Storyboards generated in just a few minutes, video previews based on a script, creation or modification of shots, synthetic voice-over: generative artificial intelligence is beginning to play a role at various stages of audiovisual production. For the director, however, the challenge goes beyond mastering new tools. It is a matter of determining what can be delegated to a model without losing artistic coherence, direction of the performers and responsibility for the production of a film.
From the Traditional Film Set to Generative Cinema
The role of a director involves a level of responsibility that begins long before the camera starts rolling. Starting with a screenplay, the director develops a vision for the film, creates a storyboard, prepares the scenes, directs the actors, and coordinates a team that includes, among others, cinematography, sound, set design, costumes, editing, and visual effects. Their role extends from pre-production to post-production. Onisep describes the director as the person responsible for the film’s creation, tasked with overseeing its various stages and communicating their decisions to both the technical crew and the actors [1].
Digital technologies have long since transformed this field. Visual effects, digital color grading, nonlinear editing, virtual production, and motion capture already made it possible to create images that had not been recorded as-is in front of a camera. The advent of generative video AI, however, introduces a different possibility: producing or transforming a sequence based on a textual description, a reference image, or an existing video.
In this article, the term “generative cinema” refers to the use of generative models within the audiovisual production process. It does not mean that a system produces an entire film on its own. Rather, it describes a set of practices in which certain images, sounds, or preparatory steps can be generated, transformed, or assisted by artificial intelligence models.
By 2026, several tools will make these applications directly accessible. Adobe, for example, describes a feature that generates videos based on text prompts, allowing users to specify the content, mood, and certain camera angles and movements. [2]. Google, for its part, notes that Veo 3.1 can use reference images to improve the consistency of characters, objects, and backgrounds, with upscaling options up to 4K depending on the available configurations [3]. This information describes capabilities announced or documented by the publishers. It does not, on its own, demonstrate that a model can maintain the aesthetic, narrative, and dramatic continuity required for a feature-length film.
The most significant change, therefore, lies less in the idea of a “film directed by AI” than in the increasing number of instances in which the director can work with images that have not yet been shot—or that may never be filmed in the traditional way.
Generating a shot and directing a film are not the same activity. A system can generate an image. The director still needs to determine why it exists, what it tells us, what precedes it, what follows it, and what it should make the viewer feel.
How AI Is Transforming Filmmaking Practices
Artificial intelligence does not replace a single action performed by the director. Instead, it is gradually being integrated into various stages of the production process, from visual research through post-production. However, it is important to distinguish between what constitutes traditional automation, what is truly based on generative models, and what remains experimental.
Storyboarding and visual research
Working from a script or scene description, generative models can quickly produce several proposals for framing, atmosphere, sets or lighting. Directors can use them to explore different directions before bringing a team together. These images remain research materials, however. They do not guarantee what can be reproduced precisely during filming.
Previsualizing sequences
Previsualization, or previs, involves representing a scene before filming to prepare elements such as the shot breakdown, camera movements and effects. Runway describes workflows that use AI to produce storyboards and animatics from a script. The company also notes that these outputs do not necessarily provide the measurable space needed to plan a physical setup, stunt, actor movement or camera equipment precisely[4]. Generative previsualization therefore does not automatically replace traditional techvis.
Generating and transforming shots
Some models can create a sequence from text or reference images, extend an image or transform the style of an existing video. This can help create inserts, concept shots or variations. However, the availability of a feature guarantees neither a character’s consistency across several scenes nor exact control over every element of the composition.
Exploring camera movements
Video tools increasingly incorporate parameters borrowed from cinematic language. They allow users to request a type of framing or movement. But asking a model for a tracking shot is not equivalent to physically organizing the relationship between camera, actor, set, focal length, movement and lighting on a film set.
Post-production and visual transformations
AI can be used to modify elements within an image, produce variations, create set extensions or prepare visual assets. The boundary between filming, visual effects and generation is becoming less distinct, making it necessary to document the origin of the elements used more carefully.
Dubbing, voices and digital performances
Systems capable of reproducing a voice, a face or certain aspects of a performance are also used in localization and character transformations. This capability is technically distinct from ordinary editing and raises specific questions about consent, contracts and the use of a performer’s identity.
These practices also change the timeline of the creative process. A visual idea that was difficult to represent used to require a sketch, a mockup, a storyboard, or a 3D preview. Today, a generative model can quickly produce multiple variations. But an increase in the number of proposals does not guarantee a better final result. It also increases the workload involved in selection, verification, and ensuring consistency.
The most tangible change, then, might be less dramatic than it seems: the director has more options to choose from, but must use his or her judgment more often to decide which ones truly serve the story.
A New Role for the Director
The integration of generative AI does not turn the filmmaker into a mere operator of models. Rather, it shifts some of the decision-making to an earlier stage and increases the number of decisions regarding the origin, coherence, and use of images. The director’s value, then, lies less in their ability to produce a large quantity of images than in their ability to articulate a vision through them.
