From Queries to Queriers: How AI Search Rewrites Discovery

1. Introduction — Why the Rules of Search are Changing

Search used to be about what people typed into a box. Organizations competed for visibility by aligning with those words – optimizing keywords, polishing metadata, and climbing rankings. That era isn’t gone, but it is no longer the full story.

Today, discovery is being reshaped by AI. Instead of just answering queries, AI interprets the individual – the querier – and responds with tailored recommendations. This shift changes not only how people find information, but also what organizations must do to remain discoverable.

At Aviaticus, we have supported enterprises in shaping large-scale digital content strategies. More recently, I’ve been reflecting on how content strategy intersects with the future of search – and, in particular, how organizations can remain competitive as traditional search gives way to AI-driven discovery.

This paper is written for marketing, experience, content, technology, and data leaders. Together, these groups shape how organizations engage with customers and are best positioned to adapt to the AI-enabled evolution of consumer expectations.

While the scale of change is profound, the steps to adapt are often evolutionary rather than revolutionary. The first step is to evaluate existing tools and processes to ensure their full potential is being realized, then determine any gaps and map them against a maturity and capability roadmap. This structured view helps organizations understand where incremental adjustments, such as rethinking roles, sharpening accountability, or reconfiguring workflows, can unlock outsized value. Business capability modeling provides a proven, unbiased way to navigate this journey without being driven by tool preference, instead focusing on the capabilities that matter most.

2. Two Eras of Search — From Queries to Queriers

For decades, organizations have optimized for queries – figuring out what keywords people type into traditional search engines and shaping content accordingly. Now, the paradigm is shifting: discovery is about optimizing for the querier – the individual, their preferences, and their context.

• Traditional Search Engines (Google, Bing, etc.):
  • Deliver ranked lists of links known as SERPs (Search Engine Results Pages).
  • Visibility depends on SEO (Search Engine Optimization), which weighs four major factors:
    • Relevance: How closely the content matches the search query.
      Example: A page titled “EV-friendly hotels in Miami” is more relevant than a generic “Miami hotels” page when someone searches for “hotels with EV charging.”
    • Backlinks: Links from other sites pointing back to yours, treated as “votes of confidence.”
      Example: A review in The New York Times linking to your restaurant carries far more weight than a small blog link.
    • Freshness: How recently the content was published or updated.
      Example: A 2025 article on the iPhone 17 outranks a 2023 review of the iPhone 15.
    • Domain Authority: The overall credibility of a site, based on age, trustworthiness, and backlink profile.
      Example: Hilton.com will usually outrank a brand-new travel blog when people search for “honeymoon hotels in Miami.”
  • Organizations can also increase visibility through SEM (Search Engine Marketing) – paid advertising on top of organic results.

• AI Search Engines (ChatGPT, Gemini, Copilot, Perplexity):
  • Deliver synthesized, conversational answers.
  • Strengths: interpret nuance, use descriptive signals like ambiance or atmosphere, and refine results in dialogue.
  • Weaknesses: can be outdated or inaccurate if not connected to the live web.

Analogy: Traditional search engines are the librarians with the freshest stack of books. AI search is the consultant who reads them and tailors the summary for you.

Not all AI search engines work the same way. Broadly, they fall into two categories:

• Model-First AI Search: Relies primarily on the AI model’s pre-training – i.e., the knowledge it absorbed from large datasets up until its last training date.
  • Pros: Can generate fluent, conversational answers even to complex or open-ended questions.
  • Cons: May be outdated, since the model only “knows” what it was trained on.
  • Example: A model trained on data up to 2024 might answer your query about “best EV chargers in 2025” with stale information.
• Retrieval-First AI Search: Pulls in live data first (from search indexes, websites, or structured feeds) and then uses AI to synthesize the answer.
  • Pros: More accurate, up-to-date, and source-driven; usually provides citations.
  • Cons: Dependent on what content is made available through websites, structured feeds, or APIs – and on how well that content is organized and exposed.
  • Example: Perplexity and Bing Copilot typically retrieve current web results and then generate a summary.

