Automation and AI in e-commerce for product data management

Changes in data management strategies in the e-commerce industry

Until a few years ago, the biggest nightmare of dynamically growing e-commerce was information chaos in the omnichannel model. If you sold the same product in your own store, on eBay and on Amazon, you probably remember this nightmare: correcting typos in three different panels, fighting different formats of product characteristics and the eternal confusion of which description is the current one.

The solution that was supposed to bring peace of mind was the implementation of PIM (Product Information Management) class systems. They promised one “source of truth” — a central hub in which a once prepared product card magically circulates through all sales channels in the right form. Companies that understood this and invested in such data architecture (which we wrote more about in the article: https://www.sagiton.pl/en/blog/pim-and-omnichannel-strategy) have actually mastered the information chaos. PIM has become the foundation, without which professional sales in many markets today are simply impossible.

Why is PIM not enough to stay competitive in the market?

However, as the dust settled after the implementation of product data centralization, many e-commerce managers faced a painful new barrier: You have a great data management system, but you still don't have the data itself.

Today, the problem is no longer where to keep information about products, but where to get it and how to prepare it on a massive scale. Even the most advanced PIM system is just an “empty rack” that you have to fill every day with thousands of technical attributes, unique descriptions and hundreds of photos. In a world where you're uploading new products every week and niche manufacturers provide information in a messy way (or don't give it at all), manual card replenishment isn't just a regular task for an employee — it has become a powerful bottleneck that inhibits your growth. Suddenly it turns out that despite having PIM in e-commerce, your team spends 80% of their time searching manufacturers' websites, rewriting tables from PDFs and fighting for unique SEO-friendly content.

This is where today's front line runs. The question is no longer “Do you have a PIM?”, but: “How quickly can you feed it with high-quality data before your offer disappears into the crowd of competitors?”. This is the moment when the theory of “data management” collides with the brutal operational reality. Even if you have a PIM, without a strong team to support it, at the scale of thousands of products, you start doing manual crafts, not modern commerce. In the following paragraphs, we will describe the four main barriers through which your e-commerce may not develop properly:

1. Playing a role of detective – struggling with attributes

When you sell products from giants like Bosch or Husqvarna, it's simple — you get a ready-made e-commerce data architecture in structured tables. The problem starts with niche or local producers. They often don't have online catalogs, and their “database” is at best a dusty PDF or spreadsheet that remembers the previous decade.

Employees of your e-commerce, instead of selling, turn into online detectives:

• They search manufacturers' websites in other languages.
• They copy data from competitors' online stores (hoping that they were not mistaken).
• They manually rewrite the technical parameters, which with hundreds of SKUs almost guarantees errors that later result in returns from dissatisfied customers and a negative impact on the brand image.

2. Description Factory: SEO vs. Marketing Channel Specificity

You know perfectly well that copy-paste from the manufacturer's description is the shortest way to a penalty from Google for duplicate content. But uniqueness is only the tip of the iceberg. The real challenge is contextual adaptation.

If you sell the same product in three marketplaces, you need to prepare three different descriptions:

• In the specialized e-shop: the client is looking for specifics, technical data and engineering language.
• On the general marketplace (e.g. eBay): you need to write in the language of benefits for the average user.
• In the marketplace on social media: you have seconds to attract attention – usually written in everyday language.

With a scale of 1000 products and 3 sales channels, you have to write 3000 unique texts. No e-commerce copywriter will maintain the pace and quality without burning out.

3. Visual efforts – the phone, Canva and Corel

In e-commerce, photos are “to be or not to be”, and witch small producers it is usually “not to be”. Very often the employee has to take the product in hand, take a photo with your phone in the back room and begin the painstaking process of editing:

• Removing the background to meet the strict requirements of Amazon (so-called white thumbnail).
• Cropping under the layout of your store.
• Overlaying arrangements so that the photo does not look amateurish.

One photo is a minimum of 5 minutes in graphics programs. If you have 200 new products to process per month, you have just lost more than two working days of one employee for just clicking “remove background”.

