Future of Business: SAIC’s Toni Townes-Whitley on Leading Strategic Transformation
HBR IdeaCast · 7 Nov 2024
Source: https://hbr.org/podcast/2024/11/future-of-business-saics-toni-townes-whitley-on-leading-strategic-transformation
Transcript: https://podcasts.opennash.com/shows/hbr-ideacast/future-of-business-saics-toni-townes-whitley-on-leading-strategic-transformation-0de443/ (via OpenNash Podcasts, machine-generated)

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I'm Alison Beard. For the next few Thursdays, we're bringing you a series of interviews with some of the world's leading tech CEOs and founders to hear their perspectives on artificial intelligence and other top-of-mind issues. Today, we'll listen to a conversation that Toni Townes-Whitley, CEO of SAIC, had with HBR editor-in-chief Adi Ignatius during our recent virtual Future of Business Conference. SAIC is a company with more than $7 billion in annual revenue and 24,000 employees.

It provides engineering, digital, AI, and mission support to defense, space, intelligence, and civilian customers. Tony took the helm about a year ago after stints as a senior executive at Microsoft, CGI Federal, and Unisys. She also serves on five boards, both corporate and nonprofit, and is a former US Peace Corps volunteer. So she has some useful insight on how organizations of all types can work together on our biggest challenges. In answering questions from Adi in the audience, she shares her thoughts on leading effective strategic transformation, upskilling employees, and guarding against AI bias.

Here's that discussion.

So let's dive in, um, talk about how as an incoming leader, you know you're you're able to figure out what needs to change and what levers you have to try to bring about that change.

A couple things are very clear. SAIC was first. It was a first in its market to have an employee-owned operating model to really to operate in the world of STEM before STEM was cool. And that was the history. Then I started to look at the data, the more current data relative to SAIC having gone a bit flat in its growth cycle, uh SAIC having to more differentiate its portfolio, and SAIC not being as considered as deeply a market leader as it had in years past.

It was in those sort of inputs that I started to think about what would be the the areas of pivot or tuning that would be helpful in some key areas that would most likely uh correlate to uh shifting the growth pattern and almost resetting SAIC back in the market in a point of leadership. So I started to consider those as hypotheses, and I came up with four hypotheses of where the company needed to tune, and I wanted to test those in the first year.

Well, so yeah, let's talk about those four, because it from the outside it looks like you decided to move on practically everything. You the product portfolio, the market strategy, the brand, the culture. Y you know, how do you get buy-in across the company and and and how do you execute on so many different fronts?

So let's start with tuning versus wholesale change. The magnitude of change in each area in each area is important to understand. We were very clear about identifying where we would change and what would stay the same in the spirit of start, stop, and continue. We had those exercises across the company to ensure that people understood that not everything was changing. We also sequenced uh the four pivots. So we started with portfolio, leaned in heavily on the portfolio with questions on whether our portfolio was truly enterprise scale, whether we had built point solutions for unique customers, or whether we had enterprise capability to go after large, large programs.

We started to ask about whether our technology was in fact state of the art and differentiated, and whether that technology was deployed across all of our programs, as well as were we bidding that into our pipeline. So we started with portfolio. We then went to go-to-market and culture, and the two came together as we identified what kind of company, not only how did we want to go to market, but who do we want to be as we go to market as individuals and collective as SAIC, and identified the pivots within those areas.

And then brand was our sort of our final move to really start to speak to, given the shifts that we're making inside the company, what's the future uh brand of SAIC? SAIC has a phenomenal brand. Our study and research indicated that we were very well known, but it had an anachronistic brand. Everyone looked back at a golden moment at SAIC, not a forward-looking brand and understanding what we were going to be as a company in the future.

So how do we get the buy-in combination of laying out what was changing and what was not moving in sequence, looking for tuning opportunities, and quite frankly, Adi, I think probably most importantly, putting an enterprise operating model in place with metrics and processes and rhythms that hardened that opportunity to change. Meaning not just sort of words and hopes and desires, but fundamental day-to-day rhythms in the business uh through metrics that we use about fourteen that I track every month, but seventy that are arrayed across the company at different levels to help us know that we're on track and that we're moving and to give signal, uh, and to quite frankly, create some listening systems in the organization.

