“I think sometimes the hardest technology problems aren’t really technology problems at all; they’re human problems.”
At 13, Jill Bruning was already working two jobs. During the day, she detasseled corn in rural Illinois. At night, she worked as a carhop at a Dog n Suds. The money was going toward something she had come to regard as essential: a college education and the ability to support herself. Growing up about 80 miles outside Chicago with two sisters and a mother whose marriages had often been difficult, Bruning had seen what financial dependence could mean for women. “I wanted a career, and I wanted to be able to earn my own money,” she recalls. She did not yet know what that career would be. She knew why she needed one.
Four decades later, Bruning is the Chief Technology Officer (CTO) of Amentum, an advanced engineering and technology company with approximately 50,000 employees working across complex government and commercial programs. Her responsibilities now include managing one of the most consequential technological questions facing large organizations: how to integrate artificial intelligence into the way people work. Yet the engineer who built her career solving technical problems has arrived at a somewhat unexpected conclusion. Some of the most difficult challenges in technology have very little to do with technology itself.
Her route into engineering began with a high school math teacher who recognized her aptitude for science and problem-solving. Bruning earned a bachelor’s degree in mechanical engineering from the University of Illinois and a master’s from MIT, entering a profession where women were still conspicuously outnumbered. She remembers trying to accommodate the prevailing corporate culture, right down to her wardrobe. “I even wore the little bow ties on my blouses in the man suits, as if that would work,” she says. The experiment in fitting in proved less valuable than the realization that followed. She found that becoming more comfortable with her own leadership style brought greater success and considerably less strain.
There were formidable technical assignments along the way. Early in her career, Bruning specialized in cryogenic engineering, developing expertise in the extremely low temperatures required for sophisticated scientific equipment. She recalls collaborating on cooling technologies associated with efforts to restore capabilities to the Hubble Space Telescope. The work gave her a connection to one of the world’s most recognizable scientific instruments, even if the engineering behind its operation was largely invisible to the public.
Another defining assignment came in the aftermath of September 11, 2001. Working in information technology, Bruning was called to Washington, D.C., to help redesign networks that would allow intelligence agencies to share information more effectively. The experience shifted her professional geography and reinforced the importance of the work itself. “I really felt like I was working on something that made a difference,” she recalls. She never left Washington.
Over the following decades, Bruning moved through senior technical and operational leadership positions at companies including CSC, NJVC, URS, and AECOM. Her responsibilities expanded from specialized engineering to large government programs and organizational management. At Amentum, she led major business operations before becoming CTO. That combination of technical depth and operational experience matters in her current position, where an important part of the job involves finding connections between engineering expertise in one part of the company and an entirely different customer’s problem.
Artificial intelligence has made those connections both more promising and more complicated. Bruning sees enormous potential in technologies that can shorten design cycles, retrieve engineering knowledge, and help technical teams evaluate their work. She describes engineers writing AI scripts to check drawings against mechanical and electrical design codes, allowing them to identify problems and improve designs more quickly. The possibilities keep expanding as employees discover what the tools can accomplish.
But speed creates its own management problems. At Amentum, enthusiasm for AI initially encouraged employees to compete over how extensively they used it. Bruning describes a shift toward recognizing the people who use computing resources most efficiently. The distinction is revealing. A technology capable of generating more work, more experimentation, and more consumption does not automatically create more value. Someone still has to decide what matters.
For Bruning, that responsibility belongs firmly with people. “I think sometimes the hardest technology problems aren’t really technology problems at all; they’re human problems,” she says. She is particularly concerned with how leaders establish accountability when work is shared between employees and increasingly capable AI systems. Which decisions can reasonably be delegated? Who checks the results? How does an organization preserve judgment, trust, and cybersecurity while changing familiar processes? She favors an approach her company calls “human in the lead, as opposed to human in the loop,” emphasizing human oversight and authority as technology assumes more tasks.
There is an interesting continuity between that position and the independence she sought as a teenager. Bruning’s early ambitions centered on acquiring the education and economic security necessary to make her own decisions. Her present work involves helping organizations decide how much authority they can responsibly give to machines. In both cases, the underlying issue is control, although the scale and consequences have changed considerably.
Education remains personal to her. Through a family foundation named for her late husband, she supports community development and educational opportunities in rural Missouri. Having grown up in a small community herself, she knows that access to education cannot always be taken for granted. Asked what initiative she would most like to expand, she returns to that work without hesitation.
For someone whose professional life now revolves around artificial intelligence, Bruning remains unusually attentive to the capabilities technology cannot supply on its own: trust, relationships, judgment, and the willingness to learn. Her career has taken her from Illinois cornfields to space technology, intelligence infrastructure, and the executive leadership of a global engineering company. The next challenge may be less about what machines become capable of doing than what their human counterparts choose to do with that capability. And for Bruning, who spent her early career learning to stop conforming to other people’s expectations, the question of how people retain their agency has a particular resonance.