Key distinction: Model-first AI is like asking a consultant to recall what they’ve read in the past. Retrieval-first AI is like asking the consultant to first check the latest reports, then interpret them for you.

3. Why Content Capabilities Matter More than Ever

In traditional search, visibility depended on keywords and metadata. In AI search, discoverability depends on how richly you can describe experiences and differentiating attributes – the details that help AI understand why your product, service, or brand is the right match.

These attributes can take many forms:

• Hospitality: Ambiance, design, scents, atmosphere, unique amenities.
• Retail: Materials, sourcing, fit, sustainability, packaging design.
• Automotive: Driving feel, cabin acoustics, upholstery, charging options.
• Healthcare: Patient experience, ease of access, personalized care elements.
• Financial services: Transparency, speed, digital experience, relationship approach.

Discoverability, however, is not just about how brands describe themselves – it also depends on how much the AI system knows about the querier. The more context it has, the more relevant and personal its recommendations become. In a sense, each of us is building a digital twin: a profile enriched over time with our preferences, values, and experiences.

Example: If you simply search “hotels in Miami,” the results will be generic. But if the AI knows that you are planning a honeymoon, you like salt water pools, travel in autumn, drive an EV, prefer lively atmospheres and that you like wood-based scents, it can recommend not just any hotel, but the hotel that feels designed for you.

This digital twin is not the same as a CDP (Customer Data Platform). A CDP aligns customer interaction history to a single ID across systems, enabling personalized marketing and customer lifecycle management. The digital twin in search is different: it’s about personal context and preference modeling so that discovery feels intuitive and even anticipatory. Over time, these digital reflections of self will increasingly determine which brands are surfaced and which are ignored.

Meeting these expectations requires rethinking content not as fixed assets but as adaptable building blocks that can be assembled and personalized in real time.

This is the essence of “atomic content”: breaking assets down into their smallest usable components. A campaign image, for instance, becomes a set of parts: background, product, tagline, metadata. Structured this way, content can be categorized, recombined, and deployed across channels, from websites to conversational AI, with systems assembling the right variation in response to customer intent. Without this modularity, brands risk being invisible in the very moments when customers are most ready to engage.

The governance and processes that once enabled SEO success are now even more critical in the age of AI search because gaps or inconsistencies no longer just lower your ranking – they may remove you from the conversation entirely.

4. Omnichannel Content – from Silos to a Single Source of Truth

Many organizations still manage content in silos with different teams in charge of website, app, social, or e-commerce. This channel-specific approach may have worked in the past, but it will not scale in the AI era.

Instead, organizations need a single source of truth that is channel agnostic, meaning the same structured content can be consistently reused across channels, with only minimal formatting adjustments rather than creating separate versions that risk inconsistency.

That source of truth typically includes:

• PXM (Product/Experience Information Management): Core product and experience attributes (evolution of PIM – Product Information Management).
• DAM (Digital Asset Management): Images, videos, and other brand assets tagged with metadata (think marketing collateral).
• MAM (Media Asset Management): Rich media such as video and audio libraries (think production-heavy content).
• Taxonomy/Ontology: Controlled vocabulary to ensure consistency.
• Virtual Experiences: 3D, AR, VR content.
• Translation & Localization Management: Ensures consistency across languages and regions.
• Personalization Engines / Dynamic Content Systems: Adapt and tailor experiences for different segments or individuals.

The publishing layer (often a CMS or equivalent system) is no longer the master record. Its role is to format and distribute centrally governed content into channels – websites, apps, social platforms, OTAs (Online Travel Agencies), and increasingly, AI feeds – without altering the underlying source of truth.

Bridging point: Historically, websites served as the “master” for Google and other search engines, but in the AI era the true master should be the omnichannel content platform. The website – and by extension Google indexing – becomes one of several distribution channels, not the central repository of record.

Emerging trend: In the near future, AI platforms may pull content directly from omnichannel systems (via APIs or ingestion portals), bypassing websites as the middleman. This makes upstream governance even more critical.

