It makes the workflow faster, which is both the danger and the opportunity for every comms team that’s been told to “use AI.”
AI does not make a bad workflow smart.
It makes it faster.
That sounds like a warning, and it is. It’s also the reason I’m still excited about this stuff. AI is useful when it gives leverage to something that already has a shape. It’s dangerous when it gives speed to something nobody has thought through.
If you run communications for an association, a church, a manufacturer or a venue, somebody has probably told you to “use AI” by now. A board member. A vendor. Your own tired brain the week before a big event.
Here’s what I keep finding. AI isn’t a shortcut around the boring parts of the work. The boring parts get more important.
Where does the source material live?
Who decides what’s good enough?
What happens when the model is wrong but confident?
What gets reviewed by a person before it goes out?
What gets remembered after something ships?
None of those sound futuristic. They sound like operations. That’s the point.
The model is rarely the whole system. It’s one station inside the system. Before it runs, something has to be captured, cleaned, selected or framed. After it runs, something has to be judged, routed, scheduled, measured or thrown away.
If those steps are vague, AI doesn’t fix the vagueness. It produces more of it.
I ran into this again building a scheduling system for my own social posts. The glamorous version would be “generate a month of content.” That part is easy. Too easy, honestly.
The real work was everything around it:
That’s the workflow. AI helped inside it, but the value came from the shape around it. The system had to know what good meant. It had to know what not to do. It had to leave records behind so future me isn’t guessing what happened.
This is where a lot of AI adoption gets weird. Teams start with the model instead of the workflow. They ask for output before they have an operating rhythm.
“Make me 30 posts.”
“Summarize this meeting.”
“Edit this clip.”
“Analyze these metrics.”
Those are fine requests. They’re also incomplete requests. The more useful questions are a little less shiny:
Answer those, and AI starts becoming leverage instead of novelty.
Here’s the simplest filter I use now.
If the task repeats, AI might help.
If you can judge the output, AI might help.
If failure is reversible, AI might help.
If a human can review the weird cases, AI might help.
If the system remembers what happened, AI might help a lot.
If all five are missing, you don’t need automation yet. You need a clearer workflow.
For a comms team, that filter sorts things fast. Pulling rough clip candidates out of a two-hour recorded session? It repeats every time you have an event, you can judge the result by watching it, a bad pick costs nothing, and a person chooses the final cut. Good fit. The statement your executive director sends after a hard week? It doesn’t repeat, it’s hard to judge in advance, and you can’t take it back. Keep that one human.
It’s the opposite. It’s respect for the tool. Powerful tools deserve better than vague jobs.
The more I build with AI, the less I think the advantage is “using AI.” Everyone is going to use AI. The advantage is the operating system around it.
Source material.
Constraints.
Taste.
Review.
Records.
Feedback.
That’s not as exciting as a demo video. But it’s what keeps the demo from turning into a mess three weeks later.
AI does not make a bad workflow smart.
It makes the truth arrive faster.
If your team is trying to figure out where AI actually fits in your video workflow, I’m always happy to compare notes.