So so this is great. I hope people are taking notes. This is kind of a master class in how you how you do strategic transformations. This is this is this is really great. Let's dig into some of these areas. Um, you know, on the on the portfolio shift uh area, you know, I I assume that a lot of that is about embracing new technologies, including AI. Curious, h how do you do that? How do you operate on the cutting edge while serving, you know, complex, in some cases government clients that need the highest levels of safety and security and reliability?

So when I talked about the hypothesis Adi that I came in on the portfolio, obviously coming from, you know, a company like Microsoft, is super focused on our capabilities and and where we were differentiated and whether we were at scale. And I found I was so pleased to find that we actually had clear differentiation in areas of what we would call sort of digital engineering, um, secure data analysis and and uh operational AI. Uh you mentioned AI, the way we look at it is a very operational sort of gritty level of AI that happens in mission critical environments, as well as uh secure cloud.

Our ability to broker and migrate uh organizations to the cloud. We also I also saw that we had some work uh in in sort of what I called horizon two. So we we were starting to turn the corner in edge computing, in understanding quantum and starting to move into the areas that will be sort of the future leaning areas for the company. So I was actually quite pleased to see that we had that capability. How do we deploy it though in with within the Department of Defense, the intelligence companies, the space agencies, and the civilian government?

That was really our conversation. And we made some investments in the first year in some to harden those areas and to create opportunities, what we would call sort of sandboxes to create the client environment. And ahead of ahead of sort of deploying it to the client, testing it, modeling it, simulating it, so the client could see what capabilities existed. We're very proud of the work that we do, uh, not just in AI. I know that's sort of the the hot topic I talk about in terms of operational AI, because AI is a category of capability, as you know, and we have it in every part of our company.

But where we focus our AI and our data is that we spend a lot of time on the security and the accuracy of data that supports AI so that the the models uh are actually um really robust uh so that when those large language models reason, they reason over the correct data. And we see the use of AI across the Department of Defense in targeting, in predictive analytics, in the ability to bring down the decision timelines, in the ability to integrate data sets.

And so uh we've seen great receptivity to that across the Department of Defense and the intel companies, uh, most notably, probably with what we'll call the combatant commands that where AI is most uh is most uh critical for them in their day-to-day operations.

You know, on the AI question, you know, you talked about about some of the use cases. This is a real civilian question, but anyway, I'm sure, you know, some people listening might feel a little uneasy about the idea of AI tools being used by the Department of Defense, other federal agencies. You know, to what to what extent are you even able to think about so-called responsible use of of of AI?

Well, it's it's kind of core and center to how we think about AI. When you think about AI and mission critical environments, AI in terms of training air traffic controllers that that secure our our airspace in the US, uh SAIC contr- uh trains every air traffic controller in this country within the Department of Transportation. AI at the border, at the southern border within the customs and border patrol, we run that capability, the technology that secures our southern border.

Uh AI that is uh part of decision analytics for uh the Department of Defense as well as for our national security uh agencies and and and organizations. We spend a lot of time on uh the the ethical aspects of AI. We are part of standard setting with NIST, which is the standards body within the Department of Commerce. We have our own trustworthy initiative we do with George Washington University. Uh, we are sort of first in uh of its kind as a as a system integrator, mission integrator that is really codifying what great AI, ethical AI looks like.

But we think about AI in the context of the data that it is reasoning over and the security of that data. And we see AI also on the civilian side. You can use AI to improve uh um scenarios like what we find in our biometric center. We have we run one of, I think, maybe the center uh uh is the only of its kind in the world where we are able to test facial recognition technology and test the ability that AI and facial recognition and image capture.

