How to Successfully Integrate AI into Media Workflows

Historically, organizations that resist disruptive technologies face a difficult path forward. While early adopters have the advantage of building foundational skills, late adopters often scramble to catch up, risking their market position. Artificial intelligence (AI) is particularly disruptive because it’s a broad enabler, affecting everything from software development to how society will function in the future. The media and entertainment industry, with its ever-growing demand for high-quality personalized content and never-ending cost pressures, is an early adopter of generative AI and is now benefiting from general AI-driven innovation across the distribution and operational spheres.

For media companies, the question has shifted from whether to integrate AI to how to do so effectively. Those who navigate this transition successfully will streamline operations and unlock new opportunities for innovation and growth.

As AI matures, it follows a well-known technology adoption cycle, moving from product and solution innovation to industry-wide disruption. Pioneers in the media and entertainment industry have already begun integrating AI and machine learning (ML) into their workflows.

Examples of where the technology is being used to enhance efficiency, personalization, and creativity are growing. Netflix’s AI-driven recommendation engine, for instance, personalizes user experiences to boost engagement. In addition, Netflix leverages AI to create compelling previews for their content, identifying which combination of highlights is most likely to create viewer engagement. Spotify’s AI DJ curates personalized playlists, blending data with creativity. Spotify also uses AI to give their “synthetic” DJ a human voice, with the ability to change tone, accent and gender to create greater resonance with their subscribers. Microsoft’s Azure platform offers AI-based content moderation tools, and Azure’s Video Indexer uses AI to analyze content and enrich the associated metadata. Freewheel has developed AI-powered ad insertion and targeting technologies, to increase the efficiency of ad monetization.

These are just a few examples of how AI is quietly revolutionizing the industry, and the use cases will only continue to grow. For media companies facing this wave of AI offerings, the real challenge isn’t whether AI can help, but how to choose the right tools and strategies for their needs.

The first step in integrating AI into media workflows is understanding the organization’s readiness to adopt an AI use case. AI solutions with broad based implications, touching multiple organizational functions and data sources, will require complex evaluation models to ensure all aspects of the business are considered. Far easier to digest are AI solutions that are narrower in scope – improving encoder performance for example – requiring simpler evaluation models to determine if they are a fit for the organization’s needs.

The second step in determining a good use case is to define a desired outcome that is measurable through clear operational KPIs. Typically, this is clearly tied to increasing efficiency (reducing cost), improving customer experience (reducing churn and increasing engagement), or driving additional revenue.

The third step is to evaluate the robustness of the available solutions and determine the threshold of performance that would define a successful outcome for a given use case. In most cases, the organization is not building and training its own AI models, but leveraging third-party AI models through APIs, and exercising that model on its own dataset or content. Having a clear understanding of the range of potential outcomes using easier-to-assess metrics such as visible quality, time taken to deliver assets, or even factors like bandwidth utilisation, will help qualify valuable use cases and help avoid disappointment. There are a number of factors that need to be considered when evaluating an AI solution for your organization. To illustrate the point, below is an example evaluation model for assessing whether or not to leverage AI in an encoding workflow. This evaluation model looks at five key factors:

• Cost and Savings: AI incorporated into encoding will save distribution bandwidth but will come with the cost of additional compute resources and potential software licensing costs. A good evaluation model will consider not only the savings in distribution bandwidth cost, but also the additional cost of infrastructure to manage the increased computational load. Having a benchmark of costs for an existing process based on a few simple metrics such as ‘time taken’ or ‘asset processed’ to compare to an AI workflow can help with TCO calculations. But always remember: AI processes go through both model and workflow improvements that tend to provide incremental benefits through subsequent versions, so TCO is an evolving calculation.
• Performance Impact: Additional AI-based processing may introduce latency into the encoding workflow. If the AI solution introduces multiple seconds of latency for a live stream, the impact on viewer experience may materially impact the business. In some cases, the AI solution may also have impacts on the client side, which may not be acceptable. A good evaluation model will consider all end-user impacts in implementing the solution and have a clear threshold for acceptable performance.
• Operational Impact: Any impacts on the day-to-day operations should be well understood. Is there additional monitoring required to ensure sustained performance of bandwidth savings and/or picture quality? Do staff need to be re-trained to understand any new performance metrics, configurations and settings? Are there sustainability implications that need to be evaluated against the organization’s ESG initiatives due to increased power consumption?
• System Integration and Risk: Are there other systems in the video encoding and distribution workflow that also use automation and/or AI? Are the end-to-end system risks well understood to mitigate any business-impacting events? Could there be potential cascading effects of a malfunctioning system feeding into another AI-enabled system, and are the current failsafes and redundancies sufficient? Running workflows initially in test and development environments as well as simulating failures is a great way to understand how failsafes and redundancy fare ahead of production deployment.
• Ethics and Privacy: Ethical and privacy considerations should always be part of every evaluation model. Can the system alter the content in any way? Is there any possibility that the AI-powered system could touch customer data? For example, there could be AI-enabled encoding systems that have built-in mechanisms for automated language dubbing or in-frame brand detection and replacement for monetization purposes. Ensuring appropriate controls and permissions to preserve content owner/creator rights is critical.

Once a use case is selected, develop gradual modes of introduction into the organization. Constrain the initial implementation so the implications to the organization are well understood, as well as the potential for achieving the desired outcomes.

Media companies like the BBC have successfully adopted this approach, piloting multiple AI-driven initiatives in limited internal settings. For example, content personalization features were launched in controlled settings before deploying them to a wider audience. The BBC also ensures that all initiatives are governed by core principles, which inform their own internal evaluation models.

It is also useful to consider scenarios where the system may perform very well as a pilot but run into significant problems at scale. Define potential issues that may affect scaling your AI-enabled solutions as part of the evaluation model and consider if rollback mechanisms may be needed.

AI is not just a tool — it’s fast becoming a strategic imperative for the media and entertainment industry. By adopting a methodical approach — starting with clearly defined use cases, supported by robust evaluation frameworks, and conducting thoroughly tested pilots in controlled environments — media companies can leverage AI to drive both efficiency and innovation.

From early adopters, it’s clear that AI isn’t a one-size-fits-all solution. Companies that excel in harnessing AI are those with a deep understanding of media workflows, technical applications, and industry pain points. These pioneers are best equipped to utilize AI effectively, customizing its capabilities to their specific needs.

Another key takeaway is the low cost of experimentation. By running pilots in parallel or within non-production environments, companies can explore AI’s potential without disrupting ongoing operations. Crucially, this trial-and-error process not only fine-tunes AI implementations but also develops critical internal AI literacy that will drive long-term value.

Make no mistake — AI is already transforming the industry. A 2023 Gartner poll of more than 1,400 executive leaders revealed that 45% are piloting generative AI solutions, and 10% have already deployed them in production. This is a sharp rise from just 15% piloting and 4% in production the previous year, underscoring the urgency with which companies are embracing AI to stay competitive. As the digital landscape rapidly evolves, those who act now to explore AI’s possibilities, while building the foundational skills and strategies, will be best positioned to unlock new growth opportunities and deepen audience engagement. AI isn’t just the future — it’s the key to staying ahead in a fast-changing world.