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Technology is pushing the boundaries of commerce like never before. Artificial intelligence (AI) is one of the primary driving technologies at the forefront of the commerce evolution, using advanced algorithms to revolutionize marketing and personalize customer experiences. As of 2024, AI adoption in e-commerce is skyrocketing, with 84% of brands already using it or gearing up to do so.
This article explores the AI revolution coming to commerce, focusing on what makes AI a driving force for e-commerce in particular, and the ways it’s reshaping how businesses engage with consumers.
Understanding the AI revolution in commerce
AI is quickly reshaping commerce as we know it by democratizing access to sophisticated tools once reserved for large corporations, breaking down functional silos within organizations, and integrating data from multiple sources to achieve deeper customer understanding. It’s paving the way for a future where every brand interaction is uniquely crafted for the individual, powered by AI systems that anticipate preferences proactively.
AI is a broad term that encompasses:
- Data mining: The gathering of current and historical data on which to base predictions
- Natural language processing (NLP): The interpretation of human language by computers
- Machine learning: The use of algorithms to learn from past experiences or examples to enhance data understanding
The capabilities of AI have significantly matured into powerful tools that can improve operational efficiency and boost sales, even for smaller businesses. They have also fundamentally changed how businesses interact with customers and handle operations. As AI continues to develop, it has the potential to provide even more seamless, personalized, and ethically informed commerce experiences and establish new benchmarks for engagement and efficiency in the marketplace.
Four benefits of the AI revolution coming to commerce
Major commerce players like Amazon have benefited from AI and related technologies for a while. Through machine learning, they’ve optimized logistics, curated their product selection, and improved the user experience. As this technology quickly expands, businesses have unlimited opportunities to see the same efficiency, growth, and customer satisfaction as Amazon. Here are four primary benefits of AI adoption in commerce.
1. Data-driven decision making
AI gives businesses powerful tools to analyze large amounts of data more quickly and accurately than a person. Through advanced algorithms and machine learning, AI can sift through historical sales data, customer behavior patterns, and market trends to uncover insights and suggest actions that might not be immediately obvious to human analysts. By transforming raw data into actionable insights, AI empowers businesses to make more informed decisions, reduce risks, and capitalize on opportunities.
As a real-world example, Foxconn, the largest electronics contract manufacturer worldwide, worked with Amazon Machine Learning Solutions Lab to implement AI-enhanced business analytics for more accurate forecasting. This move improved forecasting accuracy by 8%, saved $533,000 annually, reduced labor waste, and improved customer satisfaction through data-driven decisions.
2. A better customer experience
AI is set to make customer interactions smoother, faster, and more personalized by recommending products based on preferences and behaviors, making it easier for customers to find what they need.
When consumers visit an online store, AI also provides instantaneous help via a chatbot that knows their order history and preferences. These AI-powered assistants offer real-time help like a knowledgeable store clerk. They give the appearance of higher-touch support and can answer basic questions at any hour, provide personalized product recommendations, and even troubleshoot issues. Chatbots free up human customer service agents for more complicated matters, and these agents can then use AI to obtain relevant information and suggestions for the customer during an interaction.
3. Personalized marketing
Data-driven personalization of the customer journey has been shown to generate up to eight times the ROI, as data shows 71% of consumers now expect personalized brand interactions. Until AI came around, personalization at scale was complex to achieve. Now, gathering and processing data about a customer’s shopping experience is easier than ever based on lookalike customers and past behavior.
Many businesses have adopted AI to glean deeper insights into purchase history, web browsing, and social media interactions to drive better segmentation and targeting. With AI, advertisers can analyze behavioral and demographic data to suggest products someone is likely to love. Consumers can now browse many of their favorite online stores and see product recommendations that perfectly match their tastes and needs.
AI can also offer special discounts based on purchasing habits, and send personalized emails with products and content that interest customers to make their shopping experience more engaging and relevant. This personalization helps businesses forge stronger customer relationships.
Personalization across digital storefronts
Retail media involves placing advertisements within a retailer’s website, app, or other digital platform to help brands target consumers based on their behavior and preferences within that environment. Retail media networks (RMNs) expand this capability across multiple retail platforms to create seamless advertising opportunities throughout the customer journey. Integrating AI into RMNs can improve personalization across digital storefronts with personalized, relevant ads and custom offers in real time that improve the customer experience.
4. Operational efficiency
AI can also be beneficial on the back end, enabling more efficient resource allocation, pricing optimization, efficiency, and productivity.
Customers can be frustrated when they visit a store for a specific product only to find it out of stock or unavailable in a particular size. With AI, these situations can be prevented through algorithms that forecast demand for certain items. Retailers like Amazon and Walmart both use AI to predict demand, with Walmart even tracking inventory in real time so managers can restock items as soon as they run out.
