In today’s rapidly evolving retail landscape, the implementation of Large Action Models (LAMs) holds the potential to revolutionize the customer experience. By integrating LAMs into the multi-modal customer journey, there is an opportunity to significantly reduce the time and effort spent by shoppers throughout the pre-purchase, purchase, and post-purchase stages, whether they are shopping in-store or online. Currently, shoppers are required to navigate through multiple steps and various applications to complete their everyday shopping tasks, which not only consumes a substantial amount of time but also requires significant effort from the shopper. However, by leveraging LAMs, there is potential to simplify the process and reduce the complexity of finding the right products in stores, payments, returns, or customer care needs. This can lead to increased customer satisfaction and loyalty, as shoppers are more likely to return to a retailer that provides a positive and hassle-free shopping experience. Additionally, LAMs can also provide retailers with valuable insights into customer behavior and preferences, which can be used to improve product offerings and marketing strategies.
Let’s take an example. Updating your Uber payment method , is a multi-step process while navigating through the app . With LAM , this can easily become a conversational command which would take the action of updating the payments without the requirement of navigating through the app. The LAMs are neuro symbolic models and it interacts with other apps and services. It’s unique capability lies in its ability to represent intricate application structures as interactive and functional entities, going beyond simple token sequences. As described by Lyu, LAM can be employed with both symbolic algorithms and neural networks, enabling it to perform actions in a manner that closely aligns with human comprehension.
Unlike Language Models (LLMs), LAMs are capable of reproducing human activity on conventional interfaces, enabling them to comprehend and execute complex tasks. The adoption of Rabbit R1for example, which has already pre-sold over 10,000 units, is expected to grow, signifying the increasing embrace of LAM technology. This technology empowers apps to perceive and understand surroundings, providing unprecedented accessibility and ease of use. For instance, envision being able to instruct a device, “Take me to the nearest retailer’s app or website and place an order for my groceries, using credit card #1 for payment and get it delivered to my friend’s address at XYZ.” This would have been a time consuming, multi step journey without the power of LAMs which can convert this conversation into actionable steps and complete the entire task.
Therefore, this technology has the power to significantly impact consumer decision-making and retailer selection, as it offers a seamless customer experience. Being an early adopter of LAM is crucial, as it unlocks a multitude of new possibilities, key ones being accessibility, enhanced customer experience , streamlined post purchase journeys and real time decision making .
In 2023 and 2024, the retail industry witnessed a surge in AI adoption, with over 60% of retailers planning to boost their AI investments in the next 18 months. The emphasis on intelligent stores and their central role in the omnichannel experience, along with the transformative potential of generative AI, has propelled retailers to actively consider how AI can help them meet evolving customer preferences, address labor shortages, and drive sustainability efforts. This is where the convergence of tech readiness and accelerated adoption of LAMs in the ecosystem .
As of 2024, LAM is poised to further evolve, with researchers exploring new applications and use cases. For instance, LAM can be integrated with virtual and augmented reality to create immersive shopping experiences, and it can be used to personalize marketing campaigns based on customer behavior and preferences. Additionally, LAM can be employed to optimize supply chain management, inventory management, and pricing strategies, ultimately driving business growth and customer satisfaction. In conclusion, LAM represents a significant advancement in generative AI, offering a unique blend of linguistic fluency and task-completion capabilities. As the technology continues to evolve, it will undoubtedly shape the future of human-machine interaction, particularly in the context of shopping and customer experience.
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