Why Your Company Is Not an AI Company

I was on a call recently with someone walking through their company’s AI strategy, and by the end of it I still had the same question I had at the start: what do you actually do?

That’s the problem with the way this market is talking right now. Everyone’s an AI company. Every deck says it. Every booth says it. Every homepage finds a way to squeeze it into the first screen. The phrase is everywhere, and the more it spreads, the less useful it becomes.

In theory, calling yourself an AI company is supposed to signal relevance. In practice, it usually does the opposite. It strips away the product story, replaces it with a category label that means almost nothing, and leaves the buyer doing extra work just to figure out where the company fits. In a media market that’s already crowded with overlapping vendors and fuzzy positioning, that kind of messaging doesn’t sharpen the picture. It blurs it further.

That’s why so much of this language feels hollow. It isn’t rooted in the actual mechanics of the product. It’s rooted in the need to sound en vogue.

A company that improves how content moves through the system, whether that’s making libraries easier to search, reducing manual work in packaging and publishing, improving how content gets scheduled and distributed, or giving teams better visibility into performance, hasn’t become an AI company just because AI sits somewhere inside the stack. It’s still the same business. The real question is whether the product changes the workflow in a way that matters.

That’s the part too many companies skip.

The market doesn’t need more vendors announcing that they use AI. Of course they use AI. At this point, that’s barely worth saying. The market needs a clear explanation of what the product actually does and what gets better once it’s in place. Does it cut time out of clipping and packaging? Does it make a huge library more searchable? Does it automate metadata and captions that used to be handled manually? Does it surface monetization issues faster? Does it help smaller teams move more content with less chaos? Those are the questions that matter, and none of them are answered by slapping AI on the front of the pitch.

That’s why the label keeps falling flat. Buyers aren’t looking for a tool in the abstract. They’re looking for a solution to a workflow problem that’s already costing them time, money, or both. They want to know where the product sits, what function it serves, what friction it removes, and why it’s better than the five other companies claiming to do something similar. If the first thing they hear is “we’re an AI company,” they still have to ask every important question afterward. The pitch hasn’t done any work yet.

That’s where the confusion starts turning into a business problem. Bad positioning doesn’t just make a company sound generic. It slows things down. It drags out decision-making. It forces buyers to decode basic value in internal meetings where attention is already limited and patience is even more limited. The fuzzier the story, the harder it is for anyone inside an organization to repeat it, defend it, or push it forward.

And media doesn’t need any extra confusion right now. This is already a sector full of products that crowd each other at the edges. Some companies say workflow automation. Some say video intelligence. Some say media supply chain. Some say monetization infrastructure. Some say orchestration. Some say publishing. Put enough of them side by side and the distinctions start getting soft. Add AI branding on top of all of it and the category gets even harder to parse.

That’s why the lazy version of the AI story keeps backfiring. It sounds broad when companies want broad. It sounds futuristic when companies want futuristic. But broad and futuristic are doing the opposite of what the market actually needs. The useful story is almost always narrower than that. It lives in the workflow. It lives in the operational detail. It lives in the part where someone can say, “Here’s the exact thing that used to take hours, and here’s how it now gets done faster, cleaner, and with fewer people touching it.”

That’s a real product story.

If a system can process a large video library and generate useful context without someone manually tagging every asset, say that. If a team can search content based on concepts, moments, and events instead of relying on inconsistent labeling, say that. If content can be prepared, adapted, and published faster across formats, say that. If workflows that used to require multiple manual steps can be streamlined or automated, say that. If performance issues can be surfaced and acted on while they still matter, say that.

Those are all concrete improvements. They’re easy to understand, easy to value, and easy to repeat inside an organization.

That’s what strong positioning sounds like. It doesn’t hide the mechanism. It connects the mechanism to a workflow that somebody already understands and already feels pain around.

This is also why so much AI messaging feels oddly disconnected from the actual history of the business. Media companies didn’t suddenly become interested in efficiency because AI showed up. They’ve always been interested in efficiency. 

Talk to any Chief Operating Officer.

They’ve always wanted faster workflows, lower friction, better use of labor, cleaner distribution, and more output from the same content and teams. AI didn’t invent the pressure to do more with less. It just arrived as the latest tool being used to attack the same old problems.

That’s an important distinction, because it changes what’s worth paying attention to. The interesting question isn’t whether a company has inserted AI into its product. Nearly everybody has, or says they have. The interesting question is whether the addition of AI has made the product materially more useful inside an existing operation. Has it shortened turnaround time? Has it reduced repetitive labor? Has it improved discovery? Has it tightened packaging and publishing? Has it made monetization performance easier to see and easier to act on? Has it made the entire system less clunky to operate?

Those are the outcomes that hold up. Those are the outcomes that survive scrutiny when the meeting gets more serious and the room starts asking where the ROI actually is.

A lot of companies don’t want to do that work because it’s harder. It’s easier to market a feeling than a function. It’s easier to say “AI-first” than to explain exactly how the product changes a media workflow. It’s easier to stay high level and hope the audience fills in the gaps. That’s how you end up with messaging that sounds polished and current but collapses the second someone asks a basic follow-up.

So what do you actually do?

Who is it for?

What gets easier once it’s installed?

Why should anyone care?

Those questions aren’t unfair. They’re the whole point and is the type of work we’re doing for our clients at 43Twenty.

The companies that answer them well are usually the ones that stop treating AI like an identity. They use it as part of the explanation, not the explanation itself. They understand that the product still has to stand on its own. AI can help make the case, but it can’t be the case.

That’s where a lot of the current market goes sideways. There’s too much AI theater and not enough operational clarity. Too much language built to sound important, not enough language built to explain what changes on the ground. Too much abstraction, not enough mechanics. Too much signaling, not enough substance.

Eventually that gap catches up with people. Buyers get sharper. Categories get more crowded. Expectations rise. Once everybody says they use AI, the phrase stops differentiating anyone. At that point, the companies that still lead with the label start sounding even more generic than they did before.

That’s where this is headed. AI is becoming table stakes across media software, which means claiming it as an identity is only going to get weaker over time. The companies that come out ahead won’t be the ones making the biggest show of it. They’ll be the ones that can tie it directly to workflow improvements that make immediate sense inside a real operation.

That’s the standard now. Explain the function. Explain the workflow. Explain what gets faster, cleaner, easier, or more profitable. Skip the inflated identity play and get to the operational value.

Because if your entire pitch is that you’re an AI company, the next question is still the same one.

What do you actually do?