AI is moving the search to the behavior ranking and I foresee that content satisfaction rate will become dominant in the near future. Algorithms now follow the interaction of the user, after clicking. Google interprets that as value being achieved in case 70% of visitors remain over 90 seconds and 40% of them are clicking an internal hyperlink. I have observed this myself in the case of my own guides. Bounce rates decreased by 25% and rankings had improved within weeks without requiring any additional back links. That demonstrated the fact that AI systems reward matching content to the actual human intentions. I have been spending 17 years optimizing against all the signals that one can use, metadata, schema, but nothing is better than usefulness. AI can now read context as humans do and it can know when a page provides a better, faster and clearer answer to the question. The new metric will not be in terms of keywords, but comprehension. It does not need to be about tricking the system anymore to achieve gain in SEO, but instead about teaching it with value.
The next major ranking factor will be content authenticity and author credibility. As AI-generated material floods the web, search engines will rely more on verifiable human signals such as author profiles, brand mentions, and trust indicators like citations and expert verification. In testing across several client sites, pages tied to verified author entities and consistent topical expertise have shown 20 to 30 percent higher visibility in AI-driven search results. The future of SEO will depend less on keyword mechanics and more on demonstrating genuine expertise, authority, and trustworthiness across every piece of published content.
A year ago, we noticed a clear shift in how search engines treated our content. Long, theoretical guides that once ranked well started slipping. At the same time, short case studies, even ones with fewer words, were climbing to the top. That change pushed us to rethink our entire content approach. Here's one example. We worked with a SaaS client who used our platform to consolidate their AI model usage. By comparing usage across models and setting cost caps, they cut their monthly AI spend by 42% without losing output quality. Instead of writing a 3,000-word article about "how to reduce AI costs," we wrote a 900-word piece that showed exactly what they did, step by step, with screenshots of their before-and-after billing dashboard. The result was immediate. That case study outranked several top-ranking generic guides within three weeks. It brought in 2.4x more organic traffic and kept readers on the page 52% longer. More importantly, we saw visitors sharing it on LinkedIn and quoting the numbers in their own blog posts, something that never happened with our older, theory-heavy articles. The lesson is simple: AI-powered ranking systems seem to care more about verifiable evidence than about how much you write. Showing real outcomes is now a stronger signal than repeating best practices. My advice would be: stop telling people how something should work, show them how it did work, with real data to back it up. Best regards, Dario Ferrai Co-founder at All-in-One-AI.co (a platform where users can access all premium AI models under one subscription) Website: https://all-in-one-ai.co/ LinkedIn: https://www.linkedin.com/in/dario-ferrai/ Headshot:https://drive.google.com/file/d/1i3z0ZO9TCzMzXynyc37XF4ABoAuWLgnA/view?usp=sharing Bio: I'm a co-founder at all-in-one-AI.co. I build AI tooling and infrastructure with security-first development workflows and scaling LLM workload deployments.
I can say this without hesitation, written content that sounds natural, that has verified facts and citations, that presents information in a clear, easy to understand format will end up ranking better over a period of time. Even with AI-generated content flooding the web, information that not only sounds authoritative, but presents information or opinion with links and references to peer-reviewed human content or expert insights will continue to make it's way to the top of rankings. Content that is created with expert insight that is original and is not regurgitated from other sources via AI will outperform other types of content over the long run. Google still has processes for evaluating user satisfaction with search results, and this is a critical factor that is difficult to measure with a SEO tool, but becomes apparent when humans evaluate the rankings. Authority sites with inbound links from trusted sources and old-school seed sites will also continue to outperform sites that lack these crucial editorially placed links.
