IBC 2025 Preview: Operational AI and business intelligence reshape broadcasting infrastructure

While creative applications of artificial intelligence capture much industry attention, a parallel transformation is occurring in broadcast operations and business intelligence systems.

As professionals prepare for IBC 2025 in Amsterdam this September, companies report significant efficiency gains from AI applications that optimize infrastructure, audience engagement and monetization strategies.

Infrastructure automation and stream management

AI is increasingly being pitched to monitor performance and automatically adjust resources based on real-time conditions in broadcasting infrastructure. These applications address operational challenges that traditionally required constant human oversight and manual intervention.

“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 represents a shift from reactive problem-solving to predictive system management, where AI monitors multiple performance indicators and prevents issues before they affect 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 said.

Industry vendors report that operational AI applications often deliver value in areas that receive less attention than high-profile creative tools. These systems address backend processes that affect overall content supply chain performance.

“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.”

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Looper Insights uses machine learning to optimize content placement across digital platforms, 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 said. “Predictive models translate visibility into projected outcomes like revenue or impressions, guiding smarter decisions about where and when content should appear.”

Comprehensive lifecycle optimization for content

Some companies implement AI across entire content monetization processes, creating integrated systems that optimize scheduling, audience targeting and rights management simultaneously. These approaches move beyond isolated AI tools toward comprehensive operational intelligence.

“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.”

The system creates compounding improvements, where better predictions enable more effective scheduling. This generates 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 said. “We’re documenting 35% conversion improvements within months — intelligence that learns from every audience interaction.”

AI-driven scheduling systems can automatically substitute underperforming content and optimize channel lineups based on real-time audience response. These applications enable rapid channel deployment with minimal human resources.

“One client launched 40 channels in three days with just two staff — 80% faster than traditional methods,” added Verbesselt.

The technology also addresses rights management complexity, predicting optimal licensing windows and identifying monetization opportunities across multiple platforms and distribution channels.

On the subscriber side, AI systems can analyze viewer behavior patterns to predict cancellations and enable proactive retention strategies. These applications combine engagement analytics with predictive modeling to identify at-risk subscribers before they leave.

“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.”

The system enables dynamic content updates that keep platforms current without manual intervention, addressing audience expectations for fresh, relevant programming.

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“AI also enables dynamic, constantly updating editorial changes at scale ensuring the platform remains fresh and relevant,” Kahanam said.

Sustainability and environmental monitoring

Environmental impact monitoring represents an emerging application of operational AI, where systems track carbon emissions and energy consumption at the process level.

This approach addresses growing industry focus on sustainability while identifying cost optimization opportunities.

“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 said.

Business model integration challenges

Despite widespread AI adoption, industry executives emphasize that successful implementation requires integration into core business systems rather than superficial additions to existing workflows.

Many companies add AI tools without addressing underlying operational inefficiencies.

“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 shift toward AI as an integral component of business operations rather than an additional 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.”

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Some industry leaders argue that operational AI applications offer more immediate value than highly publicized creative tools. These systems focus on business optimization rather than 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.”

Real-world implementations demonstrate measurable improvements in resource allocation and workflow efficiency.

Realistic expectations and practical applications

Industry executives urge careful evaluation of AI implementations, distinguishing between applications that solve genuine operational problems and those driven primarily by marketing considerations. Some emphasize practical, measurable benefits over experimental technologies.

“Everyone’s so busy chasing the AI gold rush that we’ve forgotten to ask the basic question: ‘Is this actually useful, or are we just adding expensive complexity?'” said Jan Weigner, CTO of Cinegy. “The real value is in fixed-function AI — automated transcription, real-time subtitling, metadata enhancement, content cataloging. These applications deliver measurable results without copyright nightmares.”

Practical implementations focus on applications where AI performance exceeds human capabilities in specific operational contexts.

“We’ve implemented AI-powered automatic subtitling that outperforms human transcribers in high-stress environments,” Weigner said. “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 implementation and content licensing

Operational considerations include ethical AI deployment, particularly regarding content licensing and creator compensation. Some companies address these concerns by establishing licensing agreements rather than using unlicensed content for AI training.

“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.”

This approach ensures content creators receive compensation when their material contributes to AI system training, addressing ethical concerns about unauthorized content usage.

“Signing licensing deals instead of scraping content ensures ethical and transparent AI usage in video and broadcasting,” Alhajry said. “Studios and creators can now supplement their work with AI content that is ethically trained, where all contributors benefit along the production chain.”

As the broadcast industry prepares for IBC 2025, operational AI applications continue expanding beyond traditional IT functions into core business processes. The focus appears to be shifting toward systems that integrate seamlessly into existing operations while delivering measurable efficiency improvements.

The 2025 IBC will provide industry professionals with opportunities to evaluate these operational AI applications and assess their potential impact on business performance and operational efficiency.