AI can automate and streamline operational tasks to help businesses run smoother, faster, and more cost-effective operations. It can:
- Offload tedious data entry, scheduling, and order processing tasks for greater fulfillment accuracy.
- Analyze historical data and market trends, predicting demand to help businesses optimize inventory, reduce waste, track online and in-store sales, and prevent shortages.
- Forecast demand levels, transit times, and shipment delays to make better predictions about logistics and supply chains.
- Improve data quality using machine learning algorithms that find and correct product information errors, duplicates, and inconsistencies.
- Adjust prices based on competitor pricing, seasonal fluctuations, and market conditions to maximize profits.
- Pinpoint bottlenecks, identify issues before they escalate, and provide improvements for suggestions.
Future trends and predictions
If you want to stay ahead in e-commerce, it’s just as important to know what’s coming as it is to understand where things are today. Here are some of the trends expected to shape the rest of 2024 and beyond.
Conversational commerce
Conversational commerce allows real-time, two-way communication through AI-based text and voice assistants, social messaging apps, and chatbots. Generative AI advancements may soon enable more seamless, personalized interactions between customers and online retailers. This technology can improve customer engagement and satisfaction while providing helpful insights into preferences and behaviors for better personalization and targeting.
Delivery optimization
AI-driven delivery optimization uses AI to predict ideal routes for each individual delivery, boosting efficiency, reducing costs, promoting sustainability, and improving customer satisfaction throughout the delivery process.
Visual search
AI-driven visual search is quickly improving in accuracy, speed, and contextual understanding. Future developments may integrate seamlessly with augmented reality (AR) so shoppers can search for products by pointing their devices at physical objects. Social media and e-commerce platforms may soon incorporate visual search more prominently, allowing users to find products directly from images.
AI content creation
AI is already automating and optimizing aspects of content production:
- Algorithms can generate product descriptions, blog posts, and social media captions personalized to specific customer segments.
- AI tools also enable the creation of high-quality visuals and videos.
- NLP advancements ensure content is compelling and grammatically correct.
- AI-driven content strategies analyze consumer behavior and refine messaging to meet changing preferences and trends.
This automation speeds up content creation while freeing resources for strategic planning and customer interaction.
IoT integration
Integrating AI with Internet of Things (IoT) devices could help make the ecosystem more interconnected in the future. AI algorithms can use data from IoT devices like smart appliances, wearables, and sensors to gather real-time insights into consumer behavior, preferences, and product usage patterns. This data enables personalized marketing strategies, predictive maintenance for products, and optimized inventory management. AI-driven IoT data analytics can also streamline supply chain operations to reduce costs and inefficiencies.
Fraud detection and security
There will likely be an increased focus on the ethical use of AI and data privacy regulations to strengthen consumer trust and transparency. AI-powered systems will get better at detecting and preventing fraud in e-commerce transactions, which will heighten security measures for both businesses and consumers.
Chart the future of commerce with Experian
AI has changed how marketers approach e-commerce in 2024. With AI-driven analytics and predictive capabilities, marketers can extract deeper insights from extensive data sets to gain a clearer understanding of consumer behavior. This enables refined segmentation, precise targeting, and real-time customization of messages and content to fit individual preferences.
Beyond insights, AI automates routine tasks like ad placement, content creation, and customer service responses, freeing marketers to concentrate on strategic planning and creativity. Through machine learning, marketers can predict trends, optimize budgets, and fine-tune strategies faster and more accurately than ever. The time to embrace AI is now.
At Experian, we’re here to help you make more data-driven decisions, deliver more relevant content, and reach the right audience at the right time. Using AI in your commerce marketing strategy with our Consumer View and Consumer Sync solutions can help you stay competitive with effective, engaging campaigns.
Contact us to learn how we can empower your commerce advertising strategy today.
