How artificial intelligence is changing PXM (Product Experience Management)

Imagine spending long hours manually creating product descriptions, translating them into different languages and assigning hundreds of thousands of products to the right categories. Sound familiar? For many companies, this is an everyday occurrence – tedious work with spreadsheets and data copying through the night. The good news is that a revolution is coming. Artificial intelligence (AI) is entering the world of Product Experience Management (PXM) to relieve teams, automate tedious tasks and take the product experience to a new level.

In this article, we will tell you how AI is a game-changer in PXM. You will learn, among other things:

• How to automate PIM (Product Information Management) processes – from description generation to translation and tagging.
• How AI analyzes data to predict demand, suggest products and improve the supply chain.
• How to personalize product content to customer preferences using predictive algorithms and segmentation.
  
• You will learn about specific examples of AI tools in PXM (e.g. LemonHub.AI and others) and their applications.
• You will discover key benefits for B2B companies: greater data accuracy, time savings, improved operational efficiency, and higher conversions and customer satisfaction.

Artificial intelligence has become the secret weapon of PIM managers – PIM automation with AI can now effectively relieve people of the most monotonous tasks. Until recently, generating AI product descriptions or translating product content was a manual, time-consuming job. Now, artificial intelligence in description translation and content creation allows you to automatically generate and enrich product descriptions in multiple languages, making these processes unattended. In practice, this means that you can enter product data into the system and an intelligent algorithm based on AI product description generation will generate an attractive, coherent description consistent with your brand tone - and it will do so in seconds, not hours. What's more, thanks to artificial intelligence in the translation of descriptions, the system will immediately translate the content into Spanish, German or the language of the customer you are just acquiring in a new market.
But that's not all. AI is also great at categorizing and tagging products - AI product tagging and AI product classification allows you to assign the right tags, categories and attributes automatically, based on text data or even photos. AI technology has already gone beyond translations and descriptions – today, PIM automation with AI also includes automatic photo tagging, intelligent product classification and dynamic adaptation of content to different markets. Imagine adding new products to your PIM and the system – using AI in product classification – recognizes the color or material in the photos and fills in the appropriate fields. It sounds like magic, but it really happens.
A real-life example: a Polish company implementing PIM automation with AI was faced with the task of transferring 500,000 products to a new category structure. Manual classification would have taken approximately 1,400 hours of work (over 8 months of continuous hard work!) and cost around 210,000 PLN. Thanks to the use of AI in product classification and automatic category mapping, it was possible to reduce this workload by 80%, to approx. 280 hours. Savings? Nearly 168 thousand PLN directly, plus an additional ~10% savings thanks to fewer errors. In total, the company saved ~€45,000 in 10 months! They not only gained time and money – product data became more consistent and standardized, which translated into easier integration with other systems and better business reporting.
There are more stories like this. Another organization used PIM automation with AI to automatically read data from product labels (e.g. ingredients on food labels) and integrate it into the PIM system. As a result, 95% of the products were processed automatically, and only 5% required manual correction. The cost of data entry dropped from almost 1.88 million PLN to 93 thousand PLN - the company saved about 1.78 million PLN, and after taking into account the lower number of errors, even 1.97 million PLN. What a difference! Instead of laboriously crunching numbers, employees were able to focus on strategic tasks and the data in the system was more accurate.
PIM automation with AI also means an end to chaos when creating descriptions. For example, a manufacturer in the sanitary industry had 250,000 products in many variants - manual creation of technical descriptions was unrealistic. Thanks to AI product description generation, it was possible to automatically generate 80% of the content, leaving only 20% for manual processing. The time needed to create content has been dramatically reduced and the company has saved over 5 million zlotys on operating costs. In addition, the descriptions are consistent, of high quality and free of errors, which has been appreciated by both customers and installers using this information.
What does all this mean for you? No more boring, repetitive work with product data. PIM automation with AI enables intelligent content creation and information management – algorithms learn from your data and business rules. By generating AI product descriptions and automatically tagging products with artificial intelligence, you gain time (which you can spend on product or strategy development) and your data is more accurate and up to date. Companies using AI in PIM report faster product launches and fewer human errors because the algorithm does not get tired or confused. In a word: efficiency and scalability that were once only dreamed of.

