Designing AI-powered shopping experiences for the next generation of commerce

Agent-based experiences erode trust

Autonomous agents—designed to reorder pet food or book travel—can alienate users if they don’t reflect nuanced preferences. 

Clickstream data doesn’t tell the full story

Most eCommerce teams optimize AI performance based on CTR, bounce rates, and conversion data. But that tells you what happened—not why. Behavioral data alone can’t explain:

• Why customers skipped the smart filter
• Why they dropped off during a co-pilot interaction
• Why they disabled an AI assistant after one use

When AI gets the experience wrong—too generic, too pushy, too opaque—shoppers don’t just opt out of the feature. They question your brand’s relevance, trustworthiness, and ability to serve them in the future.

Understanding these behaviors requires human insight. Watching, listening, and analyzing real experiences with real people adds clarity.

The emerging paradigm: Human-AI co-shopping

We’re entering an era where shoppers won’t just browse and buy. They’ll collaborate with AI copilots, voice interfaces, and shopping agents that act on their behalf.

This shift introduces two critical imperatives for eCommerce and digital leaders:

1. Designing embedded AI experiences
   These include smart filters, voice-enabled search, dynamic product advisors, and personalized prompts embedded into digital storefronts.
2. Preparing for autonomous AI agents
   AI systems like Amazon’s Alexa, Google’s Gemini, and third-party agent networks are beginning to transact independently. These agents are interpreting customer preferences, reordering subscriptions, and even negotiating on price.

But without a deep understanding of real human behavior, these systems risk overstepping, misfiring, or simply being ignored. Trust, usability, and perceived value must be designed into every interaction.

The solution: design AI shopping experiences with human insight at the core

1. Diagnose the gaps: understand why customers opt out of AI

Before refining features or training new models, identify where your AI experience is breaking down and why.

Example: A shopper opens a “smart outfit builder” and immediately exits—not because the suggestions were wrong, but because the tool launched before they’d even chosen a category. The timing felt pushy, not helpful.

Takeaway: AI features often fail not on logic, but on context. Early exit signals can reveal emotional friction, not functional failure.

UserTesting enables brands to run rapid, real-world tests of AI experiences with target shoppers. Whether embedded in a mobile app or voice-enabled assistant, you can:

• Observe users interacting with AI features in natural settings
• Identify where intent, language, or timing feels off
• Hear—in customers’ own words—why they abandoned or ignored the AI flow.

2. Build AI-specific personas based on trust thresholds

Not every shopper wants the same level of AI involvement. 

Example: A customer regularly reorders the same dog food. One day, the AI assistant swaps in a new formula marked “better rated.” The customer cancels the order and disables the assistant—because they never asked for a change.

Takeaway: Even helpful automation can backfire if it oversteps a user’s trust boundary. Personas must reflect not just needs—but control expectations.

Design experiences based on trust thresholds:

• Delegators: High trust in automation. Willing to let AI choose and act (e.g., replenishers, subscription buyers).
• Validators: Open to AI suggestions but require review and final approval.
• Controllers: Low trust. Prefer to shop manually and opt out of AI features.

UserTesting helps you segment your customer base with precision—capturing both behavioral and attitudinal dimensions. Our research templates and QXscore framework allow you to measure how each persona group responds to different types of AI interactions.

3. Prototype and test AI features before launch

AI experiences require more testing than static UX because:

• AI doesn’t follow a fixed script—it makes decisions based on patterns, so its responses can change depending on the situation. For example, two shoppers with similar browsing behavior might receive different product recommendations if the AI interprets subtle differences in past purchases or session context.
• AI experiences shape perception in more complex ways—because unlike static interfaces, they respond dynamically. The tone of a chatbot, the timing of a recommendation, or how clearly the AI explains itself can all change how customers interpret its intent. For example, a product suggestion from a human feels like advice—but from an AI, it might feel intrusive unless the system explains why it made that choice.
• They carry emotional weight, especially in personal or high-ticket purchases

Example: A skincare brand launches a virtual product quiz with AI-generated tone analysis. In testing, users balked when the AI said, “You appear stressed—try our calming serum.” The tone felt judgmental instead of caring.

Takeaway: AI tone isn’t just words—it’s implication. Small changes in phrasing can dramatically shift how users feel about your brand.

With UserTesting, eCommerce teams can test unfinished flows, mockups, or voice prompts with real users in days not weeks. You’ll gain insight into:

• Which wording inspires trust
• Where friction or confusion arises
• What shoppers expect the AI to do next

Bonus: Our AI-powered video analysis tools summarize customer reactions instantly—flagging moments of hesitation, delight, or frustration—so you can prioritize improvements that matter most.

What to measure:

• Feature adoption rate – Are customers discovering and using the AI feature?
• Abandonment rate – Where are they dropping off or opting out mid-journey?
• Repeat usage – Do they come back to use it again (a strong proxy for trust)?
• Customer Effort Score (CES) – Was the AI experience easier than a manual one?
• AI trust score – Do customers feel confident that the AI understands their needs and acts accordingly?
• Transparency impact – Do small design changes (e.g., “Here’s why I recommended this…”) materially change how AI is received?

4. Prepare for agent-to-agent shopping by designing fail safes and control points

As AI agents begin interacting directly with commerce systems (e.g., “Buy the best protein powder under $50”), retailers must design for indirect shoppers. This introduces new challenges:

• How do you ensure AI agents can interpret your product data correctly?
• How do you preserve brand differentiation when agents standardize options?
• How do you support “reassurance layers” when humans need to override?

Example: A customer says to their phone, “Buy the best protein powder under $50.” The AI agent selects a top-rated item based on price, but misses that the customer always chooses plant-based products. Now they’re frustrated—and returning the item.

Takeaway: Agent-driven commerce demands context awareness, preference memory, and explainability. One missed cue = lost trust.

UserTesting helps you simulate these scenarios by testing how customers respond to AI agents acting on their behalf across mobile, chat, or smart home interfaces. You can test confirmation flows, fallback options, and confidence-building copy.

5. Benchmark and continuously improve the AI experience

AI-driven commerce isn’t one-and-done. These experiences must evolve as customer expectations, behaviors, and technology shift. To ensure your AI features stay effective and trusted, continuous measurement is essential—not just of conversion, but of customer perception, usability, and long-term adoption.

Start by benchmarking the experience before launch. Then monitor how each AI touchpoint performs across both functional and emotional dimensions over time.

Final word: in AI commerce, trust is the new conversion

The future of eCommerce isn’t just faster or smarter; it’s more personal, contextual, and trusted. As AI shopping agents become reality, customers will continue to ask: “Does this brand know me? Can I rely on it? Am I in control?”

UserTesting gives you the human insight to answer “yes” with clarity, confidence, and credibility.

Let’s design AI shopping experiences that customers actually want to use.