How LLMs Are Changing Search Behaviour

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How LLMs Are Changing Search Behaviour

How LLMs Are Changing Search Behaviour – And What This Means for Insurers

Introduction: A New Era of Search Begins

For nearly two decades, search behaviour on the internet has been the backbone of how consumers find information, compare products and make digital decisions. Traditional search—dominated primarily by Google—was built on keywords, backlinks, and ranking signals. It rewarded websites that understood technical SEO and could align their content to rigid ranking rules.

But this era is ending.
A dramatic transformation in search behaviour is underway—and it is happening faster than any previous shift in digital marketing.

The rise of Large Language Models (LLMs) like ChatGPT, Perplexity, Gemini, Claude, and others has fundamentally changed how people look for information online. Instead of “searching” with lists of blue links, users now ask, interact, discuss, and refine queries in natural language. The search journey is becoming conversational, contextual, and intent-driven.

This change is not a minor SEO update—it is a restructuring of the internet itself.

Most importantly, insurers and insurance brokers are among the industries most affected. Insurance is complex, trust-dependent, and highly information-heavy. When people ask questions about risks, health, travel, family or claims, they now expect personalised answers—not a list of websites to read.

This is exactly why LLMs are changing search in ways the insurance industry cannot ignore.

In this 3500+ word research blog for Digital 360, we explore:

  • How LLMs transform search behaviour

  • Why insurers must rethink their content and digital visibility

  • Key findings from new research referenced by Luisa Schmolke (ERGO Innovation Lab)

  • What LLM-first content architecture looks like

  • How brokers outperform insurers in LLM search ecosystems

  • How insurers can build API-driven, machine-readable content

  • The future: LLMs as digital agents replacing search engines

  • A step-by-step Digital 360 blueprint for insurance companies

SECTION 1: Why Search Behaviour Is Suddenly Evolving

The shift from “keywords” to “conversations”

For decades, users searched like machines because search engines were machines.
People typed:

  • “family travel insurance Germany comparison”
  • “term insurance benefits 2025”
  • “health insurance pre-existing condition rules”

But in an LLM world, the same users now type:

  • “Which family travel insurance is best for a 4-year-old child?”
  • “Explain term insurance in simple words.”
  • “Compare the top insurers in Germany and tell me which is safest.”

This shift is radical.

Why LLMs Are Changing Search

LLMs analyse:

  • Context
  • Intent
  • User persona
  • Past follow-up questions
  • Sentiment
  • Topic clarity
  • Knowledge depth

Instead of generating a link list, they generate human-like answers such as:

“For families with young children, look for travel insurance with overseas medical cover, child-friendly benefits, emergency return cover, and family-package discounts. Here are the top three options…”

The entire discovery cycle—research → comparison → clarification → decision—happens inside the LLM.

This affects every industry, but for insurers it hits the hardest.

Insurance is:

  • Complicated
  • Regulation-heavy
  • Full of legal terminology
  • Trust-based
  • Requires explanation

LLMs simplify, decode and personalise this complexity better than search engines ever could.

SECTION 2: How LLM-Based Search Platforms Work

Traditional search engines rely on:

  • Keyword matching
  • Backlinks
  • Domain authority
  • Meta tags
  • Page loading speed
  • E-E-A-T signals

LLMs use an entirely different architecture:

1. Semantic Understanding

LLMs interpret meaning, not just keywords.

2. Conversational Context

They remember previous queries and adjust answers.

3. Intent Detection

They understand what the user is trying to do, not just what they typed.

4. Multi-step reasoning

They predict the user’s next question and respond proactively.

5. Answer Generation

They do not refer to content—they synthesize it.

6. Self-curation of sources

LLMs choose which websites deserve to be referenced.

This is why LLMs are changing search so fundamentally:
they replace link lists with reasoning-driven “expert” answers.

SECTION 3: Why This Change Matters Most to Insurers

Insurance decision-making involves:

  • Comparing features
  • Understanding exclusions
  • Clarifying claims processes
  • Evaluating value vs premium
  • Minimum knowledge of legal terms

Traditional search forced users to visit multiple websites.
With LLMs, one dialogue is enough.

