Phase two of the industry’s evolution will focus on transformative experiences that not only personally engages with each consumer, but also drastically redesigns every part of the shopping value chain across marketers, designers, suppliers, manufacturers, retailers, payment brokers, etc. Artificial Intelligence (AI) coupled with edge computing and other emerging technologies will be one of the main accelerants of the future.
From Targeted to Personalized, Holistic Experience
As AI agents are developed with the self-awareness of purpose and goals, they will have the ability to continuously monitor signals, coordinate actions and make decisions on behalf of the consumers. With a seamless tailored experience retailers, designers, manufacturers, suppliers and all other actors of the shopping value chain will be able to use their customer insights to streamline the orchestration of their capabilities and become highly responsive to market trends and demands.
In an evolved ecosystem where hub and spoke-style malls have made way for hyper-focused neighborhoods, micro-communities, and other “15-minute cities,” consumer engagement will be local and hyper tailored toward true personalization. This results in the delivery of a non-obtrusive yet omnipresent experience. Nuanced by the preferences and unique behavior of customers, this enables a fluid, continuous evolution between the digital and the physical worlds. Spatiotemporal Multi-View-Based learning will fill in the gap of missing geospatial information, while federated and transfer learning along with edge computing will allow for highly contextual, continuous aggregation of learning data points about customers’ behavior. Finally, reinforcement learning applied to all the independent agents will allow them to learn by interacting with each other, as well as with their environment, and grow their expertise via a reward mechanism.
It is not all that far-fetched to imagine a scenario where digital experience permeates into the physical world. For example, let’s look at a customer who has recently booked a vacation. A series of agent’s spring to action to coordinate preparation for the trip. A local AI agent at their destination places a customized order for 3D printed clothes that meet not only the precise measurements of the customer but also their need for hypoallergenic materials and their taste in colors and patterns. In addition, the garments will provide function for the trip itself – in this case, light rain and heavy winds during hiking activities. The AI is already aware of local suppliers and uses additive manufacturing augmented with machine learning to create the customized order, optimizing freight weight and keeping waste to a minimum.
For all these channels to come together, AI agents will have to have a compute ability to analyze text, images, voice, etc. right where the consumers are. They also will need to be refined enough to ignore the large amount of irrelevant noise to only filter in what is relevant to the consumer to prevent going into hyperdrive and negatively impacting experience. Finally, they will have to grow their collective knowledge about the consumer to achieve a greater level of success in serving them.
AI agents are the force multipliers that make sense of the ever-growing consumer dataflows that seem more like noise than insights – and can deliver recommendations of actions in a matter of milliseconds before consumers move on to the next widget competing for their attention. The adoption of AI at scale will transport the industry from the era of mass engagement to personalized engagement.