From Browsing to Asking: How AI Assistants Are Changing Shopping Behavior

For over two decades, online shopping has followed a relatively predictable pattern: consumers type a query into a search bar, open multiple browser tabs, compare products across websites, and eventually make a decision. This process, often fragmented and time-consuming, has been widely accepted as the norm.

However, the emergence of large language models (LLMs) and AI-powered assistants is fundamentally altering that behavior. Increasingly, consumers are turning to conversational interfaces not just to search, but to ask.

They are no longer seeking links. They are expecting answers.

This shift from browsing to asking marks a significant transformation in how people discover, evaluate, and purchase products. It has profound implications for eCommerce brands, content strategy, and the way digital customer journeys are designed.

The Rise of Conversational Interfaces

The adoption of AI assistants has accelerated in recent months. Tools like ChatGPT, Google’s Gemini, Microsoft Copilot, and Perplexity are no longer niche tools for early adopters. They are rapidly becoming part of mainstream behavior.

According to Exploding Topics, 47% of consumers now report using AI assistants to research purchases an increase of 6 percentage points from the previous year. 

Ref - https://www.askattest.com/blog/articles/2025-consumer-adoption-of-ai-report 

Younger demographics are driving this shift, with Gen Z and Millennials accounting for over 65% of AI users globally.

This evolution reflects a deeper behavioral change. Where search engines once served as the gateway to information, users are now engaging in contextual, goal-oriented conversations with AI systems. These systems respond not with a list of results, but with synthesized answers often combining product data, customer reviews, and third-party analysis into a single response.

The distinction is critical. Browsing invites exploration. Asking implies intent.

How AI Is Redefining the Customer Journey

Traditional eCommerce journeys were nonlinear. A single purchase could involve a series of steps: search, comparison, reviews, FAQs, checkout. This process often spanned multiple platforms and required significant user effort.

In contrast, AI assistants compress the journey. A user might ask, “What’s the best running shoe for flat feet under $200?” and receive a recommendation, a price comparison, verified reviews, and a direct purchase link all within one interface.

This compression is not simply a matter of convenience. It redefines the decision-making funnel itself. Rather than navigating a website or product category, the user receives a curated response based on intent, context, and prior preferences.

Amazon’s rollout of Rufus a generative AI shopping assistant embedded in its mobile app is one prominent example. Internal forecasts reported by Business Insider suggest Rufus could generate $700 million in incremental profit by 2025, and up to $1.2 billion by 2027. Walmart, meanwhile, is exploring OpenAI-powered agents to proactively assist customers with tasks such as reordering groceries or planning events, bypassing traditional eCommerce touchpoints altogether.

Emerging Behaviors Among Digital Consumers

The implications extend beyond interface design. AI assistants are enabling new modes of product discovery and decision-making:

• Users are increasingly asking complex, context-rich queries, such as “Which plant-based protein shake causes the least bloating?” or “What’s the difference between Allbirds and Tropicfeel for travel shoes?”
  
• Trust is shifting from individual reviews and blog posts to synthesized AI recommendations.
  
• Many consumers now explore reviews, product availability, and specifications within a single chat interface, reducing the need to visit brand websites altogether.
  

At the same time, voice and conversational commerce are gaining traction. According to Deloitte, 37% of global consumers now use voice commands to shop. In social commerce contexts, this figure rises to 49%. 

However, a survey by TechRadar highlights that while 80% of users are open to AI assistance, only one-third are currently comfortable allowing AI to make purchases autonomously, underscoring the importance of transparency, control, and trust in these systems.

How Brands Can Prepare for the AI-First Shopping Era

The shift toward AI-assisted shopping introduces new layers of complexity for eCommerce brands. As consumers increasingly rely on AI assistants to guide their purchase decisions, traditional digital strategies such as ranking for high-volume keywords or optimizing product listing pages are no longer sufficient on their own.

In this new environment, the assistant becomes the interface, and brands must compete not just for visibility, but for inclusion in curated, intent-rich responses. That requires a strategic reorientation around structured content, semantic clarity, and brand trustworthiness.

To remain competitive, brands must act on five key fronts:

1. Make Your Content Assistant-Readable

AI systems do not “browse” like humans. They parse structured data, interpret semantics, and prioritize clarity. If your content isn't optimized for this mode of consumption, it risks being excluded altogether.

Actionables:

• Audit your product pages for schema markup. Implement Product, FAQ, and Review structured data.
  
• Standardize metadata (title, description, specs) across your catalog to ensure consistency.
  
• Translate blog and support content into concise Q&A and use-case formats aligned with natural language queries.

Example: Instead of “Our protein shake contains 25g of protein,” include a section that answers “How much protein is in this shake?” in plain language and with schema support.

2. Align With Conversational Queries

Consumers are no longer typing keywords like “best budget smartwatch.” They’re asking assistants: “What’s a reliable fitness tracker under $200 for beginners?” This shift demands a new approach to content strategy.

Actionables:

• Map real customer questions using on-site search, support logs, and social listening tools.
  
• Develop content clusters that address these questions in precise, assistant-friendly formats.
  
• Include decision-support copy (e.g., comparisons, pros and cons, usage recommendations) directly on product pages not buried in blogs.

Tip: Use tools like AlsoAsked or Perplexity.ai to identify how people phrase product-related questions today.

3. Build Trust Into Every Digital Touchpoint

As AI intermediates more of the shopping journey, trust becomes a critical ranking factor both for consumers and the algorithms serving them.

Actionables:

• Showcase verified reviews with structured markup and visible trust indicators.
  
• Highlight certifications, sustainability credentials, and awards using badge icons and plain language.
  
• Clarify return policies, shipping timelines, and guarantees above the fold on product pages.
  
• Avoid exaggerated claims LLMs increasingly deprioritize sources with low credibility signals.

50%+ of consumers report data privacy and trust as barriers to AI-assisted commerce. Being transparent is now a competitive advantage.

4. Extend Visibility Into AI Interfaces

It is no longer enough to rank on Google. Brands must now ensure visibility across new assistant-based ecosystems.

Actionables:

• Monitor your brand’s visibility on platforms like ChatGPT, Perplexity, and Google’s SGE beta.
  
• Explore assistant integrations, such as GPT plugins, custom GPTs, or Perplexity cards.
  
• Ensure product feeds are optimized for third-party platforms that power shopping assistants (e.g., Amazon, Shopify, Google Merchant Center).

Amazon’s AI assistant Rufus and Shopify’s Sidekick are already reshaping the role of the storefront. Prepare accordingly.

5. Future-Proof With First-Party Data and Product Knowledge

LLMs rely on high-quality, authoritative inputs. If your brand data lacks depth or is inconsistent across channels, it won’t be favored in AI-generated answers.

Actionables :

• Centralize your product information in a structured PIM (Product Information Management) system.
  
• Invest in zero-party data collection, such as preference quizzes or customer onboarding flows.
  
• Train internal teams on maintaining accurate, assistant-ready product copy and documentation.

In Summary

The shift from browsing to asking is more than a UX evolution; it is a fundamental reconfiguration of how people engage with commerce.

In this environment, traditional tactics like optimizing product pages or bidding on keywords are no longer sufficient. Brands must become answerable, assistant-friendly, and context-aware. Those that do will not only maintain visibility they will become indispensable.

The future of commerce is not just search-led or voice-enabled. It is AI-assisted, conversation-driven, and increasingly agentic.

Brands that act now by optimizing for assistant interfaces, embedding structured content, and addressing real customer questions will find themselves better positioned not just to be found, but to be chosen.

In this new era, the most successful brands won’t just rank. They’ll respond.

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