The previous article talked about bias in AI applications. This one covers some of the emerging trends in AI Applications.

First, the chatbots, generative AI and search applications will continue to leverage better, cheaper, and more purposeful Large Language Models aka LLMs. Those same LLMs also come in useful to create next generation eCommerce search and product discovery solutions to produce high-quality, hyper-personalized customer search results. According to Gartner, Generative AI and search bear a reciprocal relationship because the models used in the search are trained on domain datasets. Generative AI such as ChatGPT, on the other hand, is trained on large corpus. Therefore, they are used in combination. The highly-relevant and personalized search together with a conversational capabilities of a chatbot is immense power to both traditional use cases for shoppers as well as emerging uses cases for personalized campaigns for individuals but small and medium businesses struggle to realized that potential due to scale.

Second, social media is a valuable channel for establishing and building relationships with billions of consumers and getting them to consider products and brands by facilitating 1 to 1 personalization at scale is now possible via Generative AI. One specific demonstration of this is with A/B testing where entire campaigns and content can be generated and customers can leverage that to see more ads that better suit their taste.

Third, product catalogs can become more informative with GenAI with pages enticing customers to add the product to their cart. The multimodal nature of AI allows image, text, and other content types to be brought together assisting retailers with both informative and personalized product pages. When combined with databases like Mongo DB for its flexibility with hierarchy and labeling, these algorithms can leverage product attributes and make hyper personalized recommendations especially with new products that have little history.

Fourth, the site navigation application gets a whole new facelift which shores up losses from cart abandonment, lost sales, and customers. In addition, it facilitates dynamic navigation, filters and search experiences that are hyper-personalized to the individual customer. Site search is one of the primary use cases of search and as with the discussion of AI models, the results can be highly relevant.

Fifth, checkout and delivery that is frequently associated with past purchase behavior, current browsing session data, and real-time inventory levels can be improved by showcasing “others bought” or “style your purchase” upsells which is not only using collaborative filtering but embedded semantics to promote personalization.

Sixth, customer nurturing applications with review solicitations, loyalty and reward programs and generating word-of-mouth feedback provide a new level of personalization for the shoppers. This results in an improved site experience feeding back into the virtuous cycle of retail shopping.

One of the main challenges against all these applications is the overcrowding of relevant but useless suggestions and the way to fix it is to with intensive oversight, continuous monitoring and the best practices of AI safety and security.