Watch RTI’s Michael Kaelin on Infinia ML’s Machine Meets World.

RTI International CFO Michael Kaelin on Facing Your Team’s AI Fears

Join Infinia ML’s Ongoing Conversation about AI

James Kotecki
Sep 30 · 10 min read

Episode Highlights from Machine Meets World

This week’s guest is Michael Kaelin, RTI International’s Executive Vice President and Chief Financial Officer.

“One of the most important things is really trying to get in the hearts and minds of your team, because the natural first reaction is, ‘Oh my God, we’re going to implement this technology and I’m going to lose my job.’ I mean, that was the thing that was on their minds in 2016, 2017, so we spent a lot of time talking about why that wasn’t the case.

“There’s enough information out there that I think has proven that, yeah, their jobs are going to change, but new jobs are going to get created, and so how are you evolving as an individual or professional to take advantage of those new opportunities? Some of that we had to create new skills in the team, and some of that we had to think about jobs differently.”

“Adoption of AI, digital transformation, to me, is all about people. It’s not about the tech. I mean, you can buy the tech tomorrow if you wanted it, but if you don’t have an organization that’s aligned, excited, motivated about the change that can come from the adoption of the technology, then you’re not going to get the benefit from it.”

“Don’t get frustrated by fits and starts because you’ll have some of them, and that’s the way it’s been for us, but we know it’s a long-term important item for us, and we continue to invest and continue to move things forward. And so I think if you think of things like a journey, you won’t get as frustrated by maybe a lack of immediate results, but they will come. You will eventually get there.”

Watch the show above. You can also hear Machine Meets World as a podcast, join the email list, and contact the show.

Audio + Transcript

Michael Kaelin:
The natural first reaction is, “Oh my God, we’re going to implement this technology and I’m going to lose my job.”

James Kotecki:
This is Machine Meets World, Infinia ML’s ongoing conversation about artificial intelligence. I am James Kotecki joined by Michael Kaelin, RTI International’s Executive Vice President and Chief Financial Officer. Welcome, Mike.

Michael Kaelin:
Thanks, James, for having me. I appreciate it.

James Kotecki:
So for people who actually think they know what RTI International is, what do they think you do?

Michael Kaelin:
People that know us know that we’re an independent research institution. They may know us by having a lot of health expertise. They may know us by having a lot of education expertise. They may know us as someone who does a lot of international development, and all of those things are true. About 80% of our work is federal, but it spans pretty much all the agencies, and it’s a global organization where we’re doing work, not just domestically in the US, but in 70 countries around the world.

James Kotecki:
You’re the CFO. You’re not obviously the one doing the primary research, at least not most of the time, I’m guessing.

Michael Kaelin:
Right. Right.

James Kotecki:
What is your interest in artificial intelligence?

Michael Kaelin:
Really, for me, it started maybe a couple of years ago as I was trying to think about the future of how finance operates within RTI, and embedded in that vision was really bringing technology, artificial intelligence, other tools into what we do. And back in 2016, we weren’t really quite ready for the technology adoption, I would say. We did other things that were important and foundational. It’s not until in the last year where we’ve really started to do some work and do some pilots that really help advance what we’re trying to do as a function, but also how we support our research teams.

James Kotecki:
And is it fair to say that some of those foundational issues were getting the data set up, getting the data in the right place, getting it cleaned up?

Michael Kaelin:
It’s hard to get good clean data when it’s coming from 25 different systems and you’re trying to bring that together to try to do something with, so that’s a challenge that we’re still wrestling. The second thing is people. I was really spending a lot of time trying to get my staff and my organization mentally ready for technology adoption.

James Kotecki:
There’s almost a component of emotional readiness there. Obviously, the term AI can conjure up a lot of mixed emotions for a lot of people. Talk to me about the emotions that were welled up in RTI and how you navigated that as a leader.

Michael Kaelin:
One of the most important things is really trying to get in the hearts and minds of your team, because the natural first reaction is, “Oh my God, we’re going to implement this technology and I’m going to lose my job.” I mean, that was the thing that was on their minds in 2016, 2017, so we spent a lot of time talking about why that wasn’t the case. There’s enough information out there that I think has proven that, yeah, their jobs are going to change, but new jobs are going to get created, and so how are you evolving as an individual or professional to take advantage of those new opportunities? Some of that we had to create new skills in the team, and some of that we had to think about jobs differently.

Michael Kaelin:
So basically it’s just, for me, it was about being an open and honest and transparent about it and communicating consistently about it so that my team, when we did start to do things, they understood why we were doing them. We spent some real time really communicating to our organization, how to embrace automation and technology and AI and what it was going to mean for them and that it wasn’t a threat, that it was actually an aid. It’s actually taking what we’re doing today and making it better.

We’re using some machine learning techniques to change the way we forecast revenue for the organization. Before, that was a very manual bottoms-up build process. It’s sort of project by project. You build it up, and it’s very cumbersome. It takes a long time. But today, now we’ve got, looking at the last 15 years worth of data, we can actually create an AI model that forecasts what revenues going to look like in the future. We can do that like that. Now, is it 100% accurate today? No, but it’s a better place to start from than all of that effort building up. So we’re basically re-engineering how we do our entire revenue forecasting for the Institute.

James Kotecki:
Is there another example or two of really practical areas where you’re using AI and actually seeing now some results or some hints of results that will be practical?

