Nội dung
- Write more clearly: blog SEO now has to account for both AI and humans
- Google is starting to surface query data in AI search, but gaps remain
- Schema for AI search: don’t optimize generically, look for entity gaps
- YouTube Ads remains a major arena for those who can read viewer behavior correctly
- AI does not just recognize brands, it also places them in the right category
- 2027 marketing budgets may favor new categories instead of just increasing AI line items
- Match rate: a quiet metric that can determine data effectiveness
- Google may be moving closer to “autonomous search”
- The Cardi B and Zevia story reminds marketers that big campaigns still need the right tone
- Agentic commerce is bringing SEO closer to conversion
- A perspective for the Vietnamese market
- References
SEO is entering a very different phase: it is no longer just about optimizing to rank on search results pages, but also about being understood by machines, cited by AI, and clear enough to fit into new search experiences. For Vietnamese marketers, these changes directly affect content, product data, measurement, and even next year’s budget plans.
- Key points:
- Google and AI systems are prioritizing content that is clear in meaning, easy to extract, and better structured.
- Data on AI search, schema, entity, and shopping behavior in AI is opening up new ways to measure performance.
- Brands not only need to be “recognized” but also correctly placed by AI into the right user-need category.
- Agentic commerce is pushing SEO closer to conversion, not just traffic.
Write more clearly: blog SEO now has to account for both AI and humans
In a recent Search Engine Land article, the familiar advice to “write for a child and someone who is distracted” was updated with a new audience: large language models and AI search systems. The core message is clear: content no longer just needs to be easy for users to read; it also has to be coherent enough for AI to understand, summarize, and decide whether to use it.
That means bloggers and content teams can no longer focus only on length or keyword density. The writing needs clear intent, a logical structure, and direct answers to user questions. As AI Overviews and conversational search experiences become more common, ambiguity in an article can cause content to be overlooked, even if it would previously have been good enough to rank at the top.
Source: Search Engine Land.
Google is starting to surface query data in AI search, but gaps remain
Search Engine Journal reports that Google Merchant Center has launched a pilot providing data on shopping-related questions users ask in AI Mode and AI Overviews. This is a notable signal for retailers, because it is the first time Google has offered part of a view into demand appearing across AI search surfaces.
However, this data is not yet the “gold mine” many expected. Google is grouping questions into clusters rather than showing each query individually. As a result, marketers can see the language and topics users care about, but they still cannot know exactly how many clicks or conversions AI search has driven to the site. For e-commerce SEO, the biggest value lies in identifying product attributes that should be added to feeds and listings.
Source: Search Engine Journal.
Schema for AI search: don’t optimize generically, look for entity gaps
Search Engine Land emphasizes a more practical approach to schema in the AI search era: instead of adding markup just for the sake of it, identify “entity gaps” — the entities your content is still missing or not expressing clearly enough. This approach makes sense when AI is not only reading a page, but also comparing the semantics between brands, categories, attributes, and related entities.
For marketers, the lesson here is that schema is no longer just a technical task for the dev team. It becomes part of the content strategy and information architecture. If product pages, advisory articles, category pages, and organizational data do not align, AI will struggle to understand who you are and the context in which you should appear.
Source: Search Engine Land.
YouTube Ads remains a major arena for those who can read viewer behavior correctly
Although it is not a traditional SEO article, Search Engine Journal’s content on YouTube Ads is still highly relevant for search and content professionals. The piece shows that YouTube is a multi-context environment: Shorts serve quick-scrolling behavior, while long-form video is where viewers have higher intent and are willing to spend time on topics they care about.
The key takeaway for marketers is how a platform fragments user behavior into different attention states. This is similar to modern SEO: not every query is the same, and not every content format serves the same goal. Understanding the context correctly determines which message, which format, and which CTA will be effective.
Source: Search Engine Journal.
AI does not just recognize brands, it also places them in the right category
Search Engine Land raises a very important question: for AI to recommend a brand, is what matters more how well-known it is, or how it is framed within a specific category? According to the article, the issue is not whether AI recognizes the brand, but whether AI understands which need group the brand belongs to and whether it should be recommended in that query context.
This is the major difference between “recognition” and “recommendation.” In modern SEO, building brand awareness alone is not enough. Businesses need to ensure that third-party signals, owned media content, schema, and the way products/services are described are all consistent so AI does not misclassify them or place them in a narrower box than reality.
