E-commerce challenges in Print-on-demand industry

The global Print on Demand market is expected to reach $57.49 billion by 2033, up from $10.78 billion in 2025. The majority of this rise in sales is generated by customized and personalized apparel and accessories, with books and booklets being a significant part of the industry.

The industry benefits from the rise of e-commerce as selling personalized or highly customized products which are prepared using print on demand solutions grows in popularity along with it. On the other hand, e-commerce logic presents a huge challenge for those print-on-demand companies that are not able to remodel their operations to better fit the market.

E-commerce as a catalyst of change

E-commerce mercilessly follows the logic of delivering a good customer experience to the point of considering it one of the key differentiators and tools to build a competitive edge, and for good reason. HubSpot states that 88% of users are less likely to return or interact further with a site that provides a bad user experience.

This particular aspect, the customer experience, generates huge challenges for print-on-demand companies and works as a catalyst for change, impacting the sales operations as well as technical aspects of the online sales process.

Growing customer expectations

The e-commerce environment comes with one major drawback compared to selling with offline channels as nearly every other shop imaginable is just one click away. The same goes for print-on-demand services, where both major and minor players compete not only with each other, but also with the rest of the market, including online retailers or marketplaces.

This kind of “competition” is about the overall level of comfort and convenience provided when shopping online. Print-on-demand clients use Amazon or comparable services and there is a particular level of convenience they expect from every online shop.

Personalization vs complexity

Print-on-demand services, especially digital-to-garment printing, benefits from the increasing need for personalization and the ability to search for unique clothing pieces. From a designer or graphic artist point of view, print on demand services are an easy way to sell art, either as clothes and accessories, or printed on paper as posters and images.

Yet for personalization, complexity is the other side of the coin. If a product is to be personalized, there is a need for options to choose from. When it comes to print on demand for books or zines, we gain added complexity in the thickness of paper, type of cover, technology of printing, and dozens of other variables contributing to the complexity of the ordering process.

Mixam, one of the leading print-on-demand companies of the market, solved this challenge by moving from a traditional ordering system to an Agentic AI assistant that supports the user throughout the completion of the order. By that, one can reach a high level of customization and personalization without the inconvenience of browsing and clicking through an exhausting list of possibilities.

Integration with external platforms vs owned infrastructure

Another challenge for print-on-demand companies is the selling process itself. A popular approach is to run owned selling platform using a popular e-commerce solution, for example WooCommerce or Shopify. Around 13% of all Shopify stores use a print-on-demand app, showing how popular this method of selling is.

Yet seamless integration requires a huge degree of technical expertise and knowledge. Not to mention the fact that the shop owner may (and probably is) updating and reshaping the offer, making a stable and reliable connection even more demanding.

Apart from selling platforms, there are also marketplaces that are a great source of customers for the price of commissions that cut the profit margins of PoD businesses. These include Etsy, Amazon and comparable online marketplaces, depending on the type of good printed and target group to be reached.

Moreso, every external platform has its own set of requirements to meet, be they technical or organizational ones. APIs differ, and so do product feed integrations, so managing cooperation with popular e-commerce destinations comes with a high administrative and organizational overhaul.

Emerging platforms

Last but not least, e-commerce is constantly evolving along the rest of the internet. New platforms and new models of consumption emerge along with new contexts the company may find its customers in.

The emergence of TikTok as a sales channel is a perfect example of the need to adopt new channels even when they appear unlikely. Apart from TikTok’s overall strong drift toward accessories and fashion, more uncommon, niche societies emerge there, including BookTok, a TikTok community centered around books, especially young adult fiction.

Print-on-demand companies act as partners for authors of self-published books and other literary works. With younger generations entering the literary market and producing their own works, the need to adjust selling models to the preferences of new demographics is growing.

AI as a new shopping assistant

The challenges described above share a common thread, the need to deliver a smooth, intuitive customer experience in a domain that is inherently complex. This is where artificial intelligence enters the picture, not as a futuristic concept, but as a practical tool already deployed in the market.

Traditional e-commerce interfaces for print-on-demand services rely on filters, dropdown menus, and product categories. The customer is expected to know what they are looking for and to express it using the right terminology. In practice, this means that a first-time customer who simply wants to “print a book” is confronted with questions about binding types, paper weight, and cover finishes before they even get to pricing.

Modern AI changes this dynamic fundamentally. Instead of forcing the customer to navigate the full complexity of options, an AI-powered assistant understands the intent behind their request. When someone says “I need a hardcover photo album for my wedding,” the system recognizes the context and recommends the right product, matching it against the actual catalog of available options, ranking alternatives by relevance, and presenting the best fit first.

Mixam implemented this approach through an AI agent that acts as a product selector, matching customer descriptions to real products based on what the customer wants, not just the words they use. The system always works exclusively through the live product catalog, so recommendations reflect what is actually available for order. The result is a shopping experience closer to speaking with a knowledgeable print consultant rather than filling out a web form, except it is available around the clock and scales to any number of concurrent visitors.

Knowledge that speaks the customer’s language

Beyond product selection, there is another layer of complexity that often goes unnoticed – the knowledge gap between print-on-demand providers and their customers. Printing is a craft with its own vocabulary. Terms like “bleed,” “spine width,” or “perfect binding” are second nature to industry professionals but entirely foreign to most customers.

