The End of Keyword Search: Why AI Is Rewriting Discovery

The End of Keyword Search: Why AI Is Rewriting Discovery

Search is broken. Typing rigid keywords into a box no longer matches how people discover products—especially Gen Z, who now turn to AI chatbots and social platforms first. Traditional keyword search is fading, replaced by conversational, intent-driven discovery powered by AI.

Google’s dominance is slipping—its search market share dropped from 93.4% in 2023 to 89.7% in 2025 (Digital Commerce 360). Meanwhile, platforms like Perplexity and ChatGPT are becoming go-to tools for product research, blending answers with shoppable results.

This shift marks a new era: agentic commerce, where AI doesn’t just respond—it acts.

• AI agents can browse catalogs, compare prices, check availability, and even complete purchases autonomously
• 83% of consumers are willing to share preferences for better personalization (Accenture)
• Amazon attributes 35% of its sales to AI-driven recommendations (McKinsey)
• Netflix saves $1 billion annually and drives 75% of content discovery via AI (IndataLabs)
• IKEA boosted e-commerce AOV by 2% using AI-powered search (Google Cloud)

Take Hanes Australasia: by deploying AI to personalize real-time recommendations, they achieved double-digit revenue growth per session—a testament to hyper-relevant discovery.

AI now interprets context, not just keywords. It understands why someone is searching—whether they’re browsing casually, comparing specs, or ready to buy—and tailors results accordingly.

For marketers, this means optimizing for AI visibility, not just Google rankings. Winning requires structured product data, rich attributes, and integration with platforms where AI agents operate.

Imagine a shopper asking, “Show me breathable workout shirts under $50 that match my minimalist style.” Legacy search fails. AI-powered systems succeed—by combining zero-party data, real-time inventory, and style preferences into one seamless response.

The future isn’t just about being found—it’s about being recommended.

As AI evolves into multimodal agents capable of processing text, images, and voice within a unified “brain,” static search bars will seem as outdated as dial-up.

Marketers must act now: build for conversation, not queries. Prepare for agents, not just users.

Next up: How AI transforms product discovery from reactive to proactive.

How AI Transforms Product Discovery & Personalization

AI-powered search is redefining how shoppers find products online—moving beyond basic keyword matching to intent-driven, conversational experiences. Today’s consumers expect instant, relevant results tailored to their preferences, and AI delivers exactly that by analyzing behavior, context, and real-time signals.

This shift is not theoretical—it’s backed by results.
- Amazon attributes 35% of its sales to AI-driven recommendations (McKinsey & Company).
- Netflix saves $1 billion annually and drives 75% of content discovery through AI personalization (IndataLabs).
- IKEA saw a 2% increase in average order value (AOV) after implementing AI-powered search (Google Cloud).

These numbers reveal a clear trend: personalized discovery drives revenue.

AI achieves this by combining multiple technologies: - Retrieval-Augmented Generation (RAG) pulls accurate product data in real time. - Knowledge Graphs map relationships between products, categories, and user intent. - Multimodal AI interprets text, images, and voice to understand complex queries.

For example, a user asking, “Show me breathable workout shirts under $50 for hot weather” gets精准 results not just from keywords—but from AI interpreting use case, budget, and environmental context.

Brands like Hanes Australasia leveraged this approach to achieve double-digit percentage growth in revenue per session, proving that smarter search equals higher conversions (Google Cloud).

One standout case: A fashion DTC brand used an AI stylist powered by zero-party data—collected via an interactive quiz on fit, style, and occasion. The result?
- A 32% increase in conversion rate
- 40% of users engaged with the quiz
- Average session duration rose by 2.1 minutes

This demonstrates how active personalization outperforms passive tracking.

Critically, today’s consumers are ready to participate. 83% are willing to share personal data if it leads to better recommendations (Accenture). That trust opens the door for marketers to use zero-party data—information users willingly provide—as a cornerstone of AI personalization.

The key takeaway? AI doesn’t just react—it anticipates. It transforms static catalogs into dynamic, responsive shopping assistants.

As AI evolves into agentic commerce—where systems browse, compare, and buy autonomously—marketers must ensure their product data is structured, discoverable, and optimized for AI interpretation.

Next, we’ll explore how conversational AI is turning search into a guided buying journey, not just a lookup function.

From Discovery to Checkout: Building AI-Driven Sales Journeys

From Discovery to Checkout: Building AI-Driven Sales Journeys

Today’s shoppers don’t just search—they converse. AI-powered search is redefining e-commerce by turning queries into dynamic, personalized conversations that guide users from first interest to final purchase.

No longer limited to keyword matching, modern search uses intent recognition, real-time personalization, and agentic behavior to deliver smarter results. The result? Faster discovery, higher engagement, and increased conversions.

For marketers, this shift means rethinking how customers interact with product catalogs. Static filters and basic autocomplete won’t cut it when AI can act as a 24/7 shopping assistant, understanding context, preferences, and even budget constraints.

Key benefits of AI-driven sales journeys: - 35% of Amazon’s sales come from AI recommendations (McKinsey & Company)
- Netflix saves $1 billion annually through AI-powered content discovery (IndataLabs)
- IKEA increased average order value (AOV) by 2% using AI search (Google Cloud)

These aren’t outliers—they reflect a broader trend: personalized, conversational AI converts.

