In common with industries across the modern economy, the broadcast media industry is currently on its own artificial intelligence (AI) and machine learning (ML) journey, with organisations at various stages of evaluation and implementation. But in practical terms, how are AI and ML changing the way broadcasters work, and what is the likely direction of travel as the development of these technologies continues to accelerate?
Among the various key adoption drivers for AI and ML in the broadcast industry is the need to do more with less. In the face of inflation, rising cost of capital and corporate drive for profitability, broadcasters need to manage increasingly complex network workflows with fewer personnel while also reducing costs. In this context, AI and ML technologies can be applied to cross-workflow monitoring and configuration management, where patterns can be observed across multiple systems. This technology helps cut through the noise of meaningless alerts, guiding operators to focus their attention where it matters most, maximising efficiency and minimising wasted time. Instead, employees can focus on other important tasks – an approach that helps put the emphasis on improving productivity.
This also applies to the many broadcast organisations that are moving or have moved to software-defined infrastructures. These technologies allow for more agile workflows, comprising both on-premises and cloud assets, controlled by the broadcaster and their content and affiliate partners. Going forward, AI and ML will play a crucial role in maximising the impact of this approach by providing insights into the performance of these complex systems. They enable broadcasters to detect problems during live playout and identify emerging instabilities, ultimately boosting operator confidence. In addition, AI and ML can be employed to automatically adjust workflows based on network and content analysis, ensuring optimal performance.
Harnessing the power of AI and ML
While various current solutions utilise AI and ML to help broadcasters reduce complexity, enhance workflows, and improve operational efficiency, it’s essential to consider the broader applications of these technologies. For example, the most advanced and effective AI and ML platforms are designed to visualise irregularities and issues emerging in complex workflows over time, making it easier for operators and engineers to identify channels that require attention while also offering detailed insights into signal degradation and its potential causes, facilitating collaboration across organisational silos to address issues efficiently. At the cutting edge, real-time machine learning is also employed to proactively alert operators to problems before they occur, ensuring minimal disruptions and enhanced operational reliability.
Looking ahead, the ongoing shift towards hybrid workflows will continue to open new opportunities for broadcasters to be more dynamic, rapidly adding content and distribution partners and adapting to network challenges. However, with this increased dynamism comes greater complexity in workflow management, which will drive the demand for analytics solutions that provide early problem detection and swift response mechanisms. As broadcasters strive to maintain operational efficiency and adaptability with limited resources, AI and ML solutions will remain integral to their success. As a result, these innovations will continue to reshape the industry landscape over the long term, enabling broadcasters to thrive in the digital age.