4. The trap of static prices in a dynamic market

Customer loyalty in e-commerce is a myth, especially in marketplaces. There, one aspect is crucial: the filter “sort by lowest price”. If your price has been rigid for half a year because “we put this in a googlesheet like that and then forgot”, then it is you probably lose on the market to competitors who react with price changes every hour.

The problem is that you can't just cut prices indefinitely. You must:

• Track trends and movements of competitors in real time.
• Remember your margin and purchase price.
• Take into account the commissions of a specific sales channel.

Manually tracking prices for thousands of items and calculating your margin on the fly is a recipe for disaster — either you break the price and gain no customers, or you sell the product at a loss because you forget to update the purchase cost.

The scale effect does not forgive. What at 10 products is a "trivial matter", at 1000 becomes a crippling cost that eats away at your margin and the energy of your team.

New Standard: Systemic Data Automation

Modern technology allows you to design processes that eliminate manual labor, turning scattered information into a ready-made, unique sales product. The key is to create an ecosystem where AI handles analytics and creation, and BPA automation takes care of business logic and data flow.

It is worth noting that in the examples listed below we are not talking about another ready-made tool, but about a properly designed automation of e-commerce processes, which integrates different technologies into a single coherent ecosystem.

1. Data extraction from unstructured sources

For our e-commerce clients, we implement automations to collect technical specifications from multiple distributed locations simultaneously. These processes include:

• Automatic analysis of external sources: from manufacturers' websites and competitors' catalogs, to advanced scanning of product sheets in PDF formats.
• Active acquisition of information: the automation of product data can initiate contact with the supplier (e.g. by e-mail enquiry). After receiving a response, the algorithms analyze the content of the message and attachments, independently extracting technical parameters and feeding them to your product base.

2. Generate unique SEO content under your control

Creating SEO descriptions becomes a process that can be fully adapted to the strategy of a particular sales channel. The key is to maintain the full impact on the final effect:

• Product information management via prompts: with proper use of automation and AI in e-commerce it is possible to generate product descriptions based on precise guidelines. AI allows you to personalize your content per marketing channel: this will give you an engineering language in a specialized online store or a persuasive language of benefits in marketplaces and social media.
• Uniqueness Guarantee: each time you build new content using e-commerce automation, it eliminates the problem of duplicate content. Descriptions can also be optimized for SEO and keyword phrases, which directly translates into better visibility in search results.

3. Product photos straight from your phone — no graphics involved

Thanks to e-commerce automation, the preparation of visual materials is no longer a time-consuming process. The employee does not need to be familiar with graphic editing or waste time in complex programs:

• One photo is enough: the employee uses the phone, takes a picture of the product and additionally the packaging or the nameplate itself (for identification) or provides the product ID with its photo for pre-prepared automation (e.g. by email or messenger such as WhatsApp, Slack, Discord).
• Automatic Assignment and Processing: the rest is handled by the automation process. The system recognizes the product, assigns the image to the correct position in the database, removes the background, crops the image to the requirements of a specific channel and applies unique arrangements. With such automation of online sales, the entire processing process takes a few seconds, and the finished photo lands in the system without the graphic designer.

4. Optimization of pricing policy in real time

It is also possible to implement automatic pricing policies, responding on an ongoing basis to what is happening in the market:

• Competition monitoring: Properly designed AI automation in e-commerce can constantly track prices on selected platforms and competitors' sites, compare them with your current purchase price and your established margin.
• Dynamic Fit: a detailed automation configuration can allow you to make price changes in such a way as to maintain competitiveness and fight for the top positions, while rigorously protecting your profitability.

