As AI continues to evolve, its integration into eCommerce has become indispensable. From virtual storefronts to real-time logistics, intelligent algorithms are powering smarter decisions and more human-centric experiences than ever before. But what exactly does “AI in eCommerce” look like today—and what will it become tomorrow?
What’s the Difference Between ML and AI in eCommerce?
While often used interchangeably, Artificial Intelligence (AI) and Machine Learning (ML) play distinct but complementary roles in online retail:
- Machine Learning is focused on pattern recognition and prediction. It analyses large datasets—such as customer browsing behaviour, purchase history, and seasonal trends—to forecast future actions. For example, ML can predict what a customer is likely to purchase next or identify optimal pricing strategies.
- Generative AI, a subset of AI, is designed for content creation. It can produce personalised product descriptions, marketing copy, imagery, and even chat responses—tailored at scale to individual preferences.
Together, these technologies enable a more efficient, personalised, and scalable approach to online commerce.
The State of AI Adoption in Australian Retail
The machine learning sector is the fastest-growing part of the AI industry, expected to grow at a compound annual growth rate (CAGR) of 36%, reaching over $500 billion by 2030.
According to the 2023 PayPal eCommerce Index, 42% of Australian businesses are already integrating AI into their eCommerce operations. Key applications include:
- Customer service automation
- Inventory and supply chain optimisation
- Targeted and personalised marketing campaigns
A report from FDM Group highlights that retailers leveraging AI are seeing double-digit sales growth and approximately an 8% increase in annual profits, outperforming competitors who have not adopted these technologies.
Real-World AI Strategies: Who’s Doing What?
Afterpay
Australian fintech company Afterpay uses ML algorithms within the Cloudera Data Platform to conduct real-time streaming analytics. This technology helps identify fraudulent behaviour by flagging anomalies and duplicate transactions across its user base of over 3.6 million customers.
In addition, the company developed Afterpay iQ, an unsupervised ML model that generates customer personas by analysing merchant and product-level data. This enables more accurate segmentation and highly targeted marketing campaigns.
Coles
Supermarket giant Coles has invested heavily in automation through its Customer Fulfilment Centres (CFCs) in Wetherill Park (NSW) and Truganina (VIC). These centres employ AI-driven robotics and logistics systems capable of processing over 10,000 online grocery orders per day.
The integration of AI enhances inventory accuracy, reduces delivery times, and contributes to a more seamless online shopping experience, particularly in urban areas.
Tesco
Tesco’s AI-powered logistics system ensures that inventory is managed efficiently, particularly for perishable goods. The AI system predicts demand for specific items, allowing for smarter inventory management and reducing waste. Tesco has reported a 15% reduction in food waste since integrating AI into its supply chain operations.
Target
Target’s use of predictive analytics is a prime example of AI’s power in demand forecasting. By analysing customer shopping behaviour and historical purchasing data, Target can predict which products will be in demand during specific seasons or events. This system helps optimise inventory levels, reducing stockouts and ensuring customers can find the products they need. During peak shopping seasons, Target has seen a 20% reduction in overstocked items due to this AI-powered approach.
IKEA
IKEA has developed an AI tool to improve the accuracy of its demand forecasting, ensuring products are available to meet customer needs.
Their mobile app allows customers to visualise how furniture would look in their space, reducing returns and increasing purchase confidence.
ASOS
ASOS leverages machine learning to deliver personalised product recommendations to its customers. By analysing user behaviour and preferences, the platform suggests items that align with individual tastes, enhancing the shopping experience
In collaboration with Microsoft, ASOS utilises AI and machine learning to support demand forecasting, data-driven decision-making, and operational efficiency.
H&M
H&M employs AI algorithms to analyse data from various sources, including sales channels, warehouses, and suppliers. This enables accurate demand forecasting, optimised inventory levels, and improved routing of shipments, enhancing supply chain agility.
The company also uses AI to predict fashion trends, allowing for timely product offerings that align with customer preferences.
Future Trends: 2025–2028 Outlook
Let’s peek into what’s next in AI-driven retail.
1. Automated Replenishment Models
AI will anticipate when you’re running out of coffee, pet food, or skincare and reorder it automatically. The move from reactive to predictive commerce is already here—and will become mainstream.
2. AI Shopping Companions
Imagine an AI that knows your preferences, budget, size, and lifestyle. Personal shopping assistants will soon become the default experience on major platforms—curating outfits, suggesting gifts, and optimising your cart in real time.
3. Emotion-Sensitive Interfaces
With advances in emotion AI, retailers will be able to adapt experiences based on customer sentiment—whether through voice, text, or facial recognition—leading to more empathetic and responsive digital interactions.
4. Seamless AI Integration Across the Value Chain
AI is moving beyond customer touchpoints. Expect to see deeper integration in logistics, inventory management, and customer service. Smart warehouses with autonomous robots, predictive restocking, and dynamic pricing systems will drive speed, efficiency, and profitability across the board.
5. A Changing Workforce & Ethical Landscape
As AI transforms operations, it will also reshape the workforce. Roles will shift from repetitive tasks to strategic, creative, and tech-savvy positions. Retailers will also face increasing scrutiny over AI use, especially around data privacy, fairness, and explainability. Building trust will be as crucial as building algorithms.
6. Agentic AI in eCommerce
Agentic AI or AI agents capable of autonomous decision-making and task execution, will become increasingly prevalent, especially in enterprise settings and complex automation where they will start handling everything from supply chain tasks to smart upselling without constant human oversight.
Final Thoughts
Now is the time to explore how AI and machine learning can boost your efficiency, increase conversions, and deliver a shopping experience your customers will love. AI isn’t just shaping the next chapter of eCommerce—it’s rewriting the entire book. From smarter logistics to deeply personalised customer experiences, artificial intelligence is helping retailers respond to market shifts with speed, empathy, and precision.
Whether you’re just starting out or scaling up, embracing smart commerce strategies can set you apart in the competitive Australian and global market.
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