AI or Not

E004 - AI or Not - Angela Sheffield and Pamela Isom

• Season 1 • Episode 4

Share episode

Welcome to "AI or Not," the podcast where we explore the intersection of digital transformation and real-world wisdom, hosted by the accomplished Pamela Isom. With over 25 years of experience guiding leaders in corporate, public, and private sectors, Pamela, the CEO and Founder of IsAdvice & Consulting LLC, is a veteran in successfully navigating the complex realms of artificial intelligence, innovation, cyber issues, governance, data management, and ethical decision-making.

Can artificial intelligence revolutionize national security? Angela Sheffield, with her extensive background in applied mathematics and operations research, believes it can. In this episode of "AI or Not," Angela shares her extraordinary journey to pivotal roles at the US Department of Energy and the Department of Defense, offering us a front-row seat to the diverse applications of AI in defense. From streamlining administrative tasks to enhancing warfighting capabilities, Angela sheds light on how leveraging commercial AI advancements can significantly improve decision-making and operational efficiency both in the boardroom and on the battlefield.

Bridging the notorious "valley of death" in the national security science and technology sector is no small feat. Angela discusses the vital importance of recruiting talent from research institutions and reimagining internal teams and collaboration methods to ensure research continuity and practical implementation. She emphasizes the necessity of data-driven decision-making and the benefits of digital modernization and cloud technologies, particularly in observing and optimizing new capabilities. This conversation provides invaluable insights into the challenges of transitioning groundbreaking research from national laboratories to practical applications in the defense and federal space.

Trust in new technologies is essential, especially in the context of digital transformation in both military and civilian government sectors. Angela delves into the necessity for process changes and the importance of trust among soldiers and operators towards new technologies. The discussion extends to advancements in digital transformation within health and financial services, reflecting on the progress in governmental sectors outside of defense. By addressing complex problems through low-hanging fruit and leveraging resources effectively, Angela shares how significant progress can be made with strategic foresight and practical problem-solving approaches. Join us for this thought-provoking episode, where mission-driven efforts in AI and digital modernization are explored in depth.



Pamela Isom:

This podcast is for informational purposes only. Personal views and opinions expressed by our podcast guests are their own and not legal advice, neither health tax, nor professional nor official statements by their organizations. Guest views may not be those of the host views may not be those of the host.

Pamela Isom:

Hello and welcome to AI or Not, the podcast where business leaders from around the globe share wisdom and insights that are needed now to address issues and guide success in your artificial intelligence and digital transformation journey. My name is Pamela Isom, I'm your podcast host and we have a very special guest with us today Mrs Angela Sheffield. Angela is an accomplished director of complex programs. I know her well. She's an influential leader. She's an expert in AI. There's more that I could say. We work together at the US Department of Energy, but I'm going to turn it over to Angela and let her share her background, her experiences. Angela, tell us about your career, your journey and what motivates you in your current role. Please.

Angela Sheffield:

Thank you for the warm introduction, pam, and for having me. As you shared with your listeners, pam and I go way back. We certainly go way back when it comes to AI and the current AI era, the current AI revolution that started, you know, around 2014. We began working together not long after that and at the early point in Pam mentioned this was at the Department of Energy, at the Department of Energy's journey towards artificial intelligence and, even more broadly, in digital transformation. So just a little bit about me.

Angela Sheffield:

I'm of the age that when I interact with people who work in AI people my age, at many of our ages we came to AI from a different field and sometimes that original discipline and practice from which we came to AI characterizes a lot of how we approach it. So I came to AI from applied mathematics working with decision makers to use mathematical and data-driven techniques to inform decisions. The field of operations, research it's all about and honestly, we had kind of a hard time selling our wares before AI, because we can use models and use data to help you make decisions better, I swear. And so when data science and AI began to re-emerge into public discourse and scientific discourse again around 2014, it was really an opportunity to seize on some core techniques and goals in terms of supporting decision makers, business leaders, national security decision makers that I've always had, and so that's what brought me to AI.

Angela Sheffield:

Was this opportunity to do the field that I chose to enter to improve decision making, to improve the outcomes of decision making in large organizations like government, like large enterprises, national security, with the use of data and mathematical techniques. And again, ai comes up and we're like this is a better sales pitch for some of what we were always trying to do as operations research professionals and then also added similar tools to our toolkit. I started my career. I went to the United States Air Force Academy and started my career in national security, chose national security and government work because I knew I wanted to have an impact at a really massive scale, and those enterprises are the sort of place to do that. So I've chosen to pull these threads of digital modernization, leveraging data for decision-making, advancing the field and applications of artificial intelligence squarely out of the defense and national security space Talked about just now.

