Published on 25 Mar 2025
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Rising adoption of artificial intelligence (AI), including industries like media and entertainment, is driving the global AIOps market. Accounting for USD 11.7 Billion in 2023, it is estimated to be exceed USD 32 Billion by as early as 2028, growing at a CAGR of 22.7%.
The adoption of AI helps enhance organization results in numerous ways, from improving customer experiences to automating repetitive tasks.
Simultaneously, it is becoming increasingly difficult to keep up with data observability, thanks to the rise in hybrid, containerized deployments and the growth of data volumes. AI platforms are capable of leveraging vast (and increasing) amounts of data generated by IT infrastructure and Software components. By filtering out noise to identify relevant occurrences and patterns related to issues affecting application availability, customer experience and performance, this goes a long way toward strengthening the handling of time critical video streaming operations, including live sports.
AI applications in media and entertainment today spans real-time analytics, infrastructure management, network and security management, and application performance management. The growing adoption paradigm is being driven by robust integration with DevOps practices, renewed focus on proactive problem resolution, enhanced user experience monitoring, advanced analytics for predictive insights, hybrid and multi-cloud management, context-aware incident management.
Major companies within the Media/AIOps market are actively engaged in developing new platforms and sophisticated software solutions employing AI, machine learning (ML), and analytics to automate operations and enhance the efficiency of their IT/SRE/SRO personnel. They excel in collecting and analysing vast volumes of data from diverse sources, including monitoring and observability tools, configuration management databases, app performance monitoring service maps, and cloud orchestration systems.
The biggest trend in observability over the past couple of years, arguably, has been the increased adoption of telemetry collection and analysis tools like OpenTelemetry. We are also witnessing a rise of video streaming specific telemetry tools like MuxData, Conviva Ops Data Platform, Telestream, Touchstream probes, and AIOps solutions including Zabbix, DataDog, etc. This establishes a path toward a standardized telemetry gathering definitions for video streaming and IT infra observability data types, including, user journeys, experience paradigms, probe alerts, application and device logs, metrics, and traces. What this means, in turn, is the rise of new, innovative observability tools tailored to specific use cases.
Our experience suggests that in the coming years, forward-thinking organizations will not just collect analytics, metrics and profiles. Actively leveraging AI, they will be treating these data streams as the source for an interconnected, contextual, and holistic view of system performance, defect patterns, predictability and efficiency.
This shift toward enhanced observability is well underway, with a growing availability of robust, open-source tools and solutions. Consequently, it is clear that there is an untapped opportunity for engineering companies for developing new tools, and frameworks to help bridge any gaps in the current observability ecosystem.
AI and machine learning technology is set to drive observability forward with better insights and algorithmic forecasts, in addition to observability techniques like logs, metrics, and traces helping to piece together exactly what is going on inside of the media ecosystem hardware and software pipeline. Bringing observability and AI together is, therefore, crucial to fully benefit from AI in Media Operations. There is a need to shift left, the way we have with software development and DevOps lifecycle and many other areas of IT, so that it is actually being done as part of the design of applications and media operations pipelines.
We feel that, going forward, media operations will transition from a reactive model – fixing problems after they occur – to a proactive approach capable of predicting and resolving issues even before they manifest and cause major experience impact. The key success factor here is leveraging latest developments in LLM-based analysis, predictive analytics, and advanced machine learning models to anticipate potential failures, optimizing operational efficiency and reducing downtime.
Enterprises that embrace this approach will get a significant edge, minimizing disruptions and improving user experiences.
