SNAPSHOT
LinkedIn's global editorial, media production, and learning teams create content at a volume and pace that traditional editing workflows were never built for.
Too much time spent scrubbing timelines, hunting through recordings, and trimming dead air instead of telling stories.
When "slow" turns into "towering backlog" at enterprise scale, something has to change. So LinkedIn adopted Descript's text-first, AI-enabled workflow, and eliminated the tedium—and the backlog.
Video
Editorial
Descript changed our workflow by turning hours of timeline scrubbing into fast, review-ready roughs and batch social cuts. That has saved our team significant time.”
Video Editing Lead
KEY RESULTS
Seamless workflow integration across the whole org
LinkedIn's adoption wasn't a big-bang transformation. Editorial had already adopted Descript, but media production and learning teams needed a way to contribute without learning another heavy-duty tool.
Descript's familiar, intuitive tools made cross-company adoption simple. Over 6-9 months of targeted workshops and workflow iteration, LinkedIn scaled Descript across multiple teams. The content stayed creative, production got faster, and the specialists finally had space to focus on the parts that require craft.
A text-based workflow that fits how the team really works
LinkedIn teams replaced waveforms and timelines with auto-generated transcripts as their primary workspace. Now instead of scrubbing through hours of footage to find the right moments, producers simply read through the transcript in Descript, delete unwanted sections and rearrange passages by cut-and-paste; the video updates automatically to match. Every edit, clip, and sequence carries searchable metadata, making it easy to find and repurpose content later. Editing video by editing text saves the team ~1 hour per project and transforms what used to be a multi-step, multi-version process into a single, clean first cut from the producer.
Multiple social clips from every interview—fast
Instead of scrubbing through an hour of video to find clip-worthy moments, LinkedIn teams scan the transcript to spot compelling quotes, stories, or soundbites. Then they simply highlight the text, extract it as a standalone clip, and repeat for every clip-worthy moment they find. What used to be a tedious one-by-one process of finding moments, cutting clips, and exporting each separately now happens in minutes with Descript’s batch extraction. The transcript becomes a menu of options where producers can quickly generate 10+ clips, compare them side-by-side, and select the strongest performers for distribution.
As it stands now, we prep almost all of our projects in Descript.
Hands-off cleanup, seamless handoffs
LinkedIn made Descript the front door for production—record, transcribe, rough cut, clean, then hand off. Remove Retakes automatically spots and deletes false starts and repeated takes. AI tools clear out pauses and dead air in a click. And Studio Sound polishes audio without lots of expensive mics or plugins. Once the rough cut is clean, teams export XML files straight to Premiere with all edit decisions intact, giving video editors a polished starting point instead of a pile of raw footage. Editors get multiple hours back per project—hours they can now spend on the storytelling and creative decisions that actually require their expertise.
Most enterprises want to "move faster and do more," but few can pinpoint exactly where the time goes or how they can get it back. LinkedIn figured it out. One hour per rough cut. Ten clips from every interview. Four to five minutes of automated cleanup per composition. Multiple hours saved in the finishing workflow. It took a few months of workshops and iteration to scale Descript across editorial, media production, and learning teams. But the cross-org effort paid off—not in vague productivity gains, but in specific, repeatable time savings that let specialists focus on what they do best. LinkedIn didn't add more editors, rebuild their stack, or overhaul their creative process. They just adopted a production layer that matched how their teams already worked: text-first, AI-assisted, and built for scale.
