.png)
5 hours ago
1
This article is part of my ‘Big Ideas’ series, in which I explore the evolving landscape of the film industry. Each instalment combines data, research, and analysis to go deep on a trend, idea or case study to reshape the film business over the next decade.
Everyone seems to have a hot take on whether AI is going to radically transform the film industry, or is just flashy vapourware.
What is certainly true is that there are already a bunch of tools on offer to film professionals which change many of the tasks they have to conduct on a daily basis.
So I thought it would be useful to go through them and share with you the breadth and depth of what’s out there.
Note: Nothing in this article is an endorsement or any production, service or company. I have not been paid by any, and I am not recommending any. Examples cited are just illustrative of being offered, not what works, what’s good value, or what’s best in class. Just because someone is willing to sell you something, doesn’t mean it works!
We’ll go through seven different areas, including:
Screenwriting. AI can generate story ideas, produce outlines, draft dialogue, suggest rewrites, provide feedback on structure and market fit, and automatically tag scripts for production breakdowns. This means screenwriting may becomes more about curating and editing machine output, as well as managing floods of new iterations.
Packaging. AI can help turn a half-baked concept into a fully fleshed-out proposition overnight, identifying your audience and generating visuals, decks, and materials to make a project feel inevitable.
Financing. AI-driven platforms claim to model budgets, simulate box office, and rank casting choices by market value. Whether it’s real insight or risk cosplay, producers are being offered more data than ever to back up the numbers.
Legal. Producers with no legal background are leaning on AI to summarise contracts, generate agreements, and flag missing clauses. While risky, it’s easy to see the appeal when legal professionals aren’t exactly queuing up to work for back-end points.
Pre-production. Automated tools extract script elements, generate shooting schedules, match crew and cast to projects, scout locations using databases, and help directors visualise shot lists. Planning becomes faster and more integrated, but demands greater clarity and earlier creative decisions.
Production. Ironically, the bit that still needs people in a room with a camera may be the least affected in the short term. But streamers are already using patented tech to tag, track, and pre-process footage in real time.
Post-production. From editing and VFX to dialogue clean-up and synthetic voice work, post is where AI is having the fastest impact. Some of the biggest shakeups may come in dubbing and localisation, where traditional workflows are looking increasingly outdated.
Let’s start with where every good ideas does - in the script…
The first category we’ll look at is perhaps the furthest along, since AI-generated text is currently more developed than its image, video, or audio counterparts.
It’s also the area likely to impact the greatest number of people. Screenwriting has the lowest barrier to entry of any filmmaking role as all it takes is an idea and somewhere to type it.
A number of platforms now offer AI-powered tools that generate basic concepts or loglines based on a few words, themes, or genre inputs. These can be used to explore different angles, test combinations of setting and character, or tailor stories to specific production resources such as locations or cast.
Writers can use custom-trained models in ChatGPT or Claude, loading their project bibles, beat sheets, or tone guidelines and then asking the AI to suggest new loglines, alternative scenarios, or setting variations that stay true to their worldbuilding.
It’s also possible to bring your characters to life, conversing with them or asking them to respond to prompts or scenarios. Character.AI lets users create and converse with AI-powered characters who respond in real time and in personality. Writers use this to roleplay scenes, stress-test character voices, or explore how two personalities might interact.
Tools not designed specifically for film can still be of use to screenwriters. On the surface, NovelAI is all about “anime-inspired” stories but it’s writing platform generates story ideas, dialogue, and even full scene descriptions that could apply in any genre. Likewise, while Jasper is a general-purpose writing assistant that can output story summaries or brainstorm premises tailored to custom instructions.
Several tools move beyond loglines into full structural outlines, using three-act or genre-specific templates. These systems can suggest a series of events or turning points from beginning to end, often editable or regeneratable with a single click.
StoryLab.ai generates outlines and hooks tailored to genre or audience type, while Plottr AI offers visual timeline tools with AI-assisted suggestions for beats and character arcs. Saga uses generative AI to build full plot structures that can be exported into screenplay format.
Some platforms will draft scenes directly, generating formatted screenplay text from a brief prompt or continuing from user input. These are typically aimed at breaking writer’s block or creating quick drafts for revision.
DeepStory can autofill scenes using its story model, aiming for logical consistency, and Sudowrite includes tools like “Continue Scene” or “Rewrite” to expand or rework text in context.
Other tools are designed to help polish or revise existing writing. This includes rewriting for tone or clarity, adjusting pacing, or aligning with specific genre expectations.
