Episode Transcript
[00:00:03] Speaker A: Eric, I get quite a bit of a taste for this myself, but not as much as you, I suspect, your professional life. You're getting approached by vendors all the time and you're always trying to work out what's actually true. I'm sure I've heard you say somewhere else, possibly to me in a bar, that sometimes there's a gap when you're thinking about it or working it through.
How do you bridge that gap?
[00:00:31] Speaker B: So, okay, I mean, I have to be realistic and say, let's take Zubin, for example. You. I mean, Zubin is exuberant about what, what wise tech is right now and what it could become. If he wasn't that way, he wouldn't be in that role or he shouldn't be in that role. He would be doing his customers and his employees and his shareholders a disvalue or, you know, so I walk into every sort of conversation with a vendor knowing they're going to be far more excited about this than anybody else in the world. Right. So that's an expectation that I have in any conversation. So how does that translate into what you asked?
There's two sides and sometimes 10 sides to every story. Right. So customers, especially paying customers, have high expectations.
It's almost impossible to make them completely happy in any scenario. Right. So I really focus on not necessarily sort of the, the outlier complaints about a specific arrangement that a software user has with a vendor. I'm focusing on. Am I hearing the same thing over and over and over about the same vendor with the same, you know, sort of issue? Right. That's where I know, okay, maybe this software is not really hitting the mark on the whole, rather than, oh, this is just a bad match between this vendor and this customer. So that's one thing, you know, I think the other thing that I feel, I feel like has become a bigger issue over the last 10 years. And Zhuin touched on this a little bit like the pace of development of technology in the background.
The sort of expectation to, or I should say the expectation is not the right word. The compulsion to over promise in a lot of cases has led to expectations. It's harder to meet those expectations.
[00:02:29] Speaker A: You could give examples here, Eric, if you wanted to throw.
[00:02:32] Speaker B: I don't want to necessarily throw anybody under the bus because it's not really about that. I think there's too many examples for me to point to any one vendor. And it's really across every sort of category I cover. Right. And we're seeing it now with sort of the AI agentic sort of pure AI agentic providers. Right. So the issue is that the inner workings of software have somehow revealed themselves in a way to users in a way that they didn't before. You know, when cloud was becoming the norm rather than this sort of weird outlier 20 years ago, no one was going, well, tell me how cloud actually works. Right? It was more, how did I pay for this? How do I access it on my computer? They didn't care about, like, the inner workings. It's not like, you know, you get those watches where you can see how the mechanism in the back works, right? Yeah, like, okay, great. That's a cool thing to look at. I have no idea how it works, even if I can see it. So why do I have a compulsion to know how this thing works? And we see that. We saw it, I think, most prominently in a negative sense with blockchain, where there was this. And I was part of the problem. There was this intense drive to understand what blockchain was, how it worked, what, you know, who. Who cares? Honestly, who cares? And AI is the same thing. It should not be that everyone needs to be a master of the technology or the tool itself. It should be, what is the outcome? Is the outcome that you seek being delivered by the company that you're paying to deliver that outcome? And so I would say, I would put it on the sort of user to not get swept away by whatever a salesperson is telling them, or a CEO on a panel talking to me or you, and more about what was your expected outcome, did the outcome get achieved? And whatever the tool was to get there, whether it was a hammer and chisel or a neural network, like, that's the most important thing, and it should remain the most important thing for people in our industry.
[00:04:35] Speaker A: But do you think this is. We've got. There's so many AI products out there. I mean, I think, like, I mean, I don't know what your inbox is like, but I get so many approaches. People want to come on and tell me all about, want to talk about what's going on in the straight of Hormuz, because they're trying to promote an AI product. I was like, what do you know about what's going on in the Strait of Hormuz? I want someone in operations to tell me that.
Not someone who's trying to flog me an AI product. How do you differentiate? And how would a buyer differentiate when there's just so much noise out there?
[00:05:05] Speaker B: Well, first of all, I have an agent sorting out all those emails through a special box opens your mail. Yeah, there you go. So it's a great question. It's honestly one that I try to ask a lot of vendors because I think it's a. It's one of the more important questions. How do I tell if your AI is better than, you know, your competitors? AI? Like, show me a way. So I think if you, if you decide that, you know, you have to do some research, you have to think, you have to see if this software is, is actually addressing the problem you have or, or the thing you want to address from an investment standpoint, that's on you. Second is, okay, you've decided they are in the right category for the thing you're trying to solve, then you have to put it on them to say, show me, you know, do you need data from me? Do you need a sandbox to play in? Show me that your AI delivers a better, faster, more accurate, more robust answer to the question that I have than three other ones that I'm testing and that. And they should be able to do that. I'm sorry, in this day and age, that should be. That's a basic question to ask in an rfi, if rf, whatever the RF you choose to use.
