AI Video Editing Tools: The Complete Guide

If you are anything like me, you absolutely dread the post-production phase of video creation. I used to dread editing on my laptop, sitting in front of my computer with hours of messy, mistake-filled raw footage, knowing I'd have to spend the next several hours manually slicing out silent pauses, cutting out "ums" and "ahs," and trying to patch together a half-decent rough cut. On my YouTube channel, where I've created and edited over 600 videos, my main mission has always been to find hands-on, practical ways to bypass this soul-crushing grunt work. I don't want hype, and I certainly don't want tools that promise to click a button and generate a cinematic masterpiece from a text prompt—because we all know that the resulting "AI slop" is completely unwatchable.

We have transitioned into an era of genuinely mature, agentic systems. We are no longer talking about simple transcription engines or basic automated cuts. Modern AI video editing tools can understand context, parse visual composition, run deep-learning speech enhancement, and even allow you to command your editing timeline through natural language. If used correctly, these tools do not replace you; they act as a tireless, hyper-efficient junior editor, stripping away the repetitive tasks so you can focus entirely on creative direction, pacing, and storytelling.

This guide is my definitive, hands-on masterclass on the AI video editing landscape. I’ve spent hundreds of hours testing every major software, plugin, and agentic workflow on real footage, pushing them to their breaking points. Whether you are a solo YouTuber looking to reclaim your weekends, a freelance editor trying to double your output, or a marketing team looking to scale your content production, this pillar page is designed to cut through the marketing fluff and show you exactly what works, what falls flat, and how to build a highly optimised, modern editing workflow.

What is AI Video Editing (And What It Isn't)

To make the most of this technology, we need to demystify what AI actually does in the editing suite. A lot of developers throw the "AI" label on basic automated scripts, but true AI video editing relies on complex machine learning models—such as Natural Language Processing (NLP) for text-based editing, computer vision for framing and object tracking, and deep acoustic models for audio reconstruction.

Here is a breakdown of the core pillars where AI actually moves the needle in post-production:

• Timeline Cleanup (The Rough Cut): Removing silent gaps, finding filler words (ums, ahs, likes, you knows) and bad takes (repeated sentences where you messed up and started over).
• Text-Based Editing: Turning video editing into document editing. By transcribing your footage, the AI links the text to the timeline. When you delete a word in the text, it is instantly cut from the video track.
• Visual and Acoustic Automation: Auto-reframe (cropping horizontal video to vertical), motion tracking, smart cutout (separating subjects from backgrounds without a green screen), eye-contact correction, and speech enhancement.
• Agentic Orchestration: The newest frontier where you talk directly to Large Language Models (LLMs) connected to your editing engine, allowing the AI to execute complex multi-step editing actions on your behalf.

What AI isn't is a replacement for human taste. AI cannot understand comedic timing. It cannot feel the emotional resonance of a sudden, dramatic pause, nor can it intuitively understand which B-roll shot perfectly matches a subtle metaphor. If you let an AI editor run on 100% autopilot, the result is almost always a breathless, sterile clip that feels deeply unnatural. In my own testing, I've seen how true AI video editing needs a human in the loop. For instance, when I tested eye-contact correction, I deliberately looked off to the side of the camera reading a script the entire time I recorded. With a single click, the AI shifted my gaze so it looked like I was staring directly at the camera throughout the whole recording. I've even put my script off to the side, read it directly, and let the AI fix my eyes seamlessly on camera. It sounds crazy, but it works. The magic happens when you keep yourself in the loop—acting as the creative director whilst the AI handles the click-heavy physical execution.

How to Choose the Right AI Video Editor

There is no single "best" AI editor because every creator's workflow is vastly different. In my years of testing, I've developed a simple framework to help you categorise and choose your tools based on four key questions:

1. What is your primary content style?

If you produce talking-head videos, tutorials, online courses, or podcasts, you should prioritise text-based editors and rough-cut automators. These tools rely heavily on speech-to-text, making them incredibly accurate. If you produce vlogs, travel videos, narrative short films, or high-pacing cinematic edits, you need timeline plugins or visual-centric AI tools that can handle motion tracking, color matching, and visual object removal.

2. Where do you want to edit? (Local vs. Cloud)

Cloud-based editors require uploading massive, gigabyte-heavy raw footage to a remote server. If you have slow upload speeds, this is an absolute dealbreaker. On the other hand, desktop-first tools process your video locally on your machine’s GPU, saving you massive amounts of time.