Angela Sheffield:

Pam, that can take you more places than just the Department of Defense, more places than just the Air Force, and so I'm very lucky that my career has been a journey through many organizations, always pulling this thread of how can we better inform decisions, have better decision outcomes in these enterprises and policy places that have a huge impact on our lives.

Pamela Isom:

That's fascinating. It's so good to see you. It's good to know that you're doing well. It's interesting to hear about your journey. You are one of the early pioneers, I think one of the early pioneers, I think so. It is always interesting when we come along after you've started something, when people come along after you started it and then you can say, oh yeah, that's what we were doing. So I love how you pointed out that this is a better sales pitch. Yeah, okay. So let's talk some more about your point of view on AI. You talked about it some, but let's talk about your perspectives on AI in defense and how do you see AI supporting the defense industry. And then, if you want to integrate some of your discussions that we've had about the crystal balls, integrate some of your discussions that we've had about the crystal balls.

Angela Sheffield:

Yeah, the Department of Defense, the defense apparatus in our government, supports a really diverse set of mission cases. The DOD has a office they call it a Chief Digital and Artificial Intelligence Office, which is a bit of a kludge of the many organizations. And now I think congressional policy is that we have that government agencies have to have a chief data office. The Department of Defense has, in addition to that, a chief digital and artificial intelligence office, which is responsible for encouraging the adoption of AI and advanced technologies across the DoD. And they characterize this mission space as from the boardroom to the battlefield, because that's how diverse the DoD's missions are From missions that look really typical to what a large enterprise or enterprises of different scale, the sorts of decisions, the sorts of operations that they manage, looks really similar. That's that boardroom context manage looks really similar. That's that boardroom context. And then others that are these unique to DoD warfighting missions.

Angela Sheffield:

And one thing that we are finding as we're digging into the DoD's digital and AI journey is that there are a lot of opportunities to leverage advances in the commercial sector, advances in commercial development and applications of artificial intelligence, data software a lot there to leverage, especially in those missions that look more like the boardroom, like managing personnel, managing logistics, in what the DOD would refer to as like a readiness posture Policy, compliance. You know. A lot of that looks like what a commercial enterprise or a traditional business enterprise would do. And there are also missions that look really different and not just warfighting, like Skyborg or UAVs or intelligence. Not just that, those are different missions.

Angela Sheffield:

You don't really have a metal on metal warfighting mission, perhaps in the business, commercial sector, but not just different in that the use cases are different, but also different in that the conditions in which you implement these solutions, especially as you really are executing against your digital modernization journey, putting these concepts into action, interacting with people on your either cloud-based or on-premises, where the rubber really hits the road. That's a really important part of that journey and that looks really different for the Department of Defense, because they conceptualize deploying artificial intelligence models or AI-enabled capabilities on computing and network systems that then are used in physical environments that are managed by enemy's environment, and that has some disconnect from cloud, because we anticipate a certain sort of warfare from our adversary. There are a bunch of different technical requirements and demands for those missions. So, accordingly, I think the DoD's journey towards digital modernization and the adoption of artificial intelligence is progressing at different rates for these boardroom applications versus the battlefield applications.

Angela Sheffield:

Now, pam, you asked about the crystal ball and this is where, for something like digital modernization or artificial intelligence, I feel that the nation, that business leaders who are eager to progress their organization through a digital modernization and AI adoption journey, but willing to learn from others in terms of the best routes to take or how to buy down risk or what challenges to expect.

Angela Sheffield:

We have these crystal ball opportunities that come from, of course, the adoption of these solutions and other sectors in advance of your own sector's adoption.

Angela Sheffield:

So there's a lot of really interesting AI experimentation happening in healthcare that perhaps leaders from other sectors could learn from. But another one that I'm really passionate about because we have this in our history is I encourage business leaders to learn from research programs. Of course, if they're the sort of organization that has an IRAD, an internal research and development program, you can be doing that prototyping and experimentation internally. But you can also learn from organizations like the Department of Energy that invest significantly like significantly in advancing the broad swath of science and then applying these advances in areas like mathematics and computer science and artificial intelligence in not necessarily your exact use cases, but use cases that might look really similar. In my time at Department of Energy, I was doing a lot of AI research, development and experimentation in applications in science and defense and intelligence that I draw on as crystal balls for how to approach it now that we're beginning at scale to make those adoptions within the Department of Defense.

