Presented by commercetools
Digital commerce leaders are under immense pressure. Navigating an increasingly volatile market, while still delivering exceptional value and experiences to customers, is a precarious juggling act — and that’s why it’s time to go all-in on AI.
It’s not just about today’s benefits; it’s about preparing for a fast-approaching future. Across industries, AI is delivering on its promise, helping companies create efficiencies while creating outstanding shopping experiences, delivering on time and unifying all touch points. It’s also on the cusp of transforming how we shop, says Dirk Hoerig, Founder and Chief Innovation Officer of commercetools.
“Very soon AI will change behavior in humans, and how we interact with companies and products,” Hoerig says. “Companies need to embrace AI now, to leverage its powerful capabilities, and position themselves to take advantage of its potential when AI, not the storefront, will become the center of the customer experience.”
For digital commerce, the interaction point for shoppers has always been the storefront, on every device, and a human has done the browsing, selecting, ordering and returning. But agentic shopping is on the horizon, or AI handling all those tasks on behalf of the human consumer. For retailers, that means optimizing product and customer data, pricing, inventory and more for an AI on the hunt at the direction of the human.
“The AI is interacting with the brands, the manufacturers, the retailers, but this is not just about putting another layer in between the human and the company,” Hoerig says. “This is a fundamental shift in how shoppers experience brands and retailers, and it’s upending the customer journey, not to mention customer acquisition, marketing and sales tactics.”
For example, retailers currently design shopping experiences around human behavior, placing upsell and cross-sell opportunities where shoppers are most likely to add extra items. However, as AI-driven shopping agents become more common, this approach may fall short. These AI shoppers, focused on finding the best product match through data, aren’t swayed by impulse buys. To offset customer acquisition costs and maintain profitability, retailers must rethink their strategies to cater to AI-driven purchasing behavior.
It’s already happening, with big tech companies making moves to control the search market, which is often an entry point for shoppers. Social networks are also considering new ways to integrate commerce and product discovery into their customer experiences.
The retailers with the right data, and the kind of powerful, flexible infrastructure that composable commerce provides, are positioned to pivot in the direction of agentic shoppers, Hoerig explains.
A composable commerce platform gives retailers the ability to create shopping experiences across channels and touchpoints, in a cloud-native, component-based and tech-agnostic way that lets a company structure its data for any AI tool or agent.
For instance, organizations with traditional, monolithic commerce platforms will need to find ways to let an agent crawl an array of functions without causing any data breaches. But composable commerce not only lets brands integrate a product catalog into the agentic web, but also allows an AI agent to make a transaction, access return information and create a customer query on behalf of the human, and more, without exposing any internal data.
While agentic shopping is breaking over the horizon, AI is already changing the shopping experience here and now. Here’s a look at the AI trends brands need to know about.
AI and hyper personalization
“The term ‘hyper-personalization’ isn’t new; it’s been used to describe algorithmic optimizations of the product catalog, mostly based on past searches and cohort data,” Hoerig says. “With generative AI, we have a unique opportunity to personalize and tailor the whole experience in real time, from content to tone and presentation.”
Generative AI can rewrite the page layout, content and wording, the assortment of products based on a customer’s direct intentions, and offer personalized interactions through chat on the application and website, based on customer context. A 50-year-old shopper will have a different vocabulary and preferred style of communication than an 18-year-old shopper, for instance. Or if you’re in a rush on a travel site, interactions can be short, sweet and transactional. If you’re browsing a beauty site, it can offer more in-depth conversation.
Localization is no longer a time-consuming, expensive endeavor — for instance, a retailer won’t have to pick and choose which languages to translate and optimize for their site and content. Translation becomes efficient at scale across any language, even down to local dialects.
“It’s the kind of interactions customers crave, driving better customer loyalty, and increasing engagement,” Hoerig says. “If you asked a retailer five years ago, ‘Would you customize interactions based on buyer cohorts, adjust your language and tone to better fit each category’s needs?’ They would say that sounds like a fine idea, but they’d never do it on a large catalog. Now it’s possible.”
The power of predictive operational intelligence
AI can process huge sets of data in a very short time, and then come up with ways to improve critical facets of retail operations. That includes inventory optimization, fraud detection in user click behavior, demand forecasting, pricing optimization and more.
Supply chain AI. Many retailers have adopted sophisticated, and expensive, demand forecasting software, with algorithms that can forecast inventory trends, offer replenishment advice and more. Adding AI into the mix makes it far less expensive to build and integrate these kinds of tools, and makes them far faster, and far more precise, nearly in real time, and for significantly less money. It even improves the customer buying experience, making tools like click-and-collect more accurate.
Fraud prevention. If AI is good at anything, it’s pattern detection, which can be applied directly to fraud prevention. For instance, AI can detect anomalies in real time and determine whether your system is experiencing malicious bot traffic that’s trying to collect data and costs compute power versus an influx of interest from shoppers.
Autonomous decision making. Today, it’s critical to create efficiency gains and reduce costs, which becomes more complex when scaling in any context. Combining that with the customer expectation that everything needs to work and run seamlessly, companies are up against a lot of demands when it comes to marketing and selling profitably. Having an AI copilot on your side that can help drive intelligent decisions changes everything.
Promotions, for instance, are far more complex than sending a 20% discount code through an email campaign. Marketing managers need to know precisely what’s moving the needle, and complex campaigns require a lot of data-driven decisioning. AI can create a plan that optimizes the marketer’s efforts, with accurate forecasts and suggestions for delivering even greater revenue.
“These trends aren’t just about the tech — it requires successful execution, and a composable commerce framework is key to tapping into the value that AI brings,” Hoerig says. “The modular architecture of API-first composable commerce enables rapid innovation, unlocking agility, efficiency, new revenue streams and measurable results with AI.”
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