Artificial intelligence is rapidly reshaping the media and entertainment industry, but its impact is often discussed in narrow terms: recommendation engines, generative trailers, or synthetic presenters. At eCreation we believe the most transformative opportunity for AI lies deeper in the media supply chain itself. From the moment a piece of content is commissioned or acquired, through production, localisation, compliance, distribution, and ultimately sales and marketing, AI can be applied end-to-end to improve efficiency, reduce cost, accelerate time-to-market, and unlock new commercial value.
Traditionally, media supply chains have been complex, fragmented, and labour-intensive. They span multiple systems, vendors, formats, territories, and regulatory environments. Many processes remain manual, repetitive, or dependent on specialist human review. AI does not replace the need for human judgement or creativity, but it can fundamentally change how work is prioritised, automated, and scaled. The opportunity is not simply to “add AI” at individual points, but to re-architect the supply chains around intelligent, adaptive workflows.
At eCreation we have considered how AI can be applied across the media supply chain, from software development through to localisation and sales and marketing, and how it can deliver both operational efficiencies and strategic opportunities.
At the foundation of any modern media supply chain is software. Media asset management systems, workflow engines, encoding pipelines, compliance tools, and distribution platforms are all built and maintained through code. AI is already changing how that code is written.
Large language models (LLMs) can assist developers by generating boilerplate code, workflow definitions, API integrations, and configuration files. In a media supply chain context, this can dramatically accelerate the creation of new processing steps, such as transcoding profiles, metadata transformations, or delivery rules. Instead of manually writing code for each new customer or contract, developers can describe requirements in natural language and have AI generate a first implementation, the process is often referred to as ‘vibe coding’.
This does not eliminate the need for experienced engineers. Instead, it allows them to focus on architecture, optimisation, and governance while AI handles repetitive or well-defined tasks. Over time, organisations can build libraries of reusable AI-assisted components tailored to their supply chain.
At eCreation we have been using GitHub Copilot, Claude Code and Cursor (utilizing it range of models) with great results. We find vibe coding particularly useful for rapid prototyping.
AI is equally valuable in software testing. Media supply chains are particularly difficult to test because they involve many combinations of formats, devices, territories, and business rules. AI can assist with:
· Code reviews, from spotting spelling mistakes and repeated code, to identifying potential security vulnerabilities.
· Unit testing, by generating test cases for individual functions or services.
· Integration testing, by simulating interactions between systems such as media asset management systems (MAMs), transcoders, and delivery platforms.
· End-to-end testing, by validating that entire workflows behave as expected under different scenarios.
By automatically recommending, generating and executing tests, AI reduces regression risk and enables faster iteration, particularly when supply chains are reconfigured frequently to meet new commercial requirements.
One of the most forward-looking opportunities is the use of AI agents to dynamically construct supply chains. Instead of pre-defining static workflows, AI agents can interpret contractual requirements, content characteristics, and delivery constraints, then assemble an appropriate processing pipeline.
For example, a contract might specify territory-specific compliance rules, language requirements, accessibility standards, and delivery formats. An AI agent can translate those requirements into a tailored workflow, selecting the correct tools and steps automatically. This moves supply chains from rigid, predefined structures to adaptive, contract-driven systems.
Quality control is a critical but costly part of the media supply chain. Traditionally, this involves human operators reviewing video and audio for technical issues such as encoding errors, audio distortion, or visual artefacts. AI-based analysis can automate much of this work.
Machine learning models can detect issues such as dropped frames, incorrect aspect ratios, poor quality standards conversion, audio clipping, silence, or sync problems at scale. These systems can operate continuously and consistently, flagging only the items that require human attention. This reduces turnaround times and operational costs while improving overall quality assurance.
An example of a solution providing automated QC is TeleStream Qualify. It has been used by Sky to validate content against their technical standards, on ingest into their MediaMesh cloud platform.
Beyond technical quality, content must meet editorial and regulatory standards. AI can analyse video and audio to identify elements relevant to compliance, such as:
· Strong language or swearing
· Violence or threatening behaviour
· Sexual content or nudity
· Smoking, drug use, or other restricted activities
By automatically tagging content with these elements, AI creates a detailed compliance profile for each asset. This metadata can then be reused across territories and platforms, reducing duplication of effort and improving consistency.
The leading AI video analysis software can pick up on context and nuance, for example the use of camera angles to imply threat.
