How AI Assistants Are Rewiring Consumer Behavior & What Marketers Must Do Next

A groundbreaking study of the National Bureau of Economic Research about How People use ChatGPT reveals the true scope of AI’s consumer impact: by July 2025, ChatGPT alone reached 700 million weekly users – nearly 10% of the world’s adult population – sending over 2.5 billion messages daily. The researchers found that there is a steady growth in work-related messages but even faster growth in non-work-related messages, which have grown from 53% to more than 70% of all usage. More striking than adoption rates are the behavioral shifts documented in the research. Nearly 80% of all ChatGPT usage falls into three categories: Practical Guidance, Seeking Information, and Writing, with consumers increasingly using AI for decision support rather than simple task completion.

This isn’t just about new tools—it represents a fundamental rewiring of how consumers discover, evaluate, and purchase. The study found that 49% of messages involve “Asking” (seeking guidance and information), while 40% involve “Doing” (completing specific tasks). Consumers now expect advisory, multi-turn interactions that understand context and preferences over time, creating shortlists through trusted AI guidance rather than evaluating endless search results themselves.

The marketing implications are immediate and profound. Here’s what you’ll discover: how AI assistants impact online search, purchase behavior (both online and offline), sales funnels, and websites. We’ll examine short-term changes already happening and long-term shifts reshaping entire customer journeys. Most importantly, you’ll get concrete marketing actions to implement now while building for tomorrow’s assistant-driven landscape where brands win or lose based on assistant-visible data and on-site execution.

The New Consumer Modality: From Queries to Conversations

Advisory interactions are replacing isolated keyword searches. Instead of typing “best running shoes” and comparing dozens of options, consumers now ask assistants: “I need running shoes for daily 5-mile runs on pavement, budget under $200, with good arch support for flat feet.” The assistant collects preferences, asks clarifying questions, and maintains context across multiple exchanges.

This behavioral shift accelerates decisions and builds confidence. Consumers receive curated recommendations with explanations rather than raw search results. They open fewer browser tabs, spend less time researching, and trust assistant-generated shortlists. Attribution becomes complex, discovery might happen in ChatGPT, validation on Google, and purchase completion on your website. Marketing teams must prepare for customer journeys that span multiple AI touchpoints before reaching their properties.

Online Search Is Becoming Conversational

Short-Term: Assistant-First Discovery and “Zero-Click” Validation

Search sessions increasingly start in assistant interfaces. Consumers launch ChatGPT, Perplexity, Google’s AI Mode, or device copilots for initial exploration, then use traditional web search to validate specific claims or find transaction pages. This creates a two-layer discovery model where assistants handle research and search engines confirm details.

Query styles emphasize problems and outcomes over brand terms. Traditional searches like “Nike Air Max review” become conversational prompts like “compare running shoes for daily training under $200 with pros and cons.” Consumers request trade-offs, ask for citations, and want structured comparisons rather than marketing copy.

Your immediate action: optimize for Generative Engine Optimization (GEO). Structure your content with clear facts, comprehensive FAQs, detailed comparisons, and verifiable sources. Ensure consistent data across your website and major listings—assistants pull information from multiple sources and flag discrepancies. Schema markup becomes critical for machine readability.

Long-Term: Agent-to-Content and Agent-to-Merchant Pipelines

Assistants will pull structured data directly via APIs, bypassing search results pages. Your product information, availability, pricing, and policies need machine-readable endpoints. Traditional SERP rankings matter less when assistants retrieve verified data directly from merchant systems.

Ranking signals evolve toward trust, provenance, and verifiability. Brand authority depends on machine-readable credibility markers—verified reviews, certified sustainability claims, transparent pricing, and authoritative content citations. Freshness and consistency across data sources become primary ranking factors.

Build for the future: develop content schemas, offer APIs, and assistant partnerships. Document your products and services in structured formats. Create availability and pricing endpoints for assistant queries. Establish relationships with major AI platforms to maintain visibility as “conversational rank” replaces traditional SEO metrics. More details about the future of website engagements can be found in this blog post.

