Video production is on the cusp - but of what, exactly?
I’ve been saying for a while that traditional video production is facing a creative and technological evolution. That’s still true. But I want to be specific about what that actually means in practice, because the conversation around AI in video has become incredibly noisy.
There’s a spectrum. On one end, you’ve got people claiming AI will replace production teams entirely. On the other, you’ve got traditionalists insisting AI has no place in serious production work. Both positions are wrong.
The reality is more nuanced and more useful. AI is excellent at certain parts of the video production pipeline. It’s poor at others. And knowing the difference is the key to integrating it without losing the quality that makes your work valuable.
Where AI genuinely helps
Pre-production planning
This is one of the most underappreciated applications. AI tools can now:
- Generate shot lists and storyboards from scripts or briefs. They won’t match what a dedicated storyboard artist produces, but for internal planning and client approval, they’re fast and effective.
- Location scouting assistance. AI-driven image search and analysis can surface reference locations and visual treatments that match your brief, compressing hours of manual research.
- Budget estimation. AI tools that analyse scripts and generate preliminary production budgets based on complexity, locations, talent requirements, and equipment needs. Not perfect, but a solid starting point.
- Scheduling optimisation. For multi-day shoots, AI can help optimise call sheets and shooting schedules based on location dependencies, talent availability, and weather data.
The value here isn’t replacing the producer or director. It’s compressing the administrative and research work that eats into pre-production time.
Transcription and captioning
This is probably the most mature AI application in video. AI-powered transcription is now fast, accurate in multiple languages, and cheap. If your team is still manually transcribing footage or creating caption files by hand, you’re wasting money. Full stop.
Tools like Whisper and its commercial derivatives handle multi-speaker transcription with speaker identification, timestamp accuracy, and format export that integrates directly into editing workflows. This used to be a full day’s work for a long-form piece. Now it takes minutes.
Asset management and logging
For any production team working with significant footage volumes, AI-powered media asset management is transformative. Automated tagging of footage by content, faces, objects, locations, and even emotional tone means your rushes are searchable from the moment they’re ingested.
I’ve seen production teams cut their post-production time by 20-30% just by implementing AI-driven asset management. When your editor can search for “close-up, male, smiling, outdoors” instead of scrubbing through hours of footage, the efficiency gain is enormous.
Rough cuts and assembly
AI can now generate rough assembly cuts from footage based on scripts or briefs. Tools analyse audio, identify key moments, and assemble a basic timeline that an editor can work from.
This isn’t replacing the edit. It’s giving the editor a starting point instead of a blank timeline. The creative decisions still happen in the edit suite. But the mechanical work of initial assembly? AI handles that well.
Colour grading assistance
AI colour tools have become genuinely impressive. Scene matching, automatic colour correction, and style transfer can handle the technical side of grading - consistency, exposure correction, white balance - leaving the colourist to focus on the creative grade.
For projects where budget doesn’t stretch to a dedicated colourist, AI grading produces results that are significantly better than what most editors would achieve manually.
Audio cleanup and mixing
AI-powered audio tools for noise reduction, dialogue isolation, and basic mixing have reached a level where they’re production-ready. Background noise removal that used to require expensive plugins and manual processing is now handled automatically and convincingly.
Where AI falls short
Here’s the honest part. There are areas where AI integration doesn’t improve your production - and can actively damage it if you’re not careful.
Creative direction
AI can generate options. It can suggest visual treatments, reference materials, and stylistic approaches. What it can’t do is make creative decisions that serve a specific narrative or brand purpose. Creative direction requires understanding context, audience, emotion, and intent in ways that AI simply doesn’t.
Use AI for inspiration and reference. Keep creative direction human.
Narrative structure and storytelling
This is fundamental. AI can identify technically competent edit points. It can’t construct a narrative arc. It doesn’t understand dramatic tension, emotional pacing, or the way a well-placed pause changes everything.
The best editors I work with make hundreds of micro-decisions based on feeling and experience. That’s not something you can automate.
Emotional resonance
AI doesn’t feel anything. It can identify smiles in footage. It can’t tell you which take of an interview answer will make an audience cry. It can analyse music tempo and mood. It can’t tell you why a particular track transforms a sequence from good to extraordinary.
This is where the human craft of video production remains essential and irreplaceable.
Client relationship and interpretation
Translating a client brief into a production reality requires reading between the lines, understanding organisational politics, and interpreting what a client means rather than just what they say. AI can’t do this. People can.
A practical integration roadmap
If you’re looking to integrate AI into your video production workflow, here’s the order I’d suggest:
Phase 1 - Immediate wins (week 1-2):
- Implement AI transcription for all projects
- Set up automated captioning workflows
- Add AI-powered noise reduction to your audio pipeline
Phase 2 - Workflow enhancement (month 1-2):
- Deploy AI asset management and footage tagging
- Integrate AI colour correction for initial grades
- Build AI-assisted rough cut workflows
Phase 3 - Strategic integration (month 3-6):
- Implement AI pre-production planning tools
- Develop AI-enhanced briefing and research workflows
- Create templated AI workflows for recurring content types
Phase 4 - Advanced applications (ongoing):
- Explore AI-generated visual effects and compositing
- Test AI-driven content personalisation at scale
- Evaluate emerging tools as they mature
The integration principle
Here’s the principle I apply to every AI integration decision: automate the mechanical, protect the creative.
Every video production workflow has tasks that are fundamentally mechanical - transcription, logging, basic colour correction, file management, format conversion. Automate all of them. They don’t benefit from human judgement.
Then there are tasks that are fundamentally creative - direction, narrative, emotion, performance, timing. Protect all of them. They don’t benefit from AI automation.
The art is in knowing which is which. And in my experience, teams that get that distinction right produce better work, faster, at lower cost. The ones that try to automate everything produce technically competent content that nobody feels anything about.
Don’t be the second team.