Angela Sheffield:

So again, it's not necessarily a one for one, but very clever and forward-leaning business leaders and enterprise leaders. Necessarily a one-for-one, but very clever and forward-leaning business leaders and enterprise leaders. I think, when matched up with other experts adding to your team, other experts can leverage this research, these programs executed in other places, as a crystal ball to reveal insights about their own journey.

Pamela Isom:

Okay, so that's good, because I didn't call it the crystal ball. So I'm so happy that you brought that up earlier and we were able to discuss it here, because I didn't name it a crystal ball, but I did see it as an opportunity to learn from the experiences of others, and that's what we wanna do here, is we wanna bring some of the journey experiences that we've encountered ourselves and share that, so that others are learning from those experiences and then know where to go and who to come to for excellence in that area. And when I was at Energy, just as you were, there was, and there still remains, a lot of research going on.

Pamela Isom:

But what I always was concerned about is how are we making that information available to stakeholders outside of the government, or outside of the agency, specifically, or outside of the lab, get it out of lab A and make it available to other labs? I've been involved recently with some federated research activities for that very reason, because we're trying to join forces and do some work across the labs as opposed to isolated to one or another, and so I've been able to engage in some federated research activities, but I still think that there's the challenge of how do we find this information. So what's your perspective on that? What's a good way for the public to find research information that's pretty relevant, that we can learn from, particularly around AI in this disruptive transformation period?

Angela Sheffield:

we no longer can control what our program is at the.

Angela Sheffield:

Department of Energy. I know I feel like I can't just say and we could speak to some of the investments that I know that you were a part of that. I was a part of that because we feel that this is a really important thing. But I will step back and say there's a traditional way, there's a historic way that information flows across the research and development to technology adoption lifecycle from the national laboratories and from Department of Energy to industry and Department of Defense. But one message that I would take from being in the defense space is that we really are feeling I don't mean to say really, because I don't want to diminish the importance there is driven by risk and evidence. There is driven by risk and evidence recognition of the urgency of this moment to do things differently and better than we used to. So before we might just rely on conferences.

Angela Sheffield:

Academic industry conferences are a way that we would share information.

Angela Sheffield:

Another way that we would share information is that we would bring in interns, have them intern at the national laboratories or intern at Department of Energy and then honestly hope that they get hired by industry companies to pull that information, or postdocs. But those are no longer sufficient in this era of increased urgency, as, from the Department of Defense perspective, we refer to the very real challenge presented to the United States by a very competitive China, the United States by a very competitive China, even if the war fighting, international security aspect of that is not super important to you, which, like, that's totally fine, but this is a complex issue. It also refers to the fact that, in manners of business and manners of what becomes standards across the international community that's what this competition with China is also about we lose a lot of ability. We might, we may. The concern is that we would lose a lot of the ability to promote and operate according to ways that are in our national interests or according to our values, if we were to lose this competition with China.

Angela Sheffield:

So that manifests in defense as more of a security angle, but it is broader than just security, you know. It is values, it is economics, it is reopen and secure commerce and there is a real urgency in this competition with China presented right now that requires that we do things differently. So, in that spirit, you're brainstorming what are some different things business leaders can do, instead of just waiting for DOE to do it at all, the energy to do it the way that it always it at all, energy to do it the way that it always has been done, or defense to do it the way that. And I would encourage and this is what we're doing. So, in my role right now, I'm at a company called SIIC.

Angela Sheffield:

It's a mission integrator, largely in the defense and federal space, but I left the Department of Energy to be a way to pull these research capabilities more quickly from the national laboratories and from our programs and Department of Energy across what we in the national security science and technology business call the valley of death, which is investment in great research. Great research often does not fund integration, does not fund training, does not fund what it really takes to be successful in a digital modernization journey or another modernization journey. Call that the valley of death. Lots of programs fail, very few new capabilities. So be the change you want to see. I'm going to leave and come to industry to pull this stuff out of the labs, to reach back to people like you, pam.