We test the and challenge whether bias is introduced as skin tone gets darker. We know all of the concerns in the research, and we do this for the Department of Homeland Security to ensure that we understand what bias is introduced in facial capture. And we've been able to to learn and quite frankly, create patents and and all kinds of research and set industry standards for not only the US, but internationally on what is great image capture look like.

That's a concern that citizens have that they are going to be represented correctly, and we have over 4,000 volunteers that have come through to give us accurate testing on facial recognition. So I'm proud of using AI to actually address bias, using AI to improve mission, and also being held accountable for AI standards in the ways that we do that many do across our industry.

Yeah, so I mean, it's a really interesting example of of public private, you know, partnership, but, you know, I have to ask, how do you navigate a client base of government entities that are bureaucratic, they're beholden to congressional budget making, which isn't always the smoothest thing in the world. You know, with political turbulence, it can mean, you know, big swings in in approach and policy from one administration to another. That that sounds like an incredible challenge.

How how do you handle that kind of uncertainty running the business?

No, it's it's a very fair question, and it's a very real day-to-day experience. I will tell you, uh, look, continuing resolutions have become a norm, unfortunately, in terms of how the budget uh sort of uh locks happen, and we've we are somewhat used to uh how to manage that on a year-over-year basis. Uh, the effect really is in our customer base. Uh, those kinds of sort of budget um stalemates create great challenges for our customers and how they do long-range planning, multi-year programs, how they handle end-of-year money, uh, the signals they want to send to the private sector where to invest.

All of that assumes an ongoing and a consistent budget process that has been truncated often by continuing resolutions. Large swings in the executive branch, quite frankly, don't have as much of an effect on our on our company. When I look at where we're positioned across the defense and intel uh sectors, uh, quite frankly, those signals come more from the conflict that's happening around the world and where those customers are are focusing emerging tech. We happen to be in the areas that they're driving to, which will be sort of cloud-based com com um command and control systems.

That's where we specialize, and that's where they're spending. So we're not overly concerned there. On the civilian side, it's a fair question. Our footprint in the civilian agencies is primarily in areas in those large federal cabinet agencies that are somewhat um um not affected by political swings. Think of the VA, the Veterans Administration, uh, Department of Homeland Security, Department of Transportation, the State Department. That's where we find ourselves with our largest business. If we were in agencies that were kind of more ripe to some of the political fodder, we would we would probably be paying more attention in that area.

But right now, we we believe that our focus is continuing to drive our national imperatives around all-domain war fighting, uh, improving the citizen experience, uh, improving on undersea dominance that this uh this country has had for the last fifty years, uh, really driving towards next generation space and doing all of these uh sort of activities collectively. Um, we're not as overly concerned on the political landscape right now.

Um, so I want to go to a couple of audience questions now, and I have some more questions for you later. But this is from Adam, not sure where Adam is, but the question is what what key metrics are you using to track the transformation? Could you give some examples of, you know, what are the numbers that matter to you now?

yeah, I appreciate that. So, for our shareholders, quite frankly, and the other stakeholders, we had to return to a a mid single-digit growth company. We have been at low single-digit for the last five years. We had to improve our growth, we had to improve our profitability. We were outside of the profitability range of our peers, and we needed to position more strongly in the market as leader. So, in this in the spirit of the financial growth side, uh, we measure our growth in terms of how we bid our business and the number of submissions that we are making.

We had been declining in the number of proposals we've been submitting. Uh, we look at our win rates against those proposals, and we look at our ability to grow off of our base business. And we measure those in terms of submissions, win rate, and on contract growth. In terms of how we grow, it's not just that we grow, but how we grow. We have four key growth vectors. Civilian is one of those. We're rebalancing the business to be more towards a third of right now, a fourth of our business is civilian.

We'd like about a third of that business to be civilian. We have a large addressable market there. It's very profitable for us, and we also have great capability to support civilian agencies. So that's a growth vector. We're moving our business into more mission and enterprise IT, so back office technology for the CIOs and front office for the mission. We measure that move of our current program revenue as well as our future pipeline revenue, and then we measure our cultural shifts.