Automation is a process, not a “magic switch”

Substantive reliability requires emphasizing that the implementation of such solutions is an operational transformation, and not the purchase of a ready-made, error-free program. In order for e-commerce automation to bring real profits, it must be based on a few hard principles:

• Human-in-the-Loop: AI is a powerful support, but human supervision remains crucial to maintaining the highest quality of service. In practice, automation may not fit the product 100% unambiguously, even after analyzing the photo of the nameplate. In this case, the process can return a query on the messenger, asking you to choose the right model from among several candidates found, or to assign it manually to the product (e.g. by providing a product ID or navigating the product tree from your phone) if the algorithm's confidence is too low.
• The risks of photos which are "too beautiful”: automatic layout generation and background removal by AI is a huge time saver for the marketing department, but carries an image risk. If the algorithm smooths the product too much or changes its proportions, the customer may receive goods that, in his opinion, are “not as described”. The professional process must assume random quality control of visual materials in order to avoid the wave of complaints resulting from excessive “embellishment” of reality.
• Compliance with the Omnibus Directive: the dynamic pricing policy pursued by “robots” is a powerful tool, but it must be closely integrated with legal obligations. In EU re-pricing systems must automatically record the history of changes and present the lowest price from the last 30 days before the reduction. Without this mechanism, even the most profitable automation can expose the company to severe penalties and loss of customer trust.
• Quality of input data: the efficiency of parameter extraction directly depends on the readability and form of source materials. Example: If a manufacturer provides a fuzzy, handwritten corrected scan of a document (“dusty PDF”), algorithms may have difficulty reading the data correctly; however, if the data comes from a digital PDF with a clear table structure, product information management is carried out automatically and error-free.
• Fuses (Guardrails): automation of pricing policy requires the imposition of rigid profitability limits. Thanks to this, regardless of aggressive movements of competitors in the market, the price will never fall below the threshold you set, which rigorously protects your margin.
• Evolution and Calibration: AI-based processes require an initial calibration phase of guidelines (prompts). This joint development of standards in the first stage makes the system almost maintenance-free over time, providing unique and effective content for each platform.

When does automation (not) pay off? Economic calculation

Automation of product data management is an investment that, like any other, must have a clear justification in the spreadsheet. Not in every case, building an advanced e-commerce data architecture will make business sense. The key indicator here is the opportunity cost, that is, the real cost of human labor that technology is supposed to replace.

Assuming the cost of the employer with the minimum wage in Poland is currently approx. 5862.37 PLN per month, we can carry out simple simulations of return on investment (ROI):

• Scenario A (micro scale): if the processes of “beautification” of data occupy an employee at the lowest national level only 25% of the working time, the annual cost of these operations is about PLN 17500. In this case, if the implementation of automation costs in the range of 17-20 thousand PLN, the investment pays off in just over a year. This is a very good rate of return.
• Scenario B (specialist/expert): in larger companies, where a specialist deals with data (let's assume the cost of the employer at the national average level, about PLN 10,000 per month) and devotes 30% of his time to it, the annual cost of manual data handling is already 36000 PLN. At the cost of implementation of the order of 35-40 thousand PLN, return on investment occurs almost immediately.
• When does it not pay off? If manual product data management is only required once in a while and takes, for example, less than 5% of a single employee's working time, invest in a dedicated automation architecture probably will never pay off. With a very small scale and low assortment rotation, manual labor still remains the most economical choice.

In the above calculations, we deliberately omit the costs of human errors — mistakes in prices, erroneous technical parameters generating returns or outdated offers on marketplaces. In fact, it is precisely the elimination of these risks and the possibility of almost unlimited scaling of sales without increasing employment that make the real ROI often much higher than it would be due to the time savings alone. It is assumed that ideally designed e-commerce automation should pay off in less than a year, and a maximum of 3 years.

Automation and AI in e-commerce — an advantage that is becoming the standard

The combination of artificial intelligence and process automation, which until recently were out of reach, are now becoming your real competitive advantage. It allows not only to cut costs, but above all work faster than the rest of the market. However, it is worth remembering that what distinguishes you today will probably be the market standard tomorrow. Without implementing these solutions, your e-commerce will soon have great difficulty breaking through the competition, which is aggressively betting on automation. The question is: can you afford to still manage data manually in 2026 while others do it on autopilot?