5 Key GEO Trends Shaping the Future of Search

What lies ahead for the future of search? The emergence of AI-driven search engines such as ChatGPT, Google Gemini, and others has brought about a seismic shift in how we discover and interact with information. Let’s explore five key GEO trends that are shaping this transformative journey and redefining the role of search engines in the digital landscape.

OpenAI’s official launch of ChatGPT Search signals the dawn of a new era where generative engine optimisation (GEO) is no longer optional but essential for anyone hoping to remain visible online. The shift towards GEO underscores the growing importance of optimising for AI-driven search engines, which are now capable of generating highly relevant, conversational responses tailored to user queries.

Today’s leading AI-powered search platforms, including ChatGPT, Perplexity, and Google Gemini, have taken search capabilities to the next level by seamlessly combining real-time search functionality with conversational engagement. This evolution allows users to ask questions, refine their search, and explore topics in a more dynamic and interactive manner than traditional search engines ever allowed.

But what does this mean for SEO? It’s clear that traditional optimisation techniques must evolve to match the requirements of these next-generation search engines. GEO is not just about targeting the right keywords; it’s about positioning your brand within the broader context of AI-driven conversations. The ability to create content that is not only search-friendly but also conversational, dynamic, and relevant to ongoing discussions is now critical.

For brands, this is a wake-up call. If businesses want to remain part of the conversations that matter most to users, they need to rethink their strategies. This isn’t just a tweak to existing practices—it’s a complete rethink of how we approach visibility in the ever-changing digital landscape. By embracing these trends and adapting to AI’s evolving role in search, businesses can secure their place in the future of online discovery.

Here are five key trends in GEO that are redefining the future of search, plus how you can prepare.

1. The Evolution of Entities

Entities are once again reshaping the way we approach search. Understanding their evolving role is essential to maintaining visibility in an increasingly AI-driven landscape.

Do you remember the phrase, “things, not strings”? This concept became a cornerstone of search in 2012 when Google introduced its Knowledge Graph. At that time, it marked a monumental shift from merely matching a “string” of words in text to recognising distinct “things,” or entities. These entities could be people, places, products, or ideas, and their identification brought search engines closer to interpreting information in a more human-like way.

Fast forward to today, and the rise of AI-powered search technology has placed even greater emphasis on entities. They now play a pivotal role in how AI interprets and prioritises information, ensuring context and relevance in search results.

Entities and Knowledge Networks

Entities are embedded within structured collections known as knowledge networks, such as Google’s Knowledge Graph, Wikipedia, and Wikidata. These networks define relationships between entities and attributes, forming the backbone of how AI understands context and determines credibility.

While AI heavily relies on these established networks, it doesn’t stop there. Over time, AI develops its own dynamic connections, expanding its understanding of how various entities and attributes relate to one another in different contexts.

Entities and Their Role in Relevance

Think of entities as the AI’s way of interpreting “what something truly is.” By recognising and connecting these elements, AI builds a web that links ideas, context, and real-world relevance.

For example, let’s say a user searches:
“What’s a good beginner-friendly bike for commuting in San Francisco?”

Instead of processing this query as a string of unrelated words, AI identifies key entities and attributes and examines the relationships between them:

Bike: Product (entity).
San Francisco: Location (entity).
Beginner-friendly: Experience Level (attribute).
Commuting: Purpose (attribute).

By combining these elements, AI gains a nuanced understanding of the query. It recognises that a beginner-friendly bike suitable for commuting in San Francisco might need features tailored to the city’s terrain, such as the ability to handle hills, an upright design for comfort, easy gear shifting, or even electric assist options.

2. LLMs and RAG: The Technology Behind AI-Driven Search

The increasing significance of entities in search is closely tied to how Large Language Models (LLMs) and retrieval-augmented generation (RAG) function. Understanding this technology is essential for grasping the “why” behind Generative Engine Optimisation (GEO).

How Do LLMs Work?

