With the rise of AI-powered features, search engines are not just directing users to information but delivering answers directly.
This shift is redefining how people interact with the web, raising questions about the future of SEO, content discovery, and digital marketing.
Here’s what’s coming next.
From ChatGPT to Grok 3: The breakneck pace of AI advancements
The world has seen rapid and significant advances in AI technology and large language models (LLMs) within two years.
Looking back just three years ago, Google’s Gemini and Meta’s LLAMA did not exist, and OpenAI’s ChatGPT was later released in late November 2022.
Fast-forward to January 2025, the public was introduced to DeepSeek R1. This open-source large language reasoning model astounded the AI community with its speed, efficiency, and affordability, especially compared to OpenAI’s o1 GPT model.
A few weeks later, Elon Musk’s company xAI launched Grok 3, which impressed users by topping a key AI leaderboard with its complexity and fewer guardrails (see: unhinged mode).
More recently, Anthropic released Claude 3.7 Sonnet and Claude Code, an LLM that excels at code creation and debugging to a degree that has made many software engineers a bit uneasy.
These LLMs are just the beginning of AI’s rapid progress, with more breakthroughs on the way.
Google’s AI Mode: A glimpse of the future
AI isn’t just bringing new products – it’s transforming existing ones, too.
On March 5, Google announced they were expanding AI Overviews with a new experimental feature called AI Mode.
This interactive feature allows users to:
Engage with web search in a chat-like manner through multimodal understanding.
Refine long-tail queries in a back-and-forth manner.
AI Mode, powered by Gemini 2.0, enhances research using a “query fan-out” technique to gather real-time data from multiple sources and generate detailed, in-depth summaries.
This may make SEOs uncomfortable, as it potentially reduces clicks to publisher sites and further promotes a zero-click ecosystem.
With Google integrating Gemini 2.0 into its suite of products and its dominance of 89% of the search industry, its AI innovations demand close attention.
These technologies will likely be added to search, and AI Mode offers a preview of what’s ahead.
Two terms for the future of search: Agentic and deep research
We’ll likely hear two terms used more often in the AI and search space:
Deep research models can browse the web and focus on conducting intensive, in-depth research to provide users with informative summaries on complex topics.
Unlike previous LLMs, which use a single-step information retrieval system through RAG (retrieval-augmented generation), deep research and agentic models can:
Conduct multi-step research through a series of actions, pulling information from multiple sources to provide comprehensive summaries to the user.
Take proactive actions, such as executing tasks and complex instructions.
Google’s Project Mariner and OpenAI’s Operator already showcase these capabilities by allowing users to perform tasks within their browsers while understanding multi-modal elements such as text, images, and forms.
Dig deeper: How to use OpenAI’s Deep Research for smarter SEO strategies
How these models could change search
Suppose you want to plan a trip to Tokyo and know the best season to go, the weather, and where to stay.
Typically, this type of research takes a few days or weeks, and you gather information from various sources, such as travel websites or YouTube videos.
A deep research model can do the heavy lifting by searching the web, gathering information, and summarizing relevant content, which saves you time.
It can also “read, listen, and watch” various sources to provide a thorough answer.
An agentic model could also book your hotels and flights, navigating checkout flows to complete the purchase.
AI is moving in this direction as companies like Google work toward AGI (artificial general intelligence) – machines that can reason across diverse tasks like humans.
Deep research and agentic models are key milestones in building practical AI solutions for everyday use.
AI Overviews have already impacted click behavior and organic traffic.
Now, we must consider these AI features’ long-term effects on the content ecosystem.
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What could the future search landscape look like?
Google’s AI Overviews and agentic advancements are here to stay.
If AI Mode succeeds, it will be the first deep research feature in Google Search.
So, what’s next for the search landscape?
Here are some possibilities.
Continual rise of zero-click searches
Since launching in May 2024, AI Overviews have significantly reduced clicks to informational queries.
As AI search capabilities advance, users will likely rely even more on AI tools for quick answers rather than clicking through to websites or articles.
AI Mode and future search innovations could accelerate this shift by prioritizing fast, AI-generated summaries over traditional browsing.
As zero-click searches become the norm, you must rethink how you measure value and engagement.
Traditional KPIs may no longer accurately reflect user behavior, so focusing on brand visibility and awareness will be more critical than ever.
Increased personalization
LLMs and AI systems are revolutionizing search by personalizing responses with unmatched speed and scale, surpassing traditional algorithms.
Leveraging Google’s vast user data, AI can train on existing information and refine queries in real-time to deliver more tailored results.
As these systems continuously learn, they will become even better at recognizing, remembering, and adapting to individual user preferences.
As AI-driven search becomes more personalized, it’s worth considering whether hyper-niche content is the key to reaching your audience.
Multimodal search
Google’s AI-powered multimodal capabilities are already embedded in many of its products, including Project Astra, an AI assistant unveiled at Google I/O 2024.
During a live demonstration, Astra used multiple tools – such as Google Lens – to identify objects in real time and respond to voice queries.
In my own experience at Google I/O, the AI assistant:
Accurately classified animal figurines.
Distinguished between similar names (“Bob” vs. “Rob”).
Even created a story about the figures.
While some of these advanced features haven’t been integrated into Google Search yet, multimodal search through Google Lens and voice search is already shaping how users submit queries.
As Google develops these capabilities, you should anticipate what’s next, look beyond text-based queries, and optimize for image, video, and audio search.
Dig deeper: From search to AI agents: The future of digital experiences
Commercial queries can still draw users to websites
AI-generated results have reduced clicks for informational queries, but commercial and transactional searches still offer opportunities for website traffic.
During the decision-making process, potential buyers research extensively – comparing products, reading reviews, and exploring multiple channels before making a purchase.
While it’s unclear how AI-generated search will impact this journey, think about how AI can streamline multi-touchpoint decision-making while still driving users to your website.
When users move closer to making a purchase, user-generated content – like reviews – will still play a crucial role in conversions.
Content quality still rules
Despite AI’s growing role in search, one thing remains constant: high-quality content is essential.
Whether users rely on traditional search engines or LLMs, visibility will still depend on the strength of the content itself.
Since both Google Search and LLMs use RAG to pull from vast datasets, ensuring these systems have access to accurate, high-quality information is critical.
Content demonstrating E-E-A-T (experience, expertise, authoritativeness, and trustworthiness) will continue to rank higher in AI-driven search results.
Your brand will also play a bigger role in search visibility, making it essential to create valuable, well-optimized content across multiple formats.
Dig deeper: Decoding Google’s E-E-A-T: A comprehensive guide to quality assessment signals
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