Why Model Choice Is the Wrong AI Obsession

Neil C. Hughes (0:00)

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What if the model your company has spent months selecting matters far less than whether sales and finance agree on exactly what revenue actually means?

Well, this is a wonderfully un-glamorous problem at the heart of enterprise AI. And my guest on AI at Work today is Emma McGrattan. She's CTO at Actian. She's a self-described data obsessive who builds Lego and judges an old fashion by the quality of its eyes. I've got a feeling we're going to have a fun conversation today. But on a serious side, Emma will argue today that competitive advantage actually comes from enterprise data, shared business meaning, clear ownership and employees that are willing to question those confident AI answers. Dare I say overconfident sometimes. And we'll also explore the gap between boardroom confidence and the reality seen by data engineers in their day-to-day work, and why agents expose weak governance, and how one data product and one data contract can move a useful AI project toward production. So if your pilots look impressive, but collapse when connected to real data, hopefully this conversation will help explain why. But enough from me. Let me introduce you to my guest right now.

So thank you for joining me on the podcast today, Emma. Can you tell everyone listening a little about who you are and what you do?

Emma McGrattan (2:17)

Yeah, sure. So my name is Emma McGrattan, and I'm the Chief Technology Officer at Actian.

And I'm obsessed with Lego. That's really what I do. I make Lego and I drink old fashions.

And I have a job that funds all of that. So my job at Actian is to talk to people about data, right? And how we're at the moment, data is like the sexiest it's ever been, right? But I started my career in data over 30 years ago. And back then, data was like plumbing, right? It was kind of in the background, kind of assumed it was always going to work. And when it didn't work, things got really ugly. But now data is like a conversation piece, right? I can talk to an Uber driver about the fact that I'm helping customers get their data ready for AI or identify where they may have some problems. So a big part of what I do is speaking publicly.

And, you know, there's three topics I'm really passionate about. One is data prep for AI, right? And the second is around the ethics of AI, right? And what data we should actually be exposing to AI and making sure that as we're training AI that we're doing it with data that's appropriate and that we're not having bias built into some of the models and so on. And the third area I'm really passionate about right now is data sovereignty, right? And how do we unhook ourselves from our complete dependency on American tech, right? Because there's some challenges there, right? You see some of the cloud providers, some of the frontier model providers have over a barrel on some of these things, right? So we're trying to figure out how do we help companies get unhooked from tech. So data AI sovereignty is the third topic that I'm quite passionate about at the moment. So doing a lot of public speaking. I'll be in the UK for Big Data London next month, so quite looking forward to that. Always a great event. And this year, Louis Thoreau is doing the keynotes.

Neil C. Hughes (4:07)

That'll be cool. I must admit, you had me at building Lego and drinking old fashions as well. But you mentioned data sovereignty there, which is a huge topic over here in Europe and something I hear more and more about. And when I'm doing a little research on you, I was also reading that you argued that enterprises are possibly spending too much time debating on which AI model is the fastest, the cheapest, or even the smartest.

So on that side of things, if model choice isn't what separates the winners from everything else, the question I've got to ask is what is, what should they be debating?

Emma McGrattan (4:41)

Right. So to me, the models are almost being commoditized at this point, right?