Content automation is one thing, but AI can do something else – it can think like an analyst and planner, only on steroids (and without coffee breaks). In the world of PXM, it is not only crucial what we say about the product, but also how much of the product we have on the shelf and whether we offer it to the right person at the right time. Artificial intelligence in B2B e-commerce also shines here, especially in areas such as AI demand forecasting and AI in inventory management.

Demand forecasting

Traditional sales forecasts are often based on order history and the instinct of experienced managers. In the past, it was common for someone to order more goods for the warehouse before the holiday season based on a gut feeling. AI demand forecasting turns this guesswork into science. Machine learning algorithms analyze huge data sets – not just your sales from previous years, but also market trends, weather, consumer behavior, and even social media posts – to predict what will happen. This makes demand forecasting much more accurate and dynamic. The system can adjust predictions on an ongoing basis when a new trend (e.g. a viral video on TikTok driving up demand for a product) or anomaly (e.g. a sudden lockdown) occurs. The result? Minimizing the risk of running out of stock (a so-called stockout) or being stuck with a mountain of unsold goods. According to IBM, using AI for forecasting allows companies to better adjust their inventory levels in real time, avoiding both shortages and excesses.

For example, a clothing chain implemented AI demand forecasting to predict sales of winter jackets. The algorithm took into account weather data from the last 10 years, fashion trends from social media and current sales figures. It predicted with great precision how many jackets of a particular model and size would be needed in different regions of the country. As a result, the store transferred the right quantities of goods to regional warehouses in advance. When the cold weather arrived, customers easily found what they were looking for and the company did not experience any shortages or surpluses – supply and demand were perfectly synchronized. This is how AI works as a digital forecasting fairy.

Automation of product recommendations

AI not only predicts what you will sell and how much, but it can also suggest who to target and what to offer to increase sales. We are talking about AI-based automated product recommendations. We have all seen this in practice on e-commerce platforms (“Customers who bought X also bought Y”). In the past, these were simple rules (“if someone was looking at televisions, show them TV accessories”). Now, machine learning algorithms analyze customer behavior in real time - what they are watching, what they add to their shopping cart, what they are looking for - and personalize recommendations with surgical precision.

The result? Higher conversion rates and bigger shopping carts. Automatic product recommendations AI is a powerful tool – it is estimated that up to 35% of Amazon's revenue is generated by its product recommendation engine. Since one in three dollars of Amazon's sales comes from “recommended for you”, it is worth taking a closer look. Introducing AI to recommendations in your shop or B2B catalog can significantly increase additional and cross-selling. Personalized suggestions make the customer more willing to throw something extra into the basket because they feel that the offer is tailored to their needs. It's a bit like a good salesperson who knows a regular customer and says, “Mr. Mark, I have a promotion on product X for you, you'll probably like it.” With the difference that AI does it automatically, on a huge scale, and learns with every click of the user.

What's more, such dynamic recommendations work not only on the store's website. Artificial intelligence in B2B e-commerce supports sales departments - it suggests to representatives which product to offer to a business customer based on their order history or seasonal trends. It is a real help in cross-selling and upselling, which translates into numbers. According to research, implementing AI-based recommendations can increase conversion by a dozen (and sometimes more) percent and significantly improve the customer experience. Customers feel better served - they get what they need before they even think they want it.

Optimizing the supply chain and inventory

PXM also concerns the physical side of the product – where it is and when it is to reach the customer. This is where the enormous value of AI in inventory management comes in. Artificial intelligence plays a masterful game, optimizing the entire supply chain: warehouses, distribution centers, delivery routes and relationships with suppliers. It's like solving a Rubik's cube with a million moving parts – and AI can do it in no time at all. Humans are not able to take thousands of data points into account at any given moment (inventory levels, forecasts, delays, transportation costs, etc.) – but AI most certainly can!