Take Hanes Australasia, which implemented Google Cloud’s AI for real-time personalization and saw double-digit percentage growth in revenue per session. By analyzing user behavior and intent, the system delivered hyper-relevant product suggestions, reducing bounce rates and boosting basket size.

The technology behind these results combines Retrieval-Augmented Generation (RAG) with Knowledge Graphs to understand both product data and customer context. This allows AI agents to answer complex questions like, “Show me breathable workout shirts under $30 for hot climates,” then recommend matching items in real time.

Core capabilities of AI-driven sales agents: - Understand natural language and multi-intent queries
- Access real-time inventory and pricing
- Remember past interactions for continuity
- Recommend based on zero-party data (e.g., style quizzes)
- Guide users to checkout without leaving the chat

Platforms like involve.me enable brands to collect zero-party data through interactive quizzes, fueling AI stylists that outperform generic recommendations. With 83% of consumers willing to share preferences for better personalization (Accenture), this approach builds trust while improving accuracy.

And now, AI isn’t just guiding purchases—it’s completing them. Perplexity and Firmly.ai have integrated native checkout via PayPal, enabling end-to-end transactions inside conversational interfaces. Visa and Mastercard are aligning with these ecosystems, signaling that AI-driven payments are becoming mainstream.

Marketers must ensure their tech stack supports this evolution. That means API-first architecture, real-time inventory sync, and seamless payment integration.

The next frontier? Multimodal AI agents—systems that process text, images, and voice in a unified framework. Experts predict production-ready versions within months, not years (r/Singularity), making now the time to prepare.

As AI evolves from assistant to autonomous buyer, the sales journey will become less linear and more conversational. Marketers who embrace this shift will lead the next wave of e-commerce innovation.

Next, we explore how optimizing product data unlocks AI visibility and drives smarter discovery.

Future-Proofing Your Strategy: Preparing for Agentic & Multimodal AI

Future-Proofing Your Strategy: Preparing for Agentic & Multimodal AI

The next wave of e-commerce isn’t just smart—it’s autonomous. Agentic AI and multimodal systems are transforming how consumers discover and buy products, making traditional search tactics obsolete. Brands that adapt now will dominate the new AI-first customer journey.

Imagine an AI that doesn’t just recommend products—but shops for you. Agentic AI refers to autonomous systems capable of browsing, comparing, and purchasing without human intervention. These agents act as personal shoppers, executing tasks like “Find me eco-friendly running shoes under $120” across multiple sites.

This shift is accelerating: - Google’s search market share dropped from 93.4% in 2023 to 89.7% in 2025 (Digital Commerce 360), as users turn to AI-native platforms. - 35% of Amazon’s sales come from AI-driven recommendations (McKinsey & Company), proving the commercial power of intelligent systems. - Platforms like Perplexity now support native checkout via PayPal, turning conversational AI into transactional interfaces.

Gen Z is leading this change, with growing reliance on chatbots like ChatGPT for product discovery. The future belongs to brands visible in AI-generated responses—not just search engine results.

Example: A student uses a multimodal AI agent to upload a photo of a jacket seen on social media. The agent identifies the style, finds sustainable alternatives, compares prices, and completes the purchase—autonomously.

Marketers must shift from optimizing for keywords to optimizing for AI visibility.

Multimodal AI agents understand and generate content across text, images, audio, and code—using a unified “brain.” This enables richer interactions, like searching with a photo, voice command, or even partial sketches.

Key trends: - Mixture-of-Experts (MoE) architectures allow specialized AI models (vision, language) to collaborate under one system, improving accuracy. - Experts predict production-ready multimodal agents within months, not years (r/Singularity). - These systems rely on semantic understanding, not just keyword matching, making structured, rich product data essential.

Brands must ensure their product catalogs are: - AI-readable with detailed metadata (color, material, use case) - Visually optimized with high-quality, tagged images - API-accessible for real-time inventory and pricing updates

Case in point: IKEA’s AI-powered search uses visual and contextual signals to increase average order value by 2% (Google Cloud), demonstrating the ROI of multimodal readiness.

The shift demands a new approach: unified, flexible tech stacks that can plug into evolving AI ecosystems.

Waiting is not an option. The tools to adapt are here—and accessible.

Actionable steps for marketers: - ✅ Adopt no-code AI platforms like Google Vertex AI Search or similar solutions that enable rapid deployment of intelligent agents. - ✅ Enrich product data with automated tagging and detailed attributes to improve AI interpretability. - ✅ Collect zero-party data via quizzes and preference selectors—83% of consumers are willing to share for better personalization (Accenture). - ✅ Integrate AI with CRM and payment systems to enable end-to-end transactions in chat. - ✅ Audit your tech stack for API-first, modular architecture to support future multimodal agents.

Platforms are emerging that combine RAG (Retrieval-Augmented Generation) with Knowledge Graphs to deliver context-aware, accurate responses—critical for trust and conversion.

The goal? Become the preferred brand inside the AI agent’s decision loop.

The era of proactive, conversational, and autonomous commerce is here. The next section explores how to turn AI visibility into measurable revenue growth.