3. Standalone NLE vs. Plugin Integration?

Do you want to ditch traditional editors completely and work in a simplified, text-first environment? Or do you want to stay inside professional Non-Linear Editors (NLEs) like Adobe Premiere Pro, DaVinci Resolve, or Final Cut Pro, and use AI plugins to speed up your existing timeline workflow?

4. How do the credit systems impact your budget?

The hidden trap of cloud AI pricing is that credit limits are almost always calculated by the input minutes of your source video, not the output clips you generate. If you upload a one-hour podcast, it will burn 60 credits whether the AI generates 5 minutes of finished clips or 50. You must map your monthly raw footage volume against these pricing tiers to avoid massive, unexpected bills.

The Core AI Video Editing Stack: Hands-on Tool Reviews

Let's dive deep into the specific tools that dominate the creative landscape. I have tested each of these extensively on real, multi-camera, and talking-head footage.

Descript: The Document-Style Pioneer

If you have never used a text-based video editor, it feels like magic the first time you try it. You drop your raw video file into the interface, and within a couple of minutes, Descript transcribes it with incredible accuracy. From there, your editing timeline becomes a text document. If you delete a sentence, a word, or even a single syllable from the transcript, Descript instantly slices it from the video track.

For creators who do a lot of talking-head videos, tutorials, or podcasts, this is a massive paradigm shift. Instead of scrubbing back and forth on a traditional timeline to find the exact spot where you misspoke, you simply highlight the bad take in the text and press delete. I have written extensively on automating your rough cuts with Descript's AI features that demonstrates how to clean up silences and repetitions in literally three clicks.

Descript has evolved far beyond basic text cutting. Their built-in AI co-editor, Underlord, handles complex tasks like Studio Sound (which instantly removes echo and background noise, making a cheap USB microphone sound like a high-end studio setup), auto-captions, eye-contact correction, and AI-generated B-roll. To see how these tools come together in my personal production pipeline, you can read about my complete 8-feature Descript workflow.

In my own runs, I took a messy, 10-minute and 7-second recording and simply typed into the AI assistant that I wanted it shortened down to a four-minute edit. In just a few seconds, it edited it down to exactly 4 minutes and 3 seconds. Even better, I was reading a script off-camera the entire time, but I asked the assistant to add eye contact correction, and boom—I was looking right at the lens. To access this, you just head to settings, scroll to the "labs" area, and toggle on the "Underlord" beta.

I also tested its text-based word replacement. I said "computer" in a recording when I meant to say "laptop". I typed the mistake and the correction into the AI box, and after a few seconds, the audio played back saying "laptop" in my own voice. I've also done this to swap "producer" for "editor" ("I'm not an audio editor") and "vegan nuggets" for "chicken nuggets"—it is incredibly seamless. If your audio is too quiet, you can ask the assistant to increase the volume by 30% (or even 50%) and enhance the quality. The difference is night and day.

You can also generate visual assets directly in the chat. I typed in a prompt for a "cartoon image of a man sat at his computer with his head in his hands, crying into his hands." In seconds, it generated the image, and I just had to resize it to cover the screen. For distribution, I asked it to create 30-to-60-second clips for each main talking point. The thing nobody tells you is that it can make them too short initially, so I had to specify the length and ensure it created a new composition for each clip. When it generated a wide horizontal clip, I simply typed "convert the video to 9x16 vertical," and it instantly reframed the video and even the title screen.

My favourite trick is using a "mega prompt" at the start of a fresh edit. I paste in four instructions at once: shorten to 4 minutes, apply eye contact correction, increase volume by 30%, and enhance the audio. It runs them all in bulk. If you want to go the automated route, the "Edit for Clarity" button is insane. I ran it on a 10-minute and 7-second video with heavy intensity, and it instantly cut it down to a tight 4 minutes and 9 seconds by removing filler words, silences, retakes, and unnecessary tangents.

As you get more comfortable with the platform, you can start leveraging some of the more advanced workflows. For instance, you can use conversational text prompts to ask the AI to write a summary, pull out highlight clips, or automatically generate social media copy based on your edit. To master these shortcuts, check out 10 advanced Descript AI editing tricks to level up your editing speed.