AI enables supply chain optimization, i.e. AI in inventory management and end-to-end logistics. Thanks to machine learning, the system monitors stock levels across the entire supply network in real time, detects anomalies and delays, simulates various scenarios and automatically makes replenishment decisions. If the stock of a product in the central warehouse falls below a certain threshold, the AI can automatically generate an order from the supplier or transfer the surplus from another warehouse before anyone notices the problem. In addition, there is intelligent transportation planning – algorithms can determine optimal delivery routes (saving time and fuel) or combine loads so that trucks are full rather than half empty.

The concrete results? Companies that have implemented AI in inventory and supply chain management are seeing double-digit improvements. According to McKinsey, pioneers of artificial intelligence in B2B e-commerce and supply chains achieve up to 15% reduction in logistics costs, 35% less inventory in the warehouse and 65% improvement in customer service levels. In other words, it is cheaper, there is less capital tied up in goods, and customers still get what they ordered faster. Who wouldn't want that combination?

In practice, AI can predict bottlenecks and prevent them before they occur - this is the core of AI in inventory management. An example? A factory can order raw materials in advance if the AI's demand forecast indicates an upcoming increase. Or a chain of stores can dynamically allocate inventory to brick-and-mortar locations based on local sales trends (e.g., more sunglasses to seaside towns when the weather is expected to be sunny). AI is vigilant 24/7 – it analyzes signals from IoT (sensors, scanners), sales data, information about delays from suppliers – and reacts in real time. It's as if a team of logistics geniuses were sitting in the command center of your supply network, but in reality, it's done by one agile algorithm.

As a result, the number of “sorry, out of stock” situations decreases, the costs of express emergency deliveries are reduced, and warehouses do not burst at the seams from excesses. An intelligent supply chain also means better transparency - you can see exactly where your product is at every stage. AI helps to detect, for example, dishonest suppliers (because it analyzes the parameters of quality and timeliness of deliveries), and also ensures compliance with regulations (tracking batches of goods in terms of legal requirements or ethical standards).

Have you ever had an online shop suggest exactly what you need before you even thought of it? Or had a newsletter show you products that perfectly match your taste? It's not magic – it's AI product content personalization. Personalization is playing an increasingly important role in PXM – customers expect messages and offers to be tailored to their needs, and AI allows us to meet these expectations faster and more precisely than ever before. Artificial intelligence is the key here, because only it is able to process billions of combinations of customer and product data in a fraction of a second to offer something individual to each customer.

Dynamic content customization in e-commerce

Traditional e-commerce often presents the same shop, the same descriptions and the same page layout to all buyers. Today, this approach is outdated. Dynamic AI product pages are the answer to the expectations of modern consumers - AI makes it possible to tailor the content of a page or application to a specific user in real time. If we know (thanks to AI) that a customer has recently browsed the “fitness equipment” category and is a regular visitor to the promotions section, the home page can automatically display a banner with a new collection of sportswear on sale instead of a random offer. Another customer will see something completely different, tailored to their needs. Such personalized websites and applications are becoming the standard - 77% of shoppers want a more personalized experience, and as many as 91% say they will leave an online store that offers a poor, generic shopping experience. It is therefore worth making sure that each recipient feels that the store “knows” their needs.

AI can dynamically modify the order and selection of products on lists, the content of recommendations, and even the layout of website sections. Thanks to the personalization of product content by AI, a customer who was browsing evening dresses yesterday will see a section called “Recommended dresses for special occasions” today - because predictive recommendation algorithms predict what she will be interested in. On the other hand, someone who always buys shoes from us will get info about the new shoe collection at the top of the page. It's a bit like each customer has their own shopping advisor who tailors the assortment in the store especially for them. Implementing such dynamic, AI-driven customer experiences can significantly increase engagement and time spent on the website, which naturally translates into sales results.

