eCom Buzz

AI in eCommerce: What Works, What Doesn’t, and How to Actually Use It

A Guide to Performance ROI for eCommerce

AI isn’t magic—but it can be useful.

When applied with purpose, AI can genuinely improve eCommerce workflows, customer experience, and even revenue.

The challenge? Most teams aren’t applying it with purpose. They’re adopting tools in isolation, without understanding what each part of “AI” actually does—or how it fits into the business.

This article breaks down the three layers of AI that matter in eCommerce:

Generative AI, Machine Learning, and Automation.

What each does. Where each fails. And how they work together.

Whether you’re testing tools or building something custom, here’s how to make AI useful instead of overwhelming.

🎥 Prefer to watch the video? Check it out here

1. Generative AI

This is the most familiar layer—the one behind tools like ChatGPT, Claude, and Gemini. These platforms generate new content by analyzing patterns across massive training datasets.

In eCommerce, generative AI is helpful for:

Summarizing large batches of product reviews
Rewriting technical or repetitive PDP copy
Translating descriptions for international sites
Structuring unformatted data (e.g. product specs)
Creating Q&A blocks or SEO copy

Its value is speed and scalability. But it comes with real limitations:

It doesn’t understand your customers or intent
It can’t process large structured datasets
The quality depends on training data you don’t control
It’s not integrated—it just outputs unless you connect it
Many public tools aren’t secure (prompts can be reused for training)

In short: generative AI is a content assistant. It’s not a strategist, and it shouldn’t run unattended.

2. Machine Learning

While generative AI creates, machine learning identifies.

It’s about recognizing patterns in your historical data and surfacing likely outcomes or behaviors. In eCommerce, that can mean:

Predicting churn
Forecasting demand (e.g., for Q4 inventory)
Personalizing product recommendations
Segmenting customers based on lifetime value
Identifying price sensitivity or bundling patterns

Machine learning models are most effective when trained on your actual store data—not someone else’s. That means clean order history, product metadata, and customer behavior logs.

The catch? Machine learning can’t explain intent. It doesn’t know why a pattern exists. It only shows that it does. It’s also highly sensitive to bad input—so if your data is messy or inconsistent, your outputs will be too.

And many out-of-the-box ML tools are marketed as “intelligent,” but lack the customization needed for real-world results.

How it all works together

When applied strategically, these layers align into a simple but powerful framework:

Generative AI creates
Machine learning identifies
Automation applies

Most teams stop after the first. Some explore the second. But very few build the connective tissue that makes the system work.

That’s where the opportunity is.

Whether you’re building internal tools, evaluating a vendor, or trying to get more out of the AI tools you already have—start with clarity on which layer you’re investing in. Then ask what it needs to connect to.

That’s how AI becomes more than a demo. It becomes real leverage.

Want to see this explained in action?

This article is Episode 25 of The Ecom Buzz, SwiftOtter’s bi-weekly series for eCommerce teams who want grounded strategies delivered directly to their inbox.