The expertise gap between you and your customers was once a competitive advantage. You knew what they didn’t. You could guide them, educate them, and close sales based on knowledge they couldn’t get easily and clearly online. That dynamic is shifting, and most independent retailers need a strategy to be prepared.
Today, nearly half of all consumers use generative AI tools like ChatGPT, Gemini, and Perplexity to research purchases before they ever walk through your door or visit your website. According to a Capgemini Research Institute study, 58% of consumers have replaced traditional search engines with AI tools for product and service recommendations, up from 25% in 2023. McKinsey’s 2025 AI Discovery Survey found that 50% of consumers now intentionally seek out AI-powered search, with 44% calling it their primary source of buying insight, ahead of traditional search engines (31%), retailer websites (9%), and review sites (6%).
But here is what makes this different from traditional online research: these customers aren’t just reading reviews or comparing prices. They’re asking AI to generate vetting questions. They’re walking in with checklists. They know just enough to sound informed, but often not enough to evaluate whether the answers they get actually apply to their purchase.
This creates a new kind of customer interaction: one where the traditional “curse of knowledge” (the cognitive bias where experts assume others share their level of understanding) collides head-on with the “illusion of knowledge” (the confidence AI-generated research gives consumers who may not fully understand the context behind the questions they’re asking).
For independent retailers, this is not a scenario to ignore. It is an opportunity to reframe how you communicate, what you publish online, and how you train your team to engage with the “AI-informed” buyer.
The Expertise Blind Spot
The “curse of knowledge” was first identified in a 1989 study published in the Journal of Political Economy. The researchers found that better-informed agents consistently failed to account for what less-informed people knew, even when it was in their financial interest to do so. In retail, this shows up every day: you use industry jargon on your website, on your social media, and in customer conversations without realizing it because those terms feel as natural to you as breathing.
Consider the independent jeweler who describes a ring as a semi-mount or gemstones-and-metal on a product page, using abbreviations and other jargon. That phrase means everything to a trained gemologist and almost nothing to a first-time engagement ring buyer. Or the specialty skin care retailer whose shelf tags reference “retinaldehyde vs. retinyl palmitate” when the customer simply wants to know which product will reduce fine lines without irritating sensitive skin.
This is not a minor communication issue. Research from CXL Institute and UserTesting has consistently shown that when customers encounter unfamiliar terminology, most will not ask for clarification. They leave. They find a competitor whose language matches their understanding. Your fluency with industry terms does not signal expertise to the uninitiated customer. It creates confusion, and confused customers do not buy.
The AI-Informed Customer: Confident, Prepared, and Frequently Wrong
Now layer in what AI is doing. A 2025 Attest Consumer Survey of 5,000 consumers found that 47% are likely to use generative AI tools to research purchases, a 6-point jump from the prior year. Adyen’s 2025 Retail Report, surveying 41,000 consumers across 28 countries, found that over a third (37%) actively use AI to shop, a 47% increase from 2024. Among Gen Z, that figure is 57%.
These customers are not casually browsing. They are asking AI-specific, detailed questions, such as: “What should I ask a jeweler before buying a diamond engagement ring?” “What red flags should I watch for when choosing a skincare brand?” “How do I evaluate whether this retailer is legitimate?”
The AI obliges with authoritative-sounding checklists. And some of those questions are excellent. But many are generic, context-dependent, or pulled from information that does not apply to the specific buying scenario. The customer is unaware of the difference. They walk in with a printed list or a phone screen full of questions and absolute confidence that these are the “right” things to ask.
Example: The AI-Prepped Diamond Buyer
A customer walks into your jewelry store, having asked ChatGPT: “What questions should I ask a jeweler before buying a diamond?” The AI returns a checklist that includes questions on the 4 Cs (cut, color, clarity, carat), certification (GIA vs. IGI), and whether the diamond is conflict-free. All reasonable. But the list also includes questions about “fluorescence grading” and “light performance imaging,” which primarily apply to online diamond retailers that use technology such as ASET or Hearts and Arrows scopes. These tools are not utilized in most independent brick-and-mortar jewelry stores because they are not needed for face-to-face sales. (I had to check this item myself with one of my clients, as I am not a diamond expert 🙂) Whether you are selling online or in person, you need to be prepared to explain a variety of testing methods to your client.
If your team is not prepared to explain why that particular question does not apply to your selling environment, the customer leaves thinking you are less thorough than the online retailer. The AI gave them information without context. Your job is to provide the context.
Why This Is a Revenue Issue, Not Just a Communication Challenge
This is not a soft-skills problem. The data makes the business case clear.
