Work Smarter With AI feat. Summer Husband ’02

[00:00]Brian Jackson: Welcome to Owl Have You Know, a podcast from Rice Business. This episode is part of our Flight Path Series, where guests share their career journeys and stories of the Rice connections that got them where they are.

Joining us today is Summer Husband, a Rice Ph.D. Thank you so much for being here, Summer.

[00:17]Summer Husband: Thanks so much for having me.

[00:19]Brian Jackson: Well, great. Well, we're here because of the Women in Leadership Conference. You were hosting a workshop, or on a workshop. Could you tell me a bit about what you were working on?

[00:29]Summer Husband: Yeah. So, I had the fantastic opportunity to co-present a workshop with one of the Rice MBA school professors, Kathleen Perley, who is incredibly talented. And we spoke together about unleashing your inner cyborg and also addressing the ethics of AI. So, we spoke some about just how to empower women in their roles by really leaning in with AI, but to do that in a very mindful way, addressing the ethics and the risks that go with these very powerful tools.

[01:03]Brian Jackson: I feel like, every conversation with AI, you're on the cutting edge, because it's progressing so quickly and we're trying to all catch up. So, when you look at it in terms of ethics, you know, is it a broad definition? How are we trying to define it?

[01:18]Summer Husband: Yeah. So, that's a great question. And just laying the groundwork, too. You're absolutely right. It's not just hype that it's changing very quickly. The technology is changing so rapidly. When we look at our pipeline of AI use cases at Worley, there are things that we had tagged as not technically feasible a few months ago, last spring, last summer that now are feasible. The technology is just changing really quickly, which makes this a very exciting space to be in, but it makes it really challenging around ethics.

One of the other, I think, challenges in this space is, because it's fairly new, it's changing rapidly, so the technology is changing rapidly. The legal landscape is changing really rapidly, also. So, some of the partners that we tend to go to for playbooks in these spaces don't have a fully baked playbook in this space. A lot of organizations are needing to define and figure out their approach to ethics and AI together.

So, at Worley, we're approaching that with a pretty comprehensive approach that includes our AI experts, of course, our legal experts. We want to ensure that we've got our operations involved in that. They're the ones that are, you know, boots on the ground and are going to be using these tools. We've had to cast a pretty wide net, but we've had to do a lot of discovering and shaping for ourselves.

[02:44]Brian Jackson: So, I mean, AI can move at warp speed. It seems like the human factor is the part that's going to slow down the progression. You know, is that what you're finding? And I guess, in terms of getting to answering these questions about, okay, these are the limits in terms of ethics, you know, are we able to keep the pace? Are you able to keep the pace?

[03:05]Summer Husband: Yeah. So, there definitely are some roadblocks in keeping pace. And the risk and the ethical concerns is one of those. But I would say, also, having the right skill set in your workforce and upskilling your workforce — that's a challenge. There's a real opportunity for people who know their business very well and do the work to learn how to deploy AI in their field, in their area. It's that combination of skills. I think there are a lot of very smart people doing very smart things to build really amazing technology. What I think a lot of businesses have challenges with and where we face a bit of a roadblock is, how do you deploy those tools well in a breadth of different businesses?

So, also, it makes sense. It's an opportunity for people who are willing to, kind of, lean in and take a risk there to differentiate themselves.

[04:06]Brian Jackson: So, when you talk about upskilling, could you give me a bit of an example of that?

[04:10]Summer Husband: Yeah. So I'll give one great example. So, we just deployed something called the Sales Response Generator. So, it's a generative-AI-powered tool that our sales team uses to automate a lot of the process of responding to an expression of interest. So, we'll get an expression of interest from an organization, and we have very little control over the format. So, that could come as a PDF. It could come as an Excel. It could come as a Word. And we previously had a lot of repeated activity in going to gather, what are the documents that I need to respond to this? You know, what's the specific question that's being asked? Okay. Where have we done this before? A lot of hunting and gathering.

But it's repetitive. So, we built a generative-AI-enabled tool that automatically generates the first-draft response to an expression of interest. And then we have a human come in and refine that, to review and refine that product.