Gling AI: The Dedicated YouTube Rough-Cut Savior

Whilst Descript is a fantastic all-in-one suite, it can sometimes feel heavy and sluggish, especially if you are dealing with massive 4K files and slow internet speeds. That is where Gling AI comes in. Gling does not try to be a screen recorder, a podcast host, or a graphic design tool. It is laser-focused on one specific, painful job: taking a messy, unedited, multi-take video of you talking to a camera and delivering a perfectly clean rough cut.

Gling is a desktop application (for Mac and Windows) that processes your files locally on your machine, meaning you do not have to spend hours waiting to upload a 20GB raw file to the cloud. You drop in your raw video, and Gling's AI immediately scans the waveform and the transcribed text. It identifies all of your bad takes, silences, and filler words, highlighting them in red. You review the cuts, hit export, and Gling spits out a beautifully trimmed timeline.

I use Gling everyday, and it has genuinely saved me days of editing time. Just a few weeks ago, I used it to automate the editing on a video that went on to get 200,000 views, cutting my editing time down by hours. When you upload a raw file, you click "Cut bad takes". Gling transcribes the audio and grays out repetitions with a strikethrough. For example, at the start of one of my recordings, I repeated the word "I" back-to-back ("I... I hate video editing"), and Gling instantly cut the first one. If it makes a mistake, you can use the scissor icon to manually cut lines, or simply unclick a line to put it back in. You can also adjust the padding screen to change the time left before and after cuts.

I also tested its "B-rolls" toggle under the "Enhance" menu. Gling places purple bars on the timeline where it inserts footage. If you don't like a clip, you can click the purple bar, see the prompt used, and swap it for another option. The "Enhance audio" toggle instantly brings mic quality up to professional levels. I tested it on my own voice saying "I hate video editing," and the before-and-after difference was massive.

For captions, the "Smart captions" tool adds animated subtitles with a green box that moves along word-by-word as you speak. You can apply different styles to all clips in one click. Finally, the "Chapters" tool automatically names separate topics and creates blue timestamps on the left and black titles on the right. I just copy and paste them straight into YouTube. It routinely saves me 2 to 3 hours per edit. What used to take me 1 to 2 hours of manual cutting now takes less than 5 minutes.

What makes Gling an essential tool for my professional workflow is its integration with traditional NLEs. Instead of exporting a flattened, uneditable MP4, Gling allows you to export an XML timeline directly into Adobe Premiere Pro, DaVinci Resolve, or Final Cut Pro. This means your cuts are preserved on your professional timeline as editable clips, letting you make fine manual adjustments with ease. To understand how Gling fits into a professional creator's toolkit, read my in-depth Gling AI review.

I have tested Gling extensively against manual editing, and it routinely turns a painful two-hour rough-cut process into a five-minute automated pass. It is the single highest-ROI tool I use for talking-head videos. You can read my full breakdown of how I use Gling to automate YouTube editing to see my exact daily setup.

Agentic Video Editing: Claude & Descript MCP

One of the most exciting developments in the creative space is the rise of agentic video editing. We are moving away from fixed, rigid pipelines towards conversational, dynamic AI systems. This is best represented by Anthropic's Claude Desktop and the new Model Context Protocol (MCP) server released by Descript.

To set this up, I logged into my Descript account online, went to settings, clicked "Descript MCP", and followed the connection instructions to link it to Claude Desktop. Once connected, I asked Claude to get a transcript of my video with a timecode every two seconds. It saved it directly into my local computer folder in under a minute.

Then, I tested Claude's visual capabilities. I gave it footage where I was wearing glasses, holding money, putting on a hat, and showing a book. Most AIs only listen to audio, but Claude can actually see. I prompted it to find the moments where I was wearing a hat or holding a book and create separate clips. It watched the video, found those exact visual moments, and saved perfect clips to my computer.

I also used Claude to fix a messy 4-minute and 45-second recording where I made mistakes, paused, and even got out of my chair. I prompted Claude via the Descript MCP to shorten gaps over 1 second, remove filler words, remove retakes, and cut a specific tangent. It cleaned up the edit perfectly, bringing it down to 2 minutes and 55 seconds. I didn't even get out of my chair in the final edit!