LLMs are trained on vast datasets that include websites, forums, and structured databases such as Wikipedia and Wikidata. This extensive training enables them to process and comprehend human language with remarkable complexity.

Natural Language Understanding: LLMs learn how words, phrases, and ideas interact within various contexts. This allows them to interpret both the literal and deeper meanings behind queries, enabling them to generate intuitive, human-like responses.
Mapping Entity Relationships: Through entity recognition, LLMs establish connections between entities. For example, “San Francisco” is identified as a location associated with attributes like “hilly terrain” or “tech hubs.” These mapped relationships help LLMs create cohesive responses based on interrelated knowledge.
Contextually Relevant Answers: When responding to a query, LLMs leverage their pre-trained knowledge to generate answers that align with the user’s intent. They consider both the explicit question and its broader context, ensuring relevance and depth.

Despite these capabilities, LLMs have a critical limitation: they rely on static, pre-trained data. This can lead to outdated responses or “hallucinations,” which are plausible-sounding but factually incorrect answers.

RAG: Powering Real-Time Updates

Retrieval-augmented generation (RAG) addresses the limitations of LLMs by incorporating real-time information into responses. Instead of relying solely on static pre-trained data, RAG retrieves and integrates fresh content when processing queries. This ensures that responses are accurate, timely, and grounded in current information.

How Does RAG Work?

According to Google, RAG enhances traditional LLM workflows by combining three processes: retrieval, augmentation, and generation.

Retrieval: RAG queries pre-indexed, vectorised data from diverse sources such as news articles, APIs, Wikipedia, Wikidata, and user-generated platforms like Reddit and Quora. By leveraging semantic search, RAG merges authoritative knowledge with emerging trends for a well-rounded response.
Augmentation: Retrieved data is seamlessly integrated with the LLM’s existing knowledge. This enriched context enhances the quality and relevance of the AI’s output.
Generation: With the augmented context, RAG produces responses that are both accurate and grounded in the latest real-world information. This process combines foundational insights with current updates to ensure credibility.

Why Does This Matter for GEO?

LLMs provide a strong foundation for understanding context, while RAG ensures that responses are accurate and timely. For brands aiming to stay relevant in this evolving landscape, simply publishing content is no longer enough.

To succeed, content must be structured to integrate with the databases and knowledge networks on which AI relies. Building credibility through associations with trusted sources, authoritative mentions, and real-time engagement is equally critical.

The ultimate goal is to become the preferred source of information that AI consistently references. Achieving this begins with entity optimisation, ensuring your brand is seamlessly integrated into the AI-driven search ecosystem.

3. The New Age of Entity Optimisation

Entities play a pivotal role in how AI understands the world, making them essential for brands seeking relevance in the evolving landscape of search. However, recognising their importance is just the first step. To succeed, your brand needs to integrate itself into the AI-driven web of knowledge. Here’s how to get started.

Implement Schema Markup

Schema markup ensures that AI can accurately interpret your content and its connections to broader knowledge networks. This structured data is a cornerstone for effective entity optimisation.

Define Key Entities: Use schema markup to clearly identify your brand’s primary entities, such as people, products, places, or concepts.
Link to Trusted Sources: Use “sameAs” schema to associate your brand with authoritative profiles, including Wikipedia, LinkedIn, and other reliable platforms.
Tie Verified Profiles Together: Connect your brand’s professional and social media profiles to establish a consistent and credible digital presence.
Leverage Mentions Schema: Highlight notable entities within your content and use mentions schema to signal engagement in larger discussions.

Build Connections Within Key Knowledge Networks

Embedding your brand in recognised knowledge networks, graphs, and structured databases strengthens AI’s trust in your content and ensures better visibility.

Claim and Maintain Knowledge Panels: Keep profiles like Google Business updated with accurate and timely information to secure a trusted presence.
Develop Wikidata Entries: Establish and maintain comprehensive, reliable entries on Wikidata to integrate your brand into this vital knowledge graph.
Aim for Wikipedia Presence: While creating a Wikipedia page may not be feasible for every brand, earning mentions in reliable sources can be just as effective.

