Why shoppers prefer AI-enhanced commerce — and why the human touch still matters

TL;DR: AI agents already influence large portions of online shopping (roughly $262B of 2025 holiday spend), and traffic from third‑party AI search tools converts at far higher rates than social referrals (Salesforce). Consumers welcome anticipatory, personalized shopping but demand transparency, control and human backup for complex or emotional decisions. Practical priorities: make product data AI-readable (generative engine optimization), pilot agents for routine service, and build consent and escalation flows so trust scales with automation.

Why this matters right now

When discovery moves from feeds and search boxes into agentic interfaces like ChatGPT, Gemini and Perplexity, brands face two simultaneous pressures: capture the high-intent traffic those agents drive, and earn shoppers’ consent to use their data. During the 2025 holiday period, AI and agents influenced about $262 billion of online sales and retailer AI usage for customer service surged during peak season (Salesforce). That’s not hypothetical — it’s immediate revenue and a new battleground for visibility.

Consumers are shifting from valuing purely price-based deals toward valuing intelligence-driven experiences that provide transparency, confidence, and tailored assistance.

What shoppers actually want — clear signals from the data

• High adoption, mixed satisfaction: 73% of consumers have used AI chatbots to search for products, and 52% use virtual assistants weekly for tasks like re-ordering (industry surveys). Yet only about 57% report satisfaction with chatbot interactions, and frequent assistant users rate satisfaction nearer 46%.
• Privacy & control matter: 71% worry about how generative AI uses their data; 76% want explicit control over assistant boundaries (what the agent can do and what data it can access).
• Humans for nuance: Roughly 7 in 10 routine tasks can be automated, but 66% of consumers prefer human help for hard purchasing decisions and 74% prefer in-person assistance for in-store service.
• Branded agents move the needle: Companies with their own branded AI agents saw average YoY sales growth of 6.2% versus 3.9% for others — a meaningful advantage for early adopters.

What “anticipatory AI” looks like

A simple example—no engineering required to understand the UX:

Shopper: “I need a birthday gift for my sister — she likes running but hates clunky gear.”
Agent: “Great—based on her recent purchases and your budget, she’s due for a hydration belt. I found three lightweight options with high comfort ratings and free returns. Want me to check which fits her shoe size?”
Shopper: “Yes — and include color preferences she’s liked before.”
Agent: “Booked: 2 options fit her preference and have 4.6+ ratings. I’ll add a gift message and hold for your approval.”

That anticipatory flow reduces purchase anxiety, narrows choices, and increases experimentation—if the agent is trusted and accurate.

Key business signals: where AI actually drives value

AI-driven discovery converts. Traffic from ChatGPT, Gemini and similar tools doubled year-over-year in 2025 and converts at roughly nine times the rate of social referrals (Salesforce). Why? Agentic search surfaces higher-intent results, focuses on direct answers and reduces distraction—customers come with a clearer intent to buy.

Operational wins are concrete too: during peak season, agent-powered service conversations rose significantly and direct-action tasks (address updates, returns) handled by agents increased by large double-digits. Those are cost and time savings: AI automation deflects routine contacts and frees human agents to handle escalations, upsell, and retention work.

Definitions: the jargon you’ll see in planning decks

• AI agents: Automated assistants that act on behalf of users—searching, recommending, buying, or completing tasks.
• Agentic search: Discovery via third-party assistants (ChatGPT, Gemini, Perplexity) that synthesize information and return recommendations rather than a ranked list of links.
• Generative engine optimization (GEO): Structuring and enriching product content so generative AI systems can find, interpret and recommend your products.
• Branded AI agents: Company-owned assistants (chatbots or voice agents) that represent a brand and can be tailored to its tone, offers, and data access rules.

What marketing and product teams must change now

If your product pages aren’t AI-readable, agents will overlook you. GEO isn’t theoretical SEO — it’s practical tagging, semantic context and clear policy signals so agents can recommend your products confidently.

