recent Deloitte study found that US consumers now pay approximately $2 billion monthly for subscription-video services. And the technologies and capabilities needed to compete with these dominating digital media services are more diverse and agile than ever before. For instance, Netflix employed AI efficiently in its recommendation engine, reducing routine workload through automation. Since AI efficiently organizes and manages content, it helps solve significant issues due to unstructured video and audio data. From navigating through a bewildering mass of online content to personal recommendations, AI helped impact Netflix’s customer preferences. The company saved around $1 billion per year with the smart engine’s ability to reduce customer churn.

Like in the example above, AI and ML are critical components of any successful digital media strategy. The broadcast and media companies adopting AI/ML into their organizations are already benefiting from streamlined operations. This optimization affects every stage of the production workflow, from content creation to distribution, to build a complete viewing experience.


Often post-production teams are given increasingly large workloads and expected to deliver content in shorter time frames – and most of them are still relying on manual workflows. Under these constraints, they are forced to choose between missing deadlines, producing lower quality work, or streamlining processes to make the content compliant with IMF/OTT standards.

Each OTT platform requires content adheres to their custom specifications. Even a single error in a file or misaligned audio results in the rejection of the entire content package. To this end, it’s vital for broadcasters to make sure distributed content meets conformance requirements for all devices, channels, languages, and additional formats to support new form factors, which are emerging all of the time.

From the audience perspective, there are latency and bandwidth considerations as well. Content owners must ensure visual assets are smooth and accurate. Traditionally, all of these quality assurance processes lead to an increased need for manual resources, thus, leading to higher costs and operational inefficiencies.

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AI can automate up to 80% of manual processes, leaving employees open to manage strategic and creative tasks. This equates to hours saved from manually going through content to identify problems or compliance issues. A workflow augmented with AI can provide richer metadata for each scene and shot. It can also pinpoint potential discrepancies. Instead of sacrificing quality and missing deadlines, teams can now leverage AI to improve the core stages of their workflows.


AI allows for quick production of deliverables. As well as options to incorporate custom fixes to smooth out bumps in the workflow, it also adds new capabilities to editing tools. Content owners are enabled to narrow the gap between skilled and semi-skilled creative editors while adding more detailed tags and properties.

Data-heavy TV shows can also use AI to automate and speed up much of their backend work. Production houses can then keep up with the growing demand for content from global audiences. As AI promises to enhance human content creation and eliminate the guesswork of TV and film, it also presents new business opportunities for creative professionals who are willing to adapt to advanced technologies.


AI has already significantly impacted the media landscape, and it’s showing no signs of slowing down. Soon AI will be at the center of the media industry as a key force driving the creative process and audience behaviors. The media companies who continue to invest in AI will be at the forefront of this transformation. Those who adopt the technology will benefit from increased efficiencies and a better understanding of their customers – leading to an overall boost in customer loyalty and revenue.

It’s time to make your post-production even better with AI. Download our “Broadcasters’ Ultimate Guide to AI-Assisted Production” to get started.