How conversational AI will change SEO and UX

Today, we no longer search. We ask.

We want to get straight to what we need, without getting lost in pages, menus, or filters. We write a complete question—just as we would to a person—and expect a precise answer in real time.

This is the new standard set by generative artificial intelligence: ChatGPT, Google’s AI Overview, voice assistants, Text to Image (TTI) generators. Tools that have changed how we interact with the web—and are redefining user expectations.

The result? Traditional SEO strategies based on keywords and rigid paths are no longer enough.

Welcome to the era of conversational search: natural, intelligent, and intent-driven.

This guide shows you how to prepare your content, your website, and your digital strategy to stay visible and competitive in a world where navigation is led by conversation.

Conversational AI: new tools and new habits

Every time a technology becomes deeply embedded in our lives, it changes how we interact with other digital tools in our daily routines. To envision the future of conversational AI, it’s essential to understand the mechanisms that shape our online interactions.

What is conversational search?

Imagine walking into a furniture store and saying to the clerk: "I'm looking for a reclining chair for when I work from home; it needs to have adjustable lumbar support, be made of breathable fabric, and cost no more than 300 euros."
Instead of wandering through confusing departments, the clerk immediately directs you to the options that meet all your criteria.
This is exactly what a chatbot does during a conversational search. It doesn't give you a list of generic results—it analyzes your request and suggests exactly what you're looking for, possibly adding extra details to refine your choice. It’s a direct interaction, like speaking to an expert—but online.

Conversational search marks a profound shift in how users access information online. Moving away from classic keyword-based or fragmented queries, users now ask questions in natural language—often complex and nuanced—just as they would when speaking with a human. This approach has been made possible by advanced language models like ChatGPT and Google Gemini, which can understand context, interpret nuance, and deliver coherent, relevant, and often comprehensive responses.

What sets conversational search apart is its ability to maintain a "dialogue" with the user: not just one answer to a single query, but a sequence of interactions in which each response takes the previous ones into account. This creates a much more natural and intuitive experience, reducing the need for manual query refinements or multi-layered navigation to find what you're looking for.

The impact of generative AI on the digital experience

Generative AI is rewriting the rules of the digital experience, centering around dynamic adaptation to user intent and needs. Unlike traditional systems that simply return results based on textual matches, generative models produce personalized, contextual, and real-time content. This ability to generate relevant responses—even to vague or complex prompts—has raised expectations for usability and accessibility.

Users now expect smoother, faster, and more efficient interactions. They want to go straight to the solution, bypassing rigid and hierarchical navigation steps. The impact is felt across the entire digital ecosystem: websites, search engines, and eCommerce platforms must adapt to deliver intelligent, relevant, and personalized responses—often before the question is even fully articulated. In this context, user experience and search visibility become two sides of the same coin.

Search is changing: from search engine to prompt

Searching no longer means entering keywords into a search engine, but expressing a need in natural language. The prompt becomes the new interface: a request expressed as we would to a person, to which AI responds directly, with no intermediate steps. This represents a true revolution in digital interaction. The prompt is not just an input, but a way to guide technology toward targeted answers. This is where prompt engineering comes in: the ability to craft precise, strategic prompts to receive relevant, high-quality content. In a scenario where dialogue with AI systems takes center stage, knowing what—and how—to ask is an essential part of the new search experience.

A new UX driven by the question

User experience is evolving to match this new paradigm, favoring interfaces that enable natural interaction and immediate response. B2B eCommerce sites are integrating intelligent chatbots and advanced search systems to guide users directly to the information or products they need, minimizing intermediate steps.​

It's not just the question: who asks matters too

An answer depends not only on the question, but on who is asking. This dynamic has always been part of the search experience—just think of how Google’s SERP has long been personalized based on location, history, and device used. What’s new today is that this level of personalization can go far beyond search engines. Even eCommerce sites and websites can—and should—respond differently depending on the user, initiating dynamic and contextual conversations.

Thanks to conversational AI, every interaction becomes an opportunity to deliver a tailored, more relevant, and more immediate experience.

The evolution of Google and search engines

From early text-based queries to smart assistants, search engines have revolutionized access to knowledge, transforming into a digital archive that’s always available and instantly searchable. Now, with the rise of conversational AI, this archive is evolving once again: we no longer just search with keywords—we engage in dialogue with systems that understand natural language, anticipate intent, and offer personalized responses. In this section, we’ll explore how Google and other engines are adapting to these new habits.

AI Overview and Search Generative Experience (SGE)

Google has introduced the AI Overview—an evolution of what was known as the Search Generative Experience (SGE)—to provide summarized and contextualized answers to user queries. These features use artificial intelligence to better understand user intent and deliver relevant information, reducing the need to click through multiple results.

