The AI b-roll tools that fill the dead space in your edit

For a long time my b-roll process was: finish the edit, realise it’s eleven minutes of my own face, open a stock site, and lose ninety minutes scrolling through clips of smiling people pointing at laptops. The footage I ended up with was rarely relevant. It was just something to cut to.

AI has genuinely fixed this, though not in the way the marketing suggests. The tools that changed my workflow aren’t the ones generating footage from thin air, they’re the boring ones that read my script and tell me what to cut to and when.

What counts as an AI b-roll tool?

There are three distinct categories here, and mixing them up is why people get disappointed. Each solves a different half of the problem.

• Script-aware suggestion tools. These read your transcript and propose cutaways at specific timestamps. This is the category that actually saves time, because the hard part was never finding footage, it was deciding where footage should go.
• Semantic stock search. Normal stock libraries, but you describe a feeling or a scene in plain language instead of guessing keywords. “Someone realising they’ve made a mistake” gets you further than “person sad office”.
• Generative b-roll. Text to video, made to order. Impressive, improving fast, and still the one I use least.

Most creators need the first two and reach for the third occasionally.

Script-aware suggestions: the actual time-saver

The biggest unlock is a tool that looks at your transcript and says “cut to something here”. Editors built around a text-first workflow do this well, because they already have your words lined up against the timeline.

Descript is where I do most of this. Because the edit is the transcript, dropping a cutaway on a specific sentence is a two-second job rather than a hunt through a timeline. I wrote up the wider workflow in my Descript review, but for b-roll specifically the win is that you’re placing clips against meaning instead of against waveform.

The habit that made the difference for me: read the transcript and mark every sentence where I’m explaining something abstract. Those are the moments a viewer’s attention drifts, and those are exactly where a cutaway earns its place. Anywhere I’m being concrete and specific, my face is fine.

Semantic stock search: describe the feeling, not the object

The second category is stock libraries that finally understand plain language. The old way was keyword roulette. The new way is describing the shot you’re imagining and letting the model find near-matches.

• Describe the emotion, not the props. “Quiet relief after finishing something hard” returns better results than “man computer happy”.
• Say the shot type. Adding “slow push in”, “overhead”, “handheld” narrows things enormously.
• Batch your searching. Do all your b-roll hunting in one sitting with the transcript open, rather than stopping the edit each time.

I still pull a lot from my own phone footage, which costs nothing and always looks more authentic than stock. A folder of random clips you shot around your desk, your walk, your workspace will outperform a library subscription for personal-brand content.

Generative b-roll: useful in a narrow lane

Text-to-video has improved to the point where it’s genuinely usable, with a caveat worth taking seriously: it still looks generated to anyone paying attention. Faces are uncanny, hands are a lottery, and any text in frame comes out as alien gibberish.

Where it works well:

• Abstract and atmospheric shots. Light through a window, particles, textures, weather. Nobody scrutinises those.
• Impossible shots. Concepts you couldn’t film, like a visual metaphor for an idea.
• Sub-two-second cutaways where the clip is a beat, not a subject.

Where it fails: anything with people doing recognisable tasks, anything with product detail, and anything a viewer will pause on. If your video is about a real tool or a real product, film the real thing.

Cutting it in without it feeling random

Having clips is half the job. The other half is placement, and this is where most creators go wrong by treating b-roll as decoration rather than punctuation.

The rules I follow, learned mostly by getting them wrong:

• Cut on the sentence, not the beat. The cutaway should illustrate what’s being said right then, and leave when the point does.
• Keep them short. Two to four seconds. If you need longer, the b-roll has become the content and should be shot properly.
• Don’t cut away from your best line. When you say the thing that matters most, be on camera. Eye contact carries it.
• Vary the type. Three consecutive stock clips of the same visual register reads as filler immediately.

For fast social edits I do this in CapCut, which handles quick cutaways and auto-captions in the same pass. My CapCut review goes deeper on where it fits, but for short-form the speed matters more than the finesse. If you’re weighing the two editors against each other, CapCut vs Descript is the head-to-head.

The stack I’d actually recommend

If you’re starting from nothing, don’t buy four tools. Start with one editor that handles transcript-driven cutaways, one source of footage, and a phone.

For most solo creators that means an editor like CapCut or Descript, a stock library with decent semantic search, and a growing folder of your own clips. Add a generative tool only when you keep hitting a specific shot you can’t find or film, which happens less often than you’d expect.

If your bottleneck is captions rather than visuals, the best AI caption and subtitle tools for short-form video covers that side, and it’s honestly a bigger retention lever than b-roll for vertical video.

My take

B-roll is one of the few production upgrades where the cheap version gets you 90% of the result. A relevant two-second cutaway from your own phone beats a gorgeous stock clip that has nothing to do with the sentence it lands on.

So my honest recommendation is to spend your effort on placement, not sourcing. Use AI for the boring half, finding and suggesting, and keep the judgement about where a cut belongs firmly in your own hands. That judgement is the thing that makes an edit feel intentional, and it’s the one part none of these tools do well yet. Generated footage is a fun toy that occasionally solves a real problem. The transcript sitting in front of you, marked up with where attention drifts, is what actually improves the video.