AI vs Manual Product Categorization: What Actually Works?

As product catalogs grow and new products emerge at a rapid pace, e-commerce teams are grappling with a big question: should we categorize products manually or use artificial intelligence to do it? In 2025, AI-driven solutions are more accessible than ever, promising to save time and improve accuracy. But can they really outperform a human's careful judgment? Surprisingly, many businesses have yet to fully embrace automation here—by some estimates, only about 10% of online stores use automatic categorization tools, with the rest relying on manual methods. In this article, we'll compare manual vs AI-based product categorization head-to-head. We'll look at the pros and cons of each approach and discuss what tends to work best in practice.

The Traditional Manual Approach

Manual product categorization means human experts (like your merchandising team or content managers) decide where each product belongs in your category hierarchy. This approach has been the norm for decades. An experienced employee might read a product description, consider the product's use, and then assign it to the correct category on the website or in the product database. For example, a person sees a new style of running shoes and manually places it under "Sportswear > Footwear > Running Shoes" in the catalog.

Pros of manual categorization:

Domain knowledge and nuance: A human categorizer can understand subtle context and product nuances. They might know that a "gaming chair" should go under furniture as well as electronics accessories, or that a particular dress style appeals more as evening wear rather than casual wear. Human judgment can sometimes catch things that a generic algorithm might miss (like cultural differences or multi-functional products).
Control and intent: With people in charge, you have complete control over how your taxonomy is applied. If you have a unique categorization strategy or marketing tactic (say, grouping all "Gift Items" together even though they span departments), a human can implement that intentionally. Manual categorization gives flexibility to override rules when needed based on business strategy.

Cons of manual categorization:

Time-consuming and not scalable: Going item by item is slow. One internal study at a retailer found it takes around 3 minutes per product for a staff member to read, decide, and input category information. If you have thousands of products, this could be hundreds of hours of work. As your catalog grows, you might need to hire more people just to keep up with categorization. It's hard for a purely manual process to scale when you're adding dozens or hundreds of new SKUs every week.
Inconsistencies and human error: Different team members may apply different criteria or make mistakes, especially if guidelines aren't crystal clear. One person might tag a silicone baking mat as a "Kitchen Gadget" while another puts it under "Bakeware". Over time, these inconsistencies clutter your data. Human errors can also slip in—fatigue or distraction can lead to misplacing a product. Unlike machines, people aren't always 100% consistent, especially on repetitive tasks.
Costly in the long run: Manual labor has ongoing costs. The more products, the more employee hours needed. Training new staff to understand your taxonomy takes effort, and when employees leave, some categorization knowledge might leave with them. All this makes manual categorization an expensive operation as you scale up.

The AI-Powered Approach

AI-based product categorization uses algorithms—often machine learning models trained on lots of product data—to automatically assign categories to each item. Essentially, the AI "learns" from examples what characteristics define, say, a "smartphone" versus a "phone case" and classifies new products accordingly. Modern AI categorization tools can analyze product titles, descriptions, and even images to determine the best category. For example, an AI system could ingest a catalog of products with their correct categories, learn the patterns (like keywords or attributes associated with each category), and then apply that knowledge to categorize new products coming into the system.

Pros of AI categorization:

Speed and scalability: This is where AI shines. A well-implemented AI can categorize thousands of products in the time it takes a human to categorize a handful. One feed management provider reports their AI categorization system works 17 times faster than manual tagging. Whether you have 100 or 100,000 products, the AI can handle the volume without breaking a sweat. This means you can update categories in bulk or integrate new product lines quickly, keeping your site up-to-date without a huge delay.
Consistency: An AI model will apply the same criteria across all products, every time. Once it's trained, it doesn't get tired or have off days. This leads to very consistent categorization. If the rules it learned say a "4K Ultra HD" in the title indicates a television, it will categorize every new TV correctly by that rule. You won't have the situation of one item ending up in a stray category because someone interpreted the guidelines differently.
High accuracy potential: When properly trained on quality data, AI can achieve impressive accuracy—often rivaling or exceeding human accuracy for large datasets. In one case study, an AI model categorized 100,000 products with 99.98% accuracy, which is practically error-free. Many AI solutions boast accuracy rates in the mid-90% range, which means only a small fraction of products might need correction. And the more data (and feedback) the system gets, the better it can become over time, learning from any mistakes.
Cost efficiency: Although there's an upfront investment in an AI system or service, over time it can be cheaper than paying staff to do the work manually. You don't need to scale payroll linearly with your catalog size—the same algorithm can handle a bigger load after initial setup. This can free up your human team to focus on more strategic tasks than drudging through spreadsheets. For instance, the fashion retailer Boohoo saved the equivalent work of two full-time employees by automating its product categorization process.

