AI overview
Artificial intelligence (AI) is truly bringing about significant change for a post-production supervisor. Many of the pre-production and onset workflows remain focused around human collaborative creativity and practical delivery, areas where AI is having less direct impact. However, AI can be directly applied to the realm of visual content manipulation and post-production right now.
The main input data is already fully digitised, there are vast resources of external existing data across the internet to learn from and, outside of iteration and scope definition, human communication is less crucial in some ‘delivery’ aspects of the role.
At this moment in time, with significant changes and advancements happening on almost a month-by-month basis, keeping abreast of the nuances of legality, client and audience preferences, broadcaster and funder requirements, and moral and ethical implications is challenging - let alone getting to grips with new suites of tools and systems.
At the same time, the opportunities being offered are vast. Many talented and capable post-production supervisors already have extensive skills and capabilities working with complex digital tools, and their ability to embrace these new solutions, like Firefly, Runway and Midjourney is notable. This is creating potential to carry out their own work more quickly and to a higher standard, as well as move into other areas of visual media, especially in new forms of commercial, immersive or personalised interactive content.
We could simply dive into the incredible world of AI tools surrounding the post-production process of content generation and manipulation, which encompasses everything from automated language translation and lip-sync, through to complete cinematic quality film generation. However, many supervisors are already at the forefront of this technology, with high levels of competence and understanding. Often acting as resource of knowledge in this area to wider production teams. Instead, the focus here is to consider the broader challenges, impacts, risks and opportunities.
So what is AI?
Put simply, Artificial Intelligence (AI) is a goal. It’s an ambition to program machines and software to behave in a way that seems human-like or is ‘intelligent’.
Rather than simply obeying instructions AI systems aim to reason, learn, communicate and make decisions – mimicking the kind of traits we associate with humans.
Before you read further, have a look at the ScreenSkills AI 101 page (also linked in the resources section below).
What are post-production supervisors starting to use AI for in 2025?
Planning and workflow forecasting
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- Before the edit starts, AI could analyse changes to the script and flag how they might affect post timelines, budgets, and VFX requirements, useful for flagging risk early and planning resources properly.
Asset tracking and smart data preparation
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- AI can structure and label incoming material automatically through tagging faces, locations, and scene content, which can make search and retrieval much quicker once the edit is underway.
Document handling and preparation support
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- Useful for keeping on top of notes, reports, and team coordination through automating meeting scheduling, summarising conversations or offering a second opinion on documents before they are distributed.
Real-time notes and creative tracking
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- While the shoot is still rolling, AI can summarise notes from rushes, highlight key decisions, and start to organise material for post. Helping to lay the groundwork while the editorial waits for delivery.
Shot prep and pre-assembly
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- AI-assisted tools can start filtering dailies by emotion, action, or framing. This can be helpful for editors when building a rough cut or checking for performance options. This has the potential to make pre-assembly work quicker for a good editor.
Scripted ADR and sound preparation
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- AI tools can help match re-recorded dialogue to original performances, aligning ADR automatically to the on-screen lip sync and potentially the emotional tone and narrative. It is also being considered for speeding up transcription, subtitles and compliance documentation.
Audio and picture clean-up
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- Machine learning could handle dialogue isolation, noise reduction, and reverb control in a simple, straight forward way. On picture, AI can help remove make-up seams, rigs or crew, clean plates, and automate some of the VFX prep for roto and tracking.
Colour, continuity, and grade matching
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- AI can help analyse a reference grade and apply it to another scene, keeping things consistent. It’s good for first passes, matching pickups, or setting a base before the proper grade.
Cut comparison and change tracking
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- Track what has changed between cuts with new audio, trimmed shots, swapped takes and flag them clearly for review. Helping you keep on top of notes.
Compliance and delivery checks
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- Can help look for flashing lights, product placement, swear words and other issues flagged by broadcasters. This doesn’t replace a final check, but it can highlight most of the problems to speed up the process.
How is AI limited in helping post-production?
Recent studies and reports have shown limitations in AI when it comes to producing quality work. Quantity is certainly not a problem, but ensuring that the content we create is truly excellent is very important in high-end TV and film productions.
Apple's recent report drew findings on data quality.
"We find that data quality, much more so than quantity, is the key determining factor of downstream model performance"
Here, they are highlighting that the training data for an AI is really critical and a specific barrier to high-quality output.