The next ranking factor that will emerge is likely to be interaction signals in AI-enhanced environments: how users interact with your content once it is revealed to them by the AI assistant, chatbot, or via a voice search interface. As search moves towards conversational and generative formats, engines will prioritize content that produces meaningful engagement—clicks in AI-generated summaries, additional follow-up questions, prolonged dwell time, and, potentially, inferred user sentiment through time-on-device as well as interactive engagement. This means optimization will happen in ecosystems that extend way beyond traditional SERPs. While content creators have constructed answers in a structured way, they will pay attention to common questions related to the topic and add contextual cues that will help AI systems make connections and reveal the work in multi-turn conversations. Think of this as conversational SEO: no longer just ranking for a keyword but being the first resource when an AI assistant describes, elaborates, and/or recommends. In this new world of AI-led discovery, there is a shift away from traditional writing, where the best content will be prioritized based on quality of writing and representation of key terms.
Not only do I think this, but our early results also bolster the fact that page usefulness will become a major factor. What makes AI Overview and AI Search results so great is that they can offer benefits and drawbacks and answers to complex questions, making suggestions as part of the response. This means website owners to to prirotise appealing to the AI first and the human second - giving the AI useful content such as customised FAQ snippets, clear call-to-actions and a clear broken-down of pricing in order for the AI-driven algorithms to feature in their recommendation for the user. Essentially, give all of the information that the AI needs to give an informed response and you will be rewarded
Data Scientist, Digital Marketing & Leadership Consultant for Startups at Consorte Marketing
Answered 8 days ago
I think the next major ranking factor will center on two things: authentic authority and truly content based on unique datasets. As AI-generated text floods the web, search systems are already learning to detect not just factual accuracy, but evidence of human intention: unique perspectives, original data, and verifiable author identity. Google E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) was sufficient before the age of generative AI. Now, search engines will favor content where the creator's expertise and process are evident and truly unique. Weight will be given to content that's based on new data and linked to real people and notable organizations. In practice, that may include signals like first-party data, on-page interactions, consistent author identity across platforms, and even digital signatures.
I believe we're going full circle and meta data will matter again. As AI-driven search models parse the web, they start reading pages more like humans but still rely on structured cues to make sense of content fast. Meta titles, descriptions, and schema aren't just technical hygiene anymore; they're context markers for AI. The model interprets them before it even touches the full page, shaping how it understands topical authority and intent. I think brands that start crafting meta data with clarity, precision, and semantic consistency will have a serious edge. It's like the headline of a story, if it's off, no one reads further.
Prediction. The next major signal is verified experience & resolution. Pages that show first-hand work and help the user finish the task will rise. Real authors, real videos, original photos or test logs, clear steps, and one obvious next action. Backlinks still matter as corroboration, and internal links should point equity to pages that actually resolve the task. How search reads it. Engines watch what happens after the click. If a visit ends the search, that's resolution: fewer repeat queries, a longer last click, and more next-step completions like downloads, add-to-carts, or support avoidance. If AI answers keep choosing your page and users don't bounce back, that's a strong utility signal. Balance. No world-class system leans on one factor. Resolution sits with content quality, link corroboration, technical UX, and real freshness. Build for the bundle.
The next large signal is verifiable source authority built into the AI answers because they are weighted to their identity, originality and evidence of same. Source signals of provenance, first party datasets available through known endpoints, strict schema on entities and authors and consistent citations through assistants can achieve inclusion and stay ranked with tweaks to models. In my experience a rollout of C2PA styled provenance, markup of authorship history and retrieval friendly page sections upped the satisfaction of assistant inclusion results by 38.4 percent and branded discovery increased 24.1 percent in 8 weeks. Execution is easy and repeatable for teams. Ship signed sitemaps, source credentials at the content level, lightweight liquids for facts and pricing, authority and trust signals in experience and expertise by transparent bio's and revision postings and check for presence of answers in and completion of tasks in assistants for these are their feeding authority loop. Brands that feed models sequenced truth with evidence of it are able to outrun generic publishers even in saturated content categories.
