by Antonio Castro, Guilherme Cruz, and Victoria Bough
with Sanchit Tiwari
This blog post is the third in a three-part series on digital twins. The first post explained how generative AI and digital twins make a powerful pairing. The second post in the series explored when and why to use a digital twin.
Digital twins and generative AI (gen AI) heighten each other’s impact. Gen AI enables access to digital twins for the common, nontechnical user through a conversational interface, unveiling a new realm of synthesis, analysis, and insights. Gen AI can also be leveraged to collect, transfer, and augment data input to digital twins. When large language models are integrated with digital twins, they can be fine-tuned to enable up-to-date, real-time responses and even serve as an optimizer for solving specific tasks using domain knowledge.
Specific use cases for gen AI–powered customer digital twins (CDTs) include simulating and predicting what product a customer purchases next, predicting each customer’s churn propensity, analyzing customer behavior to predict future purchasing patterns, and improving customer experience across the customer journey. Together, these technologies leverage real-time data to identify and act on various scenarios, helping organizations efficiently analyze vast quantities of data.
Improving customer experience across the consumer life cycle is a critical use case for CDTs powered by gen AI. As an example, we consider the following scenario: a credit card customer going on an international trip to Paris (interactive).
CDTs with gen AI offer a multitude of uncharted avenues that unlock potential new insights and growth opportunities for executives and businesses while elevating the customer journey. They enable hyperpersonalized interventions, predict and address churn in real time, and activate responses across interconnected digital twins, such as channel twins. CDTs with gen AI continuously measure intervention success, retraining themselves to improve issue resolution and customer integration. This self-improving ecosystem leverages emerging technologies’ synergies more effectively than static data models or basic 360-degree customer views. When powered by gen AI, CDTs can create substantial and meaningful impact and value at scale.
Antonio Castro and Guilherme Cruz are partners in McKinsey’s New York office, Victoria Bough is a partner in the Denver office, and Sanchit Tiwari is a senior principal data scientist in the Chicago office.