For B-roll, Claude suggested five ideas based on my script (like Lego bricks and an orchestra conductor). I pasted its prompt into Descript, and it auto-generated custom B-roll clips matching my voice. I also had Claude apply audio enhancement to a low-quality clip recorded on my phone's mic, and the result was incredibly crisp. Finally, I had Claude add captions (using a specific preset), quiet background music, and a logo in the bottom right corner. The fact that you can automate all of this through Claude is mind-blowing.

This integration represents the future of content creation. It essentially gives you a highly capable assistant editor who can think, write, and execute edits simultaneously. To see this setup in action, check out advanced Claude video editing tricks to learn how to connect the tools.

Furthermore, we are seeing tools like Claude Cowork being applied directly to raw footage. Unlike traditional AI tools that only "listen" to the audio transcript, agentic tools can analyze both the visual data and the spoken word to make more intelligent, context-aware editing decisions. To understand the capabilities and limitations of this approach, take a look at my review of Claude Cowork for video editing where I put it to the test on real, multi-camera footage.

Short-Form Social Repurposing (Opus Clip, FireCut, etc.)

If you are a podcaster or a long-form creator, ignoring short-form vertical platforms (TikTok, YouTube Shorts, Instagram Reels) is leaving massive audience growth on the table. However, manually scrubbing through a one-hour podcast to find 10 highly engaging 30-second clips, reframing them to 9:16, adding animated captions, and rendering them out is a monumental waste of your creative energy.

This is where AI clipping tools shine. Platforms like Opus Clip use large language models to analyze your long-form footage, identify the most hook-centric moments, score them on a "Virality Score" from 1 to 100, and automatically crop the active speaker to keep them centered in a vertical frame. It then layers on stylish, animated captions with emojis and highlighted keywords. For OpusClip, I found it incredibly useful for finding the best highlights from long-form videos. It automatically focuses on the active speaker, adds animated captions, and lets you choose between standard horizontal or 9:16 vertical formats for TikTok and YouTube Shorts.

But what if you prefer to keep your workflow entirely inside your professional timeline? This is where Adobe Premiere Pro and DaVinci Resolve plugins like FireCut come in. FireCut acts as a supercharged panel right next to your source monitor, allowing you to run AI silence cutting, auto-zooms on key phrases, multi-cam podcast switching, and caption generation without ever leaving Premiere.

When I tested FireCut, I opened it as an extension inside Adobe Premiere Pro. I had a 30-minute podcast episode and used the "Find B-roll" button. The AI analyzed the entire episode, understood the topic, and automatically added video clips on top of the timeline at hand-picked moments. It's a massive time saver for adding those finishing touches.

If you are trying to figure out which of these apps fits your production style, you can read my comparison of the top AI tools to speed up editing, which evaluates Descript, FireCut, Opus Clip, and Gling head-to-head. I also put together a broader hands-on review analyzing the best AI video editors of the year to help you pick the right software based on your specific platform requirements.

Creative AI Generation & Traditional NLEs

The landscape is not just limited to specialized AI-first utilities; traditional consumer editors are also implementing massive AI updates to keep pace. For example, Wondershare Filmora 14 has introduced robust AI toolsets, including planar tracking, smart cutout, AI speech-to-text lip sync, and AI audio enhancements. At the same time, we are seeing the rise of all-in-one generative suites that allow beginners to script, generate, and edit entire cinematic videos in a single browser window.

I tested AIVideo.com, and the pricing alone is a massive win for beginners. Usually, to make an AI short film, you'd need Ideogram ($7/mo), Kling ($7/mo), ElevenLabs ($5/mo), Epidemic Sound ($20/mo), and Premiere Pro ($25/mo)—costing at least $64/mo. AIVideo.com starts at $20/mo, and with the code gpvideo at checkout, you get 20% off, bringing it down to $16/mo (a $48/mo saving).

In my testing, I built a sci-fi short film. I first generated a high-quality starting image using the Flux 1.1 Ultra model (three stormtroopers in a desert). Then I right-clicked the image and used the image-to-video generator with Kling 1.6 Pro to make a 10-second clip. The trick is to use a camera movement prompt, like "A slow dolly shot moves toward a small group of stormtroopers..."

I generated voiceovers for free, and then created custom sound effects from scratch. I prompted it for "A steady, pulsing hum of an active lightsaber" and "A wrapped sandwich being carefully passed... soft paper shuffle." I also tested the "AI Edit" feature, asking it to generate matching sound effects. It analyzed my desert scene and automatically created sound effects for sand kicking up in a breeze and a distant echo howl.

