Agility is the only answer in retail. In recent years, customer expectations, technology, and competition have been changing rapidly. Retailers who can’t adapt their processes quickly lose their customers even faster. For example, 70% of Dutch consumers abandon their online shopping carts without checking out. One of the main reasons? A slow or confusing checkout process, according to research by the Baymard Institute.Â
In this article, you’ll discover 5 ways to use artificial intelligence (AI) to optimize the ordering process while also improving the customer experience.
How can technology support your ordering process?
Behind every order lies a complex web of systems, manual processes, and coordination points. Moreover, returns also cost online retailers a tremendous amount of money. According to retail expert Stef de Bont of Shipcloud, returns cost an average of at least 12.50 euros each. Each step can introduce delays or errors. As a result, it becomes harder to respond quickly to market changes or meet customer needs. Many retailers face the same challenges: limited visibility, poor system integration, and cumbersome process changes.
However, innovation is desperately needed but difficult to achieve with “traditional IT,” or legacy software. These outdated systems are technically limited and difficult to integrate with modern technology. Consequently, integrating new solutions is complex and time-consuming. Legacy systems are typically built using technologies and architectures that are not, or barely, compatible with modern tools, frameworks, or cloud solutions. Therefore, it is difficult to add new functionalities or leverage technologies such as AI, data analytics, or automation.
One possible solution is to switch to low-code platforms such as Mendix. Low-code offers much greater flexibility and scalability than traditional software development. This lets organizations implement new functionality faster and more easily, without abandoning their existing IT landscape. Thanks to their scalability, these applications can meet increasing demand without compromising quality or efficiency. This makes it possible to modernize step by step while responding more quickly to changing customer or market needs.
In addition, technologies such as Artificial Intelligence (AI) also play an important role here. AI enables organizations to develop flexible solutions that learn from data, automate processes, and make decisions faster. These include predictive analytics, automated customer interactions, and smart maintenance planning. Mendix lets you integrate AI directly into your software so that you can respond in real time to customer needs and market dynamics. Low-code supports this process and helps you build a solution.
By combining AI with low-code, companies can innovate faster, operate more efficiently, and gain a competitive advantage—even if they still depend on legacy systems. Low-code best supports adopting these technologies, as it accelerates development, simplifies integration, and makes innovation accessible to both IT and the business.
5 applications of AI
AI offers opportunities to optimize and personalize the ordering process, including:
1. Automated replenishment system
AI models analyze historical sales data, seasonal trends, and external factors—such as the weather or local events—to predict future demand accurately. This helps stores optimize inventory, reduce waste, and prevent “out-of-stock” situations, leading to faster, more consistent delivery.
2. Smart returns management and personalized recommendations
AI also helps identify purchasing behavior and return patterns. This lets you predict return reasons and take targeted action, such as providing better sizing advice or product information. Additionally, AI boosts conversion rates through personalized recommendations based on past behavior, such as “You might also like this” or “Based on your purchases.”
3. Real-time order status
Smart AI chatbots can assist customers 24/7 with questions about orders, returns, or product information. They take the load off the customer service team, reduce wait times, and ensure fast, consistent responses.
4. Pricing strategy and optimized offerings
Furthermore, AI adjusts prices in real time based on demand, competition, and customer segmentation. This lets you respond faster to market changes and optimize margins without compromising the customer experience.
5. Dynamic staff scheduling
Finally, AI analyzes order history, peak times, and delivery routes to plan staff deployment—from order picking to delivery intelligently. The result? Faster delivery, lower costs, and a smoother customer experience.
Use case: modernizing the ordering process
The challenge
A leading European retailer faced a major challenge. With thousands of orders per week and a broad product range, the ordering process was reaching its limits. Outdated systems communicated poorly, forcing many steps to be done manually. This led to delays, order-processing errors, and poor visibility across the supply chain. As a result, customers did not receive up-to-date status updates, while internal teams spent significant time correcting errors. At the same time, the company lacked personalized communication capabilities, which strained the customer experience.
The solution
To respond more quickly to market changes and customer expectations, company X decided to modernize its ordering process using the low-code platform Mendix. The first step was to digitize inventory management, order processing, delivery, and returns. Next, AI capabilities were added, such as demand forecasting and customer segmentation. In addition, they introduced AI chatbots to provide 24/7 customer support. By combining data from various sources, the company gradually built a centralized overview for both customers and employees. The phased approach ensured that the team could learn and improve quickly during implementation.
The results
- Faster order processing thanks to automated workflows
- Fewer errors and returns due to improved data quality
- Increased customer satisfaction through personalized recommendations
- Lower operating costs due to fewer manual steps
Overall, this demonstrates how essential a smooth, transparent, and fast ordering process is for improving the customer experience and maintaining customer loyalty. The benefits of AI and low-code in retail are clear: more efficient processes, lower costs, and a better customer experience. But where do you start? Many companies struggle to translate strategy into concrete solutions.
Therefore, the most important thing is to take the first step. For example, you can start with a tangible business case—such as optimizing the ordering process—and then scale up based on proven value. The combination of low-code and AI makes it possible not only to react but also to think ahead.
Curious about how AI and low-code can make your ordering process smarter? Get in touch with one of our specialists via our contact page.Â
