How AI is transforming media and entertainment through orchestrated intelligence
Media and entertainment firms are adopting AI to streamline workflows, reduce costs, and accelerate production, with orchestrated intelligence linking systems across the value chain.
Media and entertainment leaders are prioritizing AI adoption to convert potential into measurable business gains, with only 10% of the sector’s workforce classified as Frontier Professionals compared to 28% in technology. Among AI users, 51% report producing work they could not have created a year ago, rising to 80% among Frontier Professionals, highlighting a widening capability gap that early adopters can exploit. Microsoft describes this approach as orchestrated intelligence, where AI agents connect people, data, and systems across the media lifecycle to improve efficiency and quality while maintaining governance and control.
AI-native workflows are delivering measurable efficiency gains, as demonstrated by Collective Artists Network’s Galleri5, which reduces movie production timelines from three years to three months and cuts costs by 60 to 70% compared with traditional methods. Kantar’s rebuilt Link AI platform on Azure accelerates ad scoring from up to an hour to minutes, enabling evaluation of tens of thousands of ads for a global beverage client within hours, transforming creative testing at scale.
Sanoma used Azure AI to automate weather forecasts tailored to 26 regions, replacing a single national update and improving localization while creating new advertising opportunities. The Premier League’s Copilot-powered Companion integrates 30 seasons of statistics, 300,000 articles, 9,000 videos, and live match data to deliver personalized experiences to 1.8 billion fans across 189 countries.
Microsoft’s open orchestration layer connects existing production, data, and distribution systems without replacing specialized tools, enabling coordinated workflows that scale while preserving governance and security. At IBC 2026, the company will showcase customer implementations, urging leaders to identify fragmented workflows and pilot AI-driven improvements to measure impact and build momentum for broader transformation.