Generative AI

Generative AI refers to AI systems that create new content—text, images, audio, video, code, synthetic data, or other modalities—rather than merely classifying, predicting, or retrieving existing information. These systems learn the statistical structure of their training data well enough to produce novel outputs that share the characteristics of that data. The dominant architectures underlying modern generative AI include transformer-based large language models (for text and code), diffusion models (for images and video), and generative adversarial networks (GANs). Generative AI is distinguished from discriminative AI by its capacity to synthesize rather than solely analyze.

Generative AI is reshaping commerce across the entire value chain. In content operations, it automates the creation of product descriptions, marketing copy, email campaigns, and localized content at scale—tasks that previously required large editorial teams. In customer engagement, it powers conversational shopping assistants capable of nuanced, multi-turn product discovery and service interactions. In design and merchandising, it generates product visualization, virtual try-on experiences, and creative campaign assets. In operations, it enables AI agents that can reason over business data, draft analyses, and take actions within enterprise systems. The business imperative is not merely to adopt generative AI capabilities but to integrate them into workflows and data systems in ways that compound proprietary advantage—since the base models themselves are increasingly commoditized, competitive differentiation derives from how they are applied, fine-tuned, and grounded in proprietary data.