The media and entertainment (M&E) industry is no stranger to innovation. From digital broadcasting to streaming platforms, technology has continuously shaped how content is produced, distributed, and consumed. Now, artificial intelligence (AI) is driving another transformation, improving efficiency, content quality, and audience engagement. But beyond the buzzword, what does AI really mean for broadcasters, content creators, and consumers?
AI’s impact on workflows and production efficiency
Managing workflows efficiently is crucial in modern media production. AI automates repetitive tasks like metadata tagging, content indexing, and real-time error detection, freeing professionals for higher-value work.
AI-powered tools detect inconsistencies such as black frames, colour imbalances, or audio disruptions, reducing manual monitoring. In live broadcasting, AI-driven systems assist with camera switching and framing, maintaining high production standards with fewer resources. Speech-to-text and natural language processing (NLP) improve automated captioning, enhancing accuracy and compliance while reducing manual transcription needs.
AI is also transforming post production. Automated video editing tools analyse shot composition and narrative structure, expediting the editing process. AI-powered colour grading ensures video consistency by adjusting hues and saturation automatically.
Beyond efficiency, AI enables real-time collaboration by automating cloud-based editing and remote workflows. AI-driven tools streamline version control, intelligent file management, and tagging, making large-scale projects more manageable and reducing bottlenecks.
Enhancing content delivery: AI-driven quality control and error reduction
As content distribution expands across platforms, maintaining quality is increasingly complex. AI-powered monitoring systems analyse vast data volumes, identifying anomalies, flagging issues, and predicting failures before they affect audiences.
Reducing false positives—incorrect alerts that waste resources—is a key challenge. Traditional rule-based monitoring triggers unnecessary alerts, while AI-driven systems learn from patterns, improving accuracy and operational efficiency.
AI enhances scalability, enabling streaming platforms to analyse video data in real-time, ensuring consistent audio and visual quality without manual intervention.
AI-driven quality control also aids compliance. Automated systems detect inappropriate content, copyright violations, and non-compliant ads, helping broadcasters maintain industry standards and avoid legal issues.
AI in live production
Live production demands real-time adaptability. AI automates camera tracking, optimises shot selection, and improves audio clarity with noise reduction. AI-generated graphics and overlays provide real-time statistics, multilingual translations, and interactive elements that boost engagement.
For example, AI-driven camera systems automatically track subjects, reducing the need for manual operators. This is especially valuable in sports broadcasting, where AI identifies key players, tracks the ball, and captures optimal angles.
Real-time graphics and augmented reality overlays enhance broadcasts. AI analyses game statistics and delivers insights instantly, engaging sports fans and news audiences alike. Beyond traditional broadcasting, AI powers virtual sets, dynamic overlays, and real-time facial recognition, creating more personalised, interactive live events and esports coverage.
These advancements help broadcasters maintain high-quality live content while optimising resource allocation.
The human factor: AI reshaping jobs, not replacing them
As in other industries, the question arises as to whether AI will replace roles in the entertainment industry as well. The answer will be similar as it is answered in other fields—AI’s goal is to make work more precise, to channel and leverage efforts into other areas, and ultimately to multiply and accelerate productivity. Still, behind every new feature or technological solution, there is a need for human oversight and decision-making before choosing an action.
AI automates routine tasks but also creates new roles. AI operators, data analysts, and machine learning engineers are essential to implementing and maintaining AI-driven tools. Successful AI adoption depends on collaboration between media professionals and technologists to ensure solutions address real industry needs.
The rise of foundational models and multimodal AI
In recent years, major tech companies have developed foundational AI models like GPT, LLaMA, and Google Gemini for applications from text generation to image analysis. Organisations can fine-tune these models or customise them with dedicated prompts, facilitating seamless AI integration without in-house expertise.
Another major advancement is the rise of multimodal AI, which enables a single model to process and understand multiple types of data—including text, images, and soon, video and audio. This development means that AI can be used more effectively across different areas of content production, from automated tagging of multimedia assets to generating cross-format metadata for better content organisation.
Additionally, the rise of AI agents—autonomous systems that execute tasks and make decisions—offers new tools for the industry. Unlike traditional AI models, they operate independently within set guidelines, streamlining workflows, enhancing user experiences, and optimising processes, which I expect will also impact AI integration in media and entertainment.
The future of AI in media
AI’s role in M&E continues to evolve. As technology advances, AI solutions will become more efficient and customised for live production, monitoring, and content personalisation.
Ethical considerations will play a greater role. As AI tools gain the ability to generate and manipulate content, broadcasters must ensure that AI-driven editing adheres to transparency and editorial integrity standards.
By integrating AI-driven analytics and automation, broadcasters can streamline operations, reduce errors, and enhance content quality. AI is not a replacement for human expertise but a tool that supports industry professionals in delivering more efficient, engaging media experiences.