Purchase Behavior – Online and Offline

Short-Term: Advisor-Driven Shortlists and Faster Conversions

Assistants assemble recommended options, typically 3–5 choices with reasoning. Consumers review one or two detailed pages before purchasing instead of comparing dozens of options. Your goal shifts from broad visibility to inclusion on assistant-curated shortlists through relevant, structured content.

Decision support reduces purchase anxiety and accelerates conversions. Consumers feel more confident in assistant-recommended choices, leading to faster purchase decisions. Shopping carts reflect assistant-curated bundles, complementary products suggested together rather than individual discovery paths.

Voice assistants become in-store shopping companions. Consumers use voice guidance for SKU comparisons, real-time availability checks, and price verification. Buy-online-pickup-in-store (BOPIS) grows as digital research seamlessly transitions to physical fulfillment.

Long-Term: Agentic Commerce and Negotiating Buyers’ Agents

Personal AI agents will transact on behalf of consumers. These agents reserve inventory, apply promotions, schedule delivery, and handle returns without human intervention. Merchants offering transparent pricing, clear policies, and service-level guarantees via APIs gain preferential inclusion in agent recommendations.

New competitive advantages emerge around API-first commerce. Businesses that expose real-time inventory, dynamic pricing, and automated customer service through machine-readable interfaces will capture more agent-driven transactions. Traditional e-commerce sites become one layer in a broader ecosystem of agent-accessible services.

Omnichannel commerce becomes truly unified. AI agents coordinate online research, inventory checks, in-store pickup, and post-purchase service as seamless experiences. Loyalty programs and subscription replenishment become automated routines managed by personal agents rather than manual consumer actions.

The Sales Funnel and Buyer’s Journey Are Compressing

Short-Term: MOFU Deepens, BOFU Accelerates

Top-of-funnel awareness changes as problem-led prompts dominate. Consumers start with outcome-focused queries rather than browsing for inspiration. Your authority content matters when assistants cite it as credible sources, but broad awareness campaigns lose efficiency compared to targeted consideration-stage materials.

Middle-funnel consideration becomes more intensive. Assistants compare attributes, risks, and proof points in detail. Your structured data about features, benefits, guarantees, and third-party validation determines inclusion in detailed comparisons. Clear trade-offs and honest limitations build trust with both assistants and consumers.

Bottom-funnel decisions accelerate with risk reduction. Return policies, warranties, transparent fees, and service guarantees surface prominently in assistant recommendations. On-site checkout assistants help complete transactions, reducing cart abandonment through guided purchase completion.

Long-Term: Tasks Replace Stages; Human Touch at Trust Checkpoints

Traditional funnel stages collapse into task-based interactions. Awareness-to-decision frequently occurs within a single assistant conversation. Human interaction concentrates at high-stakes trust moments like video product demonstrations, expert consultations, or complex configuration choices that benefit from human expertise.

Post-purchase relationships become agent mediated. AI assistants onboard new customers, manage reorders, schedule maintenance, and handle routine service requests. Customer lifetime value increases when post-purchase experiences run smoothly without human friction but requires robust backend data and process automation.

New metrics track assistant influence throughout compressed journeys. Monitor your share of assistant recommendations, assistant-attributed conversions, and agent-initiated average order value and lifetime value. Traditional attribution models break down when customer journeys span multiple AI touchpoints before reaching your direct properties.

Websites Become Copilot-Ready: From Pages to Assistants

Short-Term: High-Competence Onsite Copilots

Consumer expectations shift toward interactive, task-completing website experiences. Visitors expect on-site assistants (chatbots) that answer questions, compare options, and complete transactions like quote generation, booking confirmations, checkout assistance, and return processing. Static information pages feel outdated compared to conversational interfaces.

Success requires clean, structured data foundations. Your product attributes, service descriptions, policies, fees, FAQs, and social proof (reviews, certifications) need structured formats that power accurate assistant responses. Inconsistent or incomplete data leads to poor copilot performance and lost conversions.