For Filmora 14, I tested its text-prompt editing. I typed "make footage lighter" and it did it instantly. I even asked it "how to increase the volume" and it pointed me directly to the volume slider. I also used its AI Music generator to create a 32-second euphoric, lo-fi hip-hop track for my timeline.

I also tested HeyGen's translation tool. I spoke in English, and it translated my voice to Spanish while perfectly syncing my lip movements. Runway was another standout, letting me animate a character image using my own speaking video in one click, and using its advanced item detection to cleanly remove unwanted objects.

If you are a beginner looking to create stylized, highly produced content or even faceless narrative videos without managing multiple separate software subscriptions, all-in-one AI engines are incredibly powerful. I recommend checking out my hands-on review of AIVideo.com to see how to script and generate cinematic clips on a single budget.

Conversely, if you want a look at what both traditional and creative generative platforms are doing to automate timeline edits and translate languages, see my guide to new AI video editors like Filmora and HeyGen where I break down how to animate characters and translate localized content on autopilot.

My Step-by-Step AI Video Editing Workflow

To truly cut your post-production time by 90%, you cannot just throw random tools at your timeline. You need an integrated, logical workflow that allows these AI tools to do what they do best, whilst keeping you in the creative driving seat.

Here is the exact step-by-step framework I use for my YouTube channel:

[Raw Footage]
      │
      ▼
┌──────────────┐
│   Gling AI   │ ──► Local Waveform Cleanup & Bad Take Removal
└──────────────┘
      │
      ▼ (XML Export)
┌───────────────────────┐
│ Premiere Pro / Resolve │ ──► Structural & Creative Assembly (A-Roll & B-Roll)
└───────────────────────┘
      │
      ▼ (XML Export)
┌──────────────┐
│   Descript   │ ──► Underlord AI Studio Sound & Eye-Contact Correction
└──────────────┘
      │
      ▼ (Descript MCP Server)
┌──────────────┐
│ Claude AI    │ ──► Auto-generate Metadata, Chapters, and Titles
└──────────────┘
      │
      ▼ (Final Render)
┌──────────────┐
│  Opus Clip   │ ──► Repurpose long-form video into 5-10 Vertical Shorts
└──────────────┘

Phase 1: The Local Waveform Cleanup

I film my video, shoot my raw talking-head footage, and drop the files straight into Gling AI. I let Gling process the files locally on my Mac's GPU. Within two minutes, Gling transcribes the video and highlights all silences, filler words, and repetitive bad takes. In my tests, removing silences manually takes about 15 seconds per cut. With over 400 silences in a 10-minute clip, that takes forever. But Gling found 55 silences in my 10-minute video and cut them out in 20 seconds, reducing the timeline down to 5 minutes and 50 seconds automatically. I do a quick 3-minute visual review to make sure it hasn't cut out any intentional dramatic pauses, then I export the XML file.

Phase 2: Structural Assembly

I import Gling's XML file directly into Premiere Pro or DaVinci Resolve. Because Gling preserves the original cuts as separate clips rather than a flattened video, I have full creative freedom. I arrange my structural edit, throw in any custom animations, and lay down my primary B-roll tracks.

Phase 3: Text-Based Refining & Audio Polish

Once my edit is structurally solid, I push the sequence into Descript. I run Descript’s "Studio Sound" filter, which uses deep learning to rebuild the audio, removing room echo, computer fan hum, and background noise. I also run the eye-contact correction tool if I was looking at my notes too often during filming. I've even used speech correction to swap out single words like 'producer' to 'editor' in my own voice. Finally, I use Descript's text-based editor to make surgical trims to any sentences that feel too wordy.

Phase 4: Agentic Metadata & Distribution

With the edit complete, I activate Descript's MCP server inside Claude. I ask Claude to analyze the finalized transcript and generate five high-converting, click-worthy YouTube titles, a search-optimised video description, a timestamped chapter list, and three distinct promotional posts for social media.

Phase 5: Short-Form Extraction

After rendering and uploading the long-form video, I drop the final link into Opus Clip. Within 5 minutes, Opus parses the entire video, identifies the top 5 most engaging moments, crops the footage to 9:16 using face-tracking, generates animated captions, and schedules them for auto-posting across TikTok, Instagram, and YouTube Shorts.