Our culture is about enterprise mindset, even though we have a wonderful entrepreneurial spirit, we trade as one symbol, and the challenges that our customers face require all 25,000 members of our company to come together and all of the technology of our company come together. So we measure sort of where enterprise mindset is showing up across the company, demonstrably in terms of how we're working together towards specific programs. So those are some of the key metrics that we look at.

We're now just starting to look at some brand metrics, but think about the four pivots that we introduced, the four pivots we are measuring against each of those four pivots and looking to see if our strategy, quite frankly, is taking hold. And to date, we feel pretty good that it's starting to take hold.

Yeah, well, uh let me let me ask you more about culture. You know, you talked about, you know, that that's something that you're you're measuring as well. Tell me more, I mean, are there are there specific actions that you've taken that are showing results, whether whether on a quantifiable or or, you know, on a on a quality basis?

Sure, Adi, I think look, there were four pivots to our culture that we introduced. One, as you've heard me talk about, moving from a not just a pure entrepreneurial culture, but to a blended culture that that recognized and enterprise mindset. We also talked in our culture about being able in a very uh a third of our folks are military, former military. We have uh a very respectful, polite culture, but our ability to debate debate topics respectfully, to get more challenging in our culture and a little bit more bold and challenging and interrogating ideas, not interrogating each other.

Uh, so that's sort of a social how we collaborate type of way that we wanted to change the culture. We also talked about taking uh a little bit more being a more bold culture in terms of taking risk, um, calculated risk, but going after big bets and naming those. And finally, the pivot for our culture was relative to incubating talent. We are a great talent acquisition company. We have acquired talent very, very well. We use metrics like days to fill a requisition, but we needed to shift to building more talent in the organization.

And that meant putting metrics on managers and giving them tools to incubate talent. We call that upskilling, and we've run three pilots now across the company, and now we're investing more and more in upskilling. Upskilling are individuals who are interested in moving up with more technical skills like cloud or data or AI, moving up with more consultative skills. They've been in a mission, they want to know how to be more business development oriented. Uh, some people who are super technical, learning more mission, understanding of of of what our missions do.

So in those four pivots, we've been measuring through pulse surveys, uh, how our employees feel, uh, more enterprise mindset. We track when there's a program that, let's say, uh, is being supported at the enterprise level that requires each of the groups to step away from one of their, you know, their objectives to support the enterprise, how often that occurs. We start to measure, as I've said, uh, how much talent is being incubated through upskilling. Uh again, we f we see early indicators, our pulse surveys are up, uh, we see greater enterprise plays, if you will, and we're seeing sort of early indicators that the upskilling is a is a big hit inside the company for career development.

So that's how we're moving forward, and we'll stay down that path for the next few years.

You had uh sort of hinted earlier or or implied earlier, y- you know, about bias in in, you know, kind of AI and AI output. So this is a question from from somebody who's watching, uh, named Iris, who who's interested, you know, from your experience, how do companies address bias in AI implementation, you know, in their business? And that could range from HR processes to, you know, the the content they're putting out there in the world or the products they're developing.

How how have you come to think about that?

So, I can't speak for all companies. I'll say at SAIC, we run an AI council. We bring all of the disciplines of AI from our internal IT operation to our external innovation factory. Our every one of our functions, HR, legal, our finance function, and those that are client-facing, they all have representatives on a an AI council that meets routinely to address issues of AI in the business, how we're using AI for proposal development or recruiting or hiring, uh, but also AI, um, to to our customers, so AI from the business, which is how we are embedding AI in our solution sets going to our customers.

I think one of the best ways, and I and I've learned this over the last few years, not uh uh not only here at at SAIC, but previously at Microsoft, in terms of having a multi-dimensional group that's looking across the business to understand how AI is being designed, tested, deployed, and then reassessed, so that we'll call sensitive uses of AI are being reviewed by a broad, diverse set. It's important that that that team is not only diverse in their thinking, diverse in their background, but actually trained and have standards and metrics, and that's where I'm pleased that we are part of standard setting within the government.

