Tom Brown

▶0:00HostWelcome to the Built by People podcast, where we share the stories and insights of the world's top HR leaders. Join us as we dive deep into the minds of HR executives, uncovering their strategies, challenges, and triumphs in shaping today's workforce. I'm excited to welcome Tom to the Built by People podcast. Tom, thank you so much for joining us. And as a starting question, I always love to ask if you could share a little bit more about your career journey.

▶0:48GuestThat's great. Thanks for having me. I've had a really interesting career. I've been lucky enough to live and work in a number of different countries— the US, UK, Netherlands, Switzerland— and spend considerable time in places like Russia and China as well. Early career was spent really trying to find out what my passion was. I spent time in education. I went off and did an MBA. I spent time as a strategy consultant, and it was only latterly that I moved into HR with Dell. And Dell then moved into Vodafone, another large UK corporate, and then into Juniper Networks. Juniper Networks was when I moved across to the US in 2008, and then with them till 2010, and then a long period with eBay, and then I joined Automation Anywhere, spent a little bit of time at Cooper, and then joined Drover about 3 years ago.

▶1:35HostTom, a lot of our conversation today is going to center around AI in HR. Can you first start by telling us a time when you had to address employee concerns about AI replacing jobs? How did you approach that conversation and what were the outcomes?

▶1:52GuestYeah, it's a really interesting kind of moment in time. I mean, I think it's worth going back to kind of 1940s and you had the president of IBM saying, I can see a market for 5 computers. Or in the mid-1970s when the head of digital was going, I don't understand why anyone would need a computer in their house. And the reason we're giving that kind of preamble is we don't know. And there's lots of people out there saying, we know what's going to happen. But if you listen to the reports coming out of the World Economic Forum or Goldman Sachs or McKinsey, the range of possibilities from it's going to add tens of millions of jobs to it's going to erode hundreds of millions of jobs is really clear. So we're at the very early stage of this revolution and nobody really knows what's going to happen. And so with that, I think you need to address the, first of all, some immediate realities. AI not only will take people's jobs away, it's already taking people's jobs away. The interesting balance to that is what are the jobs it's going to create? What are the jobs it's going to augment and make better? And how is it fundamentally going to change society? And what are the potential benefits of this? And what's the speed? Because if you go back to the Industrial Revolution, you're talking decades, even centuries for that to roll through. You've had a series of, I would describe as micro revolutions since then, computing and things like that. But we're now at a point where this is going to fundamentally change society. And so I think what you have to do is get people to understand that we are at the early stage, that the winners and losers are still very undecided, that we all have agency in this, and that actually getting as far ahead as we can on knowledge of AI, what AI can do, and the opportunities, rather than getting paralyzed by the fear of what it might deliver societally, is going to be really important. So it's really trying to get people to be curious, to explore, and to take some risks with AI.

▶3:47HostTom, how does your organization approach the balance between AI implementation and workforce transformation? Can you share a specific example of how you've managed that transition?

▶3:59GuestSo we've got a few different cases. I'm lucky enough to work for an organization that is very AI forward, both in terms of our own products where we've launched AI solutions, and in terms of the culture and philosophy of the organization, which is very much this go out and learn about AI. Let's look at opportunities to how we can leverage it. And that makes it a much easier environment. We've used AI in a number of different cases. First of all, I would say generically, it's being used across the organization. There isn't a function in the company that you could touch that isn't using it in some means. A customer success organization has seen real opportunities there. And more broadly, the research is out that says about 35% operational improvement can be seen in customer services. And we're also starting to use it as we think about what can happen with our software engineering and R&D and looking at opportunities to really leverage AI. There's still a number of steps to go to really make it as effective as people would like, but there's a lot of options there. And as you go through every other function of legal, you could be looking at Harvey or Co-Counsel and saying, what are the opportunities there? You get into more graphic design, there's obviously DALL-E and others. So there's this whole suite of potential solutions. I think we have to look to embrace them as we can. And at the same time understand the inherent risks of some of these large language models, where they can potentially scrape data, where you have to be careful about information. So there is always a balancing act, but we're definitely on the front foot when it comes to this.

▶5:22HostTom, when was a time you successfully implemented AI in a department or team? What were the key learnings and how did it impact both the business and employees?

▶5:32GuestYeah, so I'll go back to that. Sorry to hark back to the last example about customer services, but That was definitely one where, first of all, it was getting everybody in the team to a certain level and understanding of how AI was going to be used and what the opportunities were for them to see it as a powerful augmentation and not as a potential kind of replacement and an important kind of philosophy to start with. And then as they start to embrace and they start to see how that was able to deflect a lot of the kind of more administrative kind of calls that they were getting, and it was better for the customers and it was better for the team. The team could then focus on more value-added work. That was really a kind of good example there. And I'd say we're starting to see that in other teams that are leaning into AI in different ways.

▶6:15HostTom, can you share with us an example of how you're preparing your workforce for an AI-augmented future? What specific programs or initiatives have you put in place so far?

▶6:26GuestSo we don't have any kind of formal sort of training per se, although we have LinkedIn Learning and that's got some great courses on AI and those are very well utilized. So I think it's more about driving this holistic approach that says, look around, see what's out there, stay curious, understand that it's an environment that is moving very fast, and take what opportunities you can to learn about the different systems. We have an AI task force in the company. So AI is carefully looked at as we implement it, but it's being looked at from the sector of we want this to happen, not we don't want this to happen. So it's really there as a checks and balance to make sure that we don't give access to any kind of confidential data or anything else that— but otherwise we are really trying to just encourage it. It's part of our conversation at almost every all-hands now. So it's about just putting it out in the ecosystem and understanding that we have a great team of curious employees who are keen to get hold of this.

▶7:24HostTom, what parting advice would you like to share with our community?

▶7:28GuestI think the big piece here is be realistic that we don't know what the future is going to hold, but we do know that the change is going to be incredibly rapid, that there are going to be winners and losers through the process. And that as with any kind of revolution, the folks that tend to do worse are the ones who are most worried about it and therefore the most risk-averse. Even if you look at this from a societal perspective and you start looking at it and saying Google is now some commission small nuclear power stations to cope with the data demands. You think about what the potential of an augmented workforce could look like in terms of productivity and whether you could then look at things like the 4-day working week as really viable solutions in an environment where demographically populations are declining. So I think there's lots of really positive upsides and I think people just need to be thoughtful about those.

▶8:20HostTom, thanks so much for joining us on the Built by People podcast today. It was a pleasure to have you involved.

▶8:25GuestThank you for inviting me. Thanks.