The Biggest Misconceptions About AI Video Workflow Automation
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Read the complete transcript of this clip:
Eric Bolten: How does one demystify this process? You need to trust within the AI/ML aspects of this. And I think that making it clear on how these tools work, what they're based on, how they actually manifest themselves in results is really important. I mean, one of the biggest hidden challenges in technical debts in the industry is root cause analysis. And an average program channel of content will spend $500,000 a year just trying to figure out what went wrong with the fiber, what went wrong with the server, et cetera, et cetera. And you know, television is a 100-year-old business, but for 60, 70, 80 years, we were running blind. The only way you even knew anything was good in a truck was you sent a signal to master control and bring back a net return to say, "Look, that's the picture that they got." That's not how this is gonna work.
Zixi is a live business. We do live streaming. So as the industry has morphed from a very video-on-demand, file-based set of offerings, as you intersect with live sports and news et cetera, there's no time to figure those things out. And the amount of content flowing is exponential. So you, you are going to need to have those correlations, the causalities presented to you in an actionable form.
Nadine Krefetz: So, do your customers, or even, like I said, your contacts, do they have any ideas that just don't resonate, that are inaccurate?
Eric Bolten: As a person who's speaks with customers like Discovery and other folks that we would all know as household names, the bucket of AI/ML is "Well, is that Datadog or ServiceNow, is this data visualization? Is it Watson?" The answer is, it's a very big ecosystem and there are different things.
We at Zixi are really focusing in on video, but not an image recognition or that part. But how do you maintain the payload that is video in an AI/ML way? So I think that now we're getting into a lexicon that is starting to land in a much more specific way, and then taking this and translating it from an engineering point of view, an operations point of view, a technological/architectural point of view, and then, ultimately, a business impact point of view. There's a lot of evangelism that's required of all of us to get to the common understanding about that. And I don't think that's clear.
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