It’s been a swift shift toward a ubiquitous use of AI. Early e-commerce retailers used traditional AI largely to create dynamic marketing campaigns, improve the online shopping experience, automate search engine optimization (SEO) initiatives 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 datasets—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.