Examples of LLM-driven search for insurance

A user searching for:

“Which health insurance in Germany covers pre-existing diabetes?”

The LLM provides:

  • Definitions
  • Comparison
  • Recommendations
  • Eligibility conditions
  • Key exclusions
  • Price insights
  • Why certain plans are better

This means:

1. Fewer clicks to insurer websites

2. Higher influence from LLM-generated summaries

3. Insurance brands must optimise for visibility in LLMs—not just Google

LLMs are changing search so drastically that insurers who don’t adjust their content now risk losing digital relevance.

SECTION 4: Key Insights from ERGO Innovation Lab + Ecodynamics Study

Luisa Schmolke’s research outlines four critical findings:

Finding 1: Content Must Be Machine-Readable

LLMs prefer:

  • Clean HTML
  • Structured data
  • JSON-LD markup
  • Clear heading hierarchy
  • Mobile-optimised content
  • Fast loading

If content is not technically accessible:

LLMs cannot interpret it.

Finding 2: Semantic Coherence Is Essential

LLMs reward content that is:

  • Well-organised
  • Logically structured
  • Thematically consistent
  • Easy to map into a knowledge graph

If your insurance page has scattered information across tabs, PDFs, and FAQs…

It will lose visibility in LLM search.

Finding 3: Trusted Sources Get Priority

LLMs give stronger weight to:

  • Author credibility
  • Brand reputation
  • Verified citations
  • Accurate regulatory information
  • Research-backed content

Insurance is a trust-based industry.
LLMs know this—and prefer credible insurers.

Finding 4: Formats Matter (Dialogue > Articles)

LLMs understand and rank:

  • Q&A blocks
  • Decision trees
  • Step-by-step guides
  • Comparisons
  • Tables
  • Modular chunks

Because they are trained on conversational structures, these formats align naturally with LLM logic.

SECTION 5: Why Brokers Outperform Insurers in LLM Search

Broker platforms are winning. Insurers are not.
The study found:

  • Broker LLM visibility: 36%
  • Insurer LLM visibility: 17%

Why is this happening?

1. Brokers use simpler language

Insurers often use legalistic or corporate language.

2. Brokers provide modular comparison formats

Perfect for LLM ingestion.

3. Brokers use structured decision trees

LLMs love these.

4. Insurer content is too brand-centric

It’s not structured for LLMs.

5. Brokers follow strong content architecture

Insurers follow traditional web content models.

This proves one undeniable fact:

The insurance industry must rebuild its content for an LLM-first world.

SECTION 6: Different LLMs, Different Rules (No Universal SEO)

Just like Google vs Bing required different SEO tactics, LLM platforms require their own optimisation rules:

ChatGPT & You.com

  • Very high volume of results
  • High hallucination rate (approx. 9.7%)
  • Less strict filters

Perplexity & Gemini

  • Strong editorial filtering
  • Prefer high-trust sources
  • Demand structured formats

There is no one-size-fits-all optimisation strategy.

Brands must optimise based on:

  • Platform
  • Content type
  • Use case
  • Industry
  • API structure
  • Trust level

This is where leading digital agencies like Digital 360 are becoming crucial partners for insurers.

SECTION 7: APIs – The New Visibility Engine for Insurers

In a traditional SEO world, your website competes with other websites.

In an LLM world:

Your API competes with other APIs.

LLMs prefer:

  • Structured data
  • Real-time updates
  • Verified content
  • Reliable metadata

If insurers do not provide API-level access:

LLMs treat their content as static, low-priority pages.

APIs allow insurers to expose:

  • Price updates
  • Policy coverage elements
  • Claims documentation
  • Product comparisons
  • Plan details
  • Regulatory updates

LLMs then ingest and use this data live.

This is why future search visibility will be determined by:

API accessibility + structured data + LLM-friendly content.