5. Keywords, Metadata, and Schema – Speaking the Language of Machines

Why highlight these terms at all? Because they are the connective tissue between human expression and machine interpretation. Traditional SEO was already built on them, but in the AI era they become even more critical. Without them, AI search cannot “understand” or surface your content no matter how compelling it looks to a human reader.

The Three Building Blocks

• Keywords: The words and phrases people see on your site.
  • Example: A hotel website that says “romantic beachfront resort with EV charging.” Those visible words help both humans and search engines connect queries to content.
• Metadata: Data about data – hidden, structured attributes that add meaning.
  • Example: The word “pool” on a site could mean a swimming pool, a billiards table, or even a data pool. Metadata removes the ambiguity: “amenity=swimming_pool” or “activity=billiards.”
  • Another example: A dish called “risotto” could be tagged with metadata “diet=vegan,” “allergen=gluten_free,” which helps AI recommend it correctly to the right audience.
• Schema/Markup: Code embedded in a webpage that exposes metadata to search engines in a structured way.
  • Analogy: Think of schema as the “nutrition label” for machines – it tells Google or an AI search engine exactly what’s inside, so it doesn’t have to guess from the packaging.
  • Example: On a restaurant’s menu page, schema might flag an item as “dish=wild mushroom risotto,” “diet=vegan,” “allergen=none.” AI then knows precisely how to answer “show me vegan-friendly Italian restaurants.”

Completeness vs. Gaps

The gap isn’t usually technical – it’s organizational. Creative and product teams often work in parallel, and unless metadata keeps pace with copy, machines cannot make the right connections.

• Ideal state: Every important descriptor used in copy is also captured in metadata and exposed via schema.
• Risk state: Copy includes descriptors not reflected in metadata.

Lesson: It’s not enough for creative teams to write rich copy. Those descriptors must be captured in structured metadata and schema so machines can recognize and use them consistently.

6. Governance & Roles – Making Humans and Machines Align

Section 4 showed how keywords, metadata, and schema are the building blocks of discoverability. But building blocks only matter if they’re applied consistently across an organization. This is where governance comes in.

Who Owns What

• Product/Data Teams: Define and manage taxonomy, metadata, and the PXM system.
• Creative/Marketing Teams: Write copy tied to that taxonomy (e.g., “romantic” in text always maps to “ambiance=romantic” in metadata.)
• SEO/Content Ops/Digital Experience Teams: Act as the bridge making sure metadata is applied, schema is implemented, and audits are conducted.

Why Governance Matters

• Example: If a restaurant’s website says “romantic rooftop dining” but the metadata doesn’t include “ambiance=romantic” or “location=rooftop,” AI may never surface it.
• Example: If creative writers prefer words like “intimate” or “cozy,” governance ensures they all connect back to the same taxonomy value (“ambiance=romantic”).

The Role of Relationships (Graph Thinking)

Attributes don’t exist in isolation, they derive power from how they connect. Governance should ensure that descriptors map into a graph of meaning, where relationships (e.g., “ambiance=romantic” linked to “location=rooftop”) make search results richer and more precise.

Keeping Everyone Honest

Governance isn’t a one-time effort; it’s an ongoing discipline. Best practices include:

• Structured audits of metadata and schema to ensure descriptors are exposed properly.
• Content consistency checks to confirm creative copy uses terms that tie back to taxonomy.
• Visibility tests to evaluate whether the brand surfaces in both traditional and AI-driven search.

Technical investments only succeed if mirrored by organizational readiness. This means clarifying ownership, training teams, and ensuring that incentives and KBOs (Key Business Objectives) are aligned with the new way of working. Without this alignment, even the best systems will underperform.

Lesson: Governance was important in the SEO era. In the AI era, it is significantly more critical.

7. Today vs Tomorrow – Visibility vs Discoverability

Today (Traditional Search Readiness):

• Success is about visibility in SERPs, ranking higher than competitors.
• Organizations invest in keywords, metadata, and schema markup.
• SEM campaigns can buy visibility when organic efforts lag.
• The publishing layer serves as the hub into web and social channels.