Secure Mentions in Reputable Sources

AI often references information from trusted publications, making it essential to position your brand in well-regarded industry outlets.

Publish Valuable Content: Create insightful resources or unique perspectives that encourage others to reference your brand.
Collaborate with Industry Experts: Partner with thought leaders on webinars, articles, or interviews to enhance your brand’s credibility.
Appear in Established Publications: Target reputable outlets within your industry to strengthen your reputation and build trust.
Engage in Targeted Digital PR: Focus on earning mentions in authoritative sources frequently cited by AI, ensuring your brand remains part of key conversations.

For example, if someone searches for a beginner-friendly bike for commuting, AI might recommend a brand because it was mentioned in a trusted article – even if the brand itself wasn’t directly linked. This illustrates the power of indirect inclusion in authoritative discussions.

Use Real-Time and Dynamic Content

AI prioritises fresh, relevant content to generate accurate insights. Keeping your content dynamic and engaging ensures your brand remains part of ongoing industry narratives.

Regularly Refresh Content: Update blogs, news articles, and product pages to reflect the latest trends.
Engage with Forums and UGC Platforms: Participate in active conversations on platforms like Reddit, Quora, and Stack Exchange to stay aligned with current discussions.
Publish High-Impact Content: Share research, insights, or thought leadership that addresses emerging trends and questions in your industry.

Align Content and Links with Entities

To strengthen your brand’s presence, ensure your content ties directly to relevant entities. This alignment makes it easier for AI to understand and prioritise your brand in relevant contexts.

Mention Known Entities: Refer to key people, places, and concepts within your niche to enhance your brand’s relevance.
Link to Verified Sources: Reinforce credibility by including links to trusted and authoritative entities.
Utilise Internal Linking: Use an ontology-based approach to interconnect related content, demonstrating your expertise.
Create Content Clusters: Organise content into thematic clusters to signal comprehensive coverage of specific topics.

Focus on E-E-A-T

Finally, build your brand’s digital footprint by adhering to Google’s E-E-A-T principles: Experience, Expertise, Authoritativeness, and Trustworthiness. Highlight author credentials, include citations, and earn high-quality backlinks to showcase credibility.

By following these steps, your brand can thrive in the interconnected landscape of AI-driven search, ensuring its place in the evolving digital ecosystem.

4. The Rise of Multimodal Search

In today’s digital landscape, users interact with information in diverse ways, leveraging voice commands, videos, images, and audio to satisfy their search queries. This shift towards multimodal engagement was once only imaginable but is now a reality shaping the way platforms deliver content.

The Evolution of Multimodal Search

Platforms like Google Lens exemplify this trend, currently processing an astonishing 20 billion visual searches each month. This reflects a growing appetite for interactive and dynamic search experiences that go beyond traditional text-based queries.

With technologies such as Retrieval-Augmented Generation (RAG), AI systems can now retrieve and process multimodal embeddings alongside text to craft richer and more comprehensive responses.

The Role of Entities in Multimodal Search

What makes these advanced search experiences cohesive? The answer lies in entities. Entities serve as the underlying framework that binds various forms of content—images, audio, video, and text—into an interconnected narrative. For brands, this underscores the need to move beyond treating media assets as isolated efforts. Success in this new era hinges on integrating all content formats into a unified, well-structured strategy.

Optimising and Connecting Media Assets

To capitalise on multimodal search, brands need to optimise their media assets across different formats while maintaining a consistent narrative. Here are some actionable steps:

Images:

Include alt text and metadata enriched with relevant entities to boost visibility.
Use ImageObject schema to provide structured data for search engines.

Voice Search:

Structure FAQ-style content using FAQ and Q&A schema for clarity and easy retrieval.

Video Content:

Add transcripts and captions for accessibility and relevance.
Use VideoObject schema to help search engines categorise and surface your videos effectively.

Audio Content:

Include transcripts and AudioObject schema to increase discoverability and integration with other formats.