Measure copilot performance through task completion metrics. Track accuracy rates, successful task completions, resolution times, and customer satisfaction scores specifically for AI-assisted interactions. These metrics become as important as traditional conversion rates for optimizing user experience.

Long-Term: Sites as APIs and Verified Sources

Websites evolve into machine-readable storefronts. Offer endpoints, eligibility criteria, inventory status, pricing, and policy information become accessible to external assistants and buyers’ agents. Your site needs to function as both human-facing experience and machine-readable data source.

Content provenance and authenticity become competitive advantages. Verified content, watermarked media, source citations, third-party certifications, maintains inclusion in assistant reasoning as concerns about AI-generated misinformation grow. Authentic, attributable content becomes a trust signal for both assistants and consumers.

Human-designed experiences focus on trust and immersion. While AI handles routine interactions, human storytelling, immersive product experiences, and relationship-building moments become more valuable. Your site balances efficient AI-driven tasks with meaningful human touchpoints at crucial decision moments.

What Marketers Must Do Now (Playbook)

1) Win GEO: Generative Engine Optimization

Implement comprehensive schema markup across your digital properties. Structure data for products and services, FAQs, locations, policies, customer reviews, and sustainability claims. Use JSON-LD format for maximum compatibility with assistant data retrieval systems.

Create decision-support content that assistants can cite effectively. Develop comparison guides, “which option is right for me” frameworks, pros and cons analyses, and practical checklists. Keep sources and data fresh across all listings as assistants flag outdated or inconsistent information as less trustworthy.

2) Ship an Onsite Copilot That Can Transact

Deploy conversational interfaces that handle end-to-end customer tasks. Your copilot should retrieve information from your knowledge base and catalog, guide product or service comparisons, generate quotes or handle booking processes, and manage returns with appropriate escalation paths.

Implement proper guardrails and accuracy measures. Review assistant responses regularly, create hallucination detection systems, and ensure safe transaction boundaries. Measure task completion rates, customer satisfaction, and identify common failure points for continuous improvement.

3) Expose Offers and Post-Purchase Data via APIs

Build live offer endpoints for real-time assistant queries. Provide current pricing, inventory status, estimated delivery times, and active promotions through machine-readable interfaces. Implement appropriate rate limits and authentication for external access.

Structure post-purchase information for ongoing customer relationships. Make product manuals, usage tips, warranty information, and replacement parts data accessible to assistants serving your customers. This enables immediate customer service without human wait times and builds long-term satisfaction.

4) Rethink Distribution and Measurement

Audit major AI assistants for your business inclusion and data accuracy. Check how ChatGPT, Claude, Google’s AI Overviews and AI Mode, and industry-specific assistants represent your brand, products, and services. Correct missing information and weak citation sources that undermine your assistant visibility.

Develop new KPIs that reflect assistant-driven customer journeys. Track your share of assistant recommendations, assistant-attributed revenue, task-to-transaction completion times, and agent-initiated customer lifetime value. These metrics complement traditional web analytics as consumer behavior shifts toward conversational interfaces.

Risks, Constraints, and How to Mitigate Them

Data inconsistency across platforms creates confusion and lost trust. Establish a single source of truth for all product, service, and policy information. Automate syndication to major platforms and directories to maintain consistency as data updates. Regular audits catch discrepancies before they impact assistant recommendations.

Compliance and brand safety concerns require careful copilot guardrails. Ensure on-site assistants provide reversible actions, clear consent processes, and appropriate escalation to human agents for sensitive issues. Document AI decision-making processes for regulatory compliance and brand risk management.

Attribution gaps complicate measurement of assistant influence on conversions. Use referral parameters, when possible, post-purchase surveys to identify assistant touchpoints, and modeled incrementality analysis to estimate assistant contribution to revenue. Accept that attribution becomes less precise as customer journeys span multiple AI platforms.

Frequently Asked Questions