Predictive algorithms recommending products

We have already discussed recommendations in the context of automation (section 2), but it is worth emphasizing their importance for personalizing the experience. Recommendation algorithms are the heart of personalized e-commerce. Machine learning analyzes the user's preferences: what they have viewed, what they have bought, how much time they have spent on a given page, what they have in their shopping cart, etc. On this basis, it builds a model of their interests and predicts what else they might like. These are predictive algorithms - they predict the customer's next steps and needs.

In practice, it might work like this: a customer is browsing smartphones and adds a new phone model to their shopping cart. Before they finalize their purchase, the system, based on predictive recommendation algorithms, suggests perfectly matching accessories (cases, headphones) and offers a screen insurance package. All of this is analyzed in real time based on data about customer preferences. If the AI knows that a particular phone model is often bought with wireless headphones, it will suggest them. If it recognizes that the customer bought the previous model a year ago, it can offer a trade-in program with a discount. Prediction allows you to anticipate the customer's needs and make an offer before the customer looks for it themselves with the competition.

What is important is that these recommendations are learned in real time. For example, if a customer suddenly starts looking at a completely different category (let's say they were previously buying electronics, but now they are looking at travel suitcases), the system will notice this and adapt accordingly. Perhaps they are getting ready to travel, so we will immediately show them an offer of plug adapters and power banks, because these are related to travel. This level of personalization would be impossible manually. AI does this for thousands of customers at the same time, offering each one something different.

Customer segmentation supported by machine learning

However, before we personalize content, it is worth understanding who our customers are and dividing them into groups with similar characteristics (segments). Machine learning customer segmentation allows us to go beyond classic demographic or geographic methods - AI analyzes behavior patterns and creates behavioral and predictive segments based on real data. Algorithms can discover patterns in customer data that a human would never think of. For example, they can detect a “bargain hunter” segment - customers who only buy in sales, but in large quantities - or a “hesitant browser” segment - customers who look at many products many times before buying.

Machine learning (e.g. clustering techniques) can process transaction data, website behavior, and campaign responses to create segments “by nature,” resulting from data rather than marketers' assumptions. This gives us more precise target groups to which we can tailor our communication. What's more, AI can predict segment changes - for example, if a customer starts to show characteristics of another segment (they have improved their financial situation and are now buying more expensive products), the system will pick up on this and move them to another group or mark them as a hybrid segment.

Why is this important? Because with a well-segmented database – created, for example, through machine learning customer segmentation – we can ultra-personalize the message. One group can be sent sale messages, another can be emphasized the prestige of new products, and yet another can be offered products complementary to those they have already bought. AI automates the segmentation process, which used to be done manually once a year. Now, the segments are updated on an ongoing basis, in line with the influx of new data. According to McKinsey research, companies using personalization of customer data (i.e. intelligent segmentation) see revenue growth of 5-15% and a reduction in marketing costs of 10-20%. This is a powerful argument for trusting algorithms in this area.

It is worth noting that the personalization of AI product content does not end with the online store. Omnichannel is the key word. AI supports the creation of a coherent and dynamic shopping experience across all channels - whether the customer visits a website, a mobile application, uses live chat or receives an email. Thanks to dynamic AI product pages and predictive recommendation algorithms, it is possible to recognize the customer and adjust the message at every stage of the shopping journey. If someone has viewed a product online and then comes to a physical store (identifying themselves with a customer card, for example), the seller can use AI to get a hint of what the customer was looking at and what they are interested in, in order to give them better advice. Connecting the dots between channels is difficult manually, but AI is great at combining data from different sources and building a unified 360° customer profile.

Now that we know what AI can do for the product experience, let's take a look at the AI tools in PXM that make these capabilities possible. The market for these types of solutions is growing as fast as their applications. Here are a few examples of technologies worth knowing:

• LemonHub.AI – a Polish solution created by LemonMind, designed specifically for the integration of AI with PIM systems. LemonHub.AI integration with PIM enables the use of modules such as Marketer (for generating product content) and Assistant (an intelligent assistant that knows data about your products, e.g. a chatbot for customers or employees). LemonHub.AI can automatically map product attributes from different sources and also implement AI product label OCR – reading data from packaging and delivering up-to-date product information where it is needed. It's a bit like attaching an AI brain to your existing PIM – suddenly it becomes much more “intelligent” and independent. We have already described LemonHub.AI application examples (automatic categorization, descriptions, etc.) – it can be seen that the tool works well in real projects, bringing measurable benefits.
  

