Feed Drop: Why Only 10% of Companies Succeed With AI With Sam Ransbotham, Professor at Boston College

**Sam Ransbotham** (0:02)
Today, we're airing an episode produced by our friends at the Modern CTO Podcast, who were kind enough to have me on recently as a guest. We talked about the rise of generative AI, what it means to be successful with technology, and some considerations for leaders to think about as a shepherd technology implementation efforts. Find the Modern CTO Podcast on Apple Podcast, Spotify, or wherever you get your podcast.

**Joel Beasley** (0:29)
Today, we're talking to Sam Ransbotham, professor at Boston College and host of the Me, Myself, and AI podcast about why only 10 percent of companies succeed with AI. You're listening to Joel Beasley, Modern CTO.

**Joel Beasley** (0:50)
All right, so your podcast, the premise of it is why do only 10 percent of companies succeed with AI. That caught my attention. I was browsing the Internet on LinkedIn or something, and I saw this, why do only 10 percent of companies succeed with AI? That's how I found you and all of this, and all your show, Me, Myself, and AI. And I want to know, is it really that big of a deal? You made an entire show about it?

**Sam Ransbotham** (1:15)
Well, I think you know how marketing works. I mean, I think that's that word. We have to lead with some statistic that gets people interested. But that's a pretty interesting one, isn't it? Given the amount of stuff that we're hearing about artificial intelligence, we were hoping that that number was bigger than 10 percent.

**Joel Beasley** (1:33)
And why are they failing?

**Sam Ransbotham** (1:34)
I see there's the trap. I don't think that we're people really failing. And so our research, it looks at and says that about 10 or actually 11 percent are getting significant financial benefits. So it's not like they're not getting any benefits. It's not like they're failing. It's just maybe falling short of this, AI is going to change everything that we're hearing so much in society. So don't cast it as failure. There's more than two options here. This is not Hobson's choice.

**Joel Beasley** (2:05)
That's funny. Yeah. So of these companies that are achieving significant returns and investments from this AI, tell me about those.

**Sam Ransbotham** (2:13)
Yeah. So we put together, we tried to look at, all right, given we have some have-nots and some haves, what's the difference? I mean, that's a natural question for an academic or for anyone to try to figure out those differences. And the first few are things that you might, I think we would expect, right? They're got to get their technology house in order. You can't have something like artificial intelligence, complicated machine learning models. If you're basically working on an outdated copy of Excel that is run on a dated PC, right? So there's a certain sort of infrastructural element to that. And also there's talent.
You have to have somebody use these tools here. Now, so what we found was that 10 percent, to get to be one of those 10 percents, you got to have some of those basic building blocks in place. And we think of those as talent, infrastructure, and strategy. And I could talk about each one or more of those, but that doesn't get you all the way there. There's a lot more after that, and I think it was more interesting for us. You can't say, for example, we're just going to take the same little thing and just do it with AI. That is not going to get you into that 10 percent. One of my fun examples is in the healthcare industry. So here's a question for you, Joel. When was the FACTS machine invented?

**Joel Beasley** (3:38)
Oh, I don't know.

**Sam Ransbotham** (3:39)
This is about rehearsing, everyone. I'm putting them on the spot, literally.

**Joel Beasley** (3:43)
I'm going to say somewhere between the 60s and early 80s.

**Sam Ransbotham** (3:51)
60s and early 80s. Now, I think you're thinking 1960s or 1980s, right? Yeah. No, much closer to 1860s. So, which is interesting because, you know, if you think about that, it predates telephone and, you know, it was working across telegraph or whatever. So, here's the point, that's a really old technology. I'm headed somewhere with this story. Don't panic.
There's a lot of interesting stuff happening. And one of the things, the industries that uses faxes left and right is healthcare. They'll fax stuff left, you know, back and forth. They're practically the only people still using faxes to this day. And so, I've read this story about people in healthcare using AI, optical character recognition, text parsing, to take an image from a fax machine and scan it, and try to get all the information out of it. And that, on the one hand, seems like a great use of artificial intelligence, because nobody wants to retype everything that comes across the slick little fax paper, right? So, again, there's value in artificial intelligence there. But what about just not sending a fax in the first place? What about sending that information from one computer system to another computer system without a fax machine at all? And so, that's the point of you can't just slap AI on top of an existing process, which is faxing. Think of some new way to do a process. And I think that's the real difference in that 10% that you were getting at.