How AI is changing SEO

The integration of AI into search engines has transformed SEO, shifting the focus from simple keyword optimization to creating content that effectively answers user questions. Relevance, authority, and clarity have become key factors for ranking.

The importance of structuring content for semantic search

To be effective in this new era of search, content must be structured to support semantic understanding by search engines. The use of structured data, semantic markup, and a clear information hierarchy helps search engines accurately interpret and display content in a relevant way.​

GEO: Generative Engine Optimization

Generative Engine Optimization (GEO) is an emerging digital marketing strategy focused on optimizing digital content for visibility within responses generated by AI engines like ChatGPT, Google Gemini, Claude, and Perplexity. Unlike traditional SEO, which aims to rank content in search engine results, GEO focuses on ensuring that information is selected and effectively presented by AIs in their responses to users.

As search engines evolve into generative systems, GEO represents a key approach for ensuring that digital content is recognized and effectively used by AI—offering a competitive advantage to those who embrace these practices.

Why GEO will become increasingly relevant

With the rise of AI-powered response engines—integrated into chatbots and new generative search engines—the way users access information is changing. Increasingly, people no longer consult a list of links but rely on direct, summarized, and contextualized answers generated by AI. In this context, online visibility will no longer depend solely on ranking in traditional search results but on being “chosen” as a trustworthy source by generative engines.

GEO therefore becomes a strategic tool for businesses: optimizing content so it is intercepted, interpreted, and returned by AI will be essential to maintaining relevance, authority, and competitiveness in a rapidly evolving digital landscape.

How to optimize for generative models

To succeed with GEO, it's crucial to create content that is clear, authoritative, and well-structured. Using reliable sources, structured data, and clear writing increases the likelihood that content will be selected by generative models. Additionally, keeping content up to date and relevant to user needs is essential. To learn more about the most effective strategies, read our article on Generative Engine Optimization.

The differences from traditional SEO

While traditional SEO focuses on factors like keywords, backlinks, and on-page optimization, GEO requires a more holistic approach. The goal is to create content that not only serves users but is also easily interpretable and usable by AI models to generate accurate and contextualized responses.

Conversational search and long-tail keywords: similarities and differences

Conversational search and long-tail keywords share a common goal: capturing specific user intent. However, there are key differences. Long-tail keywords are typically static, predefined phrases designed to match niche searches. Conversational queries, on the other hand, are dynamic, often unique, and formulated in natural language. They are driven by dialogue rather than formulaic structure. Optimizing for conversational search means focusing less on matching exact phrases and more on understanding and addressing user needs in context—anticipating follow-up questions and maintaining continuity throughout the interaction. It’s a shift from targeting words to targeting meaning.

Information Architecture: Organizing the Answers

From Hierarchical to Thematic Structures

The classic structure of web content, based on rigid hierarchical levels (home > category > subcategory > content), followed a linear navigation logic. However, in the conversational and semantic logic enabled by AI, this organization shows its limitations.
To optimize for AI-readiness, it is essential to organize content according to a thematic structure, where each piece is connected to a central node (the pillar page) and a series of related articles (topic clusters) that explore specific aspects.

This approach improves contextual understanding for generative engines and supports users in a guided, but non-linear, exploration.

Pillar Pages and Topic Clusters to Reinforce Authority

Pillar pages serve as main content pieces on key business topics. They should not be mere containers, but comprehensive access points capable of offering an overview and linking to in-depth content. If you want to explore this topic further, check out our article on Cluster Content Strategy.

Linking pillar pages to topic clusters with strategic internal links creates a strong semantic network that:

• improves indexing,
• enhances perceived authority,
• increases the likelihood of being selected by conversational engines as a reliable source,
• facilitates user navigation.

Conversational Clusters: Think in Terms of Questions, Not Just Keywords

With the rise of natural language search, it’s strategic to structure content around frequent questions, use cases, objections, and specific needs.
Each cluster should respond to concrete conversational queries, such as:

“What is the best coating for industrial environments with high temperatures?”
instead of:
“high temperature resistant paints”.
Writing with questions in mind—not just keywords—improves both user understanding and LLM selectability.

The Funnel is a Dialogue: How Navigation is Changing

The Traditional Path: From Homepage to Product

In the classic model, users land on the homepage, browse the catalog, select a category, apply filters, and sort results. While linear, this approach often proves dispersive—especially for users who already know what they're looking for.

The New Paradigm: Getting Straight to What Matters

With the adoption of conversational search, the goal is to reduce the number of steps. Users want to type a request like “fire-retardant products for humid environments certified UNI EN 13501” and land directly on relevant options.

This requires the site to interpret complex queries and provide targeted answers, going beyond filter- and category-based navigation.

