A term that tells you nothing
I’ve been working in video production and digital content for over two decades. And in that time, I’ve watched plenty of meaningless buzzwords come and go. “Synergy.” “Growth hacking.” “Digital-first.” Terms that sound impressive in a pitch deck and mean absolutely nothing in practice.
“VideoAI” is the latest one. And it’s possibly the worst offender because it actively obscures what’s actually happening in a genuinely interesting space.
Here’s the problem. When a company tells you they’re in “VideoAI”, they could mean any of the following:
- AI-powered video editing tools
- Automated subtitle and caption generation
- AI-driven video search and tagging
- Text-to-video generation
- AI video upscaling and enhancement
- Synthetic avatar and presenter tools
- Automated video repurposing across formats
- AI-assisted colour grading and post-production
- Deepfake and face-swap technology
- AI-generated B-roll
These are wildly different technologies with different use cases, different maturity levels, and different implications for production workflows. Lumping them all under “VideoAI” is like calling everything from a bicycle to a Boeing 747 “transport technology.” Technically accurate. Practically useless.
Why the term persists
It persists because it’s convenient. For investors, it signals a hot market. For startups, it’s an easy pitch. For marketing teams, it sounds cutting-edge. Nobody has to explain the specifics because the umbrella term does the heavy lifting.
But for anyone actually trying to evaluate, adopt, or integrate these tools into a production workflow? The term creates confusion, not clarity.
I’ve sat in meetings where clients have said they want “VideoAI” and meant five completely different things depending on who was speaking. The marketing director wants AI-generated social clips. The production manager wants automated transcription. The CEO read about Sora and wants to generate entire commercials from text prompts. They’re all calling it the same thing. They’re all talking about completely different capabilities.
What the landscape actually looks like
Let me break this down into categories that actually make sense.
AI-assisted production tools
These are the most mature and most practical for most businesses right now. Tools that sit within an existing editing or production workflow and automate specific tasks. Think auto-captioning, intelligent colour matching, audio cleanup, automated rough cuts from long-form footage.
Examples: Descript, Runway (editing features), Adobe’s AI tools in Premiere Pro.
These tools save time. They’re reliable. They’re genuinely useful. And they don’t fundamentally change your production model - they make your existing one faster.
Generative video
This is the category that gets all the headlines. Text-to-video, image-to-video, AI-generated footage. Sora, Runway Gen-3, Pika, Kling. This is where the excitement lives - and where the gap between demo and practical application is widest.
I’ve tested most of these extensively. The quality is improving rapidly. But for commercial production, we’re still in early days. Consistency issues, brand control limitations, and the uncanny valley problem mean these tools are useful for concept work, mood boards, and specific creative applications - but they’re not replacing your production crew any time soon.
Synthetic media and avatars
AI-generated presenters, digital humans, voice cloning. Tools like Synthesia, HeyGen, and ElevenLabs. These have found genuine commercial traction in corporate training, localisation, and internal communications.
If you need to produce 50 versions of a training video in different languages, synthetic avatars make commercial sense. If you’re trying to build an authentic brand presence, they don’t. The use case matters enormously.
Automated repurposing
Tools that take long-form video and automatically generate short-form clips, highlights, and social content. Opus Clip, Vidyo, and similar platforms. These solve a real problem - the cost and time of repurposing content across platforms - and they’re getting genuinely good at it.
Search, tagging, and organisation
AI that understands what’s in your video content and makes it searchable. Useful for large media libraries, DAM systems, and content operations at scale. Less glamorous than generation. Often more valuable.
What terms actually matter
If you’re evaluating tools or building a production strategy, here are the terms worth using:
- AI-assisted editing - tools that speed up existing workflows
- Generative video - AI that creates new visual content
- Synthetic media - AI-generated people, voices, or environments
- Automated repurposing - AI that adapts content across formats
- Intelligent media management - AI for search, tagging, and organisation
Each of these is a distinct capability with distinct maturity levels, costs, and applications. Each one deserves its own evaluation. Calling them all “VideoAI” makes about as much sense as calling every piece of software “tech.”
Why this matters for your business
When someone pitches you “VideoAI”, ask them which category they’re actually in. If they can’t answer clearly, that tells you something about their product and their understanding of the space.
When you’re building a production strategy that incorporates AI, be specific about which capabilities you need. Don’t go shopping for “VideoAI.” Go shopping for automated repurposing, or generative B-roll, or AI-assisted editing. The specificity will save you time, money, and disappointment.
The landscape is genuinely exciting. There are tools available today that would have seemed like science fiction three years ago. But navigating it requires precision, not buzzwords.
Drop “VideoAI” from your vocabulary. Start using terms that actually mean something. You’ll make better decisions as a result.
