92 handled in ways that free up editorial teams to focus on storytelling. “If there’s one thing that matters most in media, it’s speed,” says Crownshaw. “We’re getting there, especially for short-form content. The longer workflows are still being worked through.” However, technology adoption is rarely the hardest part of a deployment. Changing the culture of an organisation is a necessary step in taking advantage of AI technology but poses a significant challenge in leadership and data management. Editorial, production, engineering and commercial teams must now be working from shared data models rather than separate silos, with governance embedded from the outset rather than retrofitted. “It’s moving from a tool mindset to a system mindset,” says Crownshaw. “AI will amplify silos if they aren’t addressed. If teams are pulling in different directions, AI just makes that problem more visible and more consequential.” He points to sports leagues the NBA and the Premier League as organisations that have made this shift deliberately. Both have combined executive alignment with technical leadership and a genuine willingness to rethink process ownership from the ground up, shifting from a collection of siloed teams to something closer to a shared operational model. The cultural and structural shifts Crownshaw describes are, in his view, precisely what Microsoft’s platform is designed to support. Where others bolt AI on as a feature, Microsoft focuses on enterprise-grade orchestration, and this is what makes the difference, he argues. Rather than layering AI over existing systems, the aim is to embed intelligence across the full stack of tools a broadcaster or production house already operates. “When you look at what we do across Microsoft Azure AI infrastructure, our Copilot framework, our agent capabilities – they have to sit “ The question isn’t whether AI works. It’s whether you’re applying it to solve the right problems” FEATURE Photo: NBA/Microsoft
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