Well, for this tool to be successful, we needed… there's no way the AI team is not going to build this on their own without very active ownership and involvement from the sales team. We're actually really fortunate. Actually, it's a Rice MBA grad, Luis Rodriguez, who has been one of the business owners of this process in our organization. But he is curious and wanted to figure out how to better deploy AI for his team.

And that's really exciting. I think it's one of the things I enjoy most about my job. I love working with the technical people on my team. They're incredibly talented. I also really love working cross-functionally with folks in different business areas. So, that, you know, entrepreneurial mindset can be a huge enabler for organizations.

[06:06]Brian Jackson: When I think about your Ph.D. in computational applied mathematics to, all of a sudden… Not all of a sudden, but to a progression of vice president of data products and applied intelligence. I mean, one, your role, probably, was it around 20 years ago, 15 years ago?

[06:23]Summer Husband: No, the work that… and I like to point this out, Brian, that the work I was doing, really good work right out of my Ph.D. program at Rice in the Department of Defense space, the work that we would essentially get problems that didn't have an out-of-the-box solution. And it was our job to say, “Okay, what's the… to, kind of, structure the problem and figure out what's the right approach?”

The right approach, very often, was a machine learning algorithm. Well, 20 years ago, the entire job was to, okay, identify what's the machine learning algorithm and then to code that up. Well, that whole process has been automated, you know, at this point, that, you know, what was my entire job that I think was very valuable and I'm very proud of has been automated for years, so that, you know, you can't get married to the details of your current job. Just technology is going to change things. So, I think I've veered from your original question.

[07:18]Brian Jackson: No, no. It's a great response, because I mean, to me, it's just everything I talk about in terms of… I'm in the energy world, so data centers come up. I might as well just get it tattooed on my forehead. We talk about it so much. And that conversation has changed in six months. And the technology we're talking about, the computational power, the energy usage, water usage, I'm on the outskirts of it. I'm seeing the data centers that are, you know, housing the AI and allowing for that computational power to actually be there and available, but know nothing about what's actually machine learning.

[07:58]Summer Husband: It's a fascinating… it, really, is a fascinating space. And the speaker in the keynote this morning, she was fantastic, but she was encouraging the audience to get a minor in AI, across the board. No matter what you're doing, you need to get a minor in AI. It's going to impact your job. It's going to be… you're going to be much more empowered in your role if you're identifying, “how does AI impact my role?” You know more about your job than 99% of the world does. You are much better placed to figure out how to deploy AI to supercharge your role than anyone else does.

[08:34]Brian Jackson: So, leveraging it to be more valuable. If it can write emails for me that don't necessarily require me to sit there and sweat over for two hours, maybe I should be doing it. Is that a better use of company time?

[08:48]Summer Husband: Yes. 100%. You could even write them in the format of a poem.

[08:51]Brian Jackson: You know, I have asked AI to write an email and I said, “Do it in the style of Hemingway.” It was a great email.

[08:57]Summer Husband: I love it. Yeah, that's a joke. But seriously, I have colleagues who've said, this is actually… it's really impacted their jobs. They've struggled over adding enough emotional connection in email and they've offended people without meaning to, and that being able to write the bullet points of an email and then ask AI to, like, make it nice has actually impacted, you know, their careers.

[09:21]Brian Jackson: Empathy up.

[09:22]Summer Husband: Yes, up the empathy. Yeah, I need that chip in all my voice, up the empathy.

[09:28]Brian Jackson: So, you, kind of, hinted on a bit, and I'd love to chat about, you know, your work with the U.S. Navy Airborne Laser Mind Detection System program. What was the genesis of working on that project? And, I guess, looking at the technology from then and looking at now, you know, what are some of the lessons learned?

[09:48]Summer Husband: So, that's one of my favorite projects that I worked on. So, essentially, this was… so, first, the organization that I was with Metro, it's the job that I got right out of grad school. They hire a lot of Ph.D.s in math and computer science and are continuing to do some really fascinating work.