Cons of AI categorization:

Initial setup and training: Implementing an AI solution isn't instantaneous. You need a good amount of training data (i.e., products already correctly categorized) to train the model, or you need to use a pre-trained model provided by a vendor that understands your industry. There's also integration work to get the AI hooked into your product information management workflow. This upfront effort can be a barrier, especially for smaller retailers without a dedicated tech team.
Misclassification risks and oversight: AI isn't perfect. If it encounters a product that doesn't fit well into the patterns it knows, it might guess wrong. For example, if you start selling a brand new type of gadget that the AI has never seen, it might lump it into a closest-known category that isn't quite right. Also, if your training data had any biases or errors, the AI can inadvertently learn those. That's why some level of human oversight is often needed—at least to review AI suggestions or handle cases where the AI has low confidence. Without checks, there's a risk an AI could systematically miscategorize a batch of items before you catch on.
Less contextual judgment: While AI is getting better at understanding language and context, a human's comprehension of nuance is still superior in certain cases. AI might struggle with things like sarcasm or abstract product descriptions, or it might not infer the intended use of a product from a quirky marketing description. For instance, a human might read "This widget will be your kitchen's best friend" and infer it's a kitchen gadget, whereas an AI might not parse that metaphor and misclassify the item. These scenarios are not common, but they illustrate how pure AI can sometimes miss context that a human would catch.

What Actually Works Best?

After looking at both sides, the reality for many e-commerce businesses is that a hybrid approach often works best. AI and humans don't have to be adversaries—they can be teammates. The idea is to let AI handle the heavy lifting of categorizing the bulk of products, and have humans oversee the process and fine-tune the results.

This approach is supported by industry experiences. For example, the team at IPSY (a beauty subscription company) implemented a system where their machine learning model handled about 90–95% of the product categorizations automatically, and humans stepped in only for the uncertain or tricky cases. In practice, this meant the AI would assign a category to most items, and if it wasn't at least, say, 90% confident in its choice, it would flag those for a person to review. This dramatically cuts down the manual workload while ensuring that edge cases still get a thoughtful human decision.

We can think of it like this: AI is great as a fast first pass. It will get the easy and medium-difficulty categorizations done in a flash. Then your trained staff (or even just a quick final check by one team member) can verify or correct the few items where the AI was unsure or likely to make a mistake. Over time, the AI also learns from those corrections, getting smarter and reducing even further the number of touch-ups needed.

The hybrid model plays to the strengths of each: the AI's speed and consistency and the human's intuition and contextual understanding. Companies that have adopted this approach often report big efficiency gains. It's not unrealistic to see accuracy rates in the high 90s and a fraction of the manual effort that used to be required. Moreover, your human experts can then focus on refining the taxonomy, handling special categorization projects (like setting up new collections or seasonal categories), and auditing the AI's performance periodically, instead of grinding through every single product.

AI vs Manual Comparison

Manual Only

Slow but nuanced

Hybrid Approach

Fast and accurate

AI Only

Fast but needs oversight

AI vs Manual: Rather than an all-or-nothing choice, many retailers find the best results by combining AI's speed with human oversight. Routine categorizations are automated, while edge cases get human attention – leading to fast yet accurate product categorization.

Conclusion

So, what actually works? In 2025, the answer for most is: leverage AI where you can, but keep humans in the loop. Manual categorization alone is too slow and costly for large catalogs, and a pure AI approach without oversight can occasionally go off track. The sweet spot is a smart middle ground. By using AI to drastically speed up the process and humans to handle the exceptions, you get the best of both worlds—efficiency and accuracy.

For e-commerce teams, this means you don't have to fear AI will replace your judgment; instead, think of it as a powerful assistant that does the boring part and frees you to make the high-level decisions. If you're still doing everything manually, it might be time to pilot an AI solution on a subset of products and see the difference. The improvements in consistency and time savings will be hard to ignore. And if you've tried an out-of-the-box AI before that didn't meet expectations, consider a hybrid workflow where the AI is guided by human feedback.

At the end of the day, successful product categorization drives a better customer experience and more sales. However you achieve it—by augmenting your team with AI tools like Categorix.ai, refining your processes, or a combination of both—the goal is the same: the right products in the right place, every time. The technology now exists to make that task easier than ever. Embracing it wisely will set your online store apart, ensuring that as your catalog grows, your organization and accuracy grow with it rather than suffer. In the AI vs manual debate, the winner is whoever delivers the most organized, shopper-friendly experience—and with a balanced approach, that can definitely be you.