Beyond the creative applications of Artificial Intelligence, a significant transformation is underway in broadcast operations and business intelligence. As professionals gear up for IBC 2025 in Amsterdam, companies are reporting substantial efficiency gains from AI applications optimizing infrastructure, audience engagement, and monetization strategies. AI is increasingly used to monitor performance and automatically adjust resources based on real-time broadcasting conditions.
“Most AI talk in streaming is about analytics or content creation, but the real shift will be automation inside the infrastructure itself,” said Michael Vitale, VP of AI strategy and product intelligence at Wowza. “Developers need systems that can observe what’s happening to a stream, take corrective action, and scale resources automatically — whether it’s a surge in viewers, a bandwidth issue, or a workflow failure.” This operational focus is a shift from reactive problem-solving to predictive system management, where AI monitors multiple performance indicators and prevents issues before they impact viewers or content delivery. “That kind of operational AI is what will separate reliable streaming platforms from those that just collect data about problems after they happen,” Vitale added.
Operational AI applications deliver value in often-overlooked areas, addressing backend processes that impact the entire content supply chain. “Most discussions focus on creative applications, but the operational layer is often overlooked,” said Francesca Pezzoli, VP of marketing at Looper Insights. “Automating metadata management, placement auditing, and insight generation can reduce hidden inefficiencies that weigh on the entire content supply chain. That’s where AI delivers compounding value that isn’t always visible.” Looper Insights utilizes machine learning to optimize content placement, translating visibility metrics into projected business outcomes. “We use machine learning to value on-screen placements across digital storefronts, helping partners allocate marketing spend to the highest-impact opportunities,” Pezzoli explained. “Predictive models translate visibility into projected outcomes like revenue or impressions, guiding smarter decisions about where and when content should appear.”
Some companies integrate AI across the entire content monetization process, creating systems that optimize scheduling, audience targeting, and rights management. “We’re leveraging machine learning across our Self-Optimizing Content Monetization Flywheel to optimize the entire content lifecycle, not just recommendations,” said Ivan Verbesselt, chief product and marketing officer at Mediagenix. “Predictive Content Intelligence: Our Spideo integration analyzes engagement patterns to create ‘Smart Content Pools’ that anticipate audience preferences, delivering 40% content discovery improvements before scheduling even begins.” This creates a compounding effect: better predictions lead to more effective scheduling, generating richer audience data for even more accurate future predictions. “The flywheel effect compounds gains: better predictions enable smarter scheduling, which generates richer engagement data, powering even more accurate predictions,” Verbesselt noted. “We’re documenting 35% conversion improvements within months — intelligence that learns from every audience interaction.”
AI-driven scheduling can automatically substitute underperforming content and optimize channel lineups based on real-time audience response, enabling rapid channel deployment. “One client launched 40 channels in three days with just two staff — 80% faster than traditional methods,” added Verbesselt. The technology also handles rights management, predicting optimal licensing windows and identifying monetization opportunities across platforms. On the subscriber side, AI analyzes viewer behavior to predict cancellations and enable proactive retention. “Machine learning and predictive analytics enhance audience engagement through hyper-personalization and real-time recommendations,” said Einat Kahanam, vice president of product solutions at Viaccess-Orca. “For monetization, AI powers intelligent adtech, dynamically creating and optimizing personalized ads to boost conversion rates. Additionally, predictive analytics helps anticipate subscriber churn, allowing for proactive retention strategies.” “AI also enables dynamic, constantly updating editorial changes at scale ensuring the platform remains fresh and relevant,” Kahanam added.
Environmental monitoring is an emerging application, tracking carbon emissions and energy consumption. “The sustainability angle is particularly overlooked, yet broadcasting operations generate significant carbon footprints through server usage and data transfer,” said Lee Otterway, commercial director for Dot Group. “This isn’t just about efficiency, it’s about using AI to make broadcasting operations more sustainable and profitable simultaneously,” Otterway stated.
Successful AI implementation requires integration into core business systems, not just superficial additions. “Everyone’s talking about what AI can do, but not enough people are talking about what AI should do,” said Symon Roue, managing director at VIDA. “Too many companies are adding a chatbot or transcription tool on top of the same broken workflows.” The focus should be on AI as an integral part of business operations, not an extra technology layer. “What’s missing is the conversation about AI as part of the operating model, not a gimmick,” Roue said. “The real win is when AI stops being a sideshow and starts making core business systems smarter, more connected, and less reliant on armies of people moving files and metadata around.”
Operational AI applications offer immediate value by focusing on business optimization rather than just content generation. “The missing conversation is operational intelligence — how AI can optimise the business of broadcasting, not just the content creation,” Otterway said. “Whilst the industry focuses intensively on AI for content generation and post-production enhancement, we’re overlooking AI’s transformative potential for operational efficiency and sustainability.”
Practical AI implementations focus on areas where AI outperforms humans. “We’ve implemented AI-powered automatic subtitling that outperforms human transcribers in high-stress environments,” said Jan Weigner, CTO of Cinegy. “Unlike humans who need rotation every 15 minutes to maintain quality, our AI delivers consistent, broadcast-grade results all day long. That’s practical AI.”
Ethical AI deployment is crucial, especially regarding content licensing. “Organizations like Troveo, Adapt Global, and others have embarked on an ethical content aggregation journey by licensing content from creators to accelerate and train AI video platforms like Moonvalley, OpenAI and others,” said Majed Alhajry, CTO and interim CEO at MASV. “By collecting content from studios and creators, a new market has emerged for AI content curation.” “Signing licensing deals instead of scraping content ensures ethical and transparent AI usage in video and broadcasting,” Alhajry added. “Studios and creators can now supplement their work with AI content that is ethically trained, where all contributors benefit along the production chain.”
As IBC 2025 approaches, operational AI applications are expanding into core business processes, focusing on seamless integration and measurable efficiency improvements. IBC 2025 will offer opportunities to evaluate these applications and their potential impact on business performance and operational efficiency.