The potential of AI and automation in video production lies in seamless integration with human expertise. AI accelerates workflows, eliminates repetitive tasks, and enables scale. This frees human experts across the video supply chain to focus on what matters most: insight, creativity, and precision. It’s this human touch that ensures the quality that top-tier brands demand. This blend of strengths is transforming the industry: producing content faster, scaling globally, and maintaining the exceptional standards audiences expect. Leaders must recognize that it’s not about choosing AI or humans; it’s about finding the right blend to meet quality, scale, and budget needs. The future of video production isn’t just faster; it’s smarter, ensuring that quality stays at the forefront of innovation.

The AI transformation is at the core of Ai-Media’s focus. Ten years ago, we were doing all human captions; even five years ago, AI captioning was about 10% of the market. Now it’s more than 50%, and the things a well-positioned AI language system can do keep increasing, expanding toward language translation, natural voices, and new uses in search and information access across organizations.

We are using AI to create playlists for FAST and broadcast delivery of sports content. Also, AI is playing a huge role in personalization of content and ads, creating better user experiences and increasing monetization options for digital linear deliveries.

AI functionality is being embedded in a wide range of production tools, including editorial, VFX, color grading, and content packaging and distribution. These tools automate much of the “first pass” manual functions, reducing time and cost and enabling time to be spent on creative tasks.

The combination of software, cloud, and AI/ML is transformational for TV and broadcast production on multiple dimensions! For one, complicated static hardware — along with its tedious manual configuration — can be replaced with dynamic and configurable software. This means that resources, and hence costs, can be allocated “on-demand.” Even more important, workflows and user interfaces can be easily adapted and customized for specific productions and individual operators. Production templates and orchestration further enable automation, streamlining the entire process. Adding AI and ML to this means that a lot of the configuration and some of the operation can be automated and creative processes can be assisted or complemented with AI. Examples range from automatic color grading, to AI-driven camera switching, to real-time generation of metadata based on computer vision.

In terms of AI tools for sports broadcasters and production teams, a great example is sound separation, which offers practical, daily advantages. Say your social-media team recorded a great interview courtside but there’s music caught in the background of the clip. Or there’s so much stadium noise that the player’s interview can’t be heard. AudioShake separates audio to give you greater control of these kinds of workflows, letting you boost or remove specific sounds.

We’ve been experimenting with AI, ML, and automation for a long time, but only in the last couple of years since the advent of transformation tasks in large language models have these innovations starting to yield real-world business results for video-streaming applications. One notable example is per-title transcoding for file-based content: a.k.a., content-aware encoding. This feature enables substantial cost savings for large-scale streaming services by implementing more-efficient encoding configurations while improving video quality. Learn more at https://bitmovin.com/encoding-service/per-title-encoding/. A more recent example is AI contextual advertising, where each video frame is analyzed to provide ad servers with insights to deliver ads relevant to the content that viewers are watching and to identify the best placement opportunities based on user behavior. Learn more at https://bitmovin.com/ai/.

AI technologies are profoundly impacting all aspects of broadcast and sport. At a video-production level, AI accelerates workflows with object- and player-tracking intelligence and automated camera calibration (enabling clever graphics and augmented reality). New technologies are allowing more-personalized viewing of sports. At Champion Data, AI-powered data collection and analysis, combined with broadcast graphics, delivers innovative content that deepens storytelling and viewer engagement.

At Disguise, we have leveraged machine learning in our product to deliver some of our most powerful features and workflows. Internally, we use the latest large language model’s power to help employees onboard more quickly and our developers to write code more efficiently. Externally, we see incredible impact within the domain of content creation and the impact AI is having on computer-graphics and video creation.Photorealistic real-time 3D is extremely expensive to create, requiring a lot of processing power to deliver to large LED walls for VP. The rapid development of 3D Gaussian Splatting with AI, which allows photorealistic 3D environments to be created from stills and video, is set to revolutionize the content creation pipeline across film and broadcast, as well as AR and VR.

AI, ML, and automation are revolutionizing video production by streamlining workflows and enhancing content delivery. EditShare leverages AI-driven tools like facial recognition, object and scene analysis, and automated metadata tagging, enabling rapid asset discovery and reuse. Its advanced workflow orchestrator minimizes repetitive tasks, allowing creative teams to focus on storytelling. Features like automatic shot detection and speech-to-text transcription boost efficiency and accuracy, helping media teams meet tight deadlines without sacrificing quality.

One compelling new trend is to take the dynamic representation of media and dynamic streaming that The Content Fabric provides and combine it with AI data for personalization. Content gets automatically tagged using AI models, indexed, and used to search for video clips or full streams. These clips are virtual URLs, dynamically generated from the long-form stream, not file copies. For premium sports content, this is key because live becomes VOD and becomes clips without additional file copies or costs. Longer-term benefits of this indexing include the ability to make personalized playlists, highlight packages, or channels. Content owners can infinitely recombine the same content object’s parts into different variants without, again, paying for egress or file copies or re-transcoding.

