Your current, siloed SEO strategy is obsolete. Relying on separate teams for technical SEO, content, and local optimization is a failing model in an AI-driven search world. Google’s Search Generative Experience (SGE) and other LLM-driven models do not just “rank” your content; they “ingest” and “synthesize” it to form direct answers. Winning in this new era requires a single, unified framework. This new, holistic SEO model merges technical performance (Core Web Vitals), local context (GEO), and answer-first content (Answer Engine Optimization) into a cohesive LLM Optimization strategy. This article explains why this pivot from keyword optimization to intent fulfillment is essential for survival and how to begin implementing it.
Your technical SEO team just spent a month shaving 200ms off your Largest Contentful Paint (LCP). Your content team published five “keyword-optimized” articles. Your local agency is busy managing Google Business Profile reviews across your London offices.
And yet, your visibility in AI-generated answers is zero.
Why? Because these efforts are completely disconnected. You are meticulously optimizing for a search engine that is rapidly being replaced. The age of “10 blue links” is ending. The new battleground is the AI-generated answer box, and it plays by an entirely different set of rules.
Surviving this shift demands a radical pivot. We must stop chasing keywords and start mastering “intent fulfillment.” This requires a holistic strategy where technical performance (CWV), local context (GEO), and answer-first content (AEO) are all optimized for ingestion and validation by Large Language Models (LLMs).
LLMs Don’t “Crawl,” They “Ingest”: Your New Content Mandate
For two decades, SEO has been about “crawling.” We built sites for Googlebot. We used keywords to help it index and rank a document.
That process is now secondary.
LLMs and generative AI experiences like SGE operate on a different principle: ingestion. They do not want to just list your page; they want to consume it. They extract its information, validate its authority, and synthesize its facts into a new, combined answer.
If your content is not built for this ingestion process, it will be ignored.
AI-driven search values your content differently. Success is no longer about keyword density. It is about:
- Structured Data: Schema (like
FAQPage,Article,LocalBusiness,Product) is no longer a “nice to have.” It is the instruction manual you give the LLM. It explicitly tells the AI what your content is, what your business does, and how to use your information correctly. Without it, the AI has to guess. It will not guess. It will use a competitor’s content that is structured. - Clear E-E-A-T Signals: Experience, Expertise, Authoritativeness, and Trustworthiness are the primary validation signals for an LLM. An AI model is trained to identify and prefer sources that demonstrate authority. This means clear, detailed author biographies, a robust “About Us” page, external citations from reputable sources, and transparent contact information. A page with “By Admin” is a page that an LLM will rightly judge as untrustworthy.
- Answer Engine Optimization (AEO): This is the “AEO” pillar. You must stop writing “articles” and start providing “answers.” Your content must be formatted for synthesis. This means using clear, descriptive headings (H2s, H3s) that map to user questions. It means using concise paragraphs, bulleted lists, and tables. If a user asks a question, your page must provide the most direct, well-supported, and easily-extracted answer to that question.
LLM Optimization begins here. You are no longer writing for a user; you are writing to be the source for an AI that is serving the user.
Core Web Vitals and AI: Why Technical Performance is Now a Trust Signal
For years, many marketing directors have viewed Core Web Vitals (CWV) as a separate, technical chore. A box to be ticked by the IT department to keep Google happy.
This is a critical, and now dangerous, misunderstanding.
A slow, janky site (poor LCP, high Cumulative Layout Shift) is, first and foremost, a bad user experience. AI models are trained on massive datasets to associate poor user experience with low-quality, untrustworthy content.
Think of it from the AI’s perspective. Its primary goal is user satisfaction. If it synthesizes an answer and provides a link to your site for more information, and that page takes five seconds to load or shifts around as ads pop in, the user is frustrated. This frustration reflects poorly on the AI, not just your brand.
The AI model infers this. It understands that a site that invests in a stable, fast, and secure user experience (good CWV, HTTPS) is more likely to be a legitimate, authoritative operation. A site that cannot be bothered to fix its technical foundation is probably not a reliable source of information.
Core Web Vitals are no longer just a “Google” metric. They are a foundational trust signal.
A technically sound site is the price of entry to be considered a trusted source for LLMs. A poor CWV score is a high-friction signal. The LLM will simply get its information from a lower-friction, higher-quality source. Your excellent, well-researched content will never even be ingested because your technical foundation failed the first test.
