Five Questions to Run With Your Team

Most teams trying to figure out AI video are asking the wrong questions. They’re asking which tool, when they should be asking what for. They’re asking who knows how to use it, when they should be asking what does good look like here.

The bottleneck has moved from production to judgment. The teams that figure that out early build something durable. The ones that don’t end up with a tool stack and no strategy.

Before your next AI video investment — tool, hire, or agency engagement — run these five questions.

1. What does “good” look like, and who decides? Not in general. In your organization, for your brand, for this specific type of content. If the answer is “we’ll know it when we see it,” you don’t have a standard — you have a preference. Those are not the same thing, and the difference will cost you every time a decision gets escalated.

2. Who owns the creative judgment call? Generating is easy. Deciding is the job. When the model gives you four outputs and none of them are quite right, someone has to know what “closer” means and why. That person exists on your team right now — or they don’t. Find out before you’re in production.

3. What’s the output actually for? Not “video content.” Specifically: what format, what platform, what audience state, what action. The answer changes the entire production approach — the tools, the pipeline, the length, the pacing. If the brief doesn’t answer this, send it back.

4. How will you know if it worked? Not views. Not engagement rate. What’s the actual outcome this content is supposed to drive, and what’s the minimum signal that tells you it’s working? If you can’t answer this before production starts, you’re producing for the feeling of having produced, not for a result.

5. What happens to the knowledge? Someone on your team will get good at this. The question is whether that capability lives in their head or in a documented system — a style guide, a prompt library, a production checklist, a workflow that new people can be trained on. Individual expertise that doesn’t get externalized is organizational debt. It leaves when they leave.

The teams who win long-term aren’t the ones with the best individual AI operators. They’re the ones who externalize that knowledge into systems, workflows, and standards that survive personnel changes. These five questions are the beginning of that process.