SECTION 8: New Areas of Action for Insurers

To succeed in an LLM-driven search ecosystem, insurers must act on the following priorities:

1. Build LLM-Ready Content Architecture

Your content must be:

  • Machine-readable
  • Semantically structured
  • Mobile-first
  • Fast-loading

2. Shift from Long Pages to Modular Content Blocks

LLMs extract better from:

  • FAQs
  • Checklists
  • Decision trees
  • Conversational guides
  • Scenario-based content

3. Add Author Identity and Trust Layers

LLMs reward:

  • Transparent authors
  • Expert reviews
  • Verifiable citations
  • Fact-checked content

4. Integrate APIs Everywhere

Expose:

  • Product details
  • Pricing
  • Coverage metadata
  • Terms & conditions

APIs improve:

  • Discoverability
  • Rankings
  • Reasoning quality
  • Integration into digital agents

5. Create Dialogue-Based Content

Write content the way users ask questions:

  • “Which health insurance should I choose if…”
  • “Explain this exclusion…”
  • “Compare these two policies…”

LLMs use these structures as training-friendly text.

6. Adopt LLM-First SEO Strategies

This includes:

  • Entity optimisation
  • Topic clusters
  • Semantic networks
  • Natural-language structure
  • Data schemas

SECTION 9: The Next Paradigm Shift – LLMs as Digital Agents

Today, LLMs answer queries.

Soon, they will perform tasks:

  • Purchase insurance
  • Submit claims
  • Compare quotes
  • Update profiles
  • Recommend personalised plans

If insurers are not integrated via machine-readable APIs…

They will not be part of the transaction.

LLMs will act as:

  • Insurance guides
  • Advisors
  • Intermediaries
  • Policy selectors

And eventually…

Insurance gatekeepers.

This is the biggest threat—and opportunity—insurers have faced in digital innovation.

SECTION 10: What This Means for Insurers (Digital 360 Strategic Blueprint)

Digital 360 recommends an LLM-first transformation roadmap for insurers:

Phase 1: Technical Transformation

  • Clean up website architecture
  • Optimise loading speed
  • Ensure mobile-first delivery
  • Add JSON-LD schemas
  • Build structured metadata
  • Create a semantic content repository

Phase 2: Content Transformation

Build content for:

  • Humans
  • LLMs
  • APIs

This includes:

✔ Conversational articles
✔ Q&A-driven resources
✔ Modular blocks
✔ Comparison tools
✔ Plain-language guides
✔ Insurance explainers

Phase 3: Trust Building

  • Author identity
  • Verified citations
  • Transparent sources
  • Medical/legal review panels

Phase 4: API Integration

Expose product data, not just publish it.

Phase 5: Platform-Specific LLM Optimisation

Different rules for:

  • ChatGPT
  • Gemini
  • Perplexity
  • Claude
  • You.com

Digital 360 creates platform-specific LLM optimisation systems to secure visibility on each.

Phase 6: Monitoring & Governance

Use:

  • LLM hallucination audits
  • Brand visibility tracking
  • Prompt-level analytics
  • Competitor LLM benchmarking

Conclusion

LLMs are changing search—and insurers must change with them.
The age of keyword-driven SEO is ending.
The age of machine-readable, semantically structured, API-supported, conversational content has begun.

Insurers who adapt now will:

  • Gain visibility
  • Build trust
  • Improve conversions
  • Survive the digital-agent revolution

Those who wait will disappear from user journeys that never reach their website.

Digital 360 helps insurance companies transition into this new LLM-driven future—where search is conversational, real-time, and powered by intelligent agents that shape the customer’s entire discovery-to-decision cycle.

The new rules of visibility are already written.
Now is the time for insurers to act.

20 FAQs “LLMs are Changing Search”

1. How exactly are LLMs changing search behaviour in 2025?

LLMs are changing search by shifting users from keyword-based queries to conversational, intent-driven interactions. Instead of scanning long pages, people now expect precise, natural-language answers generated instantly by AI models like ChatGPT, Gemini, and Perplexity. This eliminates the traditional “10 blue links” search journey and replaces it with AI-curated, context-aware responses. Users can ask follow-up questions, compare products, and explore complex topics within a single dialogue thread. Because LLMs interpret context, meaning, and user intent, search becomes faster, more personalised and more human. This transformation is especially impactful for industries like insurance, where clarity and trust matter most.