This systematic approach is how I am able to run a highly polished channel whilst spending less than an hour on the actual physical editing process per video. To learn more about optimising your pipeline, read my 5 AI editing hacks to slash post-production time.

AI Video Editing Tools Comparison Table

Here is a side-by-side comparison of the primary AI video editors available, updated with verified pricing and capabilities:

Common Mistakes to Avoid with AI Editing

When creators first discover AI video editing tools, they often make several critical mistakes that actually hurt their retention and viewer experience. If you want to keep your videos looking professional, avoid these common traps:

The "Breathless" Timeline Trap

By default, automatic silence removal tools cut every single pause down to the millisecond. When I ran silence removal on my 10-minute clip, I found that cutting silences completely to 0 seconds can make the edit feel too breathless and unnatural. Always set your silent gap threshold to at least 0.5 or 0.8 seconds (or Gling's default of cutting gaps longer than 1 second down to 0 seconds, but adjusting as needed). This preserves natural pauses and allows your content to breathe.

The "Robotic Audio" Over-Processing

Descript’s Studio Sound is an absolute lifesaver, but if you crank the intensity slider to 100%, it will make your voice sound metallic, robotic, and heavily synthesised. This happens when the AI tries to rebuild speech in a room with severe background noise or echo. Keep the intensity slider between 70% and 85% to achieve a rich, clean sound that still retains your natural tone and vocal textures.

Blindly Trusting AI B-Roll

Some tools offer to "automatically insert matching B-roll" based on your transcript. If you talk about a "marketing funnel," the AI will almost certainly insert a literal plastic kitchen funnel pouring water. This looks incredibly amateurish and breaks immersion instantly. When I tested automatic B-roll tools, I've seen them insert literal interpretations of phrases. But when you use Claude and Descript together, you can prompt specific, contextual ideas like Lego bricks or an orchestra conductor to keep it highly relevant. Never let an AI choose your B-roll without a manual, frame-by-frame review. Use AI stock suggestions as a starting point, but always verify that the visual metaphor actually makes sense.

Set-and-Forget Automated Captions

No AI transcription engine is 100% accurate. They constantly misspell niche brand names, technical jargon, or acronyms, and they often misplace punctuation, completely changing the context of your sentence. Always read through your generated captions and manually fix spelling errors before exporting. A single garbled caption can instantly lose the trust of a professional audience.

FAQ

Can AI video editors completely replace human video editors?

No. AI is exceptional at automating technical, repetitive grunt work—like cutting out silent pauses, generating transcriptions, matching camera angles, and syncing audio. However, AI completely lacks human taste, emotional intelligence, and storytelling capabilities. An AI cannot understand how to build tension, deliver a punchline with perfect comedic timing, or make complex creative decisions. The best workflow involves using AI as a highly capable assistant to handle physical execution, leaving creative direction entirely in human hands.

What is the difference between text-based editing and timeline-based editing?

Timeline-based editing is the traditional method used in software like Premiere Pro and DaVinci Resolve, where you manually cut and move audio and video tracks along a linear timeline. Text-based editing (used by Descript and Gling) transcribes your video audio into a text document. Editing the video is as simple as editing a text document; when you delete or rearrange words in the transcript, the software automatically slices and rearranges the corresponding video and audio clips on the underlying timeline.

How do credit limits work on platforms like Opus Clip?

On platforms like Opus Clip, credits are consumed based on the input duration of the source video you upload, not the output duration of the clips you generate. For example, if you upload a 60-minute podcast, it will cost you 60 credits regardless of whether the AI generates one 30-second clip or ten 60-second clips. This is a crucial distinction to keep in mind when choosing a plan, as uploading multiple hours of raw footage can burn through monthly limits incredibly quickly.

Is it better to use a standalone AI editor or an NLE plugin?

This depends entirely on your level of editing experience and the complexity of your projects. If you are a beginner, solo creator, or marketer who wants to produce polished videos quickly without a steep learning curve, a standalone editor like Descript or Filmora 14 is ideal. However, if you are a professional editor or YouTuber who requires complex color grading, multi-layered visual effects, and advanced sound design, you should stay inside professional NLEs (like Premiere Pro or DaVinci Resolve) and use AI plugins like Gling (for XML export) or FireCut to automate timeline cleanup.