McKinsey projects that by 2028, $750 billion in U.S. revenue will funnel through AI-powered search. Brands that are unprepared could see a 20 to 50% decline in traffic from traditional search channels. A Quantum Metric study found that AI-referred traffic has increased 600% since January 2025, and 42% of consumers will use AI to find or compare purchases in 2025.
The implication is clear: the AI is becoming your customer’s first advisor. If the information it provides about your category does not align with what they experience in your store, you lose credibility before your team even says hello. If your website does not include the educational content that AI tools draw on, your retail store may not even be considered.
Retailers who proactively close this gap, who translate their expertise into language and content that both AI tools and customers can understand, position themselves as the authoritative voice in their category. Those who do not will increasingly be evaluated against AI-generated benchmarks they had no hand in creating.
What Independent Retailers Can Do Now
1. Ask the AI What Your Customers Are Asking
The best way to approach this is to talk to your customers or even newly hired team members. You need to start with individuals who lack the knowledge. Get the questions from them, the way they would phrase them. Then open ChatGPT, Gemini, or Perplexity and type the exact question a prospective customer would ask: “What should I ask before buying [your product/service]?” “What’s the best [your product/service] for X or in my area, and why?” Read the response carefully. Identify which questions are relevant to your business, which require context to answer properly, and which do not apply at all. Take note of the follow-up questions or suggestions from the AI. This is your new competitive intelligence.
2. Build Your “Plain Language” Content Library
Create website content, blog posts, FAQs, YouTube videos, and social media posts that answer these AI-generated questions in your own voice. Use the customer’s vocabulary, not your industry’s vocabulary. When you do introduce a technical term, define it immediately in the same sentence. For example: “A diamond’s cut grade measures how well it reflects light, which directly affects how much it sparkles.”
This serves two purposes: it educates your customer, and it trains AI tools to source your content as an authoritative reference. The more clearly and comprehensively you answer common questions on your website and regularly searched resources like YouTube, Reddit, and other digital publications, the more likely AI search tools are to surface your business.
3. Train Your Team for the AI-Informed Buyer
Your sales staff needs to know what AI is telling your customers. Run the same AI queries that your customers run and build response guides around them. When a customer asks an AI-generated question that does not apply, your team should be able to say: “That’s really a great question if you were buying X or looking for Y. Here’s why it works differently when you’re purchasing Z, and here’s what matters more in this setting.” This response validates the customer’s research while redirecting to what matters.
4. Publish a “What to Ask Us” Guide
Instead of waiting for customers to arrive with an AI-generated checklist that may or may not apply, give them a better one. Create a downloadable or web-based guide titled “10 Questions to Ask Before Buying [Your Product Category]” that reflects what matters for your specific business. Share it on your website, email list, and social channels. This would make a great popup or welcome email follow-up. This positions you as the expert before the customer ever walks in.
5. Audit Your Jargon Quarterly
Have someone outside your industry (a friend, a family member, or a new employee in their first week) review your website, social media, product descriptions, and email campaigns. Ask them to flag every term or phrase that is not immediately clear. If they cannot understand it without an explanation, your customers probably cannot either. This would also be great for a college business intern. Do this every quarter because jargon creeps back in the moment you stop watching for it.
Implementation Framework
Discovery. Run AI queries for your product category. Document every question generated. Categorize them as relevant, needs context, or does not apply. Share findings with your team.
Content Creation. Write and publish a “What to Ask Before Buying” guide for your website. Rewrite your top 10 product descriptions using customer-facing language. Update your FAQ page to address AI-generated questions directly.
Team Training. Hold a team session where staff practice responding to AI-generated questions. Build a simple reference sheet with recommended responses for questions that need context or do not apply. Role-play scenarios with the team.
Ongoing: Jargon Audit. Schedule a quarterly review of all customer-facing language. Re-run AI queries every quarter, as AI tools update their responses regularly.
The Business Case
The retailers who win this shift will be the ones who stop assuming their expertise speaks for itself and start actively translating it for an audience that is doing its own research, often with tools that lack the context to guide them properly.
With 50% of consumers already using AI-powered search as their primary buying research tool (McKinsey, 2025), 58% replacing traditional search engines with AI for product recommendations (Capgemini, 2025), and AI-referred website traffic growing 600% year over year (Quantum Metric, 2025), the cost of inaction is not hypothetical. Retailers who fail to address the AI-informed customer risk being evaluated against criteria they did not set, described in language they did not choose, and compared to competitors whose online content is better optimized for the way customers now research.
The good news: this is a differentiation opportunity, not just a defensive play. Independent retailers who create clear, jargon-free educational content and train their teams to engage with AI-informed buyers will build deeper trust, close more sales, and establish themselves as the category authority that AI tools eventually cite.