Conversational Internal Search:

Traditional internal search, often inadequate in both B2B and B2C, must evolve into an integrated conversational engine.
AI-based solutions (such as Algolia, Coveo, or custom GPT tools) allow for complex answers, product suggestions, technical spec sheets, and even guiding users to request a quote.
This area is still largely underestimated but crucial for conversion.

Conversational UX: Designing for Smart Interaction

Interfaces that Speak: Chatbots, Voice Assistants, and Smart Search

In the new digital ecosystem, conversational user experience (UX) becomes central. AI-powered chatbots, voice assistants, and smart search engines integrate the interface, offering support and speeding up navigation.

These are no longer technological gimmicks but structural elements that simplify the customer journey, especially in complex contexts like B2B.

The Benefits of Simplified, Assisted Navigation

The benefits of a conversational UX are tangible:

Reduced average search time

Greater precision in solution suggestions

Fewer requests to customer service

Higher conversion rates in lead generation phases

How to Design AI-Friendly Content

Well-written and well-structured content is more easily interpreted by generative models.
Design guidelines:

• Short sentences, clear statements;
• Divided into paragraphs with explanatory headings;
• FAQs written in natural language;
• Reliable sources and links.

In other words, content ready to be quoted, extracted, and summarized.

A New UX for Websites

User experience on websites is evolving toward more conversational models, inspired by AI interactions. Instead of classic navigation through menus and categories, a new search mode is emerging: users enter a prompt—a phrase that summarizes their need, like “I’m looking for an eco-friendly paint for light interiors”—and receive a clear, personalized response. This includes a direct suggestion, such as a link to the most relevant product page or the most useful deep dive. The site becomes more intuitive, actively guiding users to the right content, without making them “browse through pages.” This interface is designed to save time, increase interaction relevance, and offer an experience similar to generative engines—but tailored to the brand.

Prompt Search: From Intent to Answer in One Step

At the core of this new UX is a paradigm shift: it starts from user intent, not from site structure. “Prompt search” allows the interpretation of natural language and delivers contextualized responses that anticipate informational needs. This approach not only simplifies navigation but also increases conversion potential by reducing friction and guiding users directly to the most relevant action. Moreover, analyzing inserted prompts can yield valuable insights: understanding user language, identifying new needs or content gaps, and optimizing editorial and commercial strategies accordingly. In short, prompt search is both an advanced UX tool and a qualitative data goldmine.

GEO and New UX: A Strategic Duo for the Digital Future

Integrating Generative Engine Optimization with a prompt-based user experience means designing websites that can not only be found by AI but also interact effectively and naturally with users. The two strategies reinforce each other: GEO ensures that the site's content is relevant and easily recognized by generative engines; the new UX turns the site into an interactive space capable of delivering fast, accurate responses. Together, they pave the way for a smarter, more conversational, results-driven web—where visibility comes from quality content and conversions happen through tailored experiences.

Focus: B2B eCommerce

Structured Data, Tagging, and Intelligent Indexing

A well-designed semantic infrastructure facilitates indexing and comprehension by both traditional engines and generative models. Using schema.org, microdata, semantic attributes, and metadata is essential to make content “machine-readable.”

Conversational SEO and CRO (Conversion Rate Optimization)

Optimizing for conversational search also means optimizing for conversion.
If users reach the right content faster, the likelihood of action (inquiry, purchase, download) increases.
SEO and CRO are no longer separate processes but complementary parts of an integrated strategy.

Best Practices to Prepare for Conversational Search

Optimize for Voice and Text Search

Many conversational searches happen via voice assistant or mobile. Therefore, it’s essential to:

• structure short, concise responses (featured snippet-ready),
• use natural language,
• avoid unexplained technical jargon.

Build an Easily Searchable Knowledge Base

A well-organized, up-to-date knowledge base accessible via AI can become a virtual assistant within your site.
Structuring content for scenarios and FAQs improves usability and reusability by generative models.

Integrate AI Systems into Internal Search Engines

Integrations with LLM APIs and custom models enable much more effective search than keyword-based systems.
The goal is no longer just “finding” but getting complete, contextualized answers.

The Future of Search is Conversational

It’s Not Just About Visibility Anymore: Intent Matters

Organic ranking alone is no longer enough.

Being found at the right time with the right answer is crucial. This requires content designed to meet specific intents—not just to “rank.”

Towards a Smarter, Smoother, More Personalized B2B eCommerce

Conversational search enables a new form of interaction between companies and clients.
Portals that adapt will offer an experience that is:

1. more efficient,
2. more relevant,
3. more competitive.

Act Now: How to Start Optimizing for Conversational Search

• Revise your information architecture around thematic clusters
• Invest in semantic, intent-based content
• Implement AI tools for search, support, and navigation

Monitor new metrics like “response accuracy” and “time to conversion”

In short, the challenge is no longer just to attract users, but to engage in meaningful dialogue with them.
Those who succeed will lead the future of B2B eCommerce.