91 to anyone who needs it. “Instead of opening multiple dashboards or asking the data team to pull a report, people can now ask questions they couldn’t easily answer before,” he says. “Why did engagement dip in the last hour? Which clips overperformed in one region versus another? What is likely to trend in the next 10 minutes? And they’re getting useful answers, in real time.” He continues: “It’s easy to categorise the shift as just automation. But what you’re actually getting is intelligence between all the different layers. AI agents can reason across it, trigger workflows and recommend action.” For live production environments, where the stakes of a slow decision are immediately visible, that difference is especially profound. Crownshaw identifies three areas in which early adopters are consistently seeing measurable impact. The first is reduced production friction. Live environments are unforgiving: there is no time for a team to convene around a dashboard to diagnose the problem causing a graphic to fail on screen, a network scheduling issue or unexpected latency. AI is now surfacing these anomalies earlier, often before a crew member has noticed them, and making the underlying data far easier to act on. “I’m no longer having to search frantically for something and not even know if it exists,” says Crownshaw. “Using AI, I can very quickly understand what I need, when I need it.” The second is monetisation. “The optimisation that has occurred there is phenomenal,” says Crownshaw. “What you’re seeing is the ability to personalise streams – dynamic advertisement insertion is informed by real-time performance signals, making sure the messaging is aligned.” The third is content production speed. Short-form content has, he explains, become substantially faster to turn around, with tagging, clipping, localisation and archiving Photo: iStock/gorodenkoff MEDIA & COMMUNICATIONS
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