2. Why do LLMs matter so much to the insurance industry?

LLMs are changing search for insurers because insurance products are complex, detail-heavy, and require explanation. Customers no longer want to read long policy PDFs; they want simple, direct answers. LLMs translate legal language into easy-to-understand responses and guide users through comparisons, exclusions, benefits, and claim rules in a conversational way. This reduces decision friction and builds trust. People researching health, travel, or life insurance now rely on AI-driven search for clarity and personalised insights, meaning insurers must optimise content for LLM visibility. If insurance information isn’t machine-readable, it will not appear in AI-generated recommendations.

3. How do LLMs choose which insurance websites to reference?

LLMs reference insurance content based on trust signals, semantic structure, clarity, and machine readability. Because LLMs are changing search, the old rules of keyword stuffing or backlinks no longer guarantee visibility. Instead, AI models prefer clean architecture, strong author identity, transparent sources, and well-organised information. Websites with structured data, FAQs, comparison tables, decision trees, and API-based product details are more likely to be included in AI responses. If insurer content is overly branded, fragmented, or written in complex jargon, LLMs deprioritise it. This is why brokers currently rank higher—they use simpler, structured content formats.

4. Why are brokers getting more LLM visibility than insurers?

Brokers outperform insurers because LLMs are changing search to prioritise clarity, structure, and modular content. Broker websites typically offer simple language, clean product comparisons, and decision-tree formats, which align perfectly with LLM training patterns. In contrast, insurers often use corporatised language, long paragraphs, and PDF-heavy content, making it harder for LLMs to extract focused insights. Research shows brokers capture around 36% of LLM visibility compared to insurers’ 17%, not due to better products but better content structure. To compete, insurers must adopt LLM-first content architecture, conversational formats, and machine-readable structures that AI systems understand easily.

5. How are LLMs improving insurance research for customers?

LLMs are changing search by turning complex insurance research into a simplified, conversational process. Instead of browsing multiple sites, customers can ask AI questions like “Which family travel insurance is best for Europe?” and instantly receive tailored explanations based on their needs. LLMs break down exclusions, compare features, interpret legal terms, and guide customers through decision stages naturally. This reduces confusion and increases confidence. Users also appreciate the ability to ask unlimited follow-up questions without starting a new search. For insurers, this means transparency, clarity, and consumer education matter more than ever in digital visibility.

6. What happens if insurers don’t optimise their content for LLMs?

If insurers ignore how LLMs are changing search, they risk disappearing from AI-driven discovery journeys. LLMs favour structured, machine-readable, conversational content. If insurer websites rely on outdated formats, slow mobile performance, jargon-heavy language, or unstructured PDFs, AI models cannot interpret the information correctly. This means brokers, comparison platforms, and third-party sources will dominate LLM-based responses, damaging brand visibility and trust. Over time, LLMs may act as digital agents capable of recommending and even purchasing insurance. Insurers that fail to integrate APIs and semantic content structures will lose competitive advantage and customer attention.

7. How should insurers restructure content for LLM visibility?

Since LLMs are changing search, insurers must adopt LLM-first content architecture. This includes modular FAQs, step-by-step guides, semantic headings, comparison tables, and clean HTML structures. Content should use clear language, explain concepts simply, and address user intent directly. JSON-LD schemas, structured data, and API-based product details help LLMs interpret information accurately. Insurers should create conversational content that mirrors how users ask questions: scenario-based queries, claim examples, and benefit explanations. Trust elements—expert authors, citations, compliance notes—must also be visible. This shift from long text to machine-readable modules is essential for insurance visibility.

8. Why is machine readability important for insurance content?

Machine readability is critical because LLMs are changing search from page ranking to content extraction. LLMs don’t “browse” websites—they interpret structured information. If insurer content lacks semantic HTML, structured data, or proper markup, LLMs cannot identify key elements like coverage details, exclusions, or prices. This affects visibility in AI-driven search results. Machine-readable content ensures LLMs can understand, summarise, and reuse information accurately. In insurance, clarity is essential because products involve legal terminology and complex benefits. Clean structure, standardised schemas, and API-based data ensure insurers remain visible and trustworthy in AI-generated explanations.