Defining visual intent more precisely
The more quickly a system can produce variations, the more clearly directors must know what they are looking for. A general instruction such as “an unsettling scene in a street at night” leaves considerable room for interpretation. Directing consists precisely in deciding where the camera is, what the viewer knows, what the character does not know, how light structures the space and when the shot should end.
Maintaining continuity between heterogeneous images
A film is not a collection of independent shots. Costumes, eyelines, props, the geography of the set, lighting, movements and performances must remain consistent. Models are improving in visual consistency, but continuity across a long work remains a different problem from the quality of an isolated clip[3].
Choosing between live-action filming, digital effects and generation
Directors and production teams may need to decide what should be filmed, built, simulated or generated. This decision is not solely financial. It can affect actors’ performances, technical professions, aesthetics and the ability to modify a performance later.
Preserving the direction of actors
A performance emerges from direction, experimentation, movement and interactions between performers. A synthetic sequence may reproduce some visual signs of a performance, but this does not necessarily correspond to the creative process through which an actor and a director build a scene together.
Making technological choices understandable to the team
When AI is used to prepare or create a sequence, the professionals involved must be able to understand which images serve as references, which performances may be transformed and which data are being used.
The director’s role may thus evolve toward a broader creative scope, but it would be an overstatement to conclude that the director will necessarily be less present on set. The impact will depend heavily on the format. An animated film, a commercial, a TV series, a documentary, or a feature film relying on live-action performances do not share the same constraints or the same reasons for using generative AI.
What Skills Does a Director Need in the Age of Generative AI?
The core skills of the profession remain central: understanding dramatic structure, creating a shot list, directing performers, choosing a setting, collaborating with a cinematographer, coordinating a team, and establishing a rhythm. AI, however, adds new skills related to interpreting, evaluating, and managing images.
Technical and visual skills
- Understanding the actual capabilities of video models: directors must distinguish an impressive demonstration from a capability that can be used repeatedly in production. Clip length, degree of control, consistency between shots, movement rendering, resolution and the reproducibility of a character are concrete constraints.
- Building precise visual references: mastering AI is not simply a matter of prompt engineering. Knowing how to compose a mood board, choose a photographic reference, explain lighting or describe a camera movement can provide more control than piling adjectives into an instruction.
- Evaluating generated outputs: directors must be able to spot anatomical inconsistencies, implausible movements, continuity errors, failures of consistency or an aesthetic that contradicts the film’s intent.
Legal and responsible practice skills
- Understanding rights relating to voice, likeness and performance: professional agreements show that these issues are no longer marginal. In the United States, SAG-AFTRA rules cover digital replicas, consent and synthetic performers in productions subject to the relevant contracts[5]. This American collective bargaining framework illustrates the importance of negotiation, but it is not a universal rule applicable to all productions.
- Identifying the provenance of content: a tool can be technically effective without being suitable for every production. Licenses, rights associated with reference images, commercial terms and the origin of content must be examined before integration into a professional workflow.
- Understanding transparency obligations: since August 2, 2026, Article 50 of the AI Act has applied certain transparency obligations in the European Union to AI-generated or manipulated content. For deepfakes incorporated into clearly artistic, creative, satirical or fictional works, the text provides for appropriate disclosure that must not hamper the presentation or enjoyment of the work[8].
Collaborative and strategic skills
- Working with new professional profiles: directing may require closer collaboration with VFX supervisors, generative artists, post-production managers, legal teams, data specialists or production managers.
- Maintaining a critical view of automation: fast production is not necessarily well-controlled production. Directors must account for the time required to select, correct, validate and document generated content.
- Preserving the study of cinematic language: the ease of obtaining an image must not obscure the need to understand framing, editing, space, sound, performance, rhythm and storytelling. Without this background, users may produce attractive images without being able to construct a coherent scene.
The goal of training, therefore, is not to learn a list of tools that are likely to change rapidly. Rather, it is to develop a more lasting skill: understanding what a system enables, what it does not guarantee, and how to integrate it into a well-executed cinematic vision.
Can artificial intelligence make filmmaking more creative and more accessible?
The argument about democratization often comes up in discussions about the “video generation.” A director can indeed visualize a scene without immediately building a set, experiment with different camera angles, or produce an animatic with fewer material resources. This can lower certain barriers to experimentation, particularly during the development phase.
Runway, for example, presents preview AI as a way to quickly transform a scenario or reference materials into storyboards and animatics [4]. However, this documentation comes from the company that markets the tools. It demonstrates a feature and a possible use case, but does not constitute an independent study showing a generalizable reduction in production costs or an improvement in film quality.
Adobe, for its part, presents Firefly Video as suitable for commercial use and highlights a training approach based in particular on licensed and public-domain content. This feature may be important to professionals who are mindful of the origin of data. However, it remains a statement by the publisher regarding its own service and does not mean that a production using the tool is automatically exempt from all rights issues related to the final content [9].
Democratization must therefore be viewed in a nuanced way on several levels.
