Jargon is the death of communication, but there are some terms worth learning to better understand the new world of online holiday shopping. “Generative AI” and “algorithms” are two concepts worth knowing something about.
Algorithms are at the heart of almost every data-based decision— from predicting the next hot product to recommending the perfect price for a product. Whether it’s demand forecasting, inventory optimization, or dynamic pricing, businesses increasingly rely on algorithms to best compete. But here’s the catch: these powerful algorithms can generate predictions and answers, but they often leave one critical question unanswered: Why?
If you’ve ever wondered why an online store suggests a particular product to you or why a product’s price changes from one day to the next, know that behind every recommendation or price adjustment, there’s an algorithm making decisions based on large sets of data. It does so to help you have the best user experience, but it also serves the needs and objectives of the seller.
As sophisticated as these models have become, understanding why a particular result was reached is often far from straightforward. For many e-commerce companies, this “why” question is not just an academic curiosity — it’s a business necessity.
Business managers, operations planners, and marketing teams need to understand why an algorithm made a certain decision in order to trust and act on it. Without a clear explanation of the algorithm’s reasoning, it becomes much harder for companies to use data-driven insights effectively. After all, if you don’t know why a pricing model suggested a 10% discount on a product, how can you be sure it’s the right decision?
This disconnect has long been a pain point for data scientists. They spend countless hours, sometimes days, backtracking through massive datasets and complex models just to explain why a system reached a particular outcome. It’s not only time-consuming—it’s also inefficient. And in a fast-moving business environment, it often means that by the time these explanations are ready, the window to take action has passed. If a business manager is waiting for an explanation about why an inventory model predicted a spike in demand for a product, that spike may have already occurred and passed by the time they have the answer. The problem? The answer’s value is now obsolete.
This issue directly hampers productivity, especially in an industry where fast decision-making is crucial. The e-commerce world moves at a lightning-fast pace. When managers and decision-makers have to wait for data scientists to backtrack and explain an algorithm’s reasoning, the business misses the opportunity to act on timely insights. The result? Missed revenue opportunities, inefficient operations, and even lost customers.
Here’s the good news: Generative AI is poised to solve this problem by bringing much-needed clarity and speed to the decision-making process. By integrating powerful tools like Large Language Models (LLMs) into existing e-commerce systems, businesses can now engage in natural-language conversations with their algorithms to get immediate, understandable answers to their “why” questions. Instead of waiting days for a data scientist to explain the reasoning behind an algorithm, business users can now ask questions like, “Why did the pricing model suggest a discount for this product?” or “What factors are driving the increased demand for this item?” and get clear, actionable answers in seconds.
Generative AI is built to understand and process natural language, so business managers can interact with the system just like they would with a human expert. The system pulls from its integrated data sources and provides a detailed explanation, broken down into easy-to-understand terms. This capability empowers managers to quickly make decisions without waiting for a back-and-forth between data scientists and operational teams. The result is faster, more informed decision-making and a more agile business operation.
But that’s not all—Generative AI also offers something even more transformative: the ability to perform real-time what-if scenario analysis. Want to know what would happen if you raised the price of a product by 10%? Or what effect a marketing campaign might have on inventory levels? With just a simple query, business users can run simulations and test out different strategies. They don’t need to wait for a data scientist to rewrite complex models or update the algorithms. This allows businesses to quickly adjust their strategies based on the most up-to-date data, maximizing their agility in a highly competitive market.
For e-commerce businesses, the advantages of generative AI are clear. By enabling business users to understand the reasoning behind algorithmic decisions and empowering them to test different scenarios in real-time, companies can become more efficient, responsive, and customer-focused. This technology helps solve the productivity bottlenecks that have long plagued e-commerce operations, allowing businesses to act faster and with more confidence.
Generative AI is paving the way for a smarter, more agile e-commerce future. As businesses continue to rely on data science solutions to drive their operations, the ability to quickly and easily access meaningful, understandable insights will be a game-changer. No longer will companies have to wait for answers that come too late; instead, they’ll have the tools to make data-driven decisions in real-time.
In the end, generative AI isn’t just about making algorithms smarter—it’s about making businesses smarter. As this technology continues to evolve, e-commerce companies will not only be able to deliver better experiences for customers but also improve their bottom line by making more informed, faster decisions.