Yitzi: Jill, it’s an honor to meet you. Before we dive in and talk about your great work and AI transformation, our readers would love to learn about Jill Bruning’s personal origin story. We’d love to hear about your childhood, how you grew up, and particularly the seeds of all the amazing leadership that has come since then.
Jill: Well, I grew up outside of Chicago. I don’t know if you can pick that up from the accent or not, a little small town about 80 miles outside Chicago. I grew up with two sisters and a mostly single mom in between a bunch of marriages that didn’t go well for the most part. What I will say is, in that era, women looked to their spouses for their financial security. I learned a lot from my mom, and one of those lessons was that I wanted a career and I wanted to be able to earn my own money. I didn’t know what that looked like as a child, but I remember talking to people about what that would take, and they said, “Well, you’d have to go to college.” Okay, how do I do that? “Well, you have to really work hard, get good grades, and save your money.” So I was always working multiple jobs. For my first jobs at 13, I detasseled corn during the day and then worked as a carhop at a Dog n Suds at night. I did achieve my goal and went to college, and my two sisters did too. Strong females that broke the chain there.
Yitzi: Wonderful. Can you tell us a bit about how you became one of the C-level leaders of Amentum? Tell us a bit about the story.
Jill: Amentum is an advanced engineering and technology company and I am a mechanical engineer. I actually didn’t know much about engineering when I went to high school or even when I first started my college career. But I had a high school math teacher who said, “You’re good at math and science and problem-solving. You should be an engineer.” And I do love to solve problems. So I grew up in engineering. I started my career and got into the government contracting community, working on U.S. government contracts, and have spent my 40-year career working for engineering and technology companies in the government contracting space.
Yitzi: Amazing. You have to tell readers a bit about what your day-to-day looks like and what it actually is that you do. Not classified.
Jill: As the Chief Technology Officer, I’m responsible for the technology strategy for the company, developing technology solutions, and integrating technology connections across the company. We have 50,000 employees and some absolutely fantastic technical work that goes on. So a lot of times it’s, “Hey, did you know there’s this cool thing going on over here that we can connect to a customer need, maybe in a different part of the business?” I’ve been with the company almost 14 years, so I’ve been through a lot of the growth. I was on the engineering and operations side, so I know a lot about the business. It’s a lot of fun at this point to be able to connect the technical work we do in order to meet the solutions our customers need.
Yitzi: Amazing. You probably have some amazing stories from your leadership in your career, and I’m sure it’s difficult to single out. Can you share with our readers two stories that stand out in your mind?
Jill: World events often shape our careers, and certainly that was the case with me during 9/11. I was working in IT at the time, and I was asked to come to DC to help with re-architecting the network that the intelligence community needed to be able to share information with each other. That was something where I really felt like I was working on something that made a difference. And guess what? I’m still in DC, so it got me here where all the action is!
I think another one that comes to mind for me is I worked on cryogenic technology when I was a mechanical engineer. That was my technical specialty. Cryogenic technology is used to cool space sensors, like in the space telescope, like Hubble. If you recall, that wasn’t working when it first went up, and I had the opportunity to work with a lot of colleagues on developing solutions for cryogenic technology that ended up fixing the Hubble telescope.
Yitzi: Wow, that’s so cool. Amazing. That’s a great story. It’s been said that sometimes our mistakes can be our greatest teachers. Do you have a story about a humorous mistake that you made when you were first starting out and the lesson that you took away from that?
Jill: When I think about mistakes we made, and especially me, I’m a female engineer. I grew up in a career where there were mostly men, and I think I spent a lot of time trying to fit in with a male culture. I even wore the little bow ties on my blouses in the man suits, as if that would work. I think what it taught me over the years is to just be who I am, and the more authentic I became as a leader, actually the more success I had. That lesson of stop trying to bend — it also creates a lot of stress when you are not being who you really are. So that initial career of trying to look like a guy didn’t really work too well.
Yitzi: Amazing. So let’s talk about your work now. You have so much impressive work. Whatever you’re allowed to share, can you share some of the most interesting or exciting projects that you’re working on now and what you hope to be working on in the near future?
Jill: I think the thing that is consuming most of the conversation in technology today is around artificial intelligence. That certainly is a challenge. We get the opportunity to look at AI and how AI can help us not only solve problems, but change the way that we do work. When I first started this role, oh gosh, a little over two years ago, AI was not consuming the conversation that it is today. I actually asked ChatGPT, “How long are we going to have to talk about AI?” ChatGPT told me 2028. Then it’ll just be part of how we do business, hopefully! It certainly plays a role in a lot of the transformative projects that we’re doing at Amentum.
