75% of Marketing Videos Are Now AI-Assisted

There is a stat making its way through every marketing newsletter, conference deck, and agency strategy document right now.

By 2026, 75% of marketing videos are expected to be AI-generated or AI-assisted.

It is a striking number. And depending on where you sit in the video industry, it probably landed one of two ways. Either it felt like confirmation that the future arrived faster than expected, or it felt like a slow-moving threat that is now very much at the door.

Both reactions are understandable. Neither is particularly useful on its own.

The first thing worth clarifying is what the stat is measuring, because AI-assisted covers an enormous range of involvement.

At one end, a video is AI-assisted because an editor used an automated transcription tool. At the other end, a short-form social video was generated almost entirely from a text prompt with minimal human input. Both count. They are not the same thing.

Here’s a more accurate view of how AI currently integrates into most professional post-production workflows:

Automated transcription and rough assembly are now standard in most editing workflows. Tools inside Adobe Premiere Pro, DaVinci Resolve, and standalone platforms can scan footage, identify usable takes, and produce a first assembly cut in a fraction of the time it once took manually. This is genuinely useful. It does not replace creative editing. It removes the most mechanical part of getting started.

Color grading assistance is increasingly common. AI tools can analyze footage and apply baseline corrections across a large volume of clips with reasonable consistency. For teams producing high volumes of social content, this changes the pace of work significantly. For high-end brand films where color is a core part of the storytelling, human grading still leads.

Audio cleanup and noise removal have become fast and reliable. Tasks that once required careful manual work, removing background noise, balancing levels, and cleaning dialogue, can now be handled in minutes. The results are not always perfect, but they are a strong starting point.

Format resizing and multi-platform versioning. This is one of the clearest wins for AI in post-production. Automatically generating square, vertical, and horizontal versions of the same edit, adapting safe zones and reframing shots, saves meaningful time for any team managing content across multiple platforms.

Script and caption generation. AI can produce accurate captions, generate rough scripts from existing footage, and assist with subtitle creation across multiple languages. These are tasks that used to require significant time and are now largely automated.

This is what most of that 75% actually describes. Not fully generated videos that replace the production process. Faster, more efficient handling of the repetitive and technical parts of that process.

Understanding what AI can do in post-production is straightforward enough. The more important question is where it stops.

AI does not understand the brief. It cannot determine whether a video is intended to evoke emotion, convey understanding, or prompt action. It cannot weigh one edit against another based on strategic intent. It processes what it is given. It does not know what the video is for.

AI cannot feel when something is wrong. Experienced editors develop an instinct over years of work. A sense that a cut lands incorrectly, that a moment needs more room to breathe, that a scene is working against the story rather than with it. That is not pattern recognition. It is a judgment built from paying attention to thousands of hours of footage and dozens of client relationships. No model replicates it.

AI cannot navigate ambiguity. Real projects rarely arrive with perfect briefs and total clarity. The ability to read incomplete information, have the right conversations, make good decisions when things are not fully resolved, and course-correct when something is drifting, these are human skills that sit at the heart of what makes post-production valuable.

AI cannot manage the relationship. Understanding what a client actually means versus what they literally wrote. Knowing when to push back and when to adapt. Recognizing when a feedback round is about the video and when it is about something else entirely. These things require experience, attention, and genuine investment in the outcome.

The 75% number describes a real shift in how work gets done. It does not describe a shift in what makes work good.

For post-production studios, the honest takeaway is this. AI changes the how, not the what.

The value a studio offers was never really about export speeds or how fast a rough cut gets assembled. It was always about the quality of the decisions made along the way. The structural calls, the pacing instincts, the understanding of what a client needs, even when they have not fully articulated it.

AI in post-production does not diminish that. If anything, it sharpens the question of where a studio’s real expertise lies. When the mechanical parts of the work become faster and cheaper, the creative and strategic parts become more visible and more important.

Studios that use AI tools to protect time for the decisions that matter are better positioned than ever. The ones treating AI as a replacement for craft are likely to find that the tools do not solve the fundamental problems.

If you commission video work, understanding AI’s role in post-production helps you ask better questions and set more useful expectations.

Faster production does not always mean better work. AI can accelerate parts of the post-production process, but the decisions that make a video actually work still take time and attention. Compressing the timeline does not change that.

The brief still matters more than anything. No amount of AI in the editing suite compensates for a brief that has not resolved the fundamental question of what a video is supposed to do. That clarity has to come from the client side. AI can help execute. It cannot fix what was never decided.

Ask how AI is being used, not just whether it is. The most useful question to ask a post-production partner is not “Do you use AI?” The more interesting question is “where does AI stop and human judgment take over? ” That answer tells you a lot about how a studio thinks about its own work.

The quality gap between work that uses AI thoughtfully and work that relies on it entirely is already visible. It will become more visible over time.

AI in post-production is not a phase. It is not going away, and the pace of development is not slowing down.

What it is doing, and will continue to do, is separate the technical from the creative more clearly than ever before. The mechanical parts of video post-production are becoming faster, cheaper, and more automated. The human parts are becoming more valuable.

That is genuinely good news for everyone who cares about the quality of the work. The editors and studios that will thrive are the ones who are clear about what they bring to a project that a tool cannot, and who use AI to make more room for exactly that.

75% AI-assisted. 100% still needs someone who knows what they are doing.