- Four AI in commerce use cases are already transforming the customer journey: modernization and business model expansion; dynamic product experience management (PXM); order intelligence; and payments and security.
- By implementing effective solutions for AI in commerce, brands can create seamless, personalized buying experiences that increase customer loyalty, customer engagement, retention and share of wallet across B2B and B2C channels.
- Poorly run implementations of traditional or generative AI in commerce—such as models trained on inadequate or inappropriate data—lead to bad experiences that alienate consumers and businesses.
- Successful integration of AI in commerce depends on earning and keeping consumer trust. This includes trust in the data, the security, the brand and the people behind the AI.
Recent advancements in artificial intelligence (AI) are transforming commerce at an exponential pace. As these innovations are dynamically reshaping the commerce journey, it is crucial for leaders to anticipate and future-proof their enterprises to embrace the new paradigm.
In the context of this rapid advancement, generative AI and automation have the capacity to create more fundamentally relevant and contextually appropriate buying experiences. They can simplify and accelerate workflows throughout the commerce journey, from discovery to the successful completion of a transaction. To take one example, AI-facilitated tools like voice navigation promise to upend the way users fundamentally interact with a system. And these technologies provide brands with intelligent tools, enabling more productivity and efficiency than was possible even five years ago.
AI models analyze vast amounts of data quickly, and get more accurate by the day. They can provide valuable insights and forecasts to inform organizational decision-making in omnichannel commerce, enabling businesses to make more informed and data-driven decisions. By implementing effective AI solutions—using traditional and generative AI—brands can create seamless and personalized buying experiences. These experiences result in increased customer loyalty, customer engagement, retention, and increased share of wallet across both business-to-business (B2B) and business-to-consumer (B2C) channels. Ultimately, they drive significant increases in conversions driving meaningful revenue growth from the transformed commerce experience.
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Creating seamless experiences for skeptical users
It’s been a swift shift toward a ubiquitous use of AI. Early iterations of e-commerce used traditional AI largely to create dynamic marketing campaigns, improve the online shopping experience, or triage customer requests. Today the technology’s advanced capabilities encourage widespread adoption. AI can be integrated into every touchpoint across the commerce journey. According to a recent report from the IBM Institute for Business Value, half of CEOs are integrating generative AI into products and services. Meanwhile, 43% are using the technology to inform strategic decisions.
But customers aren’t yet completely on board. Fluency with AI has grown along with the rollout of ChatGPT and virtual assistants like Amazon’s Alexa. But as businesses around the globe rapidly adopt the technology to augment processes from merchandising to order management, there is some risk. High-profile failures and expensive litigation threatens to sour public opinion and cripple the promise of generative AI-powered commerce technology.
Generative AI’s impact on the social media landscape garners occasional bad press. Disapproval of brands or retailers that use AI is as high as 38% among older generations, requiring businesses to work harder to gain their trust.
A report from the IBM Institute of Business Value found that there’s enormous room for improvement in the customer experience. Only 14% of surveyed consumers described themselves as “satisfied” with their experience purchasing goods online. A full one-third of consumers found their early customer support and chatbot experiences that use natural language processing (NLP) so disappointing that they didn’t want to engage with the technology again. And the centrality of these experiences isn’t limited to B2C vendors. Over 90% of business buyers say a company’s customer experience is as important as what it sells.
Poorly run implementations of traditional or generative AI technology in commerce—such as deploying deep learning models trained on inadequate or inappropriate data—lead to bad experiences that alienate both consumers and businesses.
To avoid this, it’s crucial for businesses to carefully plan and design intelligent automation initiatives that prioritize the needs and preferences of their customers, whether they are consumers or B2B buyers. By doing so, brands can create contextually relevant personalized buying experiences, seamless and friction-free, which foster customer loyalty and trust.
This article explores four transformative use cases for AI in commerce that are already enhancing the customer journey, especially in the e-commerce business and e-commerce platform components of the overall omnichannel experience. It also discusses how forward-thinking companies can effectively integrate AI algorithms to usher in a new era of intelligent commerce experiences for both consumers and brands. But none of these use cases exist in a vacuum. As the future of commerce unfolds, each use case interacts holistically to transform the customer journey from end-to-end–for customers, for employees, and for their partners.