Source: Search Engine Land.
2027 marketing budgets may favor new categories instead of just increasing AI line items
In a commentary on Search Engine Journal, the author asks how CMOs should allocate budgets in a context where AI and the economy are changing faster than many organizations can make decisions. The standout message is: instead of simply pouring more money into AI as a separate line item, businesses should consider new categories that may drive better growth.
Although this is a strategic perspective, it is still directly relevant to SEO. When budgets are tightened, long-term foundational projects such as content architecture, schema, product data, authority building, and new measurement often compete with short-term campaigns. The question is no longer “whether to do AI,” but “where AI should support growth.”
Source: Search Engine Journal.
Match rate: a quiet metric that can determine data effectiveness
Search Engine Land warns about a metric many marketing teams are not tracking closely enough: match rate. In a fragmented data environment, the ability to correctly match users, devices, sessions, or customer profiles directly affects measurement accuracy and experience personalization.
For SEO and performance marketing, match rate becomes even more important when businesses need to connect data from multiple sources: search, CRM, ads, e-commerce, and first-party data. If the match rate is low, you will see a distorted picture of the user journey and optimize the wrong priorities. In other words, before optimizing for growth, many brands need to optimize their own data recognition capability.
Source: Search Engine Land.
Google may be moving closer to “autonomous search”
Search Engine Journal cites a conversation with Liz Reid of Google, showing that Google is thinking beyond simply answering immediate queries. The concept of autonomous search is mentioned as a type of search that can detect when there is not yet a good enough answer and return later to provide one when the information is ready.
For SEO professionals, this is a signal that search is shifting from a “ask a question – receive results” model to a “track demand – respond at the right time” model. If this develops further, content will not only need to rank for the current query, but also be ready to be selected by the system when context and data change.
Source: Search Engine Journal.
The Cardi B and Zevia story reminds marketers that big campaigns still need the right tone
Marketing Dive reports that Zevia has partnered with Cardi B for the brand’s biggest campaign to date. While this is more of a pure brand activation than an SEO story, it still shows how brands are seeking attention through strong personality, everyday language, and broad pop-culture reach.
For search marketers, the indirect lesson is consistency between brand messaging and public expectations. When a brand does well on identity and tone, external signals — from press coverage to social content — also become easier for both users and AI systems to read. That provides an important foundation for long-term visibility.
Source: Marketing Dive.
Agentic commerce is bringing SEO closer to conversion
Search Engine Journal warns that many retailers still do not fully grasp the scale of change agentic commerce is bringing. When the buying journey can happen directly inside an AI environment, the website is no longer the only mandatory stop before purchase. Discovery, comparison, decision-making, and even payment can happen within the same conversational ecosystem.
This is the biggest change for e-commerce SEO in the new phase. Optimization can no longer stop at rankings or traffic; brands must think about feed visibility, AI selection, product data readiness, and integration with new commerce protocols. If they do not prepare, businesses may still get traffic but gradually lose control over conversion touchpoints.
Source: Search Engine Journal.
A perspective for the Vietnamese market
For Vietnamese businesses, this week’s signals show that SEO is moving quickly toward a “search everywhere” and “AI everywhere” model. That is especially important for industries with large product datasets such as retail, fintech, travel, education, and B2B SaaS, where content structure and data can determine whether AI understands them correctly.
In the short term, SEO teams should focus on four things: making content clearer; reviewing schema and entities; standardizing feeds, categories, and product attributes; and rebuilding measurement so they do not only look at traffic but also at visibility across AI surfaces. Businesses that prepare early will have an advantage as Vietnamese users increasingly search and shop through conversational AI experiences.
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This article focuses on SEO 2026 with a perspective for the Vietnamese market.
References
- The new SEO rules for bloggers in 2026: Why clarity matters in AI search
- Google’s AI Search Data Is Growing, But The Gaps Remain
- Schema for AI search: How to identify and prioritize entity gaps
- How To Run A Winning YouTube Ad Campaign Without A 7-Figure Budget
- How category framing changes which brands AI recommends
- 2027 Marketing Budgets: Why New Categories Beat Bigger AI Line Items
- Why your match rate is the most important number you’re not tracking
- Google’s Liz Reid Discusses Persistent Autonomous Search
- Zevia taps Cardi B to serve up real talk in biggest campaign to date
- 70% Of Top Retailers Are Invisible To Agentic Commerce – Here’s Why