• Akeneo AI – Akeneo, a well-known PIM provider, is also experimenting with incorporating AI into its ecosystem. Currently, it uses, among other things, partner integrations (e.g. with Vaimo) for automatic translation and as Akeneo AI content generator – generating descriptions and product content. However, the trend is clear: the big PIM players are adding intelligent functions to stay competitive. In its materials, Akeneo emphasizes that AI is becoming the standard, not a luxury, when it comes to product information management.
  

• Bluestone PIM – AI Linguist and company – AI Linguist and company – Bluestone PIM is another provider that invests heavily in AI. Bluestone PIM offers the following AI modules: AI Linguist for mass translation and localization of content, and AI Enrich – for attribute recognition from images and automatic AI product descriptions. This is a good cross-section of the possibilities - you can see that the tools focus on content and product data in various aspects. Importantly, Bluestone emphasizes that it is crucial to have AI integrated directly into the PIM, rather than as a separate application - to avoid chaos and risk when transferring sensitive data between systems.

• Pimberly, Salsify, inRiver... – other PIM/PXM providers are also developing AI tools in PXM. Pimberly offers modules such as Image AI, Copy AI and Product AI for automatic AI product descriptions and image tagging. Salsify invests in algorithms to improve data quality and generate content recommendations, and InRiver uses AI to assess the completeness and consistency of product information.
  

• Description generators based on GPT-3/4 - more and more companies are using large language models (LLM) such as GPT-3 or GPT-4 to implement automatic AI product descriptions on a mass scale. This makes it possible to generate hundreds of descriptions in a matter of moments, which significantly relieves the burden on content teams. Even dedicated services are being created, e.g. Plytix offers AI Writer in its PIM for automatic description writing. Similarly, Hypotenuse AI and other startups offer to generate marketing content after entering a few keywords. This often works in the SaaS model: you send product data to their API, and you get the finished text back. However, it is important to remember that automatic AI product descriptions based on language models require supervision - AI can be creative and color facts, so it is worth taking care of the quality control stage of the generated content ;) . Nevertheless, the time saved is enormous, as copywriters are relieved of writing dozens of similar texts - they can focus on quality control and polishing unique passages.
  

• Recommendation and analytics tools – in addition to typical PIM systems, many companies implement advanced AI tools in PXM, such as Salesforce Einstein (integrated AI offering recommendations, search and personalization) or Adobe Sensei (Adobe AI for Magento for recommendations and merchandising). These tools help personalize the user experience based on behavioral data. Large retailers often create their own AI engines - for example, Walmart and Allegro invest in their own algorithms that suggest products. For companies that do not have such resources, there are ready-made services such as Google Cloud Recommendations AI or AWS Personalize, which can be integrated with your store or application to add a layer of recommendations controlled by machine learning models (trained even on your own data).
  

• Image analysis (computer vision) – in the context of PXM, let's not forget about images and multimedia (after all, part of the product experience includes photos, graphics, and videos). Here, AI in the form of computer vision can automatically tag images (e.g. recognize that the product photo is a “red sleeveless dress” and add such tags), assess their quality, and even generate images (although generating product photos is still a curiosity, because it is better to show a real product). In any case, tools such as Google Vision API or Microsoft Azure Computer Vision can help you quickly organize your product photo library by assigning descriptions and categories to them and detecting logos, backgrounds, etc. As mentioned earlier, AI can also read text from images (OCR), which is useful, for example, for processing product data sheets from suppliers or information on packaging (Case 2 in section 1 showed exactly this).
  