AI, ML, and automation enable precise reconstruction and enhancement of athletes and motion in real time, creating authentic, lifelike 3D representations. AI and ML are instrumental in optimizing capture quality, smoothing motion data, and filling in gaps for seamless volumetric visualizations. Automated 3D pipelines streamline workflows, from processing massive datasets to generating dynamic, interactive content for broadcasters and fans. Together, these advances are redefining how sports are captured, visualized, and experienced, bridging the gap between technology and storytelling in unprecedented ways.

We see first-hand how next-gen AI, in conjunction with traditional AI/ML, delivers substantial benefits to consumers by revolutionizing subscription-based business models using unprecedented personalization and operational efficiency. By applying sophisticated techniques, these advanced tools can analyze vast datasets and accurately forecast customer behaviors and preferences. These tools can truly transform the capabilities of businesses in the video-production and broadcast space to precisely tailor offerings for hyper-personalized recommendations and flexible pricing structures that enhance user engagement and satisfaction, including a GenAI-powered chat engine, churn, offer management, package prediction, and payment prediction.

AI, machine learning, and automation are transforming video production by enhancing creativity and operational efficiency. These technologies empower teams to produce dynamic, high-quality content, bridging the gap between top-tier and second-tier sports. Generative AI, for instance, enables super slow-motion replays from any camera angle without requiring costly high-speed cameras, democratizing access to advanced creative tools. Automation, independent of AI, optimizes existing infrastructures like SDI, extending their utility while improving efficiency. The rise of “flexible control rooms” offers centralized control over diverse devices and workflows through a single-pane-of-glass approach, streamlining operations and enabling seamless management of production environments. Together, these advances allow broadcasters to maximize resources, simplify processes, and deliver compelling stories, showcasing the potential of AI and automation in reshaping live production.

AI and ML help with things like predictive scheduling, real-time resource optimization, and automatically tagging and analyzing video content.

AI, ML, and automation are transforming video-production and broadcast technology, enhancing efficiency, accessibility, and personalization. AI tools automate scriptwriting, editing, and quality control, ensuring polished content with minimal effort. Real-time transcription bridges language gaps, and recommendation systems and dynamic ad replacement tailor experiences to viewers. AI-powered cameras enhance live coverage with automated framing, motion tracking, and audience-driven analytics, creating immersive, personalized content. Gcore’s solutions — like ASR (real-time transcription in 100+ languages) and AI Content Moderation — streamline workflows, ensure compliance, and support global scalability. These technologies reduce costs, optimize operations, and meet evolving audience demands. From virtual production to advanced editing tools, AI empowers broadcasters to innovate, delivering engaging, high-quality experiences in a competitive media landscape.

AI and automation are helping enhance production quality. Automation is supporting streaming services’ ability to have multiple camera angles of a single sports event, allowing the user to “direct” their own feed.

AI, ML, and automation play a transformative role in a few key areas. (1) Streamlining production/post workflows with automated editing (scene detection, color grading, audio sync, etc.); live-production automation (auto multicam switching, graphics insertion, audio balancing, etc., allowing smaller teams to produce higher-quality outputs); content management (AI-powered content-management systems allow semantic search, ontologies, and knowledge graphs, fueling a more dynamic search and browse experience). (2) Co-pilot for creativity, assisting with idea generation and refinement, as well as personalized storytelling. (3) Real-time applications, such as automatic speech recognition and natural-language processing, provide real-time captions and multilingual subtitles, expanding reach and accessibility. (4) Audience engagement and monetization through personalized recommendations and targeted advertising.

Production and broadcast are being supercharged by AI-assisted workflows in two adjacent areas: unlocking the value of existing content and streamlining new-content production. Multimodality enables media-content understanding and search at scale, enabling companies to understand a “moment” in real time, pull up a relevant “moment” from 10 years ago based on a natural-language query or automated through content understanding, and integrate the content into the live feed for a global audience in just a few clicks — adding relevance, improving engagement, and increasing monetization opportunities. Multimodality augments content-production workflows for captions, subtitles, audio description, and dubbing to improve reach and accessibility for global audiences and increase engagement.

AI is significantly impacting our ability to leverage engagement and playout data to make real-time programming decisions. AI-driven content recommendations and metadata tagging are streamlining content discovery and optimizing viewer engagement. Sports broadcasters use real time insights to dynamically choose content and revenue models, which drive significant increases in viewer engagement and interactivity. AI excels at contextually selecting ads that are relevant and brand-safe, as well as prioritizing premium placement, allocating inventory effectively, and predicting which ad slots will yield maximum value. Yield management leverages AI to maximize prices for auctions and prioritize high-value bids across ad exchanges. AI is also playing a critical role in fraud detection, identifying invalid traffic and preventing fraudulent impressions.