The problem there was you needed to be able to automatically detect mines. What it looked like at that, you know, this was 20 years ago, before ships were navigating a particular area, they'd have helicopters that scan to try to pick up, are there any mines in the area? And you had a human that would look at thousands and thousands of photographs to say, “Yes, this is a mine. No, this is not a mine.” And the false alarm rate was extremely high, so 99.9% of what an operator looked at would be a false alarm.

Humans are not well-suited to that kind of — highly repetitive, you know, very few actual hits. But what was really interesting, and it was a great way to learn about machine learning, machine learning actually wasn't… I did a little bit of it in graduate school. A lot of what you do in a Ph.D. program is learn how to learn. So, I learned a lot about machine learning in that first job. But you would sit down with a human operator who was very good at distinguishing what is a false alarm and what is not, and say, “Okay, what is it about this photo that tells you this is a false alarm?” And they would say, “Well, you know, the white spot is too big.” Then you think, as a mathematician, “Okay, well, how do I quantify that?” Well, that's the amount of light that's covered, you know, in this area. You know, how can I automate that and make that a quick measurement that comes off of…

[11:34]Brian Jackson: So, you give it parameters, right? 

[11:35]Summer Husband: You give it some parameters. And that's a feature of machine learning. What we just did is identify a feature of a machine learning algorithm.

Another one was the brightness of the spot compared to the depth that was detected at. Again, you can compare how bright is this spot, how deep was it detected? And again, that's another filter that you can put on.

So, the process, I really loved that the process of talking to a human operator, understanding what is their work, what is their world, understanding their expertise, and then figuring out, how do I automate that to make your life better?

[12:10]Brian Jackson: And that's what it is. I mean, you're automating it to make life better. Because imagine the work before. It sounds awful. You're looking at thousands of images, and you're just trying to pick which one may or may not be, and then someone else is going to look at that image, right?

[12:24]Summer Husband: There's better work for humans to do, yes. And that's a lot of what we're doing with AI. I'm going to be honest about it. There's some spaces that cause me concern, that cause a lot of people concern. But there are a lot of places where we can bring in AI and automation to automate tasks that are better suited to a machine and that free up humans to do higher level and more rewarding and satisfying work.

[12:48]Brian Jackson: What would be those areas that cause concern for you?

[12:51]Summer Husband: Oh, anything that… you know, we are in the… I'm in the engineering field. We've got to work in first principles. So, when we get to… we're starting to… we're leaning in more on some AI use cases that have to do more with the core of our business. We can't take… there are a lot of things we can't take a probabilistic approach to. First principles physics is really important. So, making sure you have the right… that you're grounded in physics, where that's really important and where you're building actual physical things, is an area that we pay a lot of attention to.

Now, that's an area of some exciting research. So, physics-based AI is an area that's really growing. There's some fascinating research that's handled that's happening in that space. So, that's one that we think about.

And, you know, another area that you always have to be really careful about is, anytime you have AI, making decisions that impact people. With the very best intentions, you can do some really terrible things. So, it's extremely important that you have a very robust approach to AI governance that asks hard questions and gets beyond what was the intent of what you're providing versus what is the capability of what you're providing.

[14:17]Brian Jackson: I've played with AI myself, and ChatGPT, I think, is great. I'll ask it questions, and sometimes the responses aren't great. But when I refine it, give it the rules, give it more background information, I'm seeing the responses improve and improve and improve. So, I mean, do you think, as we continue and it continues to learn and it continues to have new parameters and new information, you know, we're going to be able to rely on these, you know, outputs and say, “Okay, we can make an actual decision on it?” You know, do we get to that point?

[14:49]Summer Husband: Well, I think you're all… well, first, I would say there, what's probably most impactful is your learning as a user. I mean, the models are really increasing in their accuracy, but also our proficiency in being able to deploy them well is also really increasing.

