AI in eCommerce: Current Challenges and Future Trends
AI
Updated: June 12, 2025 | Published: May 16, 2025
Introduction
Artificial intelligence (AI) is no longer a futuristic concept in e-commerce—it is a present-day necessity reshaping how businesses operate and engage with customers. As global market forecasts project AI in e-commerce to grow from $9.01 billion in 2025 to $64.03 billion by 2034, companies are under pressure to adapt or risk obsolescence. With rising personalization, speed, and relevance expectations, AI has become central to differentiation and scalability in a $7 trillion global market.
This report explores how AI is used across the e-commerce landscape, drawing from interviews with C-level executives. It highlights the strategic implementations by major players like Amazon, Alibaba, and eBay and the emerging practices of smaller companies navigating automation, customer engagement, and operational efficiency. Key focus areas include personalization, predictive analytics, dynamic pricing, AI-driven customer service, and content optimization.
The report examines the broader implications of AI integration, including ethical considerations, system scalability, and regulatory challenges. It concludes with an overview of the trends shaping the near future of AI in e-commerce, offering readers insight into what leading innovators are doing today and how others can prepare for what’s next.
The Driving E-Commerce Giants Currently Implementing AI
As expected, e-commerce industry giants are the driving force behind the implementation of AI. These companies are only a few examples of how AI is actively reshaping e-commerce in one way or another, from customer experience to inventory management.
1. Amazon
Amazon has been a pioneer in leveraging AI across its operations. It uses AI for personalized recommendations, logistics optimization, fraud detection, and customer support.
Rufus AI Chatbot: A shopping assistant that interprets user intent and provides contextual product suggestions.
Source: Amazon
“Interests” Feature: Uses generative AI to create personalized, auto-updating product widgets based on user behavior.
Logistics AI: AI systems help Amazon optimize delivery routes, warehouse robotics, and inventory management.
Source: NYMag
2. Alibaba Group
Alibaba is integrating AI to support its core e-commerce and cloud businesses while also aiming to lead in Artificial General Intelligence (AGI).
Cloud Intelligence: Offers AI solutions to external clients through Alibaba Cloud.
Source: Alibaba Cloud
Smart Recommendations: AI-driven personalization engines improve customer experience.
Revenue Impact: AI initiatives contributed to an 8% year-on-year revenue increase in Q4 2024.
3. eBay
eBay is using AI to create a hyper-personalized shopping experience, similar to social media platforms.
Personalized Feed: AI recommends items tailored to individual browsing habits.
Source: Unsplash
Visual Search & Categorization: Machine learning helps categorize millions of unique products.
Inventory Tagging: AI assists sellers by auto-generating tags and product descriptions.
4. Old Navy
Old Navy has implemented an advanced inventory system using AI and RFID technology.
RADAR System: Uses RFID, AI, and computer vision to track inventory in real time.
Source: RADAR
Operational Efficiency: The staff can locate items quickly and restock shelves efficiently.
Omnichannel Fulfillment: Enhances buy-online-pickup-in-store (BOPIS) and online order fulfillment.
5. Stitch Fix
Stitch Fix blends machine learning with human stylists to personalize fashion recommendations.
Style Algorithm: Suggests items based on user data (style, size, budget).
Source: Stitch Fix
Feedback Loop: AI refines recommendations based on purchase behavior and ratings.
Increased Retention: Highly personalized experiences drive customer loyalty.
Current Use Cases: 4 Insights from C-Level E-Commerce Innovators
Below, you’ll find interesting insights from 6 executives we had the pleasure to speak with. They share their unique experiences regarding the implementation of AI as well as future plans for the use of this technology in e-commerce.
Laura Carden-Lovell – Operational Streamlining and Future-Focused AI Integration
Laura Carden-Lovell, Head of Operations at TransferTravel, discussed the company’s ongoing transformation as it reboots its platform post-pandemic. One of the core operational challenges lies in the seller-side onboarding process, which was initially a 16-step, highly manual workflow. To address this, the team has integrated automation using AI tools such as ChatGPT for product description generation and an API that pulls images from Google to pre-fill hotel data.
"When I first saw it, there were 16 steps. It’s very manual, so people get bored easily. We’ve introduced ChatGPT and an image API to make the process quicker,” she explained.
While AI is being used to alleviate pain points, broader applications are still in planning. Laura emphasized the importance of expanding AI into personalization and recommendation systems to improve the buyer journey.
