
The younger generations find the video search approach much more engaging because it combines entertainment with information. Now, learning has become fun, and boring text is history.
The search results at some video websites value human opinion more than SEO-influenced results.
Voice search
Virtual assistants like Alexa, Google Assistant and Siri have proliferated on smart speakers and smartphones. As a result, voice search is becoming more widespread and popular. As voice search becomes more accurate and personalized, end-users can ask more complex questions and receive more relevant results.
Personalization
Personalization is becoming increasingly important for search engines as they strive to deliver more relevant and personalized search results. Expect search engines to continue to build more comprehensive profiles of the demographics, interests, preferences, and locations of everyone on the planet.
In the future, search engines will use AI and machine learning algorithms to deliver personalized results based on user behaviour.
Advances coming soon
AI/ML
Artificial intelligence (AI) and machine learning (ML) are transforming search engines’ processing and ranking of content. AI/ML algorithms can analyze vast amounts of data, identify patterns, and predict user behaviour, providing more accurate and relevant search results.
We all hope this advance will reduce the off-target clutter in search results we all wade through today.
Visual search
Visual search will improve search engines’ capabilities for handling visual content, allowing users to search for products and services using images and videos rather than just text. Circle to Search, which bases a search on an image the end-user has circled with their finger, is already available on new Samsung and Pixel phones. A similar capability will become available on workstations and support uploading files and end-user sketches. Visual search will provide more relevant and personalized search results as it becomes more accurate and sophisticated.
Natural language processing
Natural language processing (NLP) improves search engines’ ability to understand and process human language. NLP software often incorporates semantic search that analyzes the meaning, context, tone, and intent of end-user queries.
Soon, NLP will enable search engines to provide more personalized and conversational search results, making search more intuitive and accessible.
Advances further into the future
Augmented reality
Augmented reality (AR) will change how we search for products and services. In the future, the superior user experience of AR will engage end-users to visualize how they would look with many products personally and in their immediate environment. AR will provide more immersive and entertaining shopping experiences. For example, with AR, we can walk around a redecorated kitchen or living room or visualize ourselves wearing a new dress or suit. For example, with AR, we can walk around a redecorated kitchen or living room or visualize ourselves wearing a new dress or suit.
Contextual awareness
Personalization will continue to increase in sophistication beyond NLP to provide end-users with:
- Related questions to consider, especially when a query is vague, too general or even internally contradictory.
- Results that are pruned to reduce the skewing caused by keyword-loaded language, paid links, and sponsored content.
- Accuracy confidence scoring or weighting of references in the results.
- Misinformation and disinformation flagging.
- Historical evolution of theories and thinking related to the query.
Contextual awareness will accelerate research while reducing inadvertently misleading results. It may even reduce the frequency of end-users accepting propaganda as fact.
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