From personalization to automation: How AI is powering future of online retail

The rapid integration of AI in e-commerce is reshaping how consumers interact with online retail platforms and how businesses compete in a digital-first economy. In a new study, researchers examine how major players in the e-commerce sector are leveraging AI to enhance customer experience, operational efficiency, and market differentiation.

Their paper, “Artificial Intelligence in E-Commerce: A Comparative Analysis of Best Practices Across Leading Platforms,” published in Systems (2025), analyzes the strategies of Amazon, IKEA, Shein, Temu, and Apple, offering insights into the alignment between AI integration, digital maturity, and competitive performance.

How AI is transforming online retail operations

The study explores how AI technologies have evolved from basic automation tools to core components of e-commerce ecosystems. By applying the Resource-Based View (RBV) framework, the authors assess how AI capabilities contribute to sustainable competitive advantage across multiple operational layers - from customer engagement to logistics and security.

Amazon emerges as the undisputed leader, with AI embedded across nearly every operational area. The platform’s advanced recommendation engines, AI-driven search algorithms, and predictive inventory management systems enable hyper-personalized shopping experiences while optimizing supply chains. Complementing these capabilities are robust warehouse automation systems and sophisticated content generation tools that dynamically update listings, promotions, and advertisements.

IKEA follows closely with a balanced approach that integrates AI in stock prediction, customer analytics, and personalized marketing. The retailer’s use of AI-driven data insights helps support its hybrid retail model, combining online and physical store experiences to meet the expectations of diverse consumer segments.

Emerging platforms such as Temu and Shein are leveraging AI for rapid agility. Temu utilizes recommendation engines, visual search tools, and logistics optimization to scale quickly in competitive markets. Shein, meanwhile, focuses on real-time demand forecasting and dynamic product recommendations, allowing it to respond swiftly to shifting consumer trends - a strategy that underpins its dominance in the fast-fashion segment.

Apple, in contrast, presents a unique case. Despite being a leader in AI innovation through its broader ecosystem, with technologies like Siri and predictive text, the study finds little evidence of AI integration within Apple’s retail platform interface. This divergence highlights Apple’s focus on ecosystem-wide user experiences rather than overt AI-driven personalization at the storefront level.

Comparative insights across platforms

The comparative analysis reveals clear correlations between digital maturity, market position, and the breadth of AI adoption. Amazon and IKEA demonstrate what the researchers describe as high-level integration, with approximately 83% coverage across the six AI functional areas examined in the study: recommendation systems, virtual assistants, warehouse automation, content personalization, automated content generation, and security applications.

Temu reflects a mid-level adoption strategy, with around 66% coverage, concentrating on rapid deployment of customer-facing AI tools. Shein exhibits a narrower application of AI, with coverage of about 33%, but maximizes its limited scope by focusing on tools that directly enhance customer acquisition and retention.

One notable insight is the silent but critical role of AI in security and fraud detection, which is largely invisible to users but pivotal in maintaining trust and platform integrity. While the public-facing elements of AI are evident in product recommendations and chatbots, the study notes that back-end applications in fraud monitoring and cybersecurity likely represent substantial, albeit less visible, investments across all platforms.

The analysis also highlights the strategic alignment between AI functionalities and platform-specific business models. Amazon’s extensive integration reflects its scale-driven approach and commitment to continuous optimization. IKEA’s selective deployment supports a differentiated value proposition, balancing efficiency with a customer-centric hybrid model. Temu and Shein focus on speed and adaptability, deploying AI tools to respond to volatile consumer trends, while Apple prioritizes its broader technological ecosystem over its retail storefront experience.

Opportunities, challenges, and the road ahead

While AI adoption delivers measurable advantages, challenges remain in transparency, governance, and ethical deployment. Platforms that rely heavily on personalized algorithms face growing scrutiny over data privacy and bias, underscoring the need for clearer regulatory frameworks and responsible AI practices.

Another challenge lies in the visibility of AI adoption. The study’s methodology, relying on publicly accessible data and walkthrough analyses, suggests that the actual scale of AI integration, particularly in areas like cybersecurity, logistics optimization, and dynamic pricing, may be even greater than reported. This underlines the complexity of accurately assessing AI capabilities without access to proprietary systems and data.

The authors recommend that platforms invest in scalable AI infrastructures that integrate predictive analytics, natural language processing, and real-time learning models. These investments not only enhance customer experiences but also enable retailers to respond dynamically to market disruptions, supply chain constraints, and evolving consumer expectations.

Furthermore, the researchers highlight the importance of strategic alignment. Simply adopting AI tools is not sufficient; businesses must integrate these technologies into their broader operational and strategic frameworks to unlock their full potential. This requires robust data pipelines, cross-functional collaboration, and continuous workforce upskilling to ensure employees can work effectively alongside AI-driven systems.