Practical Example: Optimising Content for “Core Strength Workouts”

A fitness brand aiming to dominate the search landscape for “core strength workouts” could adopt the following strategy:

Blog Post with Images: Write a detailed article covering core exercises, benefits, and tips. Enhance it with images labelled with alt text like “plank position for core strength” and apply ImageObject schema to each image.
Voice Search Optimisation: Add an FAQ section to the blog addressing common queries, such as “What are the best core exercises?” with structured FAQ schema.
Video Content Creation: Produce a step-by-step instructional video demonstrating core exercises. Share the video across platforms while embedding it into the blog. Add transcripts, captions, and VideoObject schema to maximise its impact.
Podcast Episode: Release an episode discussing core strength techniques and benefits. Link the podcast to the blog post and include a transcript using AudioObject schema for accessibility.
Entity Linking and Structured Data: Reference the video series and podcast within the blog post. Interlink all content formats (blog, video, and podcast) to establish a cohesive narrative. Apply sameAs properties to connect related content and strengthen the entity network.

Creating an Immersive Search Experience

By unifying all content formats under a single strategy, brands can deliver an informative and immersive experience that engages users across their preferred search mediums. This integrated approach ensures that no matter how users choose to search—be it through text, images, or voice—your brand remains a relevant and trusted source of information.

5. Personalised and Predictive Search Experiences Are Here

Imagine a search engine that anticipates your needs before you even type a query. Instead of simply responding to your searches, it offers suggestions and solutions tailored to your behaviour, interests, and preferences—even before you realise you need them.

Thanks to advancements in generative AI, this type of predictive search experience is no longer a concept for the future; it’s happening now.

From Personalised to Predictive Search

While personalised search already tailors results to align with your preferences, predictive search goes beyond this. It anticipates your needs based on patterns in your behaviour and engagement across the digital ecosystem.

For instance, consider planning a home garden. You might start by searching for “the best vegetables to grow in spring.” As you progress, AI could suggest planting schedules, alert you to frost warnings, and recommend nearby nurseries—just as you’re preparing to make purchases.

As your gardening journey unfolds, the AI adapts, offering seasonal care tips, connecting you with gardening communities, and delivering content in the format you prefer. This level of intuition transforms search from merely “helpful” to absolutely “indispensable.”

The Importance of Predictive Search for GEO

At the core of predictive search lies dynamic entity profiles—real-time representations of people, brands, products, and concepts that constantly adapt as new data emerges. These profiles are enriched with insights pulled from knowledge networks, ensuring they remain accurate and up-to-date.

For brands, this highlights the importance of creating agile, responsive content that evolves with user preferences and expectations. It’s about staying relevant even when users haven’t yet articulated their needs.

How Brands Can Stay Ahead

To thrive in a world dominated by predictive search, brands must take proactive steps to remain an integral part of the user’s journey:

Map Content Across the User Journey
- Identify potential user needs at every stage and develop interconnected content that smoothly transitions between related topics and formats.
Adapt with Real-Time Insights
- Use emerging trends, fresh data, and audience feedback to keep your content relevant and aligned with current interests.
Redefine Value in Predictive Experiences
- Go beyond addressing immediate queries. Provide tools, guides, and resources that users will find valuable, even if they’re not actively searching for them.

Building Trust Through Anticipation

Meeting users where they are—and where they’re likely to be next—fosters trust, establishes authority, and builds lasting loyalty. By listening to your audience and anticipating their needs, even when they’re unsure of what they’re looking for, your brand can secure a lasting position in their digital journey.

Stay adaptable

The future of search is defined by being multimodal, personalised, predictive, and deeply interconnected. These trends highlight one undeniable truth: search is no longer just about information retrieval; it’s about creating meaningful, connected experiences.

If there’s one key insight to grasp, it’s that search is continuously evolving—and staying still isn’t an option. To remain competitive, brands must refine their GEO strategies and stay informed about the technologies driving this transformation.

Adaptability is the key to success. Embracing these changes will ensure your brand stays ahead in this rapidly shifting landscape.