Yitzi: Amazing. So let’s get more granular. The topic of our interview is AI transformation. Let’s start on some basics from your perspective, and also just as a simple definition, how do you define AI transformation?
Jill: Being able to think about AI transformation isn’t just about deploying the technology. When you say AI transformation, it’s really redesigning the business around new ways of working. When you just deploy the technology, that’s like a project. The other is really looking for how we can use that for value and competitive advantage. I think it’s re-looking at ways of working and also that AI culture where we all have to let go of the way we have been doing things, to learn new skills, to adapt, and to change as leaders for how we lead in the AI era.
Yitzi: Amazing. How is AI transformation different than the buzzword of the past decade, digital transformation?
Jill: Well, I think AI is a part of digital transformation. We still have a ways to go. But now there are opportunities for AI to accelerate. If you think about AI like a curve going up, it’s like a rocket ship right now. There’s a lot of energy, and it goes faster. I think AI is a subset of overall digital transformation, but it’s certainly one that’s going faster and gives us the opportunity to really change how we work. So I think leaders have to adjust not just to the capability, but to how we redesign work. How do we define authority? How do we manage the human part of the AI-human interface? It’s a whole different world.
Yitzi: People have very strong emotional reactions to AI, and I imagine it’s stronger reactions than people had to the introduction of the computer. Maybe not, but I feel like there’s a particular emotional reaction people have to AI, and I’m sure there’s resistance to AI transformation. How have you been able to engage and overcome the resistance to AI?
Jill: Well, I’ll share a story. When I was in high school, we took typing classes on typewriters. The keyboard and the computer were just starting to come out, and people said, “Nobody’s ever going to use that. You can’t trust it. We’re going to keep teaching with the typewriter and typing skills.” So I think part of the fear is just the unknown.
It’s also a very powerful technology that requires us to learn new things and to be able to challenge how we do work. It’s not simply what AI can do, but what should be delegated to a digital AI worker. What authority are we giving that as leaders to be able to now have this blended human and AI workforce, and then how do we have confidence in that? I also feel like AI is giving us the opportunity to embrace our human side a lot more. There are things that technology, whether it’s AI or other tools, will never do. They won’t have the human elements. The ability to trust, to have judgment, to work with each other, and to have relationships are the things that are uniquely human. So we’ve got to get better at the human stuff so we can delegate the things that AI does better than us.
Yitzi: Amazing. With 50,000 employees, I probably can’t even conceive of the obstacles that are in your way to implement an AI transformation. What are the biggest obstacles, aside from resistance, and how do you implement that? How do you overcome them?
Jill: I think sometimes the hardest technology problems aren’t really technology problems at all; they’re human problems. Responsible adoption, especially for AI and AI transformation, is focusing on that human element, making sure we have clear responsibility and accountability. Then, innovation just requires that we see that possibility and sometimes act before we’re certain. We have to decide how we’re going to work differently to be successful. It’s not all about the algorithms; it’s about creating an organization that can continuously learn and adapt and reinvent itself. I think some of the most successful projects aren’t necessarily the ones that have the most advanced AI; they’re the ones that are combining the AI tool with that human potential, so that you’re making the human — we call it human in the lead, not in the loop — and using that human leadership and judgment.
Yitzi: Wow. Have you found that using AI has made it easier to ideate and engineer new products and new things? Have you found that once you introduce AI, everything’s much quicker and maybe more advanced, more robust, or more successful?
Jill: There are a lot of questions in that question, but let me start with the beginning. It certainly is going faster. We are seeing much shorter development times. What that allows you to do is try something, and you can iterate a lot faster if you didn’t get it right. You can use AI to check AI. I think a lot of the normal development time that somebody would take for a new technology is really accelerated because AI is very quick to iterate with.
I think that AI also allows us to build solutions and try them, and what we’re seeing is when we put those solutions into a customer environment, particularly, the very next thing they say is, “Oh, this is great. Now can it do more? How about this? Can we add that?” So it’s not stagnant. It’s not like, “Here’s a typewriter and you’re done.” It really is an opportunity to keep adding more and seeing more possibilities, and asking, “Oh, how about if we connect this to something else?” So we see it as an ongoing thing. It doesn’t seem to stop, which gives it a lot of positive energy.
Yitzi: Let’s say you’re on the engineering floor or engineering desk. What does it look like to use AI to engineer a safety feature for a nuclear power plant?