Angela Sheffield:

And a number of us are doing that. There are great alumni from the national laboratories at Microsoft, at AWS, at small companies doing this, pulling the work out of these research institutions, out of these research programs. We're trying to make change out of these research programs. We're trying to make change. But I would also encourage business leaders to know when you read someone's background, do some sniping of key people, pull them, attract them out of these research laboratories and recognize in their backgrounds that they will bring this understanding of applying new technologies to your mission space. It does mean you'll have to reimagine new teaming because maybe they don't know how to operate in your business space. Build an internal team a little bit differently to take advantage of all those people's talent, but that's what these folks know. So, in absence of processes which are a bit out of scope for you and I exactly right now.

Angela Sheffield:

a lot of this is in the people and I think we can mix that up in new ways that will make a lot of difference for companies and for organizations like different government agencies, in terms of again accelerating this transition of research, transition of AI and digital modernization potential technologies and buying down risk of a successful transition across the valley of death.

Pamela Isom:

And that is so good because we have so much in common. One of the things that I was really concerned with when I was in government was just that, that valley of death Like a wide, open playing field. I'm like you, I'm trying to do something about it. That's part of why this show is about. One of the things was not only that, but there would be massive amounts of information and when a research activity, when the assessment is done, it may not go anywhere. It may not go anywhere.

Pamela Isom:

So then when the idea comes about again, they may go to that prior research, maybe, and maybe not, because now it's time to start a whole new process, right, a whole new research activity, a whole new abstract, et cetera. So it depends on the furtherance of the initial research program. And so there sits that value of death where, like you said, not many projects make it out of there, and there also resides all that data. So we're not talking about cybersecurity here, but that data has to be secured, even that data, even if it's old research data. So it's taking up space, senseless space. That's important, but more important is getting that information research projects that went well and have turned into something, because it doesn't always mean that the full scope of the research effort has moved forward.

Angela Sheffield:

Or there could be a change in the environment that makes it more possible.

Pamela Isom:

Exactly, so what we need is a way to get insights into that information and continuously move it forward so that the value of that percentage of products that reside there shrinks. So I agree with that. One way to get a hold to it, like what you said a couple of things you said is don't rely on conferences. Conferences are good, but that's kind of like the traditional way is conferences. So that's one of the components. But then other thoughts would be like podcasts, like what we're doing here, where we're starting to share some of the things that how to get to information or share some ideas.

Pamela Isom:

Forums like consortium used to be pretty popular and then it's kind of died out and then I think they're coming back with a different spin and we need that because we need that collaboration and think tanks. Think tanks are good at. You can bring some of those research ideas and bring them to the think tank and let the think tanks work together, which is more of a consortium. Think tanks work together to start to move these ideas forward and then use that to go forward for the necessary funding and things that are required. I also tell my customers to watch the funding opportunity announcements. It's a total nuisance. I know it can be. But if you watch that you can see what's going on. You can see what the agencies are doing, what they're looking for and where that research needs to occur. I tell agencies to follow that to get a sense of relevance and what current events. So that would be my take on progressing and dealing with the transformation and maybe some insights for folks.

Angela Sheffield:

I do think that one thing that you get along the way with some of the processes and technologies that are now baked into digital modernization, that are baked into cloud and infrastructure as a service capabilities and enterprise IT versus local IT Some of these things that are part of digital modernization that the business leaders that we're talking to are adopting is the ability to observe the use of these capabilities in their ecosystems to the way that they, because all of that is instrumented. It's instrumented to enable optimization of the resources used to power the cloud. It's also instrumented to see how people are using this, and I think that affords us the opportunity to treat every interaction like an experiment and learn from it. And again, that's coming with the solutions that they are purchasing or that they are acquiring as part of their modern ecosystem. I'm a data-driven decision person. This is data that you can use to inform your understanding of the adoption of these technologies, the readiness.

Angela Sheffield:

Perhaps there are other ways, and a lot of it is coming baked in. It's not just plug and chug. New tech is going to result in the transformation of your ecosystem. It's new tech and then, of course, it has to be adopted. That's hard enough, but what else changes in the context of your business right now. What is your decision-making process regarding deciding to pursue different projects or prioritizing training? My procurement, yeah, where are you collecting?

Pamela Isom:

data.

