AI in Retail: Beyond the Hype

Every retail software vendor now has an “AI” slide in their pitch deck. Chatbots that answer product questions. Recommendations that feel creepy. Predictive analytics that somehow never predict anything useful.

We’re tired of it too.

AI has real potential in retail—but only when it’s grounded in actual data from actual transactions. The moment AI starts “imagining” things, it becomes useless or worse. Here’s how we think about AI at Kynetik, and why we’ve built it differently.

The problem with retail AI today

Most retail AI falls into one of two traps:

The gimmick trap: Features that sound impressive in demos but don’t solve real problems. “Our AI can have a conversation about your inventory!” Cool, but can it help me find the product a customer is describing when they don’t know the name?

The hallucination trap: Generative AI that makes things up. Ask it about a product and it invents specs that don’t exist. Ask about pricing and it guesses. In retail, made-up information isn’t just unhelpful—it’s actively harmful. A wrong price damages trust. A wrong product spec can mean a return.

Both traps stem from the same root cause: AI disconnected from real data.

AI that’s grounded in reality

Here’s our principle: AI should only speak about what it actually knows. And in retail, what it knows comes from three sources:

1. Your product catalog - Names, descriptions, prices, variants, inventory. The facts.
2. Your transaction history - What customers bought, when, how they paid, what got returned.
3. Your store patterns - Peak hours, popular products, promotional effectiveness.

When AI is grounded in these data sources, it becomes genuinely useful. Not because it’s creative, but because it’s precise.

What grounded AI actually looks like

Let’s look at specific examples from how Kynetik uses AI:

Product lookup that understands context

A customer says: “I’m looking for that red thing I bought last month.”

Useless AI response: “I’m sorry, I don’t have enough information to help you find that product.”

Grounded AI response: “Based on recent purchases on this customer’s account, the most likely match is the Crimson Ceramic Vase ($45) purchased on December 12th. There’s also the Red Leather Wallet ($85) from December 8th.”

The difference? The second response is checking actual transaction data, not guessing. It might still be wrong—maybe the customer meant something else—but it’s giving the staff member a concrete starting point based on real purchases.

We never surface another customer’s history. Clienteling stays scoped to the person in front of you (their account, their device, their consent).

Faster checkout, higher conversion

Speed matters at the counter. When the AI surfaces the exact item, the right promotion, and the preferred payment method, checkout time drops and conversions rise. No hunting through menus. No re-typing items that should auto-populate. The goal: more sales per hour, happier customers.

Endless aisle without the complexity (coming soon)

We’re building a dedicated endless-aisle mode: shoppers can browse on a kiosk or tablet with an embedded AI guide. They can:

• Build a cart via chat or browse mode with live stock and pricing
• Check out themselves (QR/terminal) or hand off the cart to any register for human checkout
• Keep promotions, taxes, and tender rules consistent with the main POS

The same guardrails apply—answers are scoped to the active shopper, never to other customers.

Promotion clarity that prevents errors

Staff member asks: “Is this jacket part of the holiday sale?”

Useless AI response: “The holiday sale typically includes seasonal items. Jackets might be included if they’re marked as seasonal merchandise.”

Grounded AI response: “No. The ‘Winter Warmth’ promotion (20% off) applies to items in the Outerwear category, but this jacket (SKU: JK-7892) is categorized as Formal Wear. No active promotions apply to this item.”

No hedging. No “might be.” Just the facts from the actual promotion rules configured in BigCommerce.

Customer context that helps without being creepy

Staff member looks up a customer for a return.

Useless AI approach: Profile pop-up with “Personality type: Bargain Hunter, Engagement Score: 6.2, Predicted Lifetime Value: $847”

Grounded AI approach: “Regular customer. 12 orders in the last year. Prefers card payment. Last purchase was a size L in the Athletic line.”

One treats customers like data points to be optimized. The other gives staff useful context that helps them provide better service.

The guardrails that matter

For AI to be trustworthy in retail, it needs hard limits:

Never invent prices or inventory counts. If AI doesn’t have current data, it says so. “I don’t have current stock levels for this item” is infinitely better than a guess.

Never make up product specifications. AI can describe products using the data from your catalog. It cannot extrapolate features that aren’t documented.

Always provide source context. When AI surfaces information, the staff member should understand where it came from. “Based on transaction history” or “From the active promotion rules.”

Human makes the call. AI provides information and suggestions. Staff make decisions. The approve button is always in human hands.

Why transaction data changes everything

Here’s what makes Kynetik’s approach different: we collect detailed event data from every transaction.

Not just “Order #1234 was placed.” We know:

• Which products were scanned and in what order
• Which got removed and why (customer changed mind, out of stock, price concern)
• How long the checkout took
• Which promotions were considered vs. applied
• Which payment methods were attempted vs. succeeded

This granular data—captured automatically as staff use the POS—becomes the foundation for AI that actually understands your store.

When a manager asks “Why were sales down Tuesday afternoon?”, AI can answer with specifics: “Transaction count was normal, but average order value dropped 23%. The top abandoned item was the Fall Collection dress (3 carts). Staff noted ‘price’ as the removal reason in all cases.”

That’s not AI magic. That’s AI doing exactly what it should: surfacing patterns in data that humans would take hours to find manually.

The future we’re building toward

We believe retail AI will become essential—not as a gimmick, but as a tool that makes store operations more efficient.

Staff will ask questions in natural language and get precise answers drawn from real data. Managers will get proactive alerts when patterns suggest problems. New employees will onboard faster because AI can answer their questions instantly.

But all of this depends on one thing: data integrity. AI is only as useful as the data it’s grounded in. That’s why we built Kynetik’s event system first, before building any AI features on top.

The foundation has to be solid. The data has to be real. And the AI has to know its limits.

That’s the difference between AI that helps and AI that’s just another thing to apologize for.

Kynetik’s AI features are built on real transaction data, not guesswork. Learn more about Kynetik AI or see how it works in practice.