WriterDuet’s ScreenplayProof is a screenplay-specific proofreading assistant that says it “helps you refine your script without compromising your unique voice”, focusing on spelling, grammar, and formatting mistakes while preserving character dialogue and style. LanguageTool AI Rewrite rephrases sentences while offering multiple options per paragraph.
GrammarlyGO provides sentence-level rewrites for tone, simplicity, or length and I’m not sure I could personally cope if Grammarly was taken away from me!
AI is also being used to evaluate completed screenplays, providing feedback on story structure, emotional tone, character journeys, and even commercial potential. This is pitched as a tool for both development and investor decision-making.
ScriptBook uses machine learning trained on thousands of scripts to predict audience appeal, genre fit, and potential box office. While both Largo.ai and Cinelytic offer analysis and market insights based on script features.
These types of tools could conceivably shift the role of the screenwriter from a purely generative one to something more akin to an editor, curator, or systems designer. The “writer” might be shaping and refining content from machine outputs rather than always starting with a blank page.
And, rather terrifyingly, these tools may just be a brief intermediary steps to the point when AI models can “write” entire screenplays tuned to tone, structure, and cast without a human insight. Whether we want that or not is different topic entirely.
Packaging is part business, part sales.
Producers need to make a series of smart decisions and compromises to turn an idea or script into an investable business proposition. AI tools can help by identifying a potential film’s audience, both in terms of overall scale and precisely who to target. Claritas and Parrot Analytics provide insight into audience behaviour and demand signals across regions.
They also need to create a sales pitch so compelling as to make the project feel inevitable to gatekeepers, investors, or collaborators. This often involves creating posters, key artwork and sales decks.
In my recent round up of the Cannes 2025 Marche du Film I noted how the early-stage sales posters have seen a dramatic rise in quality over recent years. A large part of that has been the proliferation of powerful image generation models.
While this may be acceptable at a private trade fair such as Cannes, the wider public are less forgiving. Disney got into a bit of hot water recently with a Fantastic Four poster. My favourite headline on the scandal comes from Futurism.com who said “Disney Says Its "Fantastic Four" Posters Aren't AI, They Actually Just Look Like Absolute Garbage“
Money makes the world go round, so it’s not surprisingly that AI-powered tools are being pitched to film professionals.
Although this area is still developing, a number of tools now offer data-led support. These systems claim to provide producers, financiers, and sales agents faster access to risk assessments, market intelligence and help with financial forecasting.
Budget prediction and modelling. AI platforms can now scan a script or project brief and generate budget ranges. They do this by tagging elements such as cast size, locations, effects, or specific production requirements and mapping them against data from previous projects. This support can speed up early-stage planning and give a stronger foundation for conversations with investors or co-producers.
Forecasting revenue and audience reach. Increasingly, AI models are being brought in to estimate how much income a project could generate. They use a combination of script analysis, cast information, genre, market trends, and data from similar titles. My personal opinion is that this area has a lot more hype than heft, as many services seem to address the perception of risk rather than actually minimising the actual risk. That said, maybe that’s what all film producers have done time immemorial.
Assessing casting impact on financing. Casting is often the single biggest driver of project value in a financing context. Some tools now quantify the impact of attaching certain actors to a project by simulating how cast changes affect distributor and investor interest. This can influence early casting decisions, sales planning, and even negotiations.
Creating pitch decks and finance packs. Building pitch materials and business cases is a notorious time sink. AI-driven tools now automate much of this by pulling market comparables, audience insights, and project-specific data into professionally formatted decks. These can be customised for different markets, investor types, or festival submissions.
Some of the products and services being offered in this area include:
Largo.ai analyses screenplays and production data to generate budget ranges, forecast box office and VOD revenue, and simulate how different cast choices affect a project’s financial outlook. Also provides exportable reports for investors.
Cinelytic ingests scripts or pitch decks to estimate production budgets, models potential revenue based on cast, genre, and release strategy, and offers star ranking models to show how casting impacts market value.
Filmustage breaks down scripts to highlight elements that drive costs, such as cast days, location changes, or special requirements, and presents scene-by-scene financial implications.
Vault AI uses market data and behavioural analysis to project audience demand, global reach, and income potential based on a project's creative details.
Slated generates a “project score” by combining actor value, script quality, and package strength to support investment and distribution decisions.
Despite the obvious reasons not to, huge number of producers are turning to off-the-shelf generative text models like ChatGPT to produce or adapt their contracts.
On the surface this makes sense. AI can summarise long contracts, extract key clauses, and flag missing elements, in a heartbeat, all while telling you that you’re doing a great job.
