Stanley Black & Decker is best known as the manufacturer of tools for home improvement projects, but it also makes products the average consumer seldom notices, like fasteners to keep car parts secure and the electronic doors typically used at retail stores. Me, Myself, and AI podcast hosts Sam Ransbotham and Shervin Khodabandeh sat down with Mark Maybury, the company’s first chief technology officer, to learn how artificial intelligence factors into this 179-year-old brand’s story.

During their conversation, Mark described how categorizing the technology-infused innovation projects he leads across the company into six levels, ranging from incremental improvements to radical innovations, helps Stanley Black & Decker balance its product development portfolio. He also shared some insights for organizations thinking about responsible AI guidelines and discussed how Stanley Black & Decker is increasing its focus on sustainability.

Sam Ransbotham: AI applications involve many different levels of risk. Learn how Stanley Black & Decker considers its AI risk portfolio across its business when we talk with the company’s first chief technology officer, Mark Maybury.

Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I’m Sam Ransbotham, professor of information systems at Boston College. I’m also the guest editor for the AI and Business Strategy Big Ideas program at MIT Sloan Management Review.

Shervin Khodabandeh: And I’m Shervin Khodabandeh, senior partner with BCG, and I colead BCG’s AI practice in North America. Together, MIT SMR and BCG have been researching AI for five years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities across the organization and really transform the way organizations operate.

Ransbotham: Today we’re talking with Mark Maybury, Stanley Black & Decker’s first chief technology officer. Mark, thanks for joining us. Welcome.

Mark Maybury: Thank you very much for having me, Sam.

Ransbotham: Why don’t we start with your current role. You’re the first chief technology officer at Stanley Black & Decker. What does that mean?

Maybury: Well, back in 2017, I was really delighted to be invited by our chief executive officer, Jim Loree, to really lead the extreme innovation enterprise across Stanley Black & Decker. So I get involved in everything from new ventures to accelerating new companies, to fostering innovation within our businesses and just in general being the champion of extreme innovation across the company.

Ransbotham: You didn’t start off as a CTO of Black & Decker. Tell us a bit about how you ended up there.

Maybury: If you look at my history — you know, “How did you get interested in AI?” — AI started when … literally, I was 13 years old. I vividly remember this; it’s one of those poignant memories: [In] 1977, I saw Star Wars, and I remember walking out of that movie being inspired by the conversational robots — R2-D2, C-3PO — and the artificial intelligence between the human and the machine. And I didn’t know it at the time, but I was fascinated by augmented intelligence and by ambient intelligence. They had these machines that were smart and these robots that were smart. And then that transitioned into a love of actually understanding the human mind.

In college, I studied with a number of neuropsychologists as a Fenwick scholar at Holy Cross, working also with some Boston University faculty, and we built a system to diagnose brain disorders in 1986. It’s a long time ago, but that introduced me into Bayesian reasoning and so on. And then, when I initiated my career, I was trained really globally, so I studied in Venezuela as a high school student; as an undergraduate, I spent eight months in Italy learning Italian; and then I went to England and Cambridge and I learned English.

Ransbotham:The real English.

Maybury: The real English. [Laughs.]

Ransbotham: C-3PO would be proud.

Maybury: C-3PO, exactly! … Indeed, my master’s was in speech and language processing. Sorry, you can’t make this up. I worked with Karen Spark Jones, a professor there who was one of the great godmothers of computational linguistics. But then I transitioned back to becoming an Air Force officer, and right away, I got interested in security: national security, computer security, AI security. I didn’t know it at the time, but we were developing knowledge-based software development, and we’d think about “How do we make sure the software is secure?”

Fast-forward to 20 years later. I was asked to lead a federal laboratory, the National Cybersecurity Federally Funded Laboratory at Mitre, supporting NIST [the National Institute of Standards and Technology]. I had come up the ranks as an AI person applying AI to a whole bunch of domains, including computer security — building insider threat-detection modules, building penetration testing, automated agents, doing a lot of machine learning of malware — working with some really great scientists at Mitre, in the federal government, and beyond, [in] agencies and in commercial companies.

And so that really transformed my mind in terms of how do we … for example, I’ll never forget working together with some scientists on the first ability to secure medicine pumps that are the most frequently used device in a hospital. And so that’s the kind of foundation of security thinking and risk management that comes through. I got to work with the great Donna Dodson at NIST and other great leaders. And so those really were foundational theoretical and practical underpinnings that shaped my thinking in security.

Ransbotham: But doesn’t it drive you crazy, then, that so much the world has this “build it and then secure it later” approach? I feel like that’s pervasive in software in general, but certainly around artificial intelligence applications. It’s always the features first and secure it later. Doesn’t it drive you insane? How can we change that?

Maybury: There are methods and good practices, best practices, for building resilience into systems, and it turns out that resilience can be achieved in a whole variety of ways. For example, we mentioned diversity. That’s just one strategy. Another strategy is loose coupling. The reason pagodas famously last for hundreds and hundreds of years in Japan is because they’re built with structures like, for example, central structures that are really strong, but also hanging structures that loosely couple and that can absorb, for example, energy from the earth when you get earthquakes.

So these design principles, if you think about a loosely coupled cyber or a piece of software system, and even of course decoupling things, right, so that you disaggregate capabilities, so that if a power system or a software system goes down locally, it doesn’t affect everyone globally — some of these principles need to be applied. They’re systems security principles, but they can absolutely be applied in AI. I mean, it’s amazing how effective people can be when they’re in an accident. They’ve got broken bones, they’ve got maybe damaged organs, and yet they’re still alive. They’re still functioning. How does that happen? And so nature’s a good inspiration for us.

We can’t forget, in the end, our company has a purpose for those who make the world. And that means that we have to be empathetic and understanding of the environments in which these technologies are going to go into and make sure that they’re intuitive, they’re transparent, they’re learnable, they’re adaptable to those various environments, so that we serve those makers of the world effectively.