“We plan to implement personalization, so if someone’s buying certain things, we can show them more of that and even build tailored notifications,” she said.
The team also recognizes the need to integrate AI into the verification process. Currently, every listing is manually reviewed by the outsourced customer service team in the Philippines. While AI could play a role in speeding up this process, compliance with GDPR and security standards remains a consideration.
“We manually approve every listing. AI seems like a good option, but we need to map it out carefully to ensure it aligns with data protection laws,” Laura noted.
On the payments side, the company is transitioning from Stripe and PayPal to Airwallex to reduce FX fees and potentially introduce AI-driven dynamic pricing in the future.
“We’re looking at dynamic pricing—dropping prices closer to the holiday date. It’s on the roadmap,” she confirmed.
Christian H. Müller – Ethical, AI-Enabled Infrastructure for Music Monetization
Christian H. Müller, founder of SPOZZ.club, shared his vision for transforming the music industry through decentralization, fan-to-artist monetization, and ethically driven AI integration. The platform allows artists to tokenize their music as NFTs. It enables fans to stream or purchase tracks directly using a micropayment model, bypassing traditional intermediaries like Spotify or record labels.
“We wanted to eliminate intermediaries like Meta, Google, and Apple, and let artists connect directly with fans,” Müller explained. “With our model, 80% of every stream payment goes to the artist.”
Currently, AI is not yet fully deployed on the platform. Still, Müller outlined the clear path forward: first indexing platform content, then building and integrating algorithms for discovery, personalization, and interaction. The development roadmap includes tools for artist-fan engagement, betting features for predicting hits, and community-driven music recommendations.
“We’ve built the basic infrastructure. Now we need to apply the algorithms—index everything, then build a language model that makes sense for the user experience,” he said.
Müller also highlighted a significant ethical dimension in future AI integration. As generative AI becomes more common in music, he is committed to transparency and protecting the integrity of human-made works. He envisions the platform clearly labeling whether music was created by an artist or AI, ensuring fair recognition and compensation.
“You can’t register AI-generated music under a songwriter’s name. That would be illegal,” he stated. “We want users to know—just like with food labels—what’s organic and what’s artificial.”
On the operational side, the platform is built by a core internal team supported by external development partners. Müller sees AI playing a critical role in marketing automation, artist trend analysis, and scaling content onboarding, especially as the platform’s international presence grows.
“We’re looking into automated agents for social media marketing,” he said. “Our challenge isn’t the tech—we’re strong there. It’s onboarding content and making it easy for fans to discover real, valuable music.”
Leo Fadi – Building Scalable AI for Inventory, Forecasting, and Personalized Product Discovery
Leo Fadi, the founder and CEO of Vesko, shared his company’s mission to bring omnichannel commerce to small and mid-sized retailers through scalable digital storefronts. The platform bridges online-to-offline commerce by allowing businesses to run independent, ready-to-use stores with built-in features like search, filtering, and categorization.
“We’re trying to help small retailers connect digital and physical sales without depending on expensive warehousing or infrastructure,” Fadi explained.
AI is central to Vesko’s long-term vision. The company has developed an initial model of an AI-based purchasing assistant to help users find suitable products across a vast inventory. While still in early deployment, the chatbot-style interface is intended to guide users toward optimal product choices based on preferences and context.
“We’ve created the first sales assistant model,” he said. “It works like a chatbot—users can talk to it, and it helps them find the perfect product.”
A major focus for future development is implementing real-time demand forecasting using machine learning. This is especially critical for retailers navigating seasonal and regional buying patterns without large-scale historical data.
“Forecasting demand is extremely difficult, especially for new vendors who don’t know what sizes, colors, or styles to stock,” Fadi noted. “We aim to use AI to get as close as possible to real-world accuracy.”
Fadi acknowledged the risks associated with AI recommendation errors—particularly when incorrect product suggestions could damage trust or lead to poor purchasing decisions. He also emphasized that the challenge lies more in data quality and training than in engineering.
“If the model recommends the wrong product, that’s more dangerous than swearing,” he said. “You can build anything with the right team, but if you don’t know what to build, it’s a much bigger issue.”
Vesko’s main technical challenge remains data-driven reasoning. Fadi emphasized the limitations of traditional machine learning models that rely on outdated or static data, stressing the need for AI systems that respond dynamically to real-time inputs.