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Originally appeared in Adweek This holiday advertising season, identity is the real differentiator Marketers are betting big on AI to run their holiday advertising, using it to build predictive audiences, generate creative at scale, and optimize media buys in real time. The draw is clear: greater efficiency, delivered at scale. But here’s the problem: without a solid identity foundation, AI is just guessing. And in a year when consumers are cautious and competition is fierce, guesses won’t deliver the outcomes you need. Experian’s 2025 Holiday spending trends and insights report shows that success this season will depend on connecting the right data to the right audiences in real time. Download the report now Are shoppers really using AI to make holiday purchases? Not yet. Only 12% of consumers plan to use AI tools to shop this season, mostly for finding discounts. Instead, trusted influences (like retailer websites, product reviews, and recommendations) still guide buying decisions. 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The challenge isn’t just speed or volume, it’s accuracy. By pairing AI with identity, you can adapt to real behavior instead of assumptions. You can build campaigns that are consistent across connected TV, retail media, and social platforms. And you can prove results when it matters most. AI isn’t a holiday miracle. But when it’s powered by identity, it can give you clarity in a noisy season and proof of performance when budgets are under scrutiny. Explore Experian's holiday audiences to activate this season What’s the real takeaway for marketers this season? Don’t assume AI alone will save your holiday advertising strategy. It won’t. Consumers still trust human voices more than machines, and your AI models are only as strong as the data beneath them. Identity is the difference between guesswork and accuracy, between activity and impact. This holiday season, the winners won’t be the brands that simply spend more or automate faster. They’ll be the ones that put identity at the core of their AI strategy and meet consumers where they really are. Download Experian’s 2025 Holiday spending trends and insights report to see where consumers are spending and how identity can help your holiday advertising campaigns more effective. Download now About the author Colleen Dawe VP, Advertiser Partnerships, Experian Colleen Dawe is VP, Advertiser Partnerships at Experian Marketing Services, where she oversees revenue growth and client success, helping advertisers harness data and identity to fuel marketing strategies. With over 15 years of experience spanning TV and digital media, she brings deep expertise in data, identity, activation, and measurement to help her clients connect innovation with business outcomes. Holiday advertising FAQs Why isn’t AI enough on its own for holiday advertising? AI works best when it’s grounded in accurate data. 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In our Ask the Expert Series, we interview leaders from our partner organizations who are helping lead their brands to new heights in AdTech. Today’s interview is with Paul Zovighian, VP of Marketplaces at Index Exchange. Sell-side activation vs. buy-side packaging What’s fundamentally changed with sell-side decisioning, and how does it now diverge from traditional buy-side packaging? Sell-side decisioning is programmatic’s next major evolution – one that redefines how intelligence enters the transaction. Advances in infrastructure and computing power now allow supply-side platforms(SSPs) to act in the crucial pre-bid moment, enriching impressions with context, quality, and data before they reach the buy side. This isn’t just about efficiency; it’s about unlocking new value. Smarter requests mean buyers see only the most relevant opportunities, while publishers gain recognition for the true worth of their audiences and environments. We’re still at the beginning of this shift. Many players still package inventory without engaging in real pre-bid intelligence. As the market matures, the companies that evolve toward sell-side decisioning will be the ones to set the pace for programmatic’s future. Economic shifts with scaled curation As curation scales, what economic levers shift for both publishers and buyers, and how do those shifts influence deal structure and media planning? As curation scales, one of the most powerful levers is data. It’s the industry’s most valuable asset, and on Index it keeps its full worth. We don’t take a platform cut or add hidden fees, so data partners benefit from the clearest, most efficient economics in the market. Data vendors gain confidence that their economics aren’t eroded by a platform tax. For publishers, this means stronger yield and more ad spend flowing directly into working media. 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Without it, IDs have to travel through multiple platforms, creating extra handoffs and additional risks of data loss or leakage. With sell-side decisioning, those signals are obfuscated under a deal ID and applied directly at the point of decision. That means audience, context, and propensity data are activated securely, without ever leaving the sell-side environment. For partners like Experian, it’s the cleanest path to value: fewer hops, stronger privacy protection, and clearer economics for everyone in the chain. Contact us About our expert Paul Zovighian VP of Marketplaces, Index Exchange Paul Zovighian carries over a decade of industry expertise, stemming from his analytics and optimization roots to his current post as VP, Marketplaces, where he is focused on the commercial activation of Index’s newest product, Index Marketplaces. Previously, in his role as VP of corporate development, Paul led Index’s first-ever business acquisition. In his spare time, he enjoys long walks on the beach and befriending cats in NYC’s thriving bodega community. About Index Exchange Index Exchange is a global advertising supply-side platform enabling media owners to maximize the value of their content on any screen. They’re a proud industry pioneer with over 20 years of experience connecting leading experience makers with the world’s largest brands to ensure a quality experience for consumers. FAQs What is sell-side decisioning, and why is it important? Sell-side decisioning allows publishers to add intelligence, like audience data and context, before ad impressions are sent to buyers. This makes the process more efficient and ensures advertisers see only the most relevant opportunities. How does sell-side decisioning differ from traditional buy-side packaging? Traditional buy-side packaging happens after impressions are sent to demand-side platforms (DSPs). Sell-side decisioning moves some of that intelligence upstream, enriching impressions earlier and reducing inefficiencies. What does "curation" mean in this context, and how does it benefit publishers and advertisers? Curation refers to the process of organizing and enriching ad inventory with data and context. For publishers, it leads to better yield and more ad spend going directly to their media. For advertisers, it means clearer, more transparent supply paths. How does sell-side decisioning improve privacy? By applying audience and identity signals directly on the sell side, data stays within a secure environment. This reduces the number of platforms handling sensitive information, lowering the risk of data loss or leakage. What role does Experian play in sell-side decisioning? Experian provides demographic and audience insights that are activated directly at the point of decision. 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