Online Stores (Webstores) Are About To Disappear Forever

Artificial Intelligence (AI) Agents are going to transform business across every sector, and e-commerce will be one of the industries that changes the most because every online shopper will have their own AI agent that fetches products, and brings the products to them, displaying the products and details in a format that they like, not that the retailer chooses for them. Any stores; all stores. So, there’s no real need for a store anymore. Product data feeds have existed for a long time. AI agents can ingest data feeds and serve the results in a clear and easy-to-understand display, designed by YOU. They can automatically filter the results, present the best options, and then place the orders.

Why should you have to start with a search, then browse an online catalog, read all the reviews (while having to determine which ones can be trusted), and click back and forth through styles across as many websites as you have the time to spend?

AI agents are intelligent systems that understand language and are able to perceive, analyze, and act upon complex data and/or workflows. “Act” is the key word. AI Agents take autonomous action on your behalf to complete the task or achieve the desired outcome. Need a pack of white undershirts? Feed the requirements into the agent using natural language and pick from the results that used YOUR search criteria, YOUR size, YOUR gender; not the results determined by Google or Amazon using algorithms and rankings nobody understands.

Now that may be a provocative thought experiment, but it will happen, it’s only a matter of when. But, retailers and brands will always need an Order and Inventory Management System because there needs to be a central repository for all the master product catalog data, customer data, all the transactions such as orders, returns, and cancellations, the need to capture (or receive) payments, track inventory and keep channel listings up-to-date, the ability to analyze all the data from all the sales channels and make business decisions accordingly. So an OMS/IMS is the central nervous system for all the critical business data. And now these SaaS platforms are being supercharged with AI agents to perform tasks and generate insights with unprecedented speed and accuracy. Let’s take a look at some examples:

1. Best-in-Class Inventory Management and Procurement
   Modern Order and Inventory Management Systems (OMS/IMS) predict demand with remarkable accuracy by using AI agents to analyze tens of millions of data points from historical sales data, supply chain status, seasonality, market trends, and more, to make intelligent, real-time forecasts for demand. AI agents can then automatically create Purchase Orders with vendors taking into consideration things like minimum order quantity, vendor lead time, and estimated transit days including current port congestion forecasts. And the best part is that it’s all based on natural language processing so if you can explain it, an AI agent can do it.
2. Remove Waste from Fulfillment Workflows
   Humans are typically responsible for fulfillment operations (pick, pack, and ship), but traditional workflows are notoriously error-prone and cost the company tens of thousands of dollars that could otherwise be saved by using modern AI-enabled shipping software which removes the human from all (bad) decision-making and creates the optimal pick tours in the warehouse and then creates the cheapest shipping label using the smallest box or mailer that will deliver the order safely and on time.
3. Optimize (or Better Yet, Eliminate) Reverse Logistics
   AI agents can predict potential returns and optimize the reverse logistics process by identifying items likely to be returned and streamlining the return handling procedures and communications. Further, they can proactively notify Sellers and customers about the likelihood of a return before the order is shipped (which is critical for the highest return rate categories such as women’s apparel). Groundbreaking new returns technologies such as the Cahoot AI-enabled Peer-to-Peer Returns Solution are already eliminating returns by enabling the return to be shipped directly to the next customer, which is cheaper, faster, and better for the planet. By eliminating one leg of shipping, eliminating warehouse processing, the returned item is sold faster, and the econ-conscious program enhances brand reputation.
4. Drive Business Outcomes with Advanced Analytics
   AI agents can analyze enormous amounts of data quickly, and all of it, not just the headline data points like sales and returns. AI can identify more trends and answer more questions than retailers can even think of asking. For example, AI agents can identify anomalies in sales patterns, inventory levels, or customer behavior, alerting businesses to potential issues and enabling timely intervention. Another example: Sellers can evaluate supplier performance at a micro-level comparing negotiated agreements and SLAs to actual performance and use the data to negotiate better terms on their behalf, all while humans are working on growing the business.

Summary

Online stores (webstores) may be about to disappear forever, but Order and Inventory Management Systems are getting the long-awaited boost that they needed. Artificial Intelligence (AI) agents are set to revolutionize e-commerce by acting autonomously to streamline workflows across inventory management, order fulfillment, and reverse logistics, among other things, and it’s the OMS/IMS that’s leveraging this new technology to do it. By integrating Order and Inventory Management functions with real-time/real-world news and events, AI agents predict demand with precision and automate procurement decisions and actions. By leveraging modern fulfillment and reverse logistics solutions to automate rote but essential work, workflows are optimized and errors and operational costs are reduced. Lastly, AI agents are capable of exceptionally sophisticated analysis of OMS/IMS data that helps to identify actionable insights from untapped corners of the database to drive unprecedented profitability and success.