Michael Kaelin:
One project that we worked on with Infinia, which has been very good, is really using machine learning to help us in our proposal development process. We do a tremendous amount of proposals every year, that’s the engine of the institution. What we have done is, we’ve taken something that was a very human — an individual human-led process about what’s in their brain to get it into the proposal, and we’ve been able to leverage all the collective knowledge of the Institute, meaning all the things that we’ve done over the last 10 or 12 years, and get that into a place where we can build an algorithm that says, “Okay, the requirement is this. Here’s the best set of proposals that we’ve had in the past, so let’s start with that,” and we build that in. And then we’re using our people to modify, edit, and change that baseline that we’re starting from versus basically starting from scratch.

Michael Kaelin:
It’s a big efficiency opportunity for us, but I also think it’ll help us with a better outcome on the proposal side. We rolled that out in February, and we’ve had tremendous adoption. The feedback we’re getting from our research staff is fantastic where they’re really starting to see the benefit that it provides to them. So that’s one example of something internally that’s been very successful. And I think once you have something like that that’s successful, then there’s a yearning for more, which is very helpful.

James Kotecki:
So it’s pretty clear that one of the mindsets you are leading here is that it’s not about replacement of people with AI, necessarily — it’s about AI almost giving people a head start.

Michael Kaelin:
In both cases, we’re basically eliminating all the grinding that needs to get done so that our teams can then work with that and use their intelligence and their analytical skills to then really make that beginning point better. The good news is our staff now have some examples where they see that to be the case, so they’re a little less threatened about what AI or machine learning will bring to them.

James Kotecki:
Are there ethical or AI responsibility concerns that you have as a leader that you’re either monitoring or dealing with as you roll out these technologies?

Michael Kaelin:
I think what we would like is that the outcomes from any of those efforts are objective and are free of bias. Objectivity’s one of our core values at RTI, and that’s because all the research work we do and everything that we provide for our clients, we want to make sure it’s objective. And I would hope that anything that we do with AI yields an objective outcome. What we’re trying to do like in those two cases is we validate. We look at what the model is producing. We test it against what we already know. Is it making sense? Is it dealing the outcomes? If not, what can we teach it to get us to the place where we think it’s an objective outcome? And it’s not easy.

James Kotecki:
This whole piece at the end of actually monitoring it and making sure effectively that it does what you want it to do is something that maybe executives don’t think about but probably should.

Michael Kaelin:
It’s changed a lot in the last 24 months, I would say. I think there’s a lot more acceptance, a lot more knowledge. There’s a lot more companies that are doing things. I think as they get pilots up and they recognize how to manage that going forward, they recognize there’s a piece of that that has to occur. I feel like more people are talking about it and more people are figuring out how they embed that sort of structure in their organization. They may not have got it solved yet. We don’t have a totally solved, but we recognize the issue, and so we’ve then got to structure some people and systems and things internally differently to be able to maintain this new asset that we have internally so that it can be effective.

James Kotecki:
Is there something that you commonly see executives just don’t get about AI?

Michael Kaelin:
Adoption of AI, digital transformation, to me, is all about people. It’s not about the tech. I mean, you can buy the tech tomorrow if you wanted it, but if you don’t have an organization that’s aligned, excited, motivated about the change that can come from the adoption of the technology, then you’re not going to get the benefit from it. So spending more time on getting your people excited, aligned, developing new skills in the organization and creating resources to do that, I think that’s probably where organizations maybe miss a little bit, and we did initially, and we’re spending a lot more time on that now to make sure that we bring everybody along.

James Kotecki:
Does it change the way you hire people in non-technical roles? It’s obvious that if you’re starting to do all this AI stuff, you may be hiring more people that know about AI, but other people in non-technical roles have to deal with it, understand it, hopefully not fear it from day one on their job, so does it change the way you look for candidates?

Michael Kaelin:
Yeah. Absolutely, it does. And I’ll tell you, that is one of the hardest things. Quite candidly, as hiring managers in the functions, say, in finance, you have a view of what a great finance person looks like. What I’m trying to tell my leaders is you got to change your worldview a little bit because some of those skills, yes, are relevant, but we probably need to have other skills in the team that we never even thought about before that would benefit the whole. And so yeah, it does force you to think a little differently, like “should we have a data scientist in our team?” Instead of going to a central source, it’s more impactful if it’s embedded in the organization. For us, it’s a work in process.

James Kotecki:
Mike, is there anything else that this conversation has shaken loose from your brain today that you want to say before we close out?

Michael Kaelin:
This is a journey. Don’t look at this as a specific set of goals you’re going to achieve in the next six or twelve months. I think this is a journey that’s going to happen over time. Don’t get frustrated by fits and starts because you’ll have some of them, and that’s the way it’s been for us, but we know it’s a long-term important item for us, and we continue to invest and continue to move things forward. And so I think if you think of things like a journey, you won’t get as frustrated by maybe a lack of immediate results, but they will come. You will eventually get there.

James Kotecki:
Michael Kaelin, the CFO at RTI International, thanks so much for joining us today on Machine Meets World.

Michael Kaelin:
James, thank you very much, and I appreciate the opportunity.

James Kotecki:
And thank you so much for watching. This is Machine Meets World. You can email us mmw, Machine Meets World, @infiniaml.com. You can like, comment, share. You know what to do! I’m James Kotecki, and that has been what happens when Machine Meets World.

Machine Meets World from Infinia ML

Weekly Interviews with AI Leaders

Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore

If you have a story to tell, knowledge to share, or a perspective to offer — welcome home. It’s easy and free to post your thinking on any topic. Write on Medium

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store