“Today’s AI mostly memorizes past data,” he said. “But to forecast demand and guide inventory decisions, we need models that work on live, changing data. That’s the hardest part.”
With a 12-person in-house development team and plans for global scaling, Fadi is realistic about the road ahead. He sees AI as a powerful enabler—but one that must be implemented thoughtfully, especially in industries where precision and timing are critical.
“Scalability for SaaS isn’t the hard part—it’s getting AI to reason in real time and adapt to shifting variables like user behavior, trends, and even geopolitics,” he concluded.
Dan Todor – Agent-Based AI Architecture with Human Oversight for Resilient Travel Planning
Dan Todor, CTO at JoopiterX, described his platform as a highly AI-centric travel planning system that leverages a network of intelligent agents interacting with both users and suppliers. At the core is a system designed for negotiation and personalization. AI agents plan trips, initiate supplier discussions, and adapt based on user preferences over time—all with optional human intervention at every step.
“AI is left, right, and center across our platform. Everything is based on agents with human interaction or human in the middle,” Todor explained. “The agent negotiates with hotels, flights, restaurants—basically everyone—to get the best package for the user.”
The system uses memory-based agents that evolve through continued interaction, learning user preferences over time. What sets the platform apart is its inversion of the traditional model: agents negotiate on the user's behalf instead of fixed prices to find the most cost-effective and relevant options.
“It’s not like Booking.com where you pay what they ask. Our agents ask, ‘This is what I got—do you want to proceed?’ It puts the user in control,” he said.
However, Todor also highlighted a key limitation in current AI models: their sensitivity to pre-training structures. Drawing from a recent academic paper, he described how minor variations in input order could dramatically degrade model accuracy.
“Even if you shuffle the premises slightly, accuracy can drop from 97% to nearly zero,” he noted. “The pre-training of models influences their behavior far more than you’d expect—even with chain-of-thought prompting.”
To counteract this, the team employs Markov chains of agents and a layered reward model to track reasoning paths and isolate failure points. Despite this architecture, achieving consistency remains an uphill battle.
“We’re fighting an uphill battle. Even with our reward models and evaluation layers, they still fail sometimes,” Todor admitted.
Todor, who brings over 40 years of experience in software development, still codes actively and closely manages architectural design. He expressed cautious optimism about the future of AI development tools, recognizing their potential and risks.
“If you give these coding models to junior developers, it’s a recipe for disaster,” he warned. “But in the hands of someone experienced, they save time and are getting better.”
As the platform prepares for market entry, Todor remains focused on achieving robust, context-aware AI behavior that balances automation with human control. This model prioritizes user agency, system resilience, and responsible scaling.
Pressing Challenges to AI in E-Commerce
Despite AI's numerous benefits, the industry faces challenges, including data privacy concerns, algorithmic biases, and the potential for intrusive personalization. Ethical considerations require businesses to carefully balance customer experience personalization with user privacy and fairness to ensure trust.
The following challenges rely on literature reviews by scholarly authors and our input and experience.
Data Privacy and Security
AI-driven personalization and automation require access to large volumes of consumer data, which introduces significant privacy and security risks.
Risk of non-compliance with data protection laws (e.g., GDPR, CCPA);
Increased vulnerability to data breaches and misuse;
Lack of consumer trust in how AI systems handle personal data.
“AI-powered personalization relies heavily on the analysis of vast datasets... raising significant data privacy concerns... The indiscriminate collection of user data for personalization purposes can lead to privacy breaches and unauthorized access.” (Raji et al., 2024).
Cost and Resource Constraints
High implementation costs limit access to AI, especially for SMEs.
Substantial upfront investment in infrastructure and talent;
Ongoing maintenance and retraining expenses;
Financial barrier to scaling advanced AI capabilities.
“Actualizing AI arrangements in e-commerce includes noteworthy forthright costs related to program improvement, framework venture, information procurement, and ability procurement” (Joshi, 2024).
Integration with Existing Systems
AI often clashes with legacy systems, reducing efficiency.
Incompatibility with outdated IT architecture;
Operational disruptions during AI integration;
High technical complexity in synchronizing new tools with existing workflows.
“Many international e-commerce companies still need to meet expectations, primarily because managers often struggle with effectively integrating AI with existing processes and systems.” (Sakhvidi & Saadat, 2024).