User interaction depth will be the next important ranking measure as opposed to the conventional engagement measures such as bounce rate or time on page. The search engines are moving towards measuring the completeness with which customers are consuming and taking action on content with metrics such as scroll velocity, returning to the same page, cross session behavior patterns that reflect actual value extraction as opposed to just passive browsing. Google is already experimenting with systems that compensate content generating quantifiable downstream behavior like users bookmarking resources, sharing certain passages or revisiting reference material several times in weeks. We redesigned a client site and added progress tracking features and interactive aspects that led to the encouragement of multi session interaction, and in this case, the rankings were already improving on 140 keywords without any adjustments being made to the backlinks, or other conventional elements of SEO.
Based on my work in AI SEO, I see conversational context retention quickly becoming an essential optimization signal. Between the alternatives we considered, content that sustains multi-turn engagement tends to perform best in AI-powered search environments, as I've seen with several Xponent21 campaigns. If you want to prepare, start focusing on building web pages and assets that don't just answer a question, but make it easy for AI to extend the conversation naturally.
With the emergence of AI, I see how user engagement and authenticity of content will definitely be the next big ranking signals. Search engines are increasingly better able to interpret behavioural data-on factors like dwell time, content satisfaction, and sentiment. Given that generative contents are flooding the web, AI-driven algorithms will give prominence to trust, author credibility, and human insight rather than plain keyword relevance. Simply put, in the future, SEO will not be about writing for the algorithms, but about proving that a content actually helps somebody in some way.
Working with plastic surgeons and their marketers, I've noticed something. I bet Google will soon start rewarding how different topics connect. That's why we stopped just chasing keywords. Now we build content clusters that tie a patient's questions about a procedure to the surgeon's specific technique and recovery stories. It's already getting our clients more attention now that search values real expertise.
The next major factor will be understanding and responding to the user intent and content. Instead of traditional keyword optimization, put your energy into spotting long-tail keywords and attempt to address their queries in the easiest way possible. Provide advice they can follow, resolve pain points they are suffering from, and divide your content into a funnel for faster conversions, trustworthiness, and return traffic. Another factor contributing will relate to how the content is structured and what value it adds to the existing content.
Here's what I'm seeing in my work. The next big shift isn't just about having the right answer, it's about how fast you can deliver the full answer. Platforms are starting to push content that handles everything in one go, which is exactly the approach we take at Search Party. To keep up, we need to start writing pages that answer the main question plus all the follow-ups, so nobody has to click away.
Working at FATJOE, I've seen search engines get obsessed with giving users what they actually need, not just what they type. Now with AI, that predictive intent is everything. We built targeted resource hubs instead of just chasing keywords, and our rankings and conversions jumped. So for anyone doing SEO, stop guessing. Go deeper on your topics and figure out what they'll ask next.
My prediction for the next big thing in search signals is "AI Citations" combined with genuine Entity Authority. With AI-based overviews and chat features taking centre stage, we can expect search rankings to start shifting from just page links to who's content AI models are confidently citing as a source. So, we're going to need to focus our efforts on unlinked brand mentions and this time, we're not just talking about a handful of strategically placed links. We need to create clear, concise, factual content that can easily get picked up and summarised by AI algorithms. And to top it off, your brand has to be viewed as a trusted source by search engines; a source that's got real-world Experience, actual Expertise, and a proven track record of Trust (E-E-A-T) so that AI sees you as the go-to place when it needs some info.
Search engines are getting smarter. They're not just rewarding translated content anymore. When we write articles for client sites in the actual native language, not just translated, we see better rankings and people stick around longer. Here's my take: if you want real organic growth, you need a genuine native language strategy. The automated tools can't compete with the authenticity that search engines are starting to value.
Local SEO is changing. Just getting backlinks isn't enough anymore. We're seeing Google reward sites that connect their related service pages, showing they actually know their stuff. We started linking our services more thoughtfully and have already seen ranking improvements in some tough local markets. It's worth taking another look at your own internal linking now.