Angela Sheffield:

Where does the instrumentation in your ecosystem provide you some new ways to assess metric? How can you really adopt digital modernization at scale? Data driven when can you take advantage even of these tools? But that won't be in buying another tool. Data-driven where can you take advantage even of these tools? But that won't be in buying another tool. It will be in changing your decision-making processes, your prioritization methods, changing your concept of business operations and decision-making to take advantage of, in many cases, I think, what might already be coming with your digital modernization solutions and congratulations on adopting those fully. Leverage them by making those other transformations away from tech in your programs. And I'll leave it there for now. But you can also again battle boardroom to battlefield. It's very fun and interesting to support the dod because not just changing the way that you prioritize training activities, that's incredibly important. We get to imagine how does this change warfare? How does this change the way that a soldier communicates with their senior leadership on the battlefield, now empowered by AI and digital technologies?

Pamela Isom:

How trustworthy is the insights that are harvested using AI tools. So one of the things that is really critical, and it's an imperative, is what can we do to heighten the level of trust of the AI solutions? That's another area, and I think there's crystal balls brewing in that area, because there's more and more work on one of the best ways to establish and build trust in AI, and I love what you were saying about we must look at that entire ecosystem. You can't just look at the technology. There's process changes. People have to get acclimated to using the tools. Soldiers they now have this assistant there with them. They have to trust it and they have to be able to count on the tools, whether it's a soldier in the field or an operator in the back room, or you know, I'm from the telematics days and so I supported the army and troops were in the field and I was one of the ones helping with the software so we could monitor in theater.

Pamela Isom:

At that time it was called telematics. I don't know what we call it today, but that's the AI technology that we didn't call AI at the time. A part of it was, and so it was really important to have accuracy and be able to see what was going on real time and have precision, and so it's even more so important now and more so important to be able to anticipate, anticipate what's going to happen next, and I think that's where AI really comes into the mix. I'll just say this one more thing about the digital transformation Some of the organizations that are ahead, like health in the AI space and the financial services sector as well financial services is starting to advance. What they've been doing right, so they're advancing forward. So they had a jump and then now they're starting to advance, and the government, I think, is moving forward in some areas. I feel like government outside of defense. I feel like government is starting to propel forward with caution. What's your take on that?

Angela Sheffield:

One exciting thing that I'm seeing happening in the government outside of defense and national security and the International Affairs Agency and the agencies like DOD, department of Homeland Security, state Department and these other ones and like let us remind us that, for as awesome as DOD and like State Department are and sound and they can note this prestige and they do these important missions we each engage with those federal civilian agencies every single day. We consume the services that they provide. Every single day. They keep our lights on. Well, I think that might be local. They keep our lives going, and so one thing I'm excited to see reflected in the way that they're approaching their digital modernization journey is a prioritization of user experience and the citizen or the constituents experience with the technology. And another thing that I want us all to remember is we're decision makers and we have been doing this for many years, which means we're old. Our new staff and, in the case of the federal government and local and state governments, their constituents, especially their younger constituents, have a different expectation for trust of technology, for use of technology, for their experience with technology, and it's hard for us. You can't really empathize with that you. You just have to trust it. I do think that's super important to remember because we shouldn't project our holdups around trusting a smart computer. We can't necessarily project that onto the next generation of, like we say, digital native, and this is all about data driven gathering, data. That's actual ground truth on the experience of the user with the technology from a decision-making sense. That allows us to challenge our biases, but it also is how you can, based on that feedback, make your digital modernization investments and make your technology selection choices to really deliver value to the customers. And another aspect of that I think that is important for those of us who are technologists, especially in that AI space man.

Angela Sheffield:

This is making me think of something else I do want to share is to build in either process-wise if we're talking decision-making and leadership and operations build in process-wise or technology-wise, the ability to observe the use of the system, observe its implementation, its use, its operations continuously, not just the way that we used to do it in decision-making, where you do check-ins every month. Building that opportunity for continuous improvement based on having built-in opportunities to observe, and I see that prioritization in the federal government. I think that's really exciting and these problems are so big that everyone needs to be focusing on something and all together that will drive really the true realization of digital modernization opportunities to observe the use of these technologies, to move the digital services they provide, and by which we mean you getting your licenses at the DMV, the IRS, recently I know it was a very small pilot, but they recently rolled out a software program to take over 1040 easy filing but leveraging that they interact with people. So much to tackle part of the digital modernization journey, which I think is really cool. And then, if we want a little free crystal ball from Angie, one thing I see in the spirit of increasing your opportunity to continuously observe the operations and executions of your systems and processes to improve them, and executions of your systems and processes to improve them. One tough wicket we've got ourselves wrapped around in AI is that calling this instrumentation and opportunities to observe, because I'm trying not to call this data.