Jill: Oh, I’m glad you asked that one because we have a lot of engineers, and some of our smart engineers start to say, “I don’t like to bring my engineering drawings to my boss without them being checked.” So they write a little AI script to check their drawings, and then they write a script that brings in the design codes from electrical and mechanical automatically against the drawing set. People are innovating what to do with this technology every day. Then they’re saying, “How can I look at past engineering designs and drawings that we’ve done, and how can I learn from those and bring that into my design space?” It’s really about being able to make data accessible in a way that it hasn’t been before, to really reduce the cycle time of design as well as improve the quality of what’s done, to make sure you’re always having those checkpoints. You can use AI to give you information at your fingertips that you didn’t have before — or you did, but it was in books on your shelf.
Yitzi: Wow. When AI first came out, every seat had unlimited compute and now things are different. How does it work? Do you give every employee a certain budget, like you can spend X amount? What does a huge company do with that?
Jill: Well, for our company, we have a lot of people who are out with our customers. They work in the government space, and they have access to government computers and government AI. Then we have people who are in the Amentum space, and we have our own governance and responsible use policies. Yes, we give them tools, but we’re very clear about guardrails, oversight, especially cybersecurity compliance, and making sure that we’re doing it in a safe and responsible way.
Then what you pointed out starts to happen. We have those people at the very top who are doing all the programming, and they want all the latest tools, and they want all the tokens unlimited, and then you start to rack up the dollars. So we’ve started to recognize people who are leading efficient use of AI. We used to just have the leaderboard of who’s doing it the most, and now it’s who’s doing the very clever, efficient things with their tokens, and then sharing what those people are doing with their agents and how they’re doing it with each other. I think there’s this competitive gaming element with AI that we see where people like to share what they’re doing, and they love to be up on the leaderboards, and being able to combine those with the tasks that they’re doing and the efficiency with which they’re doing it.
Yitzi: Amazing. Okay, here’s the centerpiece of our interview, our signature question. Jill, you’ve been blessed with a lot of success and you must have learned a lot from your experience. Based on your experience, can you share five things every Fortune 500 leader needs to know to successfully lead their organization through an AI transformation?
Jill: To successfully lead in the AI era, it’s not just all about just the AI, the tools, and the algorithms. It’s about leading within an organization that can adapt and learn.
- My thoughts are, first, leadership involvement. We saw it initially with AI: the leaders who were using AI themselves, who would talk about it with their teams, and who would share what they’re doing, good and bad — “What did I learn?” — really had teams that accelerated and transformed.
- Second, as leaders, we also have to redesign how work gets done. We don’t want to take a manual process and automate pieces of it with AI, or a broken process, or where we have process debt, instead of rethinking and reimagining how the work gets done.
- The third thing I would say is about leadership and leadership changes: thinking about and being able to manage this combination of digital and human work, and what can be delegated and what should be delegated — not just can AI do it, but is that the right place and the right process? Then, where is that human oversight, and where does that need to be? Where’s that human decision-making? It’s being able, as leaders, to delegate and manage for both the human side and the digital worker side.
- Fourth, you have to build confidence with your team and within your organization. Having best practices, ways to share, and inviting feedback. This is an iterative technology, so to be successful, you have to be able to be agile, to move, and to share that learning, inviting feedback and collecting that data.
- Lastly, number five — though I would say maybe it’s number one, even if I put it at number five — is that you have to be responsible and accountable. You have to make trust and accountability part of the execution. You have to provide the guardrails, the cybersecurity, and the responsible use to make sure anything you’re doing in your transformation is on a trusted foundation.
Yitzi: Amazing, fantastic. This is our final aspirational question. Jill, because of your amazing work and the company and platform that you’re leading, you’re a person of enormous influence. If you could spread an idea or inspire a movement that would bring the most amount of good to the most amount of people, what would that be? Because you never know what your idea can inspire.
Jill: Oh, well, thank you for that question. I am a big believer in education. I grew up in a small rural area, and sometimes we take it for granted that people have access to education, especially in rural areas. So definitely I would focus on something to support education. My family actually does have a foundation named after my late husband that does community development and education in rural Missouri. I’m very proud of that work, and man, if that could go bigger, I’d be all over it.
Yitzi: Really? That’s wonderful. Well, Jill, how can our readers continue to follow your work? How can they learn more about what Amentum is doing? How can our readers support you in any possible way?
Jill: Well, you can certainly go to our website at amentum.com. You can get in touch with our Marketing & Communications team, or you can follow me. I’m on LinkedIn, and happy to take more followers and connect with anybody on that platform.
Yitzi: Jill, it’s been a delight to meet you. I wish you continued success and good health, and I’d love to do this again in the near future.
Jill: Well, thank you so much. I would love to do it too. It was a real pleasure talking with you, Yitzi. Thank you.
Amentum CTO Jill Bruning: Why AI’s Toughest Challenges Are Human Problems was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.