Read the first article in our series: The Business Case for Systematic AI Implementation in Ecommerce
The global AI-enabled ecommerce market is expected to reach £16.8 billion by 2030, growing at a CAGR of 15.7%¹. Behind these impressive numbers are real-world applications that are reshaping customer experiences and business operations alike.
Machine learning (ML) is one of the artificial intelligence applications that are driving the most significant transformations in e-commerce and revolutionising how we shop online.
Companies leveraging machine learning report an average revenue increase of 10-12%, with AI projected to enhance profitability by 59% by 2035² supporting measurable improvements across:
- product performance
- supply chain accuracy
- customer satisfaction
But what makes machine learning so transformative for ecommerce?
Unlike traditional programming, machine learning (ML) systems learn from owned data, continually improving their performance without explicit programming. This continuous commitment to self-improving quality makes ML uniquely suited to tackle ecommerce's most persistent challenges.
Machine learning applications revolutionising ecommerce
Personalised product recommendations
Perhaps the most visible ML application in ecommerce, recommendation engines analyse customer behaviour, purchase history, and preferences to suggest relevant products, services or experiences. These systems typically use two approaches:
- Content-based filtering: Analysing a customer's purchase and browsing history to recommend similar products
- Collaborative filtering: Suggesting products purchased by users with similar buying patterns
The results speak for themselves: Sweaty Betty, a UK activewear retailer, saw a 62% increase in revenue after implementing AI-powered recommendation quizzes, accompanied by a 52% rise in items per transaction and a 57% higher average order value³.
Dynamic pricing optimisation
Ever wonder why prices seem to fluctuate on e-commerce sites? That's ML at work. Pricing algorithms analyse competitive pricing, customer behaviour, inventory levels, and market trends to adjust prices in real-time for maximum profitability.
Online retailers using ML for dynamic pricing have reported profit margin increases of 5-10% by analysing factors like current market demand, competitor pricing, customer price sensitivity, and inventory levels⁴.
Intelligent search and product discovery
Traditional keyword search is being replaced by ML-powered search engines that understand natural language queries, recognise customer intent, and deliver contextually relevant results.
Sephora's AI-based visual search allows customers to upload a photo to find matching products. Shoppers can snap a picture of a lipstick shade or foundation and immediately see matching products – significantly improving conversion rates and customer satisfaction⁵.
Inventory and demand forecasting
Perhaps the most impactful behind-the-scenes application, ML algorithms process historical sales data, seasonal trends, external events, and market indicators to predict future demand with unprecedented accuracy.
According to McKinsey, retailers using AI-powered demand forecasting have reduced out-of-stock incidents by up to 65% while decreasing inventory costs by 10-15%⁶. In an era of supply chain disruptions, this capability has become invaluable.
Implementation challenges and practical solutions
Despite the clear benefits, implementing ML in ecommerce isn't without challenges:
Data quality and management
ML models are only as good as the data they're trained on. Poor quality, inconsistent, or insufficient data leads to inaccurate insights.
Solution: Implement robust data governance policies, create processes to clean and validate data, and establish enterprise data warehouses that combine data from various sources⁷.
Integration with existing systems
Integrating ML solutions with legacy ecommerce platforms can be complex and disruptive.
Solution: Use APIs and modular approaches to connect ML models with current systems, implement incrementally rather than replacing entire systems, and consider cloud-based ML services that offer pre-built integration capabilities⁸.
Talent and expertise gaps
Finding and retaining skilled data scientists and ML engineers is difficult and expensive.
Solution: Partner with specialised AI service providers like us, invest in training existing staff, and consider pre-built AI-powered solutions for common use cases⁹.
Conclusion
Machine learning is one of the strategic ways an ecommerce business can leverage AI to understand customers better, merchandise products effectively, manage inventory efficiently, and optimise operations for growth.
As ML technology continues to evolve, ecommerce businesses that strategically implement these solutions will be best positioned to deliver exceptional customer experiences, optimise operations, and drive sustainable growth in an increasingly competitive digital marketplace.
If you're looking for an AI provider or partner who can support your e-commerce business, please reach out to our team for a free consultation.
Catch up on our previous article: The Business Case for Systematic AI Implementation in Ecommerce
References
[1] Masterofcode (2024) 'Generative AI in Retail: Use Cases with Real-Life Examples'.
[2] SellersCommerce (2025) 'AI In ECommerce Statistics (2025)'.
[3] Algolia (2024) 'Generative AI in ecommerce: potentially huge ROI booster'.
[4] AMZScout Blog (2025) 'ECommerce Statistics and Facts for 2025'.
[5] LinkedIn (2024) 'The Impact of Generative AI on Ecommerce'.
[6] Ufleet (2025) '10 AI Trends That Will Revolutionize E-Commerce in 2025'.
[7] SwiftERM (2024) 'The Challenges Of AI For Ecommerce - SwiftERM AI Technology'.
[8] Digital Commerce 360 (2025) 'AI's transformative ecommerce role: What to expect in 2025'.
[9] BigCommerce (2025) 'How Ecommerce AI is Transforming Business in 2025'.
[10] Multichannelmerchant (2024) 'Practical Machine Learning: Using Data to Optimize Inventory Forecasting'.