- A less expensive tool does not eliminate all production costs: you still have to select the footage, correct errors, ensure continuity, edit the shots, process the sound, manage licenses, and sometimes go through numerous passes before obtaining a usable result.
- Fast rendering does not equate to precise control: filmmaking often requires that a gesture, a glance, a prop, or a movement appear exactly in the right place at the right time. The overall visual quality of a model does not imply this level of precision.
- Apparent variety does not guarantee uniqueness: if many designers use the same models, references, and formulations, certain visual codes may converge. Creativity can then shift toward the ability to select, repurpose, combine, or reject the most obvious solutions.
- Legal protection for outputs is not uniform: in the United States, the U.S. Copyright Office considers that works produced using AI may be protected when human input sufficiently determines the expression of the work. It specifies, however, that the mere provision of prompts is not, in and of itself, sufficient to establish authorship of the generated works7. This analysis pertains to U.S. law and should not be automatically applied to French or European law.
AI can therefore reduce certain technical barriers and accelerate exploration. However, there is no evidence to suggest that it automatically makes a film better, more original, or less expensive. These effects must be evaluated on a case-by-case basis, taking into account generation time, selection, correction, rights, and post-production work.
What Will the Job of a Director Look Like Tomorrow with AI?
Several developments are plausible, but they must be distinguished from certainties. Rapid advances in video modeling make certain changes likely, though they do not allow us to predict widespread automation of film production.
- An even greater emphasis on pre-production: generative storyboards, concept art, and animatics may become more common tools for exploring creative intent. They are particularly valuable when used to communicate an artistic direction rather than to precisely calculate a physical setup.
- Increasingly hybrid productions: a single film may combine live-action footage, virtual production, digital effects, computer-generated imagery, and generative transformations. For the viewer, the line between filmed footage and computer-generated imagery may become less distinct.
- Enhanced traceability of elements: when a plan combines multiple sources, producers will be well advised to provide more detailed documentation regarding the origin of materials, the authorizations obtained, and the transformations carried out. This requirement is legal, organizational, and editorial in nature.
- Labor agreements up for renegotiation: the debate involves directors, screenwriters, actors, storyboard artists, VFX teams, editors, and technicians. In its 2026 agreement, the WGA retained the AI protections secured in 2023 and added a requirement for companies to notify the union when they license screenwriters’ works to train certain commercial generative AI systems6. Again, this is a U.S. collective bargaining framework.
- More structured transparency regarding synthetic content: In the European Union, Article 50 of the AI Act has been in effect since August 2, 2026. Affected providers and distributors must comply with transparency rules regarding certain generated or manipulated content. For works that are clearly artistic or fictional, disclosure requirements may be adapted so as not to detract from the experience of the work8.
- A profession increasingly focused on consistency and decision-making: the easier it becomes to produce footage, the more the question arises: which shots truly deserve to make it into the film? The director’s role can thus be strengthened in terms of selection, continuity, team leadership, and editorial responsibility.
However, it is still too early to say whether filmmakers will work with autonomous virtual teams capable of producing a coherent feature film on their own. Current demonstrations show rapid progress in video generation, but they do not constitute proof of comprehensive autonomy across all artistic, technical, human, and legal aspects of a film production.
Cinema Enhanced by AI, but Still Guided by Human Intent
Generative artificial intelligence is already changing certain aspects of film production. It makes it possible to visualize an idea before shooting, quickly explore visual directions, produce or modify certain sequences, and experiment with forms that would previously have required more technical resources.
But above all, the advent of these tools requires us to better define what it means to create. Creating is not just about producing images. It is about deciding where to look and for how long. It is about organizing the relationship between a performer and a space. It is about determining what the viewer should understand, guess, or feel. It also means working with people whose performances, skills, and rights cannot be reduced to resources available to a generative system.
The protections negotiated by SAG-AFTRA regarding digital replicas and synthetic performers show that the issue of authorization and control over performances has become central to productions covered by these agreements [5]. The scope of responsibility for directing and producing is therefore expanding: it is necessary to know what has been generated, based on what sources, under what authorizations, and to what extent a human performance can be transformed.
Technology can make certain images accessible more quickly. It can expand the possibilities for previewing footage and blur the lines between filming and post-production. But the sheer volume of available images does not guarantee either the coherence of a work or the uniqueness of a vision.
To learn more
To continue this discussion on artificial intelligence, imagery, and creative professions, check out four additional analyses from the aivancity blog.
References
[1] Onisep. (n.d.). Film Director. View source
[2] Adobe. (2026). Generating videos using text prompts. Firefly documentation, updated June 23, 2026. View the documentation
[3] Google. (January 13, 2026). Veo 3.1 Ingredients to Video: More consistency, creativity, and control. View source
[4] Runway. (2026). What is AI previs? A guide to AI previsualization for modern production. View source
[5] SAG-AFTRA. (2026). Artificial Intelligence: TV/Theatrical Resources and Protections. View source