Google used I/O 2026 to show a search product that is becoming more conversational, multimodal, transactional and agentic.
While just prior to I/O 2026, Google released their AI optimisation guide, which is really partly a policy document.
It tells the market what Google would like people to do. But it also confirms the retrieval mechanisms SEOs and marketers should be paying attention to for visibility in AI search. Google explicity said that GEO is SEO.
Between these two announcements, here are the key takeaways:
- AI features in search rely on Google’s index, ranking systems and quality systems.
- Apply SEO best practices to generative AI search.
- Create “non-commodity content” that’s helpful, reliable, and people-first.
- Retrieval-augmented generation grounds AI answers in relevant and fresh pages.
- Query fan-out runs related searches around the original query to fetch more useful information.
- Google says you do not need special AI files, special AI formatting, chunking of your content or inauthentic mentions for Google Search.
So while search is expanding into AI surfaces such as Ask Maps, Ask YouTube and a more integrated AI Mode, the foundations of search are still very much grounded in SEO.
As always, it’s important to test things yourself to see what moves the needle.
Is GEO just SEO like Google says? I don’t think so. I think AI adds a layer to SEO, and while the input may be much the same, chasing AI inclusion requires its own time and plan.
So with that, here are the 9 main takeaways for organic search from Google I/O 2026:
AI Mode Changes The Shape Of Demand
Google says AI Mode has passed one billion monthly active users globally, with queries more than doubling every quarter since launch. Google also says the average AI Mode query is triple the length of a traditional Search query.
That does not mean people have stopped searching short head terms. They still do. The change is what happens next.
Someone may start with “hybrid SUVs”. AI Mode can quickly pull the search into recommended secondary prompts to do with:
- budget
- fuel type
- seating
- boot space
- towing capacity
- safety, running costs
- availability
In practice, AI mode forces secondary search far more than traditional search ever has. Your follow up queries will retain context of your first search, so you’ll end up then replying with something along the lines of:
“I need 5 seats and my budget is 60k” – that will drastically fine tune your results.
For keyword research, this means the seed keyword is still useful, but the real opportunity sits in the decision journey around it.
For brands, this means your bottom-funnel detail becomes more important because AI systems need grounding when they compare options like in the above example.
The Search Box Is Becoming Multimodal
Google announced an AI-powered Search box that accepts text, images, files, videos and Chrome tabs.
For users, their search can start from a product photo, a PDF, a video, a web page open in Chrome or a messy natural language prompt.
For SEOs & brands, this pulls images, documents, videos and on-page context further into organic discovery.
Your website is still central, but it is not the only asset Google may use to understand the answer, and then serve as a result.
AI Overviews Are Becoming Conversations
Google also said users can ask follow-up questions from an AI Overview and move into an AI Mode conversation with the context carried through. Google also says links and supporting articles become more relevant as the user continues to explore and converse.
That creates a different kind of visibility consideration.
A page may be used because it answers the follow-up, not because it is the perfect result for the first visible query. That in itself adds a new layer to what SEO has always been.
A thin product page might answer the head term, but it will struggle when the user asks how the concept applies to a specific product, location, price range, use case or comparison.
This is where SEO led content needs to cover the primary answer, the edge cases and the next decision all in one.
Google is Introducing Search Agents
These agents can monitor blogs, news sites, social posts, finance data, shopping data and sports data, then send users an update when something changes.
This turns freshness and change monitoring into a search product feature that will likely be very personalised.
As an example, if a user is tracking EV SUV releases in Australia, Google needs reliable pages to watch to pull information from. That could include:
- brand pages
- dealer pages
- review sites
- YouTube videos
- pricing and spec data pages
- and social discussion.
The SEO work then moves closer to content operations. Product updates, release notes, changelogs, comparison pages, stock updates, press pages and support articles all become more useful when agents are monitoring the web for changes.
This is also where link building and brand mention work overlaps with retrieval. A broader footprint gives AI systems more places to corroborate what the brand says about itself.
Updates to Local SEO
Local SEO is becoming agent readiness with Google set to introduce “Ask Maps”.
Google gave examples around finding a private karaoke room with specific criteria, then bringing together pricing, availability and booking links. It also said users will be able to ask Google to call businesses in categories like home repair, beauty and pet care.
For local SEO, a Google Business Profile is still important, but it is not enough on its own.
They “action layer” for agents will need:
- correct NAP
- clear service pages
- pricing where possible
- booking links
- opening hours
- reviews
- real photos
- location context
- accurate categories
The old local SEO fundamentals still matter but there is now an added outcome layer, which is:
“Can an AI agent confidently choose me and complete the next step?”