We have many different initiatives for standard setting for AI to make sure we are current with all ways of testing and understanding the use of AI. And so I think it starts with having a dedicated set of individuals that are multi-dimensional within a company that are routinely looking at every aspect of AI, both the risk and the opportunity.

Now, you know, bias obviously doesn't exist only in, uh, the world of technology, and I'm curious, you know, you're you're one of two black female CEOs in the Fortune 500. You know, I I'd love your thoughts on or or, if you're willing to share, what hurdles, if any, you know, have you faced getting to this point? And, you know, to what extent is that sort of limited number? Is that changing? Do you feel that that, you know, there are more opportunities now for more people?

Have there been hurdles? Absolutely, Adi. That's a a very fair question and and supposition is is correct there. And hurdles on every uh level. Sometimes the hurdles are there that are may maybe more gender based, sometimes they are uh racially based. Uh, as I said, um, you know, African American female, I show up as both every day, and there are perceptions sometimes of others. Uh, the bar can be set inappropriately high or inappropriately low. I've had all kinds of microaggressions throughout my career, but I also want to talk about not just the hurdles I've I've seen, but my own hurdles.

Uh, there was a great book written by Becky Schaumburg 2020 years ago called It's Not Just the Glass Ceilings, it's it's the sticky floors. There were some sticky floors where I held myself back, where I was concerned about being so representative of so many groups that I was slow in some of my decision making and sometimes uncertain about my next step. So what I started to deploy over time was a career path that looks more like a staircase.

There were horizontal and vertical aspects of my career, and I started to look for the next move uh after three to four years in any role, where where I was on the horizontal and vertical, which I'll define quickly as vertically, I was looking for real stretch opportunities, almost adventurous, uh, intellectual caffeine that would challenge me in ways I had never been challenged. And then I was looking to apply the learnings from those vertical opportunities in in broader and larger uh opportunity roles in different companies.

And so if you look at my career, it is a s it's it's almost a staircase of vertical challenges and then horizontal application of what I've learned. Uh, some people stay in the vertical, they're always looking adventure seekers. Uh, some people stay in the horizontal, they become, you know, the SMEs and the most the smartest person in the room. I wanted to keep challenging, but keep applying what I've learned. That's how I built my career, and as part of that, it's given me some opportunities to to take moves like going to Microsoft was one of those vertical moments.

Uh, quite frankly, uh, leaving, uh, Arthur Anderson as it was closing as a consulting business after Enron and coming to a, uh, a business and infrastructure technology company like Unisys was a a true vertical for me as well. But I've had opportunities to apply that learning. SAIC is in the horizontal phase for me, where I'm applying so much learning in this new opportunity. And I will tell you, I've tried to address the adversity that comes, uh, the re the um fact that there are so few uh individuals that look like me with curiosity, both in terms of understanding why aren't there more uh women who look like me in this role.

What do we need to do as root cause to address those issues? Um, helping people address the biases that they uh sometimes can't see in how they think about putting women, uh, and and people of color, uh, in these roles and starting to understand that there is much more supply, uh, out there, uh, and available if we would broaden the aperture, provide access, and start to measure how we are moving people through, not just uh, careers, but getting folks into career moving opportunities.

I've had I've been blessed to have a few career moving opportunities, and that's why I'm sitting in the role now.

That was SAIC CEO Toni Townes-Whitley and HBR editor in chief Adi Ignatius speaking at our virtual Future of Business Conference. I hope you listen to all of our Future of Business series and all of the episodes we have on the HBR IdeaCast about leadership, strategy, and the future of work. Find us at hbr dot org slash podcasts or search HBR on Apple Podcasts, Spotify, or wherever you listen. And if you don't already subscribe to HBR, please do.

It's the best way to support our show. Go to hbr dot org slash subscribe to learn more. Thanks to our team, senior producers Anne Sanni and Mary Due, associate producer Hannah Bates, audio product manager Ian Fox, and senior production specialist Rob Eckert. Thanks to you for listening to the HBR IdeaCast. I'm Alison Beard.