Different countries and platforms apply different classification systems. What is acceptable in one territory may require edits or a higher age rating in another. AI can map compliance tags to jurisdiction-specific classification frameworks, providing automated recommendations for certification levels and suggested edit decision lists to reach more inclusive certification levels within each territory.
This capability is particularly valuable for global distributors managing large catalogues. Instead of re-reviewing content from scratch for each market, AI enables classification by inference, supported by human oversight where necessary.
BBFC stands out as a solution provider in this field, working on various AI-driven compliance products. Among these is CLEARD, which uses standardised compliance tagging to determine classification across different jurisdictions. Additionally, Mira supports this by automatically creating detailed, standardised compliance metadata that is not tied to any specific territory.
Others working in this area include Spherex.
AI’s role does not stop at analysis. Increasingly, it can assist with compliance modification. This includes suggesting edits to achieve a desired classification, such as muting or replacing specific words, trimming scenes, or altering visuals that trigger compliance issues.
More advanced techniques allow direct modification of content, such as removing cigarettes from a scene or altering on-screen elements to meet regulatory requirements. While these capabilities raise creative and ethical considerations, they offer powerful tools for adapting content efficiently to different markets.
Metadata is the backbone of discoverability, distribution, and monetisation. Creating high-quality metadata at scale is time-consuming, particularly for large archives. AI can generate synopses by analysing narrative structure, dialogue, and visual cues within content.
AI can use various sources to construct metadata, not just video essence files, including subtitles, scripts, production metadata.
These AI-generated synopses can be used as a starting point for editorial teams, significantly reducing manual effort while maintaining consistency across catalogues. Some AI systems can provider a confidence level in their output, enabling editorial teams to prioritise their manual reviews.
Metadata generation can go beyond synopsis creation and include scene detection or tagging actors using facial recognition.
Choosing the right artwork (e.g. thumbnails) has a measurable impact on audience engagement. AI can analyse video content to identify visually compelling frames, emotional moments, or recognisable characters, then select or generate thumbnails optimised for different platforms, audience groups or individual users.
This allows broadcasters and streaming services to test and deploy multiple variants at scale, improving click-through rates without manual curation.
At eCreation, we are currently working with a major client on a proof-of-content to generate artwork for use across their platforms. In this case the AI tool selects a range of frames, for use as artwork for a programme episode, the reasoning for selection and a ranking are provided with each selected frame. A human editor then selects the most appropriate frame.
Subtitling is both an accessibility requirement and a localisation foundation. AI-driven speech-to-text systems can generate subtitles quickly, accurately and at scale, dramatically reducing cost and turnaround time. Human editors can then focus on correction and stylistic refinement rather than full transcription.
Speech-to-text was an early use of AI and there are many vendors in this space examples include: AI-Media, PHONT, CaptionHuband Lingopal.
Audio description enables visually impaired audiences to engage with video content. AI can analyse scenes and generate either descriptive narration or proposed transcriptions aligned with existing dialogue and subtitles. While human review remains essential, AI accelerates the creation of accessible versions of content that might otherwise be excluded due to cost constraints.
AI can separate dialogue from background music and sound effects, enabling the creation of enhanced audio tracks for accessibility. These techniques can also support object-based audio workflows, offering viewers greater control over their listening experience.
An example of a live service using such technology is ARD’s ‘Klare Sprache’. This provides to viewers an alternative audio track with enhanced speech. Viewers who find noises or music mixes too dominant can switch to the ‘Klare Sprache’ audio track for improved speech intelligibility.
Localisation begins with metadata. AI-based translation enables rapid, consistent translation of synopses, titles, and descriptions into multiple languages. This ensures that content can be marketed effectively in different regions without extensive manual effort.
Once subtitles exist, AI can translate them into multiple languages, maintaining timing and structure. This dramatically reduces the cost and time associated with multi-language distribution, particularly for long-tail catalogue content.
Recent advances in AI enable automated dubbing that retains the voice characteristics of the original actor. This preserves performance authenticity while making content accessible to new audiences. Combined with language translation, this opens new markets for content that might previously have been uneconomical to localise.
An emerging solution provider in this space is ElevenLabs. They support subtitle generation, translation and dubbing with a range of products. Their Dubbing product can translate content across 29 languages in seconds with voice translation and speaker detection.
More advanced localisation involves modifying lip movements to match dubbed dialogue. AI-driven video manipulation can adjust facial movements to create convincing lip-sync, improving audience acceptance of dubbed content. While still emerging, this technology has significant implications for global distribution strategies.
