Broadcasters are increasingly leveraging artificial intelligence and machine learning to unlock the value hidden within their extensive content libraries and to cultivate innovative revenue streams. This shift comes as traditional advertising models undergo significant change, and media companies face growing pressure to effectively monetize content across various platforms. These AI systems, capable of analyzing viewer behavior and automating content management, are crucial tools in navigating this evolving landscape.
The primary goal of this technology is to maximize revenue from existing content while simultaneously adapting to evolving viewer habits and the shifting demands of advertisers. “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. “From personalizing ads to AI-generated content, AI is unlocking new monetization opportunities and commercial models.”
Many broadcasters possess substantial untapped potential within their content libraries. AI systems are now capable of identifying and categorizing this material on a massive scale, allowing media companies to surface relevant content much more efficiently. “AI’s ability to efficiently and accurately search, tag and categorize content can be used to help surface content that closely aligns individual viewer preferences, and that may otherwise remain hidden,” noted Stefan Lederer, CEO and co-founder of Bitmovin.
This automated content analysis goes beyond simple categorization. Broadcasters are now using AI to identify opportunities for content repurposing, creating themed programming packages and anniversary specials from archived material without incurring substantial production costs. This is particularly valuable for free ad-supported streaming television (FAST) channels, where programming decisions have a direct impact on advertising revenue. AI systems analyze viewing patterns across FAST channels to optimize scheduling and create thematic channels, enabling broadcasters to identify high-performing content and adjust strategies based on viewer behavior.
At the individual viewer level, AI processes numerous data points to refine content recommendations, representing a shift from broad demographic targeting towards personalized experiences. “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,” explained Kathy Klinger, chief marketing officer at Brightcove.
The influence of AI extends beyond content discovery, reshaping advertising strategies as well. Current systems analyze content in real-time, allowing for contextual ad placement that was previously impossible with traditional methods. “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,” Lederer added. These systems also optimize 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,” Lederer further explained.
The technology’s applications also encompass inventory management and pricing. Dave Dembowski, senior vice president of global sales at Operative, stated that broadcasters utilize 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.
As viewing habits continue to evolve, AI analysis provides broadcasters with detailed insights into viewer behavior, leading to new revenue models that go beyond traditional advertising. “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,” Thakare predicted.
Even rights management, traditionally a very labor-intensive process, now benefits from AI automation. “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,” Lederer noted.
Despite the advantages, significant implementation challenges remain. Yang Cai, CEO and president of VisualOn, highlighted “high implementation costs, the complexity of integrating AI with existing workflows, and a lack of technical expertise among staff” as major obstacles. Data privacy concerns and building trust in AI systems pose additional hurdles. Success necessitates substantial investment in both technology and staff development. “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,” Klinger emphasized.
In conclusion, as the broadcast industry continues its evolution, AI tools empower media companies to develop monetization strategies that adapt to changing viewer behavior while maintaining advertising effectiveness and preserving the value of their content. The technology’s influence spans the entire broadcast ecosystem, from content discovery to ad placement, indicating substantial changes are on the horizon for media monetization strategies.