Traditional approaches to bridging this gap include FAQ sections, glossaries, and knowledge bases. The problem is that these tools still require the customer to search, and searching requires knowing what to look for. When a customer types “how much space should I leave at the edge of my design,” a keyword-based search engine is unlikely to surface an article about bleed margins.

AI-powered knowledge systems work differently. Rather than matching words, they understand meaning. When a customer asks a question in plain language, the system finds the most relevant answer from the company’s documentation, regardless of whether or not the customer used the “correct” terminology. Mixam deployed this approach across its six regional operations, giving customers instant access to printing expertise in a conversational format, grounded in the company’s own documentation and updated centrally.

What makes this approach particularly powerful is its adaptability. The same system that explains bleed margins in simple terms to a first-time self-publishing author can switch to professional language when speaking with an experienced graphic designer. It adjusts not just to the question, but to the person asking it. Moreover, it works in any language the customer uses, removing yet another barrier in an increasingly global market.

This conversational capability goes beyond answering questions. The AI can proactively guide customers through the specification process, asking follow-up questions, suggesting options based on the project described, and even comparing multiple quotes to present the best offer. Think of it as having a knowledgeable print consultant available at every step, one that listens first and recommends second.

Customer support at scale

Customer expectations do not end at the moment of purchase. Post-sale interactions, order tracking, complaints, modifications, reorders, are equally critical to building lasting customer relationships. For print-on-demand companies operating across multiple markets, maintaining consistent and responsive customer support is a significant operational challenge.

The economics are straightforward: hiring and training customer support agents for every market, language, and time zone is expensive. Yet the cost of poor support is even higher. A delayed response to a complaint about a misprinted batch can escalate into a lost client and a negative review that discourages future customers.

AI-powered customer support agents offer a practical middle ground. By applying the same conversational and knowledge capabilities used in the ordering process, specialized AI agents can handle a wide range of support tasks, such as answering questions about order status, processing complaints, advising on reorders, and explaining production timelines. The key distinction is that these agents work from the same real-time data and company knowledge as the sales assistant, ensuring consistency across the entire customer journey.

The human element remains essential. Well-designed AI support systems recognize their own limitations and route complex or sensitive cases to human advisors seamlessly. The goal is not to replace customer support teams, but to let them focus on the interactions where human judgment and empathy matter most while the AI handles the volume of routine inquiries that would otherwise overwhelm the team.

From impressive demos to reliable operations

The potential of AI in e-commerce is easy to demonstrate. Building a chatbot that handles the happy path, a straightforward order placed by a cooperative customer, can be done in a matter of days. The real challenge, and the real differentiator, lies in what happens when things go off script.

What if a customer requests a book size that cannot be physically produced? What if the page count does not match the requirements of the chosen binding type? What if the AI, as AI systems sometimes do, confidently generates a quote for a product that does not exist?

These are not theoretical concerns. AI systems are known for “hallucinations,” producing plausible-sounding but factually incorrect outputs. In e-commerce, hallucinations translate directly into operational chaos: impossible orders, frustrated customers, and wasted production capacity.

Mixam addressed this challenge by building validation layers that verify every specification against actual production capabilities before a quote is generated. If a customer asks for a 13x12cm book and the production facility only supports 12x12cm or 14x14cm, the AI does not invent a non-existent option, it detects the mismatch and suggests the closest available size. Similarly, page counts are verified against the requirements of each binding type, catching errors before they reach the production floor.

Equally important is a connection to real-time data. Rather than working from static product catalogs that may be outdated by the time a customer interacts with them, the AI pulls live pricing, availability, and production timelines directly from the production systems. When a quote is generated, it reflects the actual state of the business: current paper stock, real delivery dates, and functional links that customers can use to complete their orders immediately. This kind of integration ensures that what the AI promises is what the company can deliver.

Building AI as a business asset

There is a significant gap between deploying an AI prototype and running AI as part of core business operations. Many companies have experimented with AI-powered tools, but few have made the transition from “interesting project” to “reliable infrastructure.”

The distinction comes down to discipline. A production-ready AI system requires systematic testing, not just of the code, but of the AI’s behavior. This means simulating real customer conversations, including edge cases like customers who do not know what binding method they need, orders that mix color and greyscale pages, or requests for custom sizes that are not in the standard catalog. Each scenario needs to be tested, validated, and monitored over time.

Mixam’s approach illustrates what this maturity looks like in practice. The system is validated against dozens of distinct real-world scenarios, from straightforward orders to deliberately tricky edge cases. Test suites simulate full customer conversations end-to-end, ensuring that the AI not only gives correct answers but behaves appropriately throughout the interaction. This is a fundamentally different level of rigor compared to running a few manual tests before launch.

Beyond testing, operational maturity means monitoring performance continuously, managing configurations with version control, and evolving the system based on data rather than assumptions. It means treating AI the same way a well-run company treats any other critical piece of business infrastructure, with accountability, measurement, and a plan for continuous improvement. This is the difference between companies that experiment with AI and companies that build a lasting competitive advantage with it.

Summary

Print-on-demand companies surf on the modern wave of demand for personalized and unique products, benefiting from the explosion of human creativity powered by the emergence of generative AI tools. On the other hand, AI tools and the creativity mentioned above have both disruptive and empowering potential. The good news is that proven production-ready solutions already exist oin the market, turning these challenges into tangible competitive advantages for those willing to adopt them. Thus it becomes necessary to seek guidance and consultation to not stay behind the market, not only within your own industry, but across the whole, especially when competing in the digital environment.