AI Powers E-Commerce, But Scaling Up Presents Complex Hurdles

E-commerce giants increasingly use artificial intelligence to power customer experiences, optimize pricing, and streamline logistics. However, an expert in the field says that scaling AI solutions to handle the massive volume of data and real-time demands of large platforms presents a complex set of architectural, data management, and ethical challenges.

Andrey Krotkikh, a machine learning specialist with experience at AliExpress CIS, highlighted the intricacies of implementing AI in a dynamic e-commerce environment.

“One of the main challenges when scaling up is the inference of models in real-time,” Krotkikh said. “You need to provide the user with information within a short time frame without compromising the user experience.”

He cited delivery time prediction as an example, where each user’s data is unique and depends on numerous factors, precluding pre-caching. This necessitates a robust system design that accounts for data collection, model training, inference, and adaptation to evolving conditions.

“To create a system that stands the test of time, it is necessary to qualitatively collect all the information that can affect model inference and design the project, including how the model will be trained, inferred, and adapted to new conditions due to data shifting,” Krotkikh said.

He also stressed the importance of considering future projects and company plans, advocating for simple, resource-efficient models to minimize potential losses from changing priorities.

Data management is another critical area. Krotkikh described a typical scenario where data is collected across different domains with varying standards, leading to inconsistencies and outdated information.

“Usually, the situation is that data is collected by different domains in different ways, with everyone having different agreements on naming conventions,” he said. “Added to this are the problems of data becoming outdated, and the situation where data has stopped being updated is quite common.”

He suggested that a Feature Store can help manage preprocessed data and facilitate cross-team usage, while a centralized Data Warehouse (DWH) domain can unify data preparation and migration.

“From the data side, this is resolved through centralized data preparation using a DWH (Data Warehouse) domain,” Krotkikh said. “This team prepares tables and dashboards in a unified manner, initiates data migration, and acts as a proactive side in cross-team interaction.”

Deploying advanced AI techniques like reinforcement learning for dynamic pricing and recommendation systems also presents challenges, particularly in aligning with business requirements.

“In general, problems can be divided into three parts: business requirements, model training, and data,” Krotkikh said. “The most challenging problems (in my experience) are considering business requirements and learning to adapt to them.”

He emphasized the need to consider the impact of AI solutions on other company products and ensure synergistic collaboration between teams.

“Your development does not exist in isolation, but in the overall ‘atmosphere’ of the company’s products, and it is impossible to think that it does not affect other products,” Krotkikh said. “Therefore, most of the time, you need to think about how to validate the absence of the impact of your solution on other company products and how to ensure synergistic work of your project with other projects.”

Ethical considerations are paramount, particularly regarding price discrimination. Krotkikh warned against practices that are both illegal and unfair to users.

“The most important point that all companies should consider is the absence of price discrimination against users,” he said. “Such practices are punishable in many countries and, in general, are unfair to users.”

He recommended proactive discussions between machine learning and business teams to ensure fairness and prevent unintended consequences, such as price changes during sales.

“ML and business should discuss these things in advance, such as how to ensure ‘fairness,’” Krotkikh said. “One similar example is the absence of price changes during sales; ML can, on its part, analyze how best to ‘engage’ the model with such constraints to achieve good results overall for the entire sale.”

As AI continues transforming e-commerce, companies must navigate these challenges to build scalable, reliable, and ethical solutions that benefit both businesses and consumers. By prioritizing data quality, architectural robustness, and ethical considerations, e-commerce platforms can harness the full potential of AI while mitigating potential risks.