As you can see, the ecosystem of tools is rich. The choice depends on the needs of your company. If you are already using a specific PIM, check if it offers AI modules or integrations - it is often easiest to use something compatible. If you are just looking for a solution, consider those that have “AI inside” - it can save a lot of work when scaling your business. Also, remember about the data security aspect – make sure that when you transfer data to AI tools (especially cloud-based ones), you comply with security and privacy policies, especially if it is sensitive data (e.g. wholesale prices for B2B partners, etc.). Fortunately, many modern platforms give you the option to host AI models in your infrastructure or to securely encrypt data.

Finally, let's take a look at what all this gives companies, especially in the B2B sector. Because the benefits of AI in PXM are not just nice-sounding slogans - they are concrete numbers and competitive advantages. Here is a summary of the most important benefits:

• Greater accuracy and consistency of data – in B2B, where product catalogs can have hundreds of thousands of items, keeping things organized is essential. The quality of AI product data significantly reduces human error when entering and updating information, which translates into better purchasing decisions and greater customer confidence. Automatic data classification and validation means your databases are always up to date and free of typos and mistakes. This is hugely important for B2B – business customers often make purchasing decisions based on specifications and technical data. If they can always find reliable, accurate information on your website, they will be more willing to work with you (because they will avoid mistakes, e.g. ordering parts with the wrong catalog number). In short: AI improves data quality, and data quality means a professional image and a lower risk of complaints.
  
• Saving time and resources – task automation directly translates into fewer man-hours that you have to spend on product information management. Whether it's content creation or inventory planning – AI does it faster. People can focus on what they are good at: building customer relationships, creative marketing, product development. One case study showed huge savings thanks to PIM automation – 1,400 working hours reduced thanks to AI for product classification. In another case, almost 12,000 hours were gained thanks to OCR and automatic label processing. This proves that automating B2B product data management really works. These numbers are impressive. For B2B companies, this also means saving labor costs – money that would have to be spent on overtime or hiring additional staff can be used elsewhere. AI is scalable – when your product range grows, it simply works harder (or you need a slightly more powerful server), whereas without automation, you would have to increase the size of your data team proportionally.
  
• Improving operational efficiency with AI is not just an empty slogan, but a real benefit. Faster implementation of new products, shorter time-to-market, fewer errors, cheaper logistics and greater flexibility. The automation of B2B product data management translates into a concrete financial result. Optimizing the supply chain means lower storage and transportation costs, and better forecasts mean financial liquidity (you don't freeze money in excessive inventory). Companies that have invested in AI are seeing real savings: for example, the aforementioned intelligent supply chains have resulted in 15% lower logistics costs and 35% fewer inventories. In B2B, where margins are sometimes lower than in B2C, such improvements simply feed into the bottom line. On top of that, there is something immeasurable: flexibility and scalability. When you have AI in your processes, it is easier for you to grow (because machines will do more work faster) and to cope with volatility (because AI will adjust plans faster when the market changes). This is a significant advantage over competitors stuck in the old, manual model of operation.
  
• Increased conversion with AI PXM is a direct result of accurate content and data-driven recommendations. Your descriptions, recommendations and product availability are always tailored to your customers, which translates into more sales and a better customer experience. Customization makes customers feel spoiled – they receive offers and messages tailored to their interests. This translates into concrete indicators: companies that are highly personalized (often with the help of AI) generate up to 40% more revenue from personalization activities than the average. Other sources state that personalization can increase total revenue by 5-15% (McKinsey) and even double the ROI of marketing campaigns in some cases. In the B2B sector, personalization is also of paramount importance - according to research, 77% of B2B buyers will not make a purchase if they do not receive personalized content. This is a clear signal: tailor your offer to the customer or they will go elsewhere. AI enables such personalization on a mass scale by analyzing the preferences of up to thousands of business customers and sending personalized recommendations to each of them to salespeople or marketers. The result is increased sales efficiency, higher conversion rates at all stages of the funnel (from lead to order completion) and loyalty – because a customer who feels understood stays longer. A good customer experience becomes a differentiator, and AI helps to deliver it consistently and proactively.