Navigating the AI Hype and Building Data-Driven Cultures | Juan Gorricho

**Les** (0:07)
Data Storytellers, special episode here on The Data Storytellers Podcast, because I have a guest who's not new to the show. I mean, those of you who are veterans and have been following us for a while, we had an episode with, at the time, a gentleman working for Visa, and it was a very, very popular episode. We had a great conversation, and it's my pleasure to welcome back on the show Juan Gorricho.

**Juan Gorricho** (0:37)
Thank you, Les, I appreciate it. Thanks for having me again. As you said, we had a great time last episode, almost three years ago, I think. Time flies, and I'm very happy and very honored to be back.

**Les** (0:49)
Absolutely. We're catching you in an interesting time of your career because you just left your role over at TD, and you're embarking on the new stage of your journey. What I wanted to do today is just to talk to you about some of the hot topics in data and AI because there's a lot happening. We are recording this in June 2025, which I think we'll look back at these couple of years, and we'll see that a lot happened, like huge transformations took place, and we are living through them right now. And I wanted to reflect on your career and extract some lessons to those who want to achieve similar feats and want to take some on the road to make sure that they can capture value in this exciting stage of data-driven development and transformation. So first of all, like now that you're kind of in between roles, what are you most excited about today? There's a lot to talk about, but maybe we can explore a few of these themes, especially when it comes to AI.

**Juan Gorricho** (1:57)
Yeah, exactly. So as you just pointed out, I think the state of data is in complete flux, right? I mean, it's been like that almost forever. It feels like it's getting faster, right? At the degree, the amount of change keeps on accelerating, right? And I think what we're excited about is everything that is going on and how the new capabilities, AI, the amount of data, everything that is going on, how is it enabling more people, more businesses to just have a larger impact, right? And I think the speed at which things are moving is quite fast, but it's exciting, right? Like every day, you read about a new application, a new use case, when you're able to cut through the noise, right? I think there's definitely a lot of noise. I think there's definitely a lot of AI washing. I was reading a fantastic article yesterday about the risks and dangers of AI washing and labeling everything AI. But once you see through what's going on, it's definitely a lot of very exciting things going, a lot of new applications.
A lot of just like, I was just playing in this, to your point, in this sort of interim period, I was just playing with a couple of those AI-based development tools for applications and literally with a prompt and a few hours, you just built a complete prototype that otherwise would have taken years to build. So it's exciting to hear all that and to see all that. I mean, one can only imagine what the future will hold for us.

**Les** (3:24)
And this would have been inconceivable only a couple of years ago.

**Juan Gorricho** (3:28)
Exactly. Exactly.

**Les** (3:30)
So AI washing is an interesting topic in and of itself. So why do you think people want to label everything AI today? How is this different from, you know, AI has been around as we think about AI. I mean, we're still not talking about machines that can think and reason like humans, that still stays kind of like a sci-fi dream. I know that we sometimes get a different impression about the projections around AGI. Well, let's just, we can stay on the ground of reality for a second, but AI has been around for a while, especially machine learning, but AI entered the mainstream. And some say, I'm in that camp too, that this has been developing for a while, this kind of fire has been building for a while. It was just Gen. AI that really was the spark that lit everything on fire.
But why do you think today everyone wants to label everything AI?

**Juan Gorricho** (4:29)
I think we've seen the same thing with data and big data. A couple of years ago, everything was big data, all data was big data, everybody was doing big data. And so I think to your point exactly, when Gen. AI came to the picture to open AI and TATP, it's going to be almost three years ago when the first things were launched and it became mainstream, I think because it was easy to grasp.