Oct 1, 2026 · 45 min · 11 segments
**Title:** Signal Discipline: How Brands Use AI, Customer Signals, and Memory to Build Trust **Host:** Nick Brunker (Managing Director of Experience Strategy, VML) **Guest:** Adam Troyak (Managing…
Adam TroyakGuestNick BrunkerHostSo let's get into the, the macro because there's a stat in the first paper that, that you guys had, uh, put together, and I know you, you put, uh, fingers to keys to that I think really is the whole ballgame, and the, the data point is 77% of organizations say their AI adoption is already ahead of their governance.
And what I think is interesting about that number is it's not really a technology problem, right? Nobody's saying we can't do it.
Th- they're saying we're doing it faster than we figured out how to do it responsibly.
Is, is it just, uh, everybody sprinting to deploy and nobody wanting to be the person who slows it down, or do you think there is something more structural going on inside these organizations?
Yeah, I mean, a, a little bit of it is everyone's racing, right? And I think, um, a little bit of it is everyone's racing and everyone's racing in silos, uh, so that's a part of it as well.
Um, you know, most data conversations that I've had over the years is, uh, feels like they've always been solely focused on more and, and, and scraping and getting as much as you can, and I've had very few conversations around what data is worth more than others.
What data should we hold onto? When should we surface that data in the right moment? Um, what's the data that's worth leaving behind? And, you know, um, sitting in and thinking about how AI is, uh, affecting kind of our ability to remember more about customers, um, you know, it, it seems like it's less of an infrastructure problem, like the pipes seem to work, um, but what I've noticed that is that nobody's really built that layer that decides kind of what's worth surfacing and, and thinking about that in a, in a structured way.
You know, I've got a line in, in, in the, in the three-part series that I really like, and it's...
And so we have to get really specific around, um, you know, the data that means something, the data that means something in the right moment, and making sure that we're prioritizing that so, uh, AI can, you know, scale what we remember about clients in the most effective way.
We'll be linking to your three-part piece in our show notes, so for those that are listening-
One of the things you do early in one of those, uh, those pieces that I really appreciated is that you separate two words that I think most of us can completely use, uh, interchangeably, data and signals.
So let's get into the, the macro because there's a stat in the first paper that, that you guys had, uh, put together, and I know you, you put, uh, fingers to keys to that I think really is the whole ballgame, and the, the data point is 77% of organizations say their AI adoption is already ahead of their governance.
And what I think is interesting about that number is it's not really a technology problem, right? Nobody's saying we can't do it.
Th- they're saying we're doing it faster than we figured out how to do it responsibly.
Is, is it just, uh, everybody sprinting to deploy and nobody wanting to be the person who slows it down, or do you think there is something more structural going on inside these organizations?
Yeah, I mean, a, a little bit of it is everyone's racing, right? And I think, um, a little bit of it is everyone's racing and everyone's racing in silos, uh, so that's a part of it as well.
Um, you know, most data conversations that I've had over the years is, uh, feels like they've always been solely focused on more and, and, and scraping and getting as much as you can, and I've had very few conversations around what data is worth more than others.
What data should we hold onto? When should we surface that data in the right moment? Um, what's the data that's worth leaving behind? And, you know, um, sitting in and thinking about how AI is, uh, affecting kind of our ability to remember more about customers, um, you know, it, it seems like it's less of an infrastructure problem, like the pipes seem to work, um, but what I've noticed that is that nobody's really built that layer that decides kind of what's worth surfacing and, and thinking about that in a, in a structured way.
You know, I've got a line in, in, in the, in the three-part series that I really like, and it's...
And so we have to get really specific around, um, you know, the data that means something, the data that means something in the right moment, and making sure that we're prioritizing that so, uh, AI can, you know, scale what we remember about clients in the most effective way.
We'll be linking to your three-part piece in our show notes, so for those that are listening-
One of the things you do early in one of those, uh, those pieces that I really appreciated is that you separate two words that I think most of us can completely use, uh, interchangeably, data and signals.
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