Monetizing Content with AI: Going Beyond Traditional Advertising
Broadcasters are increasingly turning to artificial intelligence (AI) and machine learning (ML) to unlock the value of their vast content libraries and develop new revenue streams. This shift is occurring as traditional advertising models evolve and media companies face mounting pressure to monetize content across multiple platforms. AI systems, designed to analyze viewer behavior and automate content management, are at the forefront of these changes. The goal is to maximize revenue from existing content while adapting to fragmented viewing habits and shifting advertiser demands.
“AI is enabling broadcasters to optimize revenue generation beyond traditional advertising and subscription models,” said Zeenal Thakare, senior vice president of enterprise solutions architecture at Ateliere.
Maximizing Content Value Through AI Analysis
Content libraries represent significant untapped potential for many broadcasters. AI systems are now efficiently identifying and categorizing this material at scale, enabling media companies to surface relevant content more efficiently. Stefan Lederer, CEO and co-founder of Bitmovin explains, “AI’s ability to efficiently and accurately search, tag and categorize content can be used to help surface content that … may otherwise remain hidden.”
This automated content analysis extends beyond basic categorization. Broadcasters are using AI to identify opportunities for content repurposing, creating themed programming packages and anniversary specials from archived material without significant production costs. This technology is proving especially valuable for free ad-supported streaming television (FAST) channels, where programming decisions directly impact advertising revenue. AI systems analyze viewing patterns across FAST channels to optimize scheduling and create thematic channels. This helps broadcasters identify high-performing content and adjust strategies in response to viewer behavior.
At the user level, AI processes multiple data points to refine content recommendations, marking a shift from broad demographic targeting to personalized experiences. Kathy Klinger, chief marketing officer at Brightcove, notes, “By analyzing vast amounts of data, AI ensures viewers are presented with content they’re most likely to enjoy, keeping them engaged and reducing churn.”
AI-Driven Ad Optimization
The impact of AI extends beyond content discovery to reshape advertising strategies. Current systems analyze content in real-time, enabling contextual ad placement not possible with traditional methods. Lederer adds, “AI contextual advertising analyzes video and audio content to provide hyper-personalized ads for viewers based on the content they are watching, resulting in more ad-generated revenue.” These systems also determine optimal ad timing by analyzing user engagement patterns.
“If you combine AI contextual advertising with predictive analytics, it’s possible to predict user engagement and conversion rates at different points of the video so that the ad can be placed when the viewer is most likely to convert,” says Lederer.
The technology’s reach extends to inventory management and pricing. Dave Dembowski, senior vice president of global sales at Operative, explains that broadcasters use AI to optimize inventory allocation. “AI can help broadcasters know what to sell up front, at what price, and what inventory to hold back based on likely demand closer to delivery,” he said.
Revenue Diversification Through Data Insights
As viewing habits evolve, thorough AI analysis gives broadcasters detailed insights into viewer behavior, enabling the development of new revenue models beyond traditional advertising. Thakare points out that “Monetization strategies that will take front row seats with AI include content licensing and distribution optimization, sponsorship and brand integrations, targeted subscription and pay-per-view and bundle models, all driven by audience analytics, behavioral targeting and predictive analytics.”
Rights management, traditionally a labor-intensive process, now benefits from AI automation. Lederer elaborates, “With AI, broadcasters can automate many of the manual and time-consuming tasks involved in these processes such as contract analysis, monitoring content usage in real time to ensure rights are being enforced and analyzing data to detect potential breaches.”
Implementation challenges, however, remain significant. Yang Cai, CEO and president of VisualOn, cites “high implementation costs, the complexity of integrating AI with existing workflows, and a lack of technical expertise among staff” as primary barriers. Data privacy and building user trust in AI systems also present major hurdles.
Success requires substantial investment in both technology and staff development. Klinger believes that “Organizations should cultivate a culture of continuous learning, equipping teams with the skills to use AI tools effectively while understanding the ethical implications and regulatory frameworks that govern their use.”
As the broadcast industry progresses, AI tools are enabling media companies to develop monetization strategies that adapt to changing viewer behavior. This adaptation maintains advertising effectiveness and the sustained value of content. AI’s impact spans the broadcast ecosystem, from content discovery to ad placement, suggesting broader changes are ahead for media monetization.