Use case 1: AI for modernization and business model expansion
AI-powered tools can be incredibly valuable in optimizing and modernizing business operations throughout the customer journey, but it is critical in the commerce continuum. By using machine learning algorithms and big data analytics, AI can uncover patterns, correlations and trends that might escape human analysts. These capabilities can help businesses make informed decisions, improve operational efficiencies, and identify opportunities for growth. The applications of AI in commerce are vast and varied. They include:
Dynamic content
Traditional AI fuels recommendation engines that suggest products based on customer purchase history and customer preferences, creating personalized experiences that result in increased customer satisfaction and loyalty. Experience building strategies like these have been used by online retailers for years. Today, generative AI enables dynamic customer segmentation and profiling. This segmentation activates personalized product recommendations and suggestions, such as product bundles and upsells, that adapt to individual customer behavior and preferences, resulting in higher engagement and conversion rates.
Commerce operations
Traditional AI allows for the automation of routine tasks such as inventory management, order processing and fulfillment optimization, resulting in increased efficiency and cost savings. Generative AI activates predictive analytics and forecasting, enabling businesses to anticipate and respond to changes in demand, reducing stockouts and overstocking, and improving supply chain resilience. It can also significantly impact real-time fraud detection and prevention, minimizing financial losses and improving customer trust.
Business model expansion
Both traditional and generative AI have pivotal and functions that can redefine business models. They can, for example, enable the seamless integration of a marketplace platform where AI-driven algorithms match supply with demand, effectively connecting sellers and buyers across different geographic areas and market segments. Generative AI can also enable new forms of commerce—such as voice commerce, social commerce and experiential commerce—that provide customers with seamless and personalized shopping experiences.
Traditional AI can enhance international purchasing by automating tasks such as currency conversions and tax calculations. It can also facilitate compliance with local regulations, streamlining the logistics of cross-border transactions.
However, generative AI can create value by generating multilingual support and personalized marketing content. These tools adapt content to the cultural and linguistic nuances of different regions, offering a more contextually relevant experience for international customers and consumers.
Use case 2: AI for dynamic product experience management (PXM)
Using the power of AI, brands can revolutionize their product experience management and user experience by delivering personalized, engaging and seamless experiences at every touchpoint in commerce. These tools can manage content, standardize product information, and drive personalization. With AI, brands can create a product experience that informs, validates and builds the confidence necessary for conversion. Some ways to use relevant personalization by transforming product experience management include:
Intelligent content management
Generative AI can revolutionize content management by automating the creation, classification and optimization of product content. Unlike traditional AI, which analyzes and categorizes existing content, generative AI can create new content tailored to individual customers. This content includes product descriptions, images, videos and even interactive experiences. By using generative AI, brands can save time and resources while simultaneously delivering high-quality, engaging content that resonates with their target audience. Generative AI can also help brands maintain consistency across all touchpoints, ensuring that product information is accurate, up-to-date and optimized for conversions.
Hyperpersonalization
Generative AI can take personalization to the next level by creating customized experiences that are tailored to individual customers. By analyzing customer data and customer queries, generative AI can create personalized product recommendations, offers and content that are more likely to drive conversions.
Unlike traditional AI, which can only segment customers based on predefined criteria, generative AI can create unique experiences for each customer, considering their preferences, behavior and interests. Such personalization is crucial as organizations adopt software-as-a-service (SaaS) models more frequently: Global subscription-model billing is expected to double over the next six years, and most consumers say those models help them feel more connected to a business. With AI’s potential for hyperpersonalization, those subscription-based consumer experiences can vastly improve. These experiences result in higher engagement, increased customer satisfaction, and ultimately, higher sales.
Experiential product information
Al tools allow individuals to learn more about products through processes like visual search, taking a photograph of an item to learn more about it. Generative AI takes these capabilities further, transforming product information by creating interactive, immersive experiences that help customers better understand products and make informed purchasing decisions. For example, generative AI can create 360-degree product views, interactive product demos, and virtual try-on capabilities. These experiences provide a richer product understanding and help brands differentiate themselves from competitors and build trust with potential customers. Unlike traditional AI, which provides static product information, generative AI can create engaging, memorable experiences that drive conversions and build brand loyalty.