Post production supervisors often have access to that incredible data themselves, but training an AI with it can prove difficult, costly and at risk of copyright contravention or breach of confidentiality and IP.
Other areas are also currently limited in managing continuity between shot footage and merging that with a piece of generative AI content, or simply between two pieces of generative AI content. This is improving, but it has some way to go when capturing the essence of a performance. It poses the problem of what is considered acceptable and still ‘authentic and real’ to an audience, director or indeed the performer being represented.
Although AI assistants can be a brilliant tool to summarise and help analyse production schedules or changes of note. But working through a schedule and understanding it and the implications yourself is still crucial. Rather than replacing your laborious jobs with AI, it’s worth thinking about how they could be made more enjoyable, faster or easier to engage with.
Post-production supervisors' work often involves many aspects of collaboration with others, so much effort is put into creating the right kind of environment where people can give their best contribution. These things are very difficult for an AI to manage; crucially, the systems often don’t have access to the important real-time data, and secondly, the previous training data can be very poor. Being able to respond when things don’t quite go to plan or when personalities are creating conflict means that many aspects of collaboration are going to be managed by a human for quite some time.
When working with teams, it can be challenging to communicate AI guidelines in such a fast-paced field. Even if you or the team don’t know the answer to these questions, it is still valuable to challenge production teams to consider the implications of their actions.
A quick ‘checklist’ summary of things you might want to consider before you use AI in a project.
- Do I have permission to use the data I’m sending to an AI?
- Who owns the output that will be created?
- Is my input data stored or reused?
- Was this AI trained ethically (with consented or licensed material)?
- How might this use impact creative jobs?
- Is the output accurate, verifiable or likely to cause reputational risk?
- Could the output unintentionally imitate someone else’s work?
- Am I being transparent about how this AI was used?
- Does this use align with my employer's or customers' policies?
Preparing for the future as a post-production supervisor
It is broadly accepted that AI and ML will play a strong role in all forms of post-production in the future; generally speaking, it already is. Understanding how these tools might develop, and gaining personal insight into how to optimise them for your benefit, is crucial.
Many of the benefits of AI & ML tools and assistants are realised by the high-quality collection, preparation, analysis and application of real-time production data, not just the shot footage itself. Thinking about how you might be able to have access to this in the production process, in an acceptable and safe way, could prove to be very valuable.
To truly reap the advantages of these systems, it is worth considering expanding your knowledge beyond Movie Magic and sophisticated Excel sheets into AI-optimised systems like Airtable and Python scripting languages and automation with agentic AI approaches. This can be intimidating, and certainly is a learning curve, but a great reward is to be found when being able to automate processes across a production. Some post-production supervisors are working with in-house programmers and developers to create the kinds of automation and data collection that empower incredibly efficient and flexible processes. AI in this context can be hosted directly on local machines, ensuring data confidentiality throughout. This can create unique selling points and give your approach advantages over others.
Studios and broadcasters are also starting to do this, some creating online facilities to ensure that their content IP is only edited or manipulated in a safe environment on their privately hosted IT servers.
New forms of data collection are also available now, which can be less distracting to common workflows. Approaches like video capture to text, audio transcription and summarisation.
Learning how to use AI to catalogue, label and archive your own data, including working drawings, final finished documents, along with images and video of the completed projects, and in-camera shots. As well as the costings, plans, schedules and timings of production, will enable you to train AI in the future to automate tasks and projects, or assist in providing useful estimates to other departments.
It will be important to keep abreast of developments utilising new forms of AI computing in real-time, especially those like virtual production, techniques for automated environment creation and volumetric capture. These kinds of solutions can, if embraced wholeheartedly, bring about great efficiency, and if implemented poorly, generate huge costs. This field is changing rapidly, and a great opportunity lies within it.
Care could be considered when working with freelancers, who may hold differing views and thoughts on how they are allowed to use AI in a production. Clear communication of the funders and production policies is valuable, and if they are vague or insufficient, then at least to set basic expectations around data confidentiality and content authenticity.
Engaging in training through ScreenSkills and other online resources will be valuable for producers aiming to stay at the forefront of the industry. Producers who embrace these new technologies will be better equipped to lead projects. Delivering a blend of technical understanding and traditional oversight, achieving harmony and efficiency.
Links to other ScreenSkills resources
Discover the post production supervisor job profile
Read the post production supervisor skills checklist
Explore more AI-related training, events and opportunities with ScreenSkills
Read AI 101, an overview of some aspects of AI in the UK film and TV industry