Context is King: How GEO and AEO Create Relevance for LLMs
LLMs thrive on context. A query like “best Sunday roast” or “compliance software” is functionally meaningless on its own.
In the old model, the user would have to refine their search. In the new model, the AI does it for them.
AI models are integrating user data by default. The most important contextual signal is location (GEO). That “best Sunday roast” query, coming from a user in London, is instantly understood as “best Sunday roast near me” or “best Sunday roast in Islington.”
A query for “compliance software” from a device located in the City ofs London is understood as “MiFID II compliance software for UK-based financial firms.”
Your content must be explicitly optimized for this contextual intent. This is where GEO (Local SEO) and AEO (Answer Engine Optimization) converge into a single, powerful tool for LLM Optimization.
Look at your current content.
- Bad Content: A blog post titled “Our 10 Favorite Sunday Roasts.”
- Good Content: A local landing page titled “The Best Sunday Roast in Islington, London.” This page is structured with clear AEO-driven Q&As (“What time is Sunday roast served?”, “Is it kid-friendly?”, “What is the average price?”, “What are the vegetarian options?”). It is marked up with
LocalBusinessandRestaurantschema, has an embedded map, and lists opening hours.
This “Good Content” example is now the perfect, ingestible source for an AI. When a user asks their phone, “Where can I get a good Sunday roast near Angel station that’s good for kids?”, the AI can confidently synthesize an answer directly from your page.
You are no longer just optimizing for a user searching on Google Maps. You are optimizing to be the definitive source that the AI uses to answer that user’s specific, location-aware, and high-intent query.
This Isn’t More Work, It’s Smarter Work: The Compounding Returns of a Holistic SEO
I speak to marketing directors and in-house SEO managers in London every week. The immediate pushback is predictable: “My teams are already at capacity. We cannot manage another ‘optimization’ trend. We are stretched thin managing our current SEO, content, and technical backlogs.”
This reaction is based on a false premise. This is not another trend to add to the pile. It is the unification of your existing, scattered, and inefficient efforts.
Right now, you have three different teams (or agencies) running on three separate treadmills, producing three separate, low-impact assets:
- Tech Team: Fixes a sitewide CLS issue. (Impact: Marginal)
- Content Team: Writes a 1,500-word blog post on a broad keyword. (Impact: Low)
- Local Team: Updates holiday hours on GMB. (Impact: Minimal)
This is a massive waste of resources.
The new, unified model creates a single, high-impact asset. Imagine your team is building a new page for a key commercial service.
- The Process: The Content Strategist, GEO Specialist, and Technical SEO work together from the start.
- The Asset:
- The page structure is pure AEO. It is built as a series of direct answers to the most common user questions (“What is [service]?”, “Who needs [service]?”, “How much does [service] cost in London?”, “What is the process?”).
- The content is enriched with GEO signals. It explicitly mentions the London boroughs or industries it serves. It includes
LocalBusinessschema, client testimonials with locations, and embedded maps. - The page is validated by CWV. The technical team ensures this specific page loads instantly, is perfectly stable, and is flawless on mobile.
The Payoff: This single asset now creates compounding returns. It serves all search masters simultaneously.
- It ranks in traditional search for its target keywords.
- It appears in local search and map packs for its GEO-specific terms.
- It is now the perfect, ingestible, validated source for an LLM to use in an AI-generated answer.
You have stopped bailing water with three different buckets. You have unified your team to build a single, faster boat. This isn’t more work; it’s smarter, more focused work.
Stop Auditing in Silos: Your First Step to a Real AI Search Strategy
Your current reports are lying to you.
A “green” Core Web Vitals score means nothing if your content is unstructured mush that an AI cannot ingest. A high-ranking blog post is a vanity metric if an AI bypasses it entirely by providing a direct answer sourced from a competitor.
The fundamental problem is that you are measuring the components, not the system. You are admiring the individual bricks while your house is being redesigned by someone else.
The first step is to get an honest baseline. You must stop commissioning a “Technical Audit,” a “Content Audit,” and a “Local SEO Audit” as if they are unrelated. You must see how these elements perform together, in the context of your main competitors.
Stop auditing your site in silos. At OnDigital, we have moved beyond these fragmented, outdated reports. It is time for a unified “AI Readiness Audit” that benchmarks your CWV, GEO,AEO, and LLM signals against your top competitors.