Tomorrow (AI Search Readiness):

• Success will be about discoverability in conversations, being the option an AI recommends when asked, “What’s right for me?”
• Requires omnichannel content platform as the single source of truth.
• Rich experiential descriptors and contextual details so AI can match offerings to personal context.
• Direct connections to AI ingestion portals, bypassing websites as the sole gateway.
• Strong governance processes to ensure creative and product teams speak the same structured language.
• New success metrics that move beyond visibility to capture:
  • Intent Coverage: How well does available content align with how customers describe their needs?
  • Accuracy of Representation: Are customer-shared experiences consistent with what the brand communicates?
  • Contextual Memory & Personalization: Do AI interactions leverage a customer’s “digital twin” (interaction history, preferences, behaviors)?
  • Engagement Depth: For how long can AI-powered assistants meaningfully engage before running out of relevant, high-quality information?
  • Trust and Relevance: Are responses perceived as credible and personalized in a way that builds confidence?
• These new metrics shift the focus from being seen to being truly understood.

Key shift: Traditional search is about climbing the rankings. AI search is about becoming the trusted recommendation.

8. Conclusion – From Ranking to Trusted Recommendation

Traditional search has always been about optimizing for queries. AI search flips the lens: it is about optimizing for the querier – understanding the individual, their preferences, and even their unspoken needs.

That shift requires more than traditional SEO practices focused on technical optimization. It requires organizations to:

• Move from describing products → to describing experiences with attributes, atmosphere, and emotional markers.
• Move from channel-specific content → to channel-agnostic omnichannel management.
• Move from ad-hoc publishing → to taxonomy-first governance and connected meaning.

These practices have always mattered in SEO. In the AI era, they are significantly more critical. Organizations that treat governance and content structure as strategic assets will not only appear in results – they will become the answers AI systems recommend.

At first glance, this shift can feel complex and even overwhelming, but it doesn’t need to be. The path forward can be approached in a pragmatic, structured way – starting small, focusing on readiness, and building momentum over time. That’s why the first step is knowing where to start.

Where to Start

1. Evaluate current people, processes, and technology readiness.
2. Using business capability modeling, identify gaps and map them on a capability roadmap.
3. Prioritize organizational and technical enablers that unlock quick wins.
4. Build a starter strategy with first actionable steps.

At Aviaticus, we work with leaders to make this transformation possible, helping them move from vision to actionable steps. The question isn’t whether AI search will change discovery. It’s whether your brand will be ready to be found, trusted, and recommended in this new world.

Martin Stolfa

September 2025

Supporting Notes (Transparency & References)

Author’s Note on Process

This article was developed iteratively with AI support. I defined the perspective, questions, and direction, while AI helped structure and refine the narrative. In this way, AI served as a tool I actively guided – much like an editor or research assistant – to accelerate clarity and precision. The core thinking, insights, and message are my own.

Glossary of Key Terms & Abbreviations

• AI Search: Artificial Intelligence-driven search that synthesizes answers (e.g., ChatGPT, Gemini).
• Backlinks: Incoming links from other sites that point to your content, serving as “votes of trust.”
• CDP (Customer Data Platform): Unifies customer interaction history under a single ID across systems.
• DAM (Digital Asset Management): Organizes and tags images, videos, and brand assets.
• Domain Authority: A measure of a website’s overall credibility, based on age, trust, and backlinks.
• Freshness: A ranking factor measuring how recent or frequently updated content is.
• MAM (Media Asset Management): Organizes rich media like video and audio.
• Metadata: Data about data; machine-readable attributes that clarify meaning (e.g., “pool=swimming_pool”).
• OTA (Online Travel Agency): Third-party booking platforms like Expedia or Booking.com.
• PIM (Product Information Management): Manages detailed product data (now evolving into PXM).
• PXM (Product/Experience Information Management): Manages product plus experiential attributes.
• Publishing Layer: The system that formats and distributes centrally governed content into channels.
• Relevance: How well content matches a query.
• Schema/Markup: Code that exposes metadata in structured form for search engines.
• SEM (Search Engine Marketing): Paid search engine advertising.
• SEO (Search Engine Optimization): Practices that improve ranking in traditional search engines.
• SERP (Search Engine Results Page): The ranked list of results a search engine provides.