Ecommerce SEO Now Includes Agentic Shopping Inputs
The shopping update is a major SEO angle. Google is tying Search, Gemini, YouTube and Gmail into Universal Cart. It says people shop across Google more than a billion times a day, powered by a Shopping Graph with more than 60 billion product listings.
Universal Cart can track:
- price drops
- back-in-stock alerts
- price history
- compatibility issues
- loyalty perks
- and checkout paths
Google is also building around agent payments and commerce protocols.
For ecommerce SEO, product data becomes discovery infrastructure. Page copy matters as it always has, but so do feed attributes, variants, stock, delivery, returns, compatibility, reviews, images and price history.
I’d argue those things have always mattered in SEO. AI is just putting them in the spotlight for search.
As a practical example:
A product page with a title, one paragraph and a price is not enough if an agent is comparing waterproof hiking jackets under $300, available in black, size medium, from Australian retailers, with free returns.
YouTube Is A Search And Source Surface
The I/O recap and YouTube’s official update both point to a bigger role for video. The clearest example is Ask YouTube, where users can ask complex questions, refine with follow-ups and get an interactive response compiled from long-form videos and Shorts.
This is a very obvious organic search angle. YouTube is not just a video platform, it’s actually the 2nd biggest search engine in the world. It is a search surface, a source surface and a transcript surface.
YouTube transcripts feed Google AI Overviews majorly, that’s why by simply having a YouTube video is a great way to further optimise for AI Overviews.
If a video is used to answer “new X product features and price”, the transcript needs to include the feature names, product names, price points, comparisons and use cases.
Video production may not sit with the SEO team, but video structure increasingly should be influenced by SEO.
Treat important videos like landing pages. Make the sections clear. Say the entities out loud. Cover the decision points a user or AI system would need to extract later.
Chrome Points To Agent-Friendly Websites
Chrome announced WebMCP, a proposed standard that lets websites expose structured tools like JavaScript functions and HTML forms to browser-based agents.
This is not mainstream SEO work tomorrow morning, but it is directionally important.
Google’s own AI SEO guide now tells site owners to explore agentic experiences and agent-friendly websites where relevant.
The technical SEO question may eventually move from “can Google crawl this page” to “can an agent use this website”.
Forms, filters, booking flows, calculators, product selectors and checkout paths may need to be machine-operable, not just visible.
I mean already I am using Codex to action website changes for me. Codex can login, navigate and publish content. It actually helped me publish this very article… So the technology is already there, it’s just not baked into mainstream search, yet.
Provenance And Visual Discovery Change Trust Signals
Google is expanding SynthID and C2PA verification across Search, Gemini, Chrome, Pixel and Cloud. Users will be able to ask whether media was made with AI through Lens, AI Mode, Circle to Search and Gemini in Chrome.
This is not a ranking factor announcement. But it is still relevant to search because trust is becoming more inspectable.
This announcement also supports Google’s stance on “non-commodity” content.
The intelligent eyewear announcements point in the same direction. Google showed Gemini helping users understand the world around them, including finding reviews for a restaurant they are walking past, decoding signs, navigating and finding nearby places based on preferences.
For brands, original product photos, real team photos, real location media, review patterns, local citations and clear source provenance become more valuable as the web fills with synthetic content.
Non-commodity content is not just better writing. It is better proof.
What SEOs Should Do Now
SEO work is not to invent a new checklist and call it GEO. The work is to expand the SEO checklist to match how Google is expanding Search. So with that:
- Keep crawlability, indexability, canonicals, snippets, JavaScript rendering and page experience clean.
- Build content around decision criteria, not just exact-match keywords. Think of the secondary search.
- Make product feeds, variants, stock, delivery, returns, reviews and compatibility data complete and highly visible.
- Keep Google Business Profile data, local citations, phone numbers, booking paths and service details accurate.
- Treat YouTube transcripts, chapters and spoken entities as organic search assets.
- Build real third-party coverage through PR, reviews, links, useful off-site assets and brand mentions.
- Prepare technical teams for agent-friendly site experiences where forms, filters and actions can be used reliably.
- Track AI Overview inclusion, AI Mode surfaces, impression coverage, citations, brand mentions and the queries that trigger follow-up behaviour. You need to reverse engineer these results.
That is where AI SEO is heading in practice. Not away from SEO services, but further into product, content, local, PR, video, feed management and technical implementation. It’s a broad layer on top of organic search.