GFS Insights - August 2026 Edition
What we're seeing
AI adoption across the screen industry is growing quickly.
A British Film Institute (BFI) and CoSTAR report highlighted several indicators of this shift:
- A survey of UK producers found that 17% had already used generative AI in production, while a further 40% planned to use it
- A 2024 survey of US media and entertainment decision-makers found that 49% were already using generative AI within their organisations
- In France, a survey of 794 screenwriters, producers, directors and cinematographers found that 40% had used generative AI. Of those users, 77% continued to use it at least occasionally, including 35% who used it regularly or daily
However, adoption is moving faster than formal skills development.
The BFI found that AI education across the UK screen sector remains more informal than formal, with freelancers in particular often lacking access to structured training and development.
The industry is beginning to respond. In January 2026, ScreenSkills launched a new AI skills programme, supported by its Film Skills Fund, alongside role-specific AI guidance for positions including producers, directors, cinematographers, designers and post-production teams.
Access to AI tools is becoming easy. Knowing how to use them effectively within a professional production environment is becoming the more important capability.
Why this matters on set
Different levels of AI knowledge across a production can create very different outcomes.
Some crew may already be using AI extensively, while others may have little or no practical experience.
This can affect:
- How effectively AI tools are used
- The quality and consistency of AI-assisted work
- Whether genuine efficiencies are achieved
- How confidently teams can assess AI-generated outputs
- How knowledge is shared between departments
- Whether productions become reliant on a small number of AI-capable individuals
AI does not automatically create efficiency. The benefit depends on whether the people using it understand both the technology and the production context in which it is being applied.
Practical controls
- Identify where AI skills are becoming relevant across production roles
- Provide role-specific training rather than relying on informal or self-directed learning
- Test AI workflows on lower-risk tasks before introducing them more widely
- Share effective workflows and lessons between departments
- Cross-train personnel where important workflows depend on a small number of AI-skilled individuals
- Compare actual time, cost and quality improvements before embedding AI into standard workflows
It’s clear that the opportunity is not simply having access to the technology, but having people who know how and when to use it.
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