9. How do LLMs handle insurance comparisons for users?

LLMs are changing search by enabling dynamic, real-time insurance comparisons. Instead of directing users to comparison websites, LLMs can instantly generate side-by-side analysis of benefits, exclusions, premiums, and eligibility criteria. They synthesise multiple sources, explain differences in simple language, and highlight what matters most for the user’s profile. This eliminates manual research and helps customers make informed decisions quickly. However, LLM accuracy depends on how structured, clear, and accessible insurer data is. Insurers that provide API-based, semantically organised content will appear more often in AI-driven comparisons and recommendations.

10. Are LLM search results completely reliable for insurance decisions?

LLM responses are rapidly improving, but they are not flawless. While LLMs are changing search behaviour positively, they may still hallucinate or oversimplify certain insurance details. Some platforms like ChatGPT have higher hallucination rates, while Perplexity and Gemini apply stronger editorial filtering. Users should treat LLM outputs as guidance, not final decision-making. Insurers must help improve accuracy by providing structured data, transparent product descriptions, and verified citations. Clear machine-readable APIs also reduce misinformation. When insurers optimise for LLMs, the accuracy of AI-generated insurance answers becomes significantly more reliable and customer-friendly.

11. What formats do LLMs prefer when analysing insurance content?

LLMs are changing search by prioritising formats that align with conversational data. They prefer modular structures such as Q&A blocks, decision trees, comparison tables, premium breakdowns, claim steps, risk scenarios, glossary terms, and structured guides. These formats help LLMs extract meaning efficiently. They also favour semantic HTML, JSON-LD schemas, and text with clear topic segmentation. Traditional long-form articles, dense paragraphs, and PDF-only documents are harder for AI models to interpret. Insurers must shift from narrative-heavy content to machine-friendly formats that mirror how users ask questions in real-world insurance contexts.

12. How do APIs help insurers appear in LLM search responses?

APIs are becoming essential now that LLMs are changing search behaviour. LLMs rely heavily on structured, up-to-date data to generate accurate insurance recommendations. APIs allow insurers to expose coverage details, premiums, policy descriptions, claim procedures, and regulatory changes directly to AI platforms. This turns insurer websites into machine-consumable data sources. Without APIs, LLMs may use outdated or third-party information, reducing accuracy and visibility. API-driven content also supports automated interactions where LLMs act as digital agents capable of comparing and even initiating insurance tasks. Insurers that invest in APIs gain long-term visibility and control.

13. Will LLMs replace traditional insurance search engines entirely?

While traditional search won’t disappear immediately, LLMs are changing search so dramatically that they will eventually become the primary discovery tool for insurance. AI-generated answers reduce the need to browse multiple websites. As digital agents evolve, LLMs may help users purchase plans, submit claims, or manage renewals. Search engines like Google are already integrating AI responses prominently. In the next few years, the majority of insurance research and interactions may occur through conversational interfaces instead of traditional search pages. Insurers must prepare by building LLM-ready content architectures and real-time API integrations.

14. How will LLMs influence insurance purchase decisions?

LLMs are changing search by acting as personalised advisors. They understand user context—age, family size, destination, risk profile—and offer customised suggestions. When a user asks, “Which health insurance is best for my parents in 2025?” the LLM analyses coverage needs, highlights suitable products, and compares options instantly. This guidance accelerates purchase decisions and builds confidence. Insurers with machine-readable, trustworthy content will appear more frequently in these AI-driven recommendations. As LLMs evolve, they may also facilitate transactions, making them powerful intermediaries in the insurance buying journey.