Talent Shortage
There is a growing demand for AI-specialized talent that exceeds supply.
Limited availability of skilled AI and data science professionals;
Difficulty retaining interdisciplinary talent (technical + domain expertise);
Delays in project timelines due to understaffing.
“Quick advancement of AI innovations in e-commerce has made a noteworthy expertise hole within the workforce, with a deficiency of qualified experts with skills in machine learning, information science, and AI building.” (Joshi, 2024).
Ethical Considerations and Bias
AI models may unintentionally replicate historical biases or generate unfair outcomes.
Discriminatory outputs due to biased training data;
Lack of transparency in algorithmic decision-making;
Need for explainable AI (XAI) frameworks to ensure accountability.
“Algorithmic bias... can result in discriminatory outcomes, disproportionately impacting certain demographic groups... biased product recommendations, pricing discrepancies, or discriminatory targeting in marketing efforts.” (Raji et al., 2024).
“Bias reduction and fairness in XAI systems also continue to be inadequately resolved in the e-commerce domain... even explainable versions can generate biased outcomes.” (Sarkar et al., 2025).
Customer Experience Risks
Poor AI implementation can degrade user satisfaction.
Irrelevant recommendations or misaligned personalization;
Unintuitive chatbot interfaces leading to frustration;
Over-automation reducing the human touch in service.
“While consumers appreciate personalized experiences, they also value their privacy and may become uneasy if they feel their online activities are overly monitored or exploited.” (Raji et al., 2024).
“Their (chatbots’) opaque nature can result in disappointment when individuals fall short to recognize the thinking behind computerized responses.” (Sarkar et al., 2025).
What’s Next: AI Trends Shaping the Future of E-Commerce
The presence and continuously increasing importance of AI in the e-commerce industry are actively strengthening long-term trends dominating the field, while also creating new ones. As a result, we can boldly state that the following trends will determine the future of the e-commerce industry for the foreseeable future.
Hyper-Personalization at Scale
A long-standing trend of hyper-personalized experiences continues to persist. 71% of consumers expected personalization, and 75% got frustrated when not receiving it as of 2021. AI is seen as the perfect solution to this problem with its advanced algorithms and ability to analyze vast datasets like browsing histories and purchase patterns.
AI offers unprecedented benefits for personalized experience via:
Product recommendations;
Personalized marketing;
Personalized search results.
DBB Software already engages in the area of applying AI in e-commerce products for personalized service provision. Our team created an AI-powered virtual design assistant for Renovai. This solution provides users with personalized furnishing designs based on their preferences that they describe in a questionnaire. You can read more about this case study on our website.
Predictive Analytics and Buyer Behavior Prediction
AI-driven predictive analytics is a cornerstone for forecasting customer needs, optimizing inventory, and anticipating market trends. Currently, companies are starting to recognize how advanced algorithms analyze historical sales data and real-time market conditions to predict demand, helping businesses avoid stockouts and overstocking.
This trend enables e-commerce companies to make data-driven decisions, reduce operational costs, and enhance customer satisfaction. For example, JD Logistics reportedly used AI during Black Friday campaigns to handle a 70% increase in orders by forecasting sales accurately.
Generative AI for Content Creation and Automation
Currently, AI companies are actively implementing advanced image generation. OpenAI, Grok, and other industry giants are making large leaps in this direction, which will revolutionize content creation. This will affect the e-commerce industry, especially the automation of marketing materials creation and ad campaigns.
Over half of C-suite practitioners ranked generative AI as extremely important for customer experience, and 26% use it for marketing. Considering the increasing dominance of AI-generated images in social media and popular culture, their perception as a source for marketing materials will shift from negative to positive, especially with further advancements in the quality of AI-generated images.
Conclusion
As e-commerce enters a new era of digital innovation, AI is no longer a differentiator—it is a requirement for sustainable growth and customer-centric operations. Findings in this report indicate that AI is reshaping how companies attract, serve, and retain customers. Industry leaders such as Amazon, Walmart, and Stitch Fix are already setting benchmarks through strategic deployments of AI, and other companies are picking up the pace.
Our interviews with C-level executives also provided insights into how companies see AI as a part of e-commerce. We saw the emphasis on streamlining operations and enhancing customer support through automation and conversational interfaces. Furthermore, they were focused on building ethical, AI-powered platforms that prioritize transparency, personalization, and real-time responsiveness.
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