Script-to-Video Production Flow
Build AI video through a staged script, image, storyboard, and generation pipeline.
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 7
- Confidence
- 92%
This production flow breaks AI video creation into controllable stages rather than asking a model for a finished film immediately. The marketer begins with a concise script, decomposes it into shots, creates reference images, and arranges those images into a flowboard that defines sequence and composition. The video model then generates clips from the planned scenes, which are reviewed and assembled through editing. When the model confuses dialogue attribution—especially when a person and an unusual speaking object share a shot—the composition is redesigned so the speakers appear separately. The method accepts that production-quality AI video still requires iteration, but concentrates that effort in a repeatable pipeline where narrative, visual continuity, and model limitations can be addressed systematically.
Origin
Extracted from Marketing Against The Grain, where Kieran Flanagan described the workflow developed during roughly 20 hours of experimenting with Veo 3.1.
Core principles
- 01Plan the narrative before generating footage.
- 02Use intermediate visuals to control scene composition.
- 03Treat generation as production, not a one-shot prompt.
- 04Reduce ambiguity by separating conflicting speakers or actions.
How to run it
- 1
Write the script
Define the message, dialogue, timing, and desired action before generating any visual material.
Pro tip Keep each scene focused on one communicative purpose.
Watch out An overloaded script creates too many simultaneous generation constraints.
- 2
Break it into shots
Translate the script into a shot list that identifies speakers, locations, actions, framing, and transitions.
Pro tip Keep generated shots short enough to regenerate independently.
Watch out Long scenes increase continuity and dialogue-attribution failures.
- 3
Create reference images
Generate or select still images that establish characters, objects, composition, and visual style for each shot.
Pro tip Reuse stable references to maintain visual identity.
Watch out Inconsistent reference images lead to inconsistent footage.
- 4
Build the flowboard
Arrange the reference images in sequence to test whether the visual story works before spending time on video generation.
Pro tip Review the flowboard without dialogue to check visual clarity.
Watch out Skipping this stage hides sequencing problems until expensive iterations.
- 5
Generate scene clips
Generate video for each planned shot, including audio only where the model can reliably attribute it.
Pro tip Regenerate individual scenes rather than the entire video.
Watch out A model may assign dialogue to the most visually obvious human speaker.
- 6
Redesign ambiguous scenes
If characters, objects, or voices are confused, simplify the composition or place speakers in separate shots.
Pro tip Use editing to imply a conversation across alternating shots.
Watch out Repeatedly rephrasing the same impossible prompt may waste time without changing the visual ambiguity.
- 7
Edit and validate
Assemble the clips, refine timing and audio, and review the complete video for continuity, accuracy, and brand fit.
Pro tip Watch once with sound off and once without looking closely at the visuals.
Watch out A technically impressive clip can still fail to communicate the marketing message.
In the wild
A marketer tries to generate a scene containing a human speaking with an object. The model repeatedly gives the object's dialogue to the human, so the marketer restructures the sequence into separate shots and edits them together as a conversation.
→ Dialogue attribution works once the visually ambiguous speakers are separated.
Common mistakes
Calling iteration a one-shot
Production-worthy AI video may require substantial planning, generation, and editing even when the final result looks effortless.
Generating before storyboarding
Skipping script, shot, and flowboard stages makes it harder to diagnose why the footage fails.
Keeping ambiguous speakers together
When a model consistently assigns speech incorrectly, changing shot composition is often more effective than repeating the prompt.
Is it for you?
Best for
Marketers producing short AI-assisted videos who need more control over story, shots, and dialogue.
Not ideal for
Projects requiring long continuous performances, exact physical continuity, or fully deterministic output.
From the transcript
“Where I have a specific flow that goes from how you create a script, images, flowboard, and then the video.”
“And so I've actually edited it in a way where they're usually not in the same shot and it worked perfectly fine.”
“I would say I'm up to like 10 hours.”
From the episode
5 AI Tools The Smartest Marketers Are Using In 2026