Smart search and recommendations
Generative AI can revolutionize search engines and recommendations by providing customers with personalized, contextualized results that match their intent and preferences. Unlike traditional AI, which relies on keyword matching, generative AI can understand natural language and intent, providing customers with relevant results that are more likely to match their search queries. Generative AI can also create recommendations that are based on individual customer behavior, preferences and interests, resulting in higher engagement and increased sales. By using generative AI, brands can deliver intelligent search and recommendation capabilities that enhance the overall product experience and drive conversions.
Use case 3: AI for order intelligence
Generative AI and automation can allow businesses to make data-driven decisions to streamline processes across the supply chain, reducing inefficiency and waste. For example, a recent analysis from McKinsey found that nearly 20% of logistics costs could stem from “blind handoffs”—the moment a shipment is dropped at some point between the manufacturer and its intended location. According to the McKinsey report, these inefficient interactions might amount to as much as $95 billion in losses in the United States every year. AI-powered order intelligence can reduce some of these inefficiencies by using:
Order orchestration and fulfillment optimization
By considering factors such as inventory availability, location proximity, shipping costs and delivery preferences, AI tools can dynamically select the most cost-effective and efficient fulfillment options for an individual order. These tools might dictate the priority of deliveries, predict order routing, or dispatch deliveries to comply with sustainability requirements.
Demand forecasting
By analyzing historical data, AI can predict demand and help businesses optimize their inventory levels and minimize excess, reducing costs and improving efficiency. Real-time inventory updates allow businesses to adapt quickly to changing conditions, allowing for effective resource allocation.
Inventory transparency and order accuracy
AI-powered order management systems provide real-time visibility into all aspects of the critical order management workflow. These tools enable companies to proactively identify potential disruptions and mitigate risks. This visibility helps customers and consumers trust that their orders will be delivered exactly when and how they were promised.
Use case 4: AI for payments and security
Intelligent payments enhance the payment and security process, improving efficiency and accuracy. Such technologies can help process, manage and secure digital transactions—and provide advance warning of potential risks and the possibility of fraud.
Intelligent payments
Traditional and generative AI both enhance transaction processes for B2C and B2B customers making purchases in online stores. Traditional AI optimizes POS systems, automates new payment methods, and facilitates multiple payment solutions across channels, streamlining operations and improving consumer experiences. Generative AI creates dynamic payment models for B2B customers, addressing their complex transactions with customized invoicing and predictive behaviors. The technology can also provide strategic and personalized financial solutions. Also, generative AI can enhance B2C customer payments by creating personalized and dynamic pricing strategies.
Risk management and fraud detection
Traditional AI and machine learning excel in processing vast volumes of B2C and B2B payments, enabling businesses to identify and respond to suspicious trends swiftly. Traditional AI automates the detection of irregular patterns and potential fraud, reducing the need for costly human analysis. Meanwhile, generative AI contributes by simulating various fraud scenarios to predict and prevent new types of fraudulent activities before they occur, enhancing the overall security of payment systems.
Compliance and data privacy
In the commerce journey, traditional AI helps secure transaction data and automates compliance with payment regulations, enabling businesses to quickly adapt to new financial laws and conduct ongoing audits of payment processes. Generative AI further enhances these capabilities by developing predictive models that anticipate changes in payment regulations. It can also automate intricate data privacy measures, helping businesses to maintain compliance and protect customer data efficiently.
The future of AI in commerce is based on trust
Today’s commercial landscape is swiftly transforming into a digitally interconnected ecosystem. In this reality, the integration of generative AI across omnichannel commerce—both B2B and B2C—is essential. However, for this integration to be successful, trust must be at the core of its implementation. Identifying the right moments in the commerce journey for AI integration is also crucial. Companies need to conduct comprehensive audits of their existing workflows to make sure AI innovations are both effective and sensitive to consumer expectations. Introducing AI solutions transparently and with robust data security measures is imperative.
Businesses must approach the introduction of trusted generative AI as an opportunity to enhance the customer experience by making it more personalized, conversational and responsive. This requires a clear strategy that prioritizes human-centric values and builds trust through consistent, observable interactions that demonstrate the value and reliability of AI enhancements.
Looking forward, trusted AI redefines customer interactions, enabling businesses to meet their clients precisely where they are, with a level of personalization previously unattainable. By working with AI systems that are reliable, secure and aligned with customer needs and business outcomes, companies can forge deeper, trust-based relationships. These relationships are essential for long-term engagement and will be essential to every business’s future commerce success, growth and, ultimately, their viability.
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