Integrating AI into Post-Production: A Practical Approach
Building smarter systems for teams that value clarity, structure, and creative focus.
Post-production has always been a balance between creativity and control. Between what’s captured and what’s crafted.
For documentary and unscripted teams, that balance is more complex than ever — and AI tools are entering the space with promises to simplify, automate, and even “redefine” the process.
But the truth is simpler: AI doesn’t replace post-production. It supports it.
Understanding Where AI Fits
In practice, AI isn’t a single breakthrough. It’s a collection of small, useful improvements — automation in areas that used to consume hours or days of manual effort.
Tools that can instantly generate transcripts, label footage, clean dialogue, or suggest cuts aren’t new in concept; what’s new is their speed, accuracy, and accessibility.
For producers, this means time regained. For editors, it means fewer repetitive tasks. For teams, it means the ability to move faster without breaking the creative flow.
Still, integration matters more than invention. Most AI tools work best when they extend, not replace, traditional systems like Avid or Premiere. You can use Descript for script-based editing, Runway for quick visual cleanups, or Pika for automated shot replacements — but these tools don’t stand alone. They work best when woven into your existing process, not bolted on top of it.
Avoiding the “All-In” Trap
One of the most common missteps we see is the all-or-nothing approach: teams trying to rebuild entire workflows around a single AI platform.
That approach almost always fails.
The best results come from a targeted mindset — identifying pain points, testing tools, and refining their role over time. AI shouldn’t be the centerpiece of your workflow. It should be the connective tissue — quietly bridging the gaps between creative intent and technical execution.
The question isn’t “How do we use AI everywhere?” It’s “Where does AI actually help us work smarter?”
What Works Today
Transcription and Script-Based Editing – Tools like Trint, Descript, and Simon Says are now reliable enough for production. They make logging and rough assemblies faster, though editors still refine pacing and structure manually.
Visual Enhancement and Cleanup – Runway, Topaz, and Pika are helpful for quick upscaling, artifact repair, or subtle compositing — not replacements for VFX, but strong complements.
Asset Organization – AI-assisted tagging in Frame.io and Blackbird can identify shots, people, and even emotional tone, making footage search less chaotic.
Audio and Dialogue Polish – Adobe’s Speech Enhance and similar tools can dramatically improve field audio, though human mixing remains essential for nuance.
Each of these examples improves workflow without interrupting it. That’s the threshold to look for: if it adds complexity, it’s not helping.
Where AI Still Struggles
AI isn’t ready to make narrative decisions. It can’t sense tone shifts or creative intent. It doesn’t understand pacing, structure, or when silence says more than dialogue.
In post-production — especially in documentary work — these subtleties define the difference between an edit that’s efficient and one that’s emotionally resonant.
Until AI can perceive those nuances, it remains a tool for speed and consistency, not storytelling.
The Resonant Works Approach
At Resonant Works, we help production teams integrate practical AI tools into their workflows without losing the human element that makes their stories worth telling.
We research, test, and tailor solutions for real-world production environments — ensuring each tool enhances creative clarity rather than adds friction.
Our goal is simple: to help teams work smarter, focus on what matters, and deliver stories that resonate.