 

Perplexity, the AI-powered search engine backed by Jeff Bezos, Tobi Lütke, and other notable investors, has just launched a new feature called Buy with Pro. This AI-assisted shopping search engine will let US-based Perplexity Pro subscribers purchase products without leaving the AI search engine. Unlike traditional search engines such as Google, returning a list of ranked sources you must read through to get the answer to your query, Perplexity’s AI search instead reads those ranked sources and summarizes the answer.

 

The AI Commerce and Shopping Search Experience

The AI commerce experience aims to add a layer on top of AI search that helps users find products that meet their criteria using conversational messages instead of keywords you would type in Google. Users interact with Perplexity in natural language, asking questions and the “prompts,” and then Perplexity will understand that query and return search results, including product recommendations.  The new Buy with Pro button allows users to check out using one click on Perplexity’s website. In addition, they have a nice feature called Snap to Shop, which is essentially a visual search like Google Lens. You take a picture of an item, and Snap to Shop can find similar products.

The Old vs. The New

With a traditional Google Shopping Search, the Shopper enters some keywords into a very old-school interface, which generates a list of product pages that take them to each retailer’s website. After browsing through the images, users must click on each product image that appeals to them and read product specs, read lots of reviews, and figure out whether it’s the right fit or at least a contender.

Whereas what Perplexity is trying to do is make things more intelligent for the Shopper by returning not just a list of product pages but a list of what they call product cards, which summarizes all the information for you with statements like “Buy this if you want a luxurious, oversized cashmere sweater that combines comfort with a timeless, stylish design.” Product cards include high-resolution photos, a summary of the reviews with sources, key features, etc. These are within the Perplexity conversation and in a straightforward format that the Shopper can digest.

Also helpful…users can ask follow-up questions and/or refine the results by updating the conversation, etc. For example, add guidelines such as color, flavor, size, or price.

 

Our Real Test for an Intended Product

We started our test with a very simple query: “Looking for a cashmere sweater for a party to go with brown pants.” The first results were all the women’s pants!! We didn’t ask for pants; we asked for a sweater to go with pants. And none of the results had the option to Buy with Pro, only to visit the merchant’s website.

We started a new interaction with the identical prompt. This time, we received brown sweater suggestions, which is closer (do people wear brown sweaters with brown pants? maybe…), but still all women’s styles, while our user profile is configured as a male. So we should be receiving suggestions for men’s sweaters. Again, there were no Buy with Pro options, but we did find a Buy with Shop Pay option this time. Clicking the button brings the user to the Seller’s Shopify store directly to the checkout page to complete the purchase outside the Perplexity environment.

On the third attempt, we appended the word ‘mens’ to the end of the prompt. This time, the results were mostly what we would expect, besides disagreement about what color sweaters would go with brown pants, though there was still no Buy with Pro option.

 

Perplexity Missed the Mark

While this use case for GenAI seems to be a natural fit for GenAI technology, it’s missing the mark. Search results were poor in our tests, suggesting product-market fit might be a long way away. The current solution lacks enough data or the needed intelligence or context.

We’re optimistic about the future of this technology, but in our limited trial/quick test, we weren’t impressed. Generative AI should be able to understand our profile and what we’re looking for and return a men’s sweater and not pants.

 

It Ain’t All Bad

While search results for the sweater didn’t deliver against expectations, the results did include some thoughtful suggestions about shades and tones to consider for different looks that the shopper might be interested in achieving, as well as tips for accessorizing the outfit:

So, there is promise, but is the product ready? We don’t think so. Again, it’s early, and this was our first couple of queries. But for paid Perplexity Pro users, that’s their first impression, and Shoppers will be disappointed, and Shoppers are very discerning.

 

Final Thoughts on Perplexity

Perplexity is still a young startup and may figure it out. But trying to compete with the OpenAI’s of the world and launching products prematurely may be a bad idea.

Whether expanding from the Shopify partnership to increase the scale of Buy with Pro or working on understanding the queries better and returning better results, the next move should be figuring out and bridging the gaps. In the era of AI, capturing interest and early users for only $20/month is the easy part. Keeping them, not so much. You can also listen to a recent Perpelixty AI Commerce Search podcast here .