Artificial intelligence is a hot topic, and while everyone is talking about its benefits, not everything labeled as AI truly is. Some companies are using the AI label on technologies that are just self-contained modules collecting data without offering real analysis.
For retailers, using real AI, rather than fake AI, can make a big difference in things like sell-through rates and inventory management. Here’s what retailers need to know about fake AI and how to recognize it.
Fake AI is isolated
Fake AI is essentially a standalone data set that isn’t connected to other data. Because of this, it can’t offer the full analysis needed to get an accurate picture of your retail operations. In contrast, real AI platforms pull data from multiple sources and analyze them together, giving retailers the insights they need to predict how much product in specific sizes and colors they need at each location.
This is where terms like “AI module” and “AI native” come into play. An AI module adds AI features to an existing system but doesn’t make AI a core part of its function. For example, a smartphone may use AI for its camera to enhance photo quality, but AI is just an add-on.
On the other hand, AI-native platforms make AI the central component of their design. AI is not just an extra feature but an integral part of the platform that drives business decisions and processes from the ground up.
How to spot fake AI
The simplest way to identify fake AI is by paying attention to the terminology used. But sometimes, you’ll need to dig deeper into the technology to fully understand how it works.
One key sign of fake AI is the use of individual modules that create isolated data sets. These modules can’t communicate with each other because AI isn’t at the center of the system, tying everything together. These modules rely on scientists to analyze the gathered data manually, which makes it harder for retailers to use the information effectively.
Another red flag is the term “forecasting module.” This module simply presents data side by side, like a copy-paste job, which doesn’t integrate seamlessly into the system. It’s a basic form of forecasting, not the kind of real-time analysis that AI can provide.
Building an AI module is quick and easy, but embedding AI into a platform is much more complex. It’s like adding AI to the platform’s DNA, where every part of the platform is built to work with AI, creating a system where AI is a fundamental part of everything.
Real AI in action
In retail, up to 60% of merchandise either ends up sitting on the shelf or gets out of stock due to poor assortment planning. Real AI can greatly improve accuracy and gets better over time as more data is processed and industry experts make adjustments.
For instance, size profiling is crucial when planning assortments, allocations, and replenishments. Real AI continuously adjusts the ideal size distribution for each store, ensuring that products don’t sit on shelves for too long or sell out too quickly. It fine-tunes this for every store and applies it to the retailer’s style-color-base assortment plan.
Another example of real AI is in open-to-buy analysis, which predicts stock shortages. This is a more advanced form of forecasting, not just a module that presents data side by side. Real AI uses the full range of information to generate insights.
When AI works
With all the hype around AI, it’s important to note that when AI works properly, users don’t even need to think about it. They’ll just use the platform and gradually realize the data it provides is spot-on.
In contrast, AI modules tend to generate lots of data that users have to analyze themselves. If they misinterpret that data, it can lead to big problems.
Real AI works seamlessly in the background, like a brain behind the platform. It doesn’t require attention or extra focus to work—it just gets the job done without any fuss.