I think we are going to need humans in the loop and in a lot of… in many places, and we'll need to be very thoughtful about where we're doing that. But agentic AI is something that is technology that has really picked up speed, where you're deploying AI as an agent. So, it's taking action. You're not just getting information, it's taking action. So, we're definitely getting to some places where we put more trust in AI. But we're going to need to be really careful and thoughtful about that.

[15:42]Brian Jackson: Yeah. We still need, I call it the human factor. We can call it human intuition. Like, that needs to be a part of it, right? Do you think we ever get to, you know… I guess, really what I want to know is, where do you see human intuition and decision making still holding a strong advantage over AI? Is it just in these situations we're talking about complex problem solving with physics or, you know, actual decision having to be made?

[16:10]Summer Husband: So, first, I think it would be incredibly arrogant for me to say with a lot of confidence, this is absolutely the way it's going to look. You know, even two years from now, I think there's, you know, a lot of room for people to be extremely wrong. But right now, I can tell you the way we're actually deploying AI and some of the challenges that we're seeing where we really need people is coming up with, what are the right problems to solve? What are the interesting areas for us to be pointing AI towards?

That's, you know, a lot of what we were getting at in the session today on, you know, unleashing your inner cyborg and really leaning in with AI, is figuring out where the opportunities are, where to deploy it in your job. That is less explored than you think. I think most people under-appreciate what kind of an open field there is and figuring out where to deploy AI well in your job.

So, asking the right question is something that still is an area where humans have a lot of impact. People will catch up, but I think that you need the right combination of skill set. And I think it takes some intention. We've really… you know, this time a little over a year ago, we had an AI team of seven people that was functioning more as an R&D group. I feel very fortunate to be in an organization with executive leadership and a CEO who identified AI as a critical driver for transformation. He made significant investment in AI. We brought together our strategy group, our subject matter experts in that technical team, to really make a very intentional push and play around AI. We now have a team of 50-plus. It includes those AI experts. It also includes full stack developers. And we're very intentional about how we're partnering across the business.

So, this is what I'm talking about, too, about the right skill set in employees of putting… it's not just building up technical skills. It's putting the right mix of skills together. And I think you can do that more in individuals. I think it's going to be increasingly powerful to have expertise in your field, know your field really well, and have that minor in AI where you can identify, “Hey, this is where I can supercharge what I'm doing with AI.”

And, you know, in my career, some of the… I would say, if I were, kind of, thinking through who were the top five most impactful employees I've been privileged enough to have working on my team, that list is overwhelmingly populated with people who were not AI experts. But although, I want to be fair, my AI experts are fantastic. But the people who know their business really well and understand enough about the technology to be able to help direct, “Hey, no, no, no. This is actually the problem that you need to solve.” I had a really smart team member who helped redirect me on that. I had a model that I was so excited about. You got a very detailed probability curve of, you know, how likely a job was to be filled over time.

You got so much information out of the probability curve. I really was very attached to it. She had to, kind of, come in and say, “Nobody cares about that probability curve and it's actually getting in the way. What they want is red, yellow, green. You're getting in the way.” She said it much nicer than that, but that's essentially what she was saying.

[19:52]Brian Jackson: You’re really attached to this.

[19:55]Summer Husband: I really was. I really like all the information. But, you know, that kind of understanding, she understood the person who was going to be using the output of the model, she understood what they really needed, and she understood the UX, you know, the user experience of what we were doing. So, the technical skills are really important. You do really need that. But there are a lot of other skills that you need to have in the mix.

[20:18]Brian Jackson: It just sounds like humans aren't going obsolete, so we don't have to be afraid.

[20:21]Summer Husband: 100%. Yeah.

[20:23]Brian Jackson: But, you know, it's the truth. I think we're all, like, nervous in how to approach it. And be it ethics, be it your data, your information, the security of your confidential information, like, none of us are exactly sure where it goes out into the data sphere.

[20:38]Summer Husband: And I think it's also the, like, perceived risk versus actual risk. It feels risky. And there's a significant startup cost, I think, to really lean in with it. But it's also a very significant risk to do nothing. But you just don't feel it. It's not felt as much. And it doesn't have the startup, you know, kind of, that initial bump to get over.

