How AI & LLMs Transform Search & Marketing: 2025 Research

Abstract

This research investigates how large language models (LLMs) and AI technologies are reshaping search engines and marketing websites. It examines changes in user behavior, search algorithms, and SEO strategy, providing actionable insights for marketers and developers in an era defined by conversational, context-driven search.

Introduction

Search engines traditionally relied on keyword matching to deliver results. With the integration of advanced AI—especially LLMs—search has evolved to understand user intent and context, enable natural language queries, and generate more comprehensive answers.

Evolution of User Behavior

Modern search users increasingly phrase queries as complex, conversational questions. AI-powered engines interpret these queries more contextually, delivering richer, directly useful responses. For example, 47% of Google search results in 2025 now trigger an AI Overview, greatly impacting user engagement patterns and organic traffic.

Chart: Growth of AI Overview Triggered Google Search Queries (2023–2025)

AI Integration in Search Engines

Google: Rolls out the Search Generative Experience (SGE), making AI-generated summaries and contextual answers available to millions of users.
Bing: Integrates Microsoft Copilot and generative AI to supplement traditional rankings with dynamic, visual responses.
Perplexity: Acts as an AI-powered intermediary, summarizing and clarifying results from existing indexes.

Feature	Google SGE	Bing Copilot/GenSearch	Perplexity
AI Overview/Summary	Yes	Yes	Yes
Visual Responses	Moderate	High	Moderate
Voice Search Integration	Advanced	Moderate	None
Multimodal Search	Yes	Yes	Limited
Citation in Results	Sometimes	Frequent	Always

‍

Search engine strategies are shifting from keyword-focused to contextual, AI-driven result generation.

SEO and Marketing Implications

SEO now goes beyond keywords; content must align with user intent and provide meaningful, context-rich answers.

The rise of AI-generated summaries and features—such as AI Overviews—has led to an estimated 15–25% drop in organic web traffic from SERPs.

Chart: Estimated Decline in Organic Website Traffic Due to AI-Powered Search (2023–2025)

Optimizing for multimodal inputs—text, audio, images—is also growing in importance as user behaviors shift.

Year	Organic Traffic Change (%)	CTR Change (%)
2023	0%	0%
2024	-10%	-8%
2025	-20%	-15%

‍

Case Studies: AI SEO Performance

Flyhomes (Real Estate): Increased content from 10K to 425K pages, achieving 1.1M monthly visits, with AI guides responsible for 55.5% of all traffic.
Xponent21: Saw 4,162% organic traffic growth in 12 months by targeting rankings in AI-powered engines like Perplexity and Google AI Mode.

Industry	AI Overview Adoption (%)	Traffic Drop (%)	AI Content Share (%)
E-commerce	60	25	40
SaaS/B2B	45	18	35
Local Services	35	10	20

‍

Expert Insights

“AI has leveled the playing field for scrappy, tech-aware businesses…If you aren’t using AI in your daily strategy, you risk falling behind within the next 12–18 months.”— Dan Shaffer, Director, SEO.com

“The Google App sees about 20% of searches through voice commands…Users speak naturally in voice searches.”— Industry interview, 2025

‍

Bing vs. Google AI Search - Key Differences in 2025

Feature	Bing	Google
AI Features	Microsoft Copilot, Generative Search	Google Gemini, Search Generative Experience (SGE)
Visual Presentation	Infographic style, visually rich	AI overviews, fewer traditional links
Local Search Approach	Less hyper-localized	Hyper-localized with advanced SERP features
Market Share	10-11%	Around 90%
AI Summaries	Complements links	Sometimes replaces organic links

‍

‍Chart: Search Engine Market Share Distribution in 2025

Voice Search and New Modalities

Voice queries are becoming a significant share of search—rising from 10% of Google queries in 2023 to 20% by 2025.

Chart: Share of Google Searches Conducted by Voice (2023–2025)

Year	% Voice Searches	Example Use Case
2023	10	Simple questions
2024	15	Navigation, shopping
2025	20	Complex queries, conversational use

‍

Methodology

This research synthesizes the latest 2025 industry reports, market surveys, and case studies from e-commerce, SaaS, and local businesses. Data sources include global SEO surveys, expert interviews, and public performance metrics, ensuring objectivity and broad relevance.

Conclusion

Search is swiftly moving to an AI-first model. Marketers and developers must:

Prioritize context, user intent, and rich information quality
Optimize for AI-generated features and multimodal input types
Track industry benchmarks, expert commentary, and shifting performance metrics

AI Search Feature Level	Content Focus	Technical SEO Priority	Monitoring Metrics
Minimal (2023)	Keywords	On-page basics	SERP position, Pageviews
Moderate (2024)	Context & Semantics	Structured data	AI Overview appearances
High (2025)	Conversational & Informational	Multimodal, voice, video	AI snippet CTR, Engagement