Nobody outside your company can answer this, and that includes a general-purpose AI assistant. The answer lives in your own records: invoices, the list of apps people sign in to, IT's software inventory, and the threads where someone started a trial. An assistant connected to your workplace documents and chat can search those for you. One that isn't connected will just guess. The fastest reliable answer takes about an hour: check where your video actually lives and which AI features are already switched on there, then list what anyone has trialled.
Where to look
Start with money and logins. Finance or your expense tool shows recurring software charges. IT's sign-in dashboard shows which apps people use with company accounts. Between them, those two lists catch most tools, including ones a single team bought on a card.
Then follow the trial trail. Search your chat and docs for "trial," "demo" and "video library." That usually turns up evaluations that never became a purchase, along with the reasons they stalled. That covers most of the "looking at" half of the question.
If your company's AI assistant can read those sources, ask it the same question with names in it: "which video tools did the content team trial this year, and what happened?" If it can't read them, any list it gives you is a list of popular products. It isn't your stack.
The AI features you may already be paying for
Video AI often comes bundled in tools you bought for other reasons. Some cloud storage services label files automatically or let you search images and video by what's in them. Editing software transcribes. DAMs add tags on upload. Meeting and webinar platforms produce a transcript of every recording. Check the admin settings for each one, because some of these features ship switched off. Automatic tagging is the most common of them, and the one most likely to be running without anyone noticing.
What teams look at when the current stack falls short
Once the inventory is done, the gap is usually specific. One pattern: storage works fine, editors are happy with it, and nobody wants to migrate. What's missing is a way to search what's inside the footage. It hurts most for remote editors, who can't lean over and ask a colleague where something is. That leads to two very different evaluations. One replaces the storage with a MAM that has search built in. The other keeps the storage and adds a separate search layer.
The second route is less disruptive to try, and narrower in what it does. Search layers such as Vivu belong in that column, with one line worth putting in the comparison doc: the footage you want searchable is uploaded to a project and indexed there once, and your storage stays as it is.
Turning the list into something useful
For each tool, write down what it does with video, who owns it, which question it answers, and when someone last used it. Tools with no owner and no recent use are candidates to cancel. Questions that no current tool answers are your real shopping list. Where old brand assets end up is often the first gap this exercise exposes, because finished files tend to scatter across whatever was easiest to use at the time.
When you don't need anything new
If the inventory turns up an AI feature you already pay for that is switched off, turn it on and use it for a month before evaluating anything else. If the open questions are about approvals or versions, the fix is a process or a DAM. AI search won't help there. And if the team rarely reuses footage, the inventory itself may be the most useful result: it tells you what to stop paying for.
Which side you're on
If the inventory shows that tools you already have can answer your team's video requests, the job is switching features on and assigning owners. If the requests that go unanswered are about what's inside the footage, that is the one gap worth a new evaluation. The comparison doc should then list exactly which requests the new tool has to answer.
FAQ
Can ChatGPT or Claude tell me which software my company uses?
Only if it's connected to sources that record it, such as your company's documents, chat or IT systems, through connectors your admin has set up. Without that access, any list it produces comes from public information about popular tools. It doesn't reflect your company's actual stack.
Does our cloud storage already have AI search for video?
Possibly. Some storage services label files automatically, or let you search images and video by what appears in them, often only on certain plans. Check the admin console and your plan details. Even when the feature exists, test whether results point to a moment inside a video or only to the file.
What should go in an AI video asset management evaluation doc?
Start with the requests the tool must answer, written in your team's own words. Add the footage you'll test it on, where that footage has to live for the tool to work, who will own the tool, and how you'll judge the results. Leave the vendor feature list out of the first draft, because it pulls the comparison toward what vendors sell and away from what your team asks for.