One provider we have been watching in this space is Flawless. Their solutions perform performance editing which can enable lip-sync localisation and compliance changes.
Different platforms have different scheduling and advertising constraints. AI can analyse narrative structure and provide suggested edits to meet specific length requirements, such as fitting a one-hour programme into a broadcast slot with advertising breaks, without damaging the story arc.
With growing use of vertical video by major media organisation, there is interest in solutions that can transform traditionally filmed landscape content into the portrait. An example of a solution for this is Luma AI, which tracks faces, actions, and key points of interest to generate the reframed video.
AI can identify natural scene breaks and emotional peaks within content, enabling smarter ad break placement. This can increase viewer retention while maximising advertising effectiveness by creating deliberate cliff-hangers or pacing breaks.
AWS’s Media2Cloud solution supports Scene and Ad break detection using a combination of AWS technologies. It uses Amazon Transcribe to generate transcription from the audio dialogues of a media asset, then the Anthropic’s Claude 3 Haiku model to analyse the conversation and identify chapter points based on significant topic changes. In parallel, it generates a scene grid from video frames using the Amazon Titan Multimodal Embedding model to group frames into shots and then group shots into scenes based on visual similarity.
By analysing scenes, objects, and brands, AI can identify potential product placement opportunities within existing content. This allows rights holders to monetise archives in new ways by retrofitting placements where appropriate.
Beyond identification, AI can modify video to insert logos or objects, enabling dynamic or region-specific product placement. This transforms content into a more flexible commercial asset, capable of generating ongoing revenue long after its initial release.
A strong player in this area is Mirriad. Their solutions can dynamically inserts brands into premium television content. They not only provide the technology but also a product placement marketplace, consisting of over 4,000 hours of premium content.
Sales teams often need to identify content that matches specific buyer requirements, such as genre, tone, classification, or audience appeal. AI-powered chat interfaces can search both structured metadata and unstructured content analysis, allowing users to query archives in natural language.
For example, a sales executive could ask “find me family-friendly drama series with a hopeful tone suitable for early evening scheduling,” and receive relevant results without manually filtering databases. This would be enabled by the AI based metadata extraction detailed in a previous section.
Such natural language interfaces can also be used to find existing localisations, a key issue when trying to rapidly respond to global sales enquiries. For example, “find me family-friendly drama series with a hopeful tone suitable for early evening scheduling which are available dubbed into Spanish.”
AI can analyse viewing figures, sales data, and audience behaviour to identify patterns associated with successful content. These insights can then be applied to archive catalogues to uncover under-exploited assets with similar characteristics.
This shifts sales strategy from reactive to proactive, using data-driven insight to unlock new revenue streams.
Promotion is one of the most resource-intensive yet commercially critical parts of the media lifecycle. Every title requires trailers, cut-downs, social assets, platform-specific artwork, promotional copy, email campaigns, and sales presentations, often tailored for multiple territories and audience segments. AI dramatically changes the scale and speed at which this can be delivered.
By analysing the narrative structure, tone, emotional beats, characters, and key visual moments within a piece of content, AI systems can automatically generate promotional materials aligned to different campaign objectives.
The true value of AI in the media supply chain lies not in isolated use cases, but in integration. When AI-driven development, quality control, compliance, localisation, and commercialisation are connected through intelligent workflows, the supply chain becomes faster, more flexible, and more resilient.
Rather than replacing human expertise, AI augments it, handling scale and repetition while enabling people to focus on creative, editorial, and strategic decisions. As media organisations face increasing pressure to do more with less, AI offers a path to sustainable growth and innovation.
The opportunity now is for media businesses to move beyond experimentation and begin systematically identifying their own valuable sources of metadata and embedding AI into their supply chains, transforming them from static pipelines into intelligent, adaptive systems designed for a global, on-demand future.
AI is not a bolt-on feature. It requires thoughtful integration across workflows, systems, data, and commercial strategy. Whether you are modernising legacy operations, launching new services, or exploring how AI can unlock new value from your catalogue, the opportunity is significant, but so is the need for the right architecture.
eCreation specialises in designing and delivering intelligent, cloud-native media supply chains that combine automation, AI, and commercial insight. From workflow engineering and compliance automation to localisation, monetisation, and sales enablement, we help media organisations turn complexity into competitive advantage.
If you would like to explore how AI can transform your media operations, from code to commercialisation, you can book a no-obligation consultation directly with Simon Butler, CEO of eCreation. Choose a convenient time slot and start the conversation here.
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