Creating and Implementing AI-Powered Conversational Search to Drive Viewer Engagement

In the past year, Generative AI has transitioned from a novel technology to a pivotal tool in media and entertainment, shifting the focus from its initial promise to its practical application in solving industry-specific challenges cost-effectively. Amidst expectations for the global artificial intelligence market to surpass $2.5 trillion by 2032 [1], early adoption and strategic technology planning are essential for media and entertainment (M&E) providers aiming to capitalize on AI marketplaces and applications that attract and retain audience engagement. This paper presents a technical demonstration of how broadcast and streaming services can swiftly integrate Generative AI to enhance consumer search and discovery, addressing the inefficiencies and frustrations with current systems. By utilizing Large Language Models (LLMs) for conversational search, the proposed solution speeds up content discovery and becomes more accurate over time by learning from interactions. Unlike traditional AI approaches that rely on limited metadata, our method leverages the vast information available on the web, allowing for richer, voice-command-driven search experiences. The demonstration will cover the technical integration within existing streaming infrastructures, highlighting the role of content management systems (CMS) in processing voice commands and facilitating web-based searches, thus offering a glimpse into potential future applications such as personalized highlight reels and AI-generated content.

Naveen Narayanan | Quickplay | Toronto, Ontario, Canada