Most leadership teams learn the lesson in this chart only after their first big AI video project goes over budget.

AI video tools are excellent at ordinary shots: one person talking to camera, a product on a table, a slow walk down a hallway. Those come out clean and believable most of the time.

As a shot gets more complex or unusual, the results change quickly. Crowds, hands doing detailed work, physics like water or collisions, and anything the model has rarely seen all drive hallucinations and mistakes up and accuracy to real life down. Past a certain point, you're generating dozens of versions to get one usable take, and an artist still has to fix it.

If you're building your own AI video team, here's what that means:

🟢 Budget by shot type. Two 30-second spots can differ several times over in generation and cleanup time, depending on what's in them.

🟢 Write for the safe zone. Creative teams who understand the tools' limits will save you more money than any software license.

🟢 Keep traditional skills in the room. Compositing, animation and editing are how shots get rescued from the red zone.

🟢 Treat accuracy as a compliance issue. In regulated industries like pharma, health and finance, a wrong detail on screen can stall a review.

AI video is a real production capability today. The companies that succeed with it plan around where it's strong and staff for where it isn't.

(Curve is illustrative, based on what I see in production day to day.)

I'm John Eagan. I've spent 20 years in video production and animation, and I built an AI video production department from the ground up at the largest advertising agency in the world. If your company is planning its own AI video team, send me a message. I'm glad to talk through where to start.

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