[21:01]Brian Jackson: That's a great point. So, I mean, today, being woke, we're here on campus and you're talking about AI with folks, I'm curious, in your workshop, what types of questions were you getting? What's the general feeling around AI? Is it positive? Is it nervous?

[21:18]Summer Husband: Yeah. Well, it's a mix, as it would be in any audience. There were some questions from people you can tell are thinking deeply, who are already doing really cool things, and, you know, trying to figure out how to go from, you know, proof of concept models that they're already building. And how do you put that into production? And what are the right skill sets that you need? I mean, that is so super, super impressive. There's also, you know, some tentativeness that I think is a lot of what we were wanting to address in our session today, of, “Hey, this is an opportunity. Lean in. You don't need to ask for permission.” I mean, you've got to be following, you know, your company's guidelines around safe use of AI, but to show some leadership in that space, it's a huge need. And lean into that, yeah.

[22:10]Brian Jackson: Yeah, I agree. It's an opportunity to lean in. And trying to catch up five years from now, I don't think, is ideal. So, the attendees today, what do you hope that they take? If they could take one thing, you know, from the activity, be it from your workshop or just the conference in general, what's one thing you hope they take away?

[22:31]Summer Husband: So, I think it's really powerful for women to be around other very capable and talented women. I didn't realize what an impact that had. I've loved all the organizations that I've worked for.

My first job, there was only one other woman on the technical side. I didn't think that impacted me. But my next organization had a lot of women in executive leadership. And I suddenly started to feel like taking a more senior role was a possibility for me in a way that I had not… I just hadn't perceived that to be a possibility for me before. And I never would've, you know, connected those dots, but I really appreciate Rice opening up the opportunity for women to hear from other women. There's just a different… it's just a little different when it's mostly women in the room and you feel safe to ask some questions that you just don't feel safe to ask in some other spaces. That's just, kind of, the way that goes. So, I appreciate the opportunity.

I also… you know, someone brought up, there were some questions around imposter syndrome and how to deal with that. And that's something, I think, everyone's dealt with, male or female, women for sure, is something that we, kind of, work through. I've had an approach to that that has worked really well for me. And that's just thinking through, what's the value that I bring in this room? And to be brave and honest about that.

So, sometimes I ask, “Okay, why am I here? What is the value I bring?” And sometimes I have expertise in a space that nobody else has and I need to embrace that, and I need to speak out bravely in the, you know, the area that's my job to speak to. In some other areas, I will feel… I will know I'm out of my depth. Actually, there's a lot more expertise in this space, in this room that I have. And this is an opportunity for me to learn. And that's also okay. That's actually good. If I'm ever in a job where I never feel a little on my back foot sometimes, or a little outta my depth sometimes, then I probably need to look for another job.

So, just that kind of… look at that honestly, and be brave when you need to speak up, and be totally comfortable with the fact that you've got some things to learn when you're in a space where you have some things you need to learn.

[25:05]Brian Jackson: This is the space to do it. And I think you're right, it's hard to envision yourself in a position when you've never seen anyone else like you-

[25:13]Summer Husband: Yes.

[25:14]Brian Jackson: … stand there and take it. And we're so fortunate that we have so many women who have these incredible backgrounds and careers and stories. And, you know, this opportunity to sit down and ask them candid questions, and how do you deal with failure and how do you, you know, sit back and push norms or, you know, challenge the status quo? Like, this is that space. And one, thank you very much for sitting with me and for sharing your story. And yeah, it has been such a pleasure.

[25:45]Summer Husband: Oh, it's been my pleasure, too. Thanks for a great conversation. And I really appreciate the opportunity to speak with you. And it's been a great day at the conference.

[25:54]Brian Jackson: Thanks for listening. This has been Owl Have You Know, a production of Rice Business. You can find more information about our guests, hosts, and announcements on our website, business.rice.edu. Please, subscribe and leave a rating wherever you find your favorite podcast. We'd love to hear what you think. The hosts of Owl Have You Know are myself, Brian Jackson, and Maya Pomroy.