Generative AI has captured the imagination of industries worldwide, widely hailed as a catalyst for innovation and efficiency. Yet, real-world adoption remains limited. While approximately 15% of organizations experiment with generative AI for specific objectives, only 5% report successful implementation. Barriers such as scaling challenges, hallucination risks, and the need for business-aligned outputs persist.

The term “AI” has come to encompass a broad spectrum of analytical techniques—from the emerging capabilities of large language models (LLMs) to the proven effectiveness of classical machine learning. In this session, I will share how Appriss Retail combines these AI modalities to address fraud and abuse in both point-of-sale and e-commerce environments. Generative AI assists analysts in exploratory data analysis and investigating organized retail crime. Meanwhile, traditional machine learning models drive real-time decisions to detect and mitigate returns fraud and abuse. This hybrid approach illustrates how the strengths of generative and predictive AI can be leveraged together to deliver practical, scalable solutions.