Generative engine optimization checklist

• Structured data: implement schema markup and rich product attributes (materials, dimensions, use cases, warranty, return policy).
• Semantic clarity: avoid vague adjectives; include concrete context like “fits true to size” or “best for beginner runners.”
• Actionable metadata: mark fields for price, shipping, availability, and verified reviews so agents can assess intent and risk.
• Consent flags: include machine-readable signals that indicate what data an agent can access (e.g., email for receipts, address for shipping).
• Test across agents: sample queries in ChatGPT, Gemini and Perplexity to see how your content surfaces and what gets lost.

Quick wins you can start today

1. Tag top 100 SKUs for GEO: Add structured attributes and clear return/shipping policies—start with your bestsellers.
2. Pilot an agent for returns and address updates: Those are high-volume, low-risk tasks that deliver cost deflection and measurable KPIs.
3. Publish a consent UI and agent boundary page: Tell customers what your assistant can do and how to turn features on/off.

Three-step playbook for leaders

Treat this as Pilot → Scale → Govern. Below is a practical timeline and ownership model.

Pilot (0–3 months)

• Owner: Product + CX
• Actions: Launch a small agent pilot for a single routine task (returns, address changes, reorders). Tag 50–100 products for GEO and A/B test consent wording.
• Metrics: Contact deflection rate, resolution time, CSAT for agent interactions.

Scale (3–12 months)

• Owner: Product + Marketing + Engineering
• Actions: Expand GEO to the top 20% of catalog, add conversational commerce flows tied to promotions, integrate with CRM for personalization, and launch a branded agent experience.
• Metrics: AI-influenced revenue, conversion lift from agent referrals, average order value, organic agent-driven traffic.

Govern (ongoing)

• Owner: Legal + Privacy + Product
• Actions: Maintain consent dashboards, audit training data and model outputs regularly for hallucinations and bias, and publish an assistant transparency page.
• Metrics: Consent opt-in rates, privacy-related complaints, frequency of human escalations for corrections.

Governance, trust and the human fallback

Consumers want control. Practical controls include default privacy-preserving settings, simple toggles for data sharing, and clear escalation paths to humans. Without those, efficiencies become liabilities.

AI assistants are moving from background tools to active decision‑makers that can anticipate needs and suggest products before consumers ask.

Sample consent microcopy you can use:

“Allow ShopAssistant to access your order history to suggest items and speed checkout. You can revoke access anytime in Settings. ShopAssistant will never share your data with third parties without explicit permission.”

Risks and limitations to monitor

• Hallucination and inaccuracy: agents sometimes fabricate facts—always provide an easy human escalation and verification step for purchases above a risk threshold.
• Data leakage: guard customer PII and payment flows; use tokenization and required authentication for transactional actions.
• Regulatory exposure: privacy and AI transparency rules are evolving—keep legal in the loop for consent models and logging.
• Brand experience drift: poorly tuned agents can damage loyalty through bad recommendations—monitor CSAT and complaint volume closely after launches.

Metrics every leader should track

• AI-influenced revenue (absolute $ and % of total)
• Conversion rate from agentic search vs. other channels
• Contact deflection rate and cost per contact saved
• CSAT for agent interactions and for human escalations
• Consent opt-in rate and retention of opt-in users

FAQs

What are AI agents in retail?
AI agents are automated assistants that can search, recommend, and perform tasks for shoppers—ranging from product suggestions to completing returns.

How does AI automation improve conversion?
Agents reduce friction by narrowing options, pre-filling data, and surfacing high-intent offers—traffic from third‑party AI tools has shown substantially higher conversion than social referrals (Salesforce).

Can customers trust ChatGPT or Gemini to shop for them?
Some shoppers already trust these tools for discovery, but most want transparency and the ability to limit what the assistant can do. Trust is built through clear consent, reliable recommendations, and easy human escalation.

Final balancing act and next step

AI-enhanced shopping is choreography, not replacement. When agents handle routine discovery and operational tasks, humans can concentrate on judgment, empathy and retention. That hybrid model boosts conversion and frees capacity—if brands design for trust, transparency and discoverability.

Actionable next step: this month, run a three-item audit—(1) tag your top SKUs for GEO, (2) deploy a pilot agent for a high-volume routine task, and (3) publish a clear consent toggle and escalation path. Those steps surface quick wins while protecting the customer relationships that sustain long-term growth.