15. How can insurers ensure their content appears in AI-generated answers?

To stay visible as LLMs are changing search, insurers must focus on semantic clarity, structured formats, and trust signals. Content should use simple language, modular blocks, JSON-LD schemas, and consistent topic structures. FAQs, tables, glossaries, and step-by-step guides help LLMs extract insights. Author identity, citations, and compliance notes strengthen trust. API-driven data ensures LLMs receive real-time, accurate information. Insurers must also create platform-specific strategies for ChatGPT, Gemini, Perplexity, and others, as each has different ranking logic. An LLM-first content strategy ensures long-term digital visibility.

16. Do LLMs help users understand insurance jargon better?

Yes. LLMs are changing search by simplifying jargon-heavy insurance concepts. They can translate complex terms—like deductibles, co-payments, underwriting, exclusions, or cashless claims—into everyday language. Users can ask follow-up questions without embarrassment or misinformation. This helps insurers build transparency and reduces decision confusion. For example, an LLM can explain “waiting period” or “pre-existing disease coverage” in a friendly, conversational tone. This improves customer confidence, especially for first-time insurance buyers. Insurers must ensure that their definitions and explanations are structured clearly so LLMs do not misinterpret their meaning.

17. How can insurers reduce the risk of LLM hallucination in search results?

To minimise hallucinations as LLMs are changing search, insurers must provide structured, verified, and machine-readable information. LLMs hallucinate when content is outdated, unclear, or unstructured. Insurers should publish consistent definitions, maintain updated product data, and include clear citations. APIs reduce hallucination by delivering real-time details. Content governance—such as compliance review, expert validation, and metadata standardisation—further improves accuracy. Insurers must also monitor how LLMs describe their products using audits, prompt testing, and visibility tracking. The more structured and credible the content ecosystem, the lower the hallucination rate.

18. What role does trust play in LLM search visibility for insurers?

Trust is a core ranking factor now that LLMs are changing search. Insurance involves financial risk, legal terminology, and customer protection. AI systems therefore prioritise content with transparent authorship, credible citations, regulatory compliance signals, and expert-reviewed information. Insurers must highlight certifications, disclaimers, and professional identities to increase trustworthiness. Google’s E-E-A-T principles now extend to LLMs, meaning insurers with strong expertise and authority appear more often in AI answers. Trust-driven content helps prevent misinformation and ensures insurers—not brokers—shape how their products are interpreted by LLM search engines.

19. How should insurers prepare for LLMs becoming digital agents?

Insurers must plan for a future where LLMs are changing search and becoming active digital agents capable of performing actions like comparing policies, generating quotes, answering claims queries, or even initiating purchases. To prepare, insurers need robust APIs, structured product metadata, machine-readable documentation, and conversational content. User-friendly claim guides, risk-based recommendations, and step-by-step pathways will allow LLMs to act responsibly. Insurers that fail to integrate early risk being excluded from automated insurance interactions. This shift positions AI systems as intermediaries, making digital accessibility a strategic advantage.

20. What steps should insurers take to stay visible in an LLM-first world?

Insurers must adopt a multi-layered approach now that LLMs are changing search. First, they need semantically clean, mobile-optimised sites with strong structured data. Second, they must create modular, dialogic content using FAQs, comparisons, and scenario-based guides. Third, insurers must publish trustworthy, transparent information validated by expert authors. Fourth, APIs should be integrated to provide real-time product data to AI platforms. Fifth, insurers need platform-specific optimisation strategies for ChatGPT, Gemini, Perplexity, and others. Finally, continuous LLM monitoring, hallucination audits, and content governance ensure sustained visibility in AI-generated insurance recommendations.

How Digital 360 Helps Insurers Lead the LLM Search Revolution

Discover how LLMs are changing search with conversational, AI-driven insights that are reshaping how customers find and evaluate insurance products online. At Digital 360 — the best digital marketing company in Noida — we help insurers navigate this new AI-first landscape with LLM-ready content, semantic SEO, structured data, and machine-readable architectures. As search shifts from keywords to intelligent conversations, insurers must evolve to stay visible, trusted, and competitive. Our advanced LLM optimisation strategies ensure your brand appears in AI-generated answers, enhances customer trust, and remains future-proof in the era of AI-driven digital discovery.

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