u/Classic-Schmosby-534

Amazon has discontinued Rufus and replaced it with "Alexa for Shopping," which integrates AI directly into Amazon's search bar rather than as a separate chatbot.

More than 300 million customers used Rufus in 2025, and experts believe Alexa for Shopping's reach could be even greater. The pricing implication is quite significant: AI-assisted product discovery on Amazon is no longer a side feature, but the primary interface.

How your products are priced, described, and positioned now affects whether an AI recommends them, not just if a shopper clicks on them.

Source: ReailDetailEU

Is AI now also going to replace our water cooler convos, because they started chit-chatting with each oter? Well... probably (hopefully) not. But AI agents are increasingly talking more and more to each other:

AI shopping assistants like Rufus, Perplexity Shopping, and Google's Shopping AI are now actively comparing prices, specs, and reviews across retailers in seconds on behalf of consumers. On Black Friday 2025, Salesforce tracked $14.2B in global sales influenced by AI agents.

McKinsey just surveyed 400+ pricing executives and found that only 5–10% have fully scaled agentic AI in their pricing workflows today, even though 65–85% plan to in the next three years. (Worth noting: McKinsey's data is B2B, but the adoption gap holds in retail too.)

Of course, this is a very recurrent topic that, I assume, pricing teams are very aware off/are already dealing with, but I thought the visualization is quite interesting to fully understand AI's impact on purchasing behaviour and pricing strategy handling of retailers

Sources:
Example of AI pricing agent
Salesforce survey
Agentic pricing/buying deep-dive
McKinsey survey

GMV hit $22.2B in Q1 2026, up 18%. Revenue grew 19% to $3.1B. CEO Jamie Iannone credited C2C commerce and "recommerce" (secondhand goods) as the main growth driver.

This matters for pricing in the broader marketplace context. eBay is effectively being reinvented by consumers who want to buy and sell pre-owned fashion, and the platform's pricing dynamics are completely different from a traditional retail marketplace. There's no MAP enforcement, no standardized pricing, and no algorithmic buy box in the same way Amazon runs it. Prices are set by individual sellers competing on condition, photos, and trust signals.

For brands selling on eBay: the secondhand version of products is now competing with new products in ways that can undercut a seller's own pricing architecture. Premium brands in particular should be watching the secondhand pricing of their hero products closely.

Some insights into how pricing analytics tools work in relation to eBay

Meta just introduced Muse Spark, a new AI model embedded into Instagram and Facebook that can suggest outfits, style rooms, and gift ideas. It pulls data points from creators and brands that are already active on the platform. And just like with other AI tools, one-tap checkout is on the way.

Now, with more data being used to recommend products, the implications for pricing are getting less subtle. Because Meta Muse recommends products BASED on context. When someone asks "what should I get my friend who likes minimalist fashion under €100," Muse is making a judgment call that combines price, and product relevance.

This means that pricing strategies of retailers and brands also becoming a filter in a recommendation system. Combined with OpenAI's Walmart and Target integrations and Amazon's Buy For Me feature, AI-assisted shopping is compressing the window between intent and purchase.

Most people think of IKEA as a low-price retailer that just... is cheap. The reality is more deliberate.

On the competitive side, IKEA runs an AI system that continuously scrapes competitor sites and uses machine learning (combining image recognition and NLP) to match comparable products across brands, even when names and specs differ. Pricing intelligence that used to take analysts weeks now runs in the background across all markets.

On the supply side, their demand forecasting tool pulls from up to 200 data sources per product (local salary cycles, weather, seasonal patterns) to reduce overstock. Their logic: excess inventory means markdowns, and markdowns are a pricing cost. Better forecasting means fewer forced price cuts.

The question for the rest of retail: at what point does competitive pricing stop being a weekly task and start being infrastructure?

Source: Merlien

David's Bridal, the largest bridal store chain in the United States (selling 25-30% of all wedding dresses in the country), just partnered up with OpenAI and Microsoft to ride the AI wave.

David's Bridal launched end-to-end shopping directly inside ChatGPT and Microsoft Copilot. Real product cards, live inventory, and a buy button, without leaving the chat.

"The next generation of brides isn't going to open a browser and type in a URL. They're going to describe what they want," said the company's CTO.

The system runs on a proprietary platform they built to make their product data AI-readable. Every silhouette, fabric, neckline, price point, and availability signal had to be structured and tagged in a way AI systems can actually consume and act on.

"There's this perception that you can just point AI at your data and it'll figure everything out. That's just not the case."

AI interfaces don't respond to visual merchandising or "was €99.99" the way humans do. They read structured data. This is another good example that, if pricing information isn't clean, tagged, and machine-readable, products are not getting surfaced, regardless of how competitive the actual price is.

Is anyone already thinking about/actively working on what "AI-ready" pricing data looks like in practice?