Angela Sheffield:

We've been talking a lot about data in the AI digital modernization journey Data, data, data, data data.

Angela Sheffield:

Dod says data is a strategic asset, and by that they're data data data data. Dod says data is a strategic asset, and by that they're really talking about the data that makes up your training and test data set as part of a machine learning algorithm or part of the development of a data analytic technique, the input data to which you apply your machine learning, deep leaning or traditional statistical algorithm to get your result. It is the training data. The emphasis has been on that data and we've forgotten that you have to observe, you've got to instrument your system to make those other observations, also data to see how it's working, and so a lot of investment has been in improving the training data, lots of investment on all of these CDO data standardization activities. All of that is around improving the quality of the training data in, which is very important. But let's also remember we're digital modernization professionals. We don't get too wrapped around the axle with AI. Let's remember you also have to create these opportunities to gather data on the use of it.

Pamela Isom:

The building of that system around more data.

Angela Sheffield:

The ecosystem yeah, the ecosystem I'm really excited about. I think that might be the next wave of our investment in AI to really be and that will afford us a lot more information to make the AI better than just focusing on the training data. But I think we've gotten a little confused because we've been so focused on that just in data and have forgotten that there's also all this data of the AI interacting with your ecosystem that will also help us along the AI journey help us along the AI journey Exactly.

Pamela Isom:

I just love that. So today we've got a many. Many organizations have started and are in the middle of the long-term digital transformation journey and we've got technologies and process changes and new factors that are weighing in on the equation of success and their challenge left and right, and sometimes it's overwhelming because you don't know what to do, you don't know how to prioritize. So you've got BR, you've got augmented reality and gen AI. You've got all these got spatial. Now Can't call that BR because it's spatial. So organizations are struggling with well, how is this going to help my mission? And I don't want to be like some of the providers who didn't do anything and then were swallowed up, basically, and went out of business. So organizations are struggling with that because they don't want to be left behind. People are the same way. People are trying to grow, develop, cultivate themselves, keep sharp, and we don't want to be left behind. So, with what you were saying, I think that it's really important to in the middle of this transformation journey. Some people set a long roadmap, some people set a shorter roadmap. It's time to take a look at. Some people set a shorter roadmap. It's time to take a look at.

Pamela Isom:

Now that we've got this insertion of AI and the next evolution of AI, how do we move forward today? Because you can't just say I'm not going to do anything, I can, and then you will be like some of those vendors that got overtaken. So we don't want to do that, but we're to the place where it's not use or lose right now. So it's a good place for organizations to start to experiment and take the time and build that into the plan. Like you're saying, now is the right time for that, and so I really like that. You brought that up, because we always say when you're using agile, allow for the incremental approach, allow for the iterations and allow for short run, so you can always build on top of that.

Pamela Isom:

But now, with the big word in the day and time of AI and maturing your digital transformation programs, for me, the big word is the continuous. You've got continuous testing. You've got continuous monitoring, monitoring from the standpoint of even when you roll out products and then you decide that, okay, now it's time to retire these products. You still have to pay attention to the net effect of the retirement, why? Because of the interconnectivity. Like you said, there's so many components connected until we really have to start to think through that part of the equation, too, from a continuous perspective. So we retired it.

Pamela Isom:

But what are the interconnections, what are the interdependencies, and how long before they can replace the dependency that they had with something else? That's what I mean by that. So I think that that was really good, really good insights. So we are almost out of time here. A good discussion. One last thing that I'll ask you is I don't think you really have any concerns, and you've voiced that already. So tell us, is there any words of wisdom, any perspectives that you would want the listeners to hear and to understand and that you can walk away with some parting words for them?

Angela Sheffield:

Yes, and I hope this resonates. But two things come to mind. First, I would say everything is founded in your business operations and use cases, by which I mean we bring in new technologies, like digital modernization, like AI, to enhance the operational efficiency of something that you're doing or to create new capability that helps you achieve your mission more In defense of national security, especially in the business that I have supported most frequently. We were trying to find bad guys.

Angela Sheffield:

So when I do my catalog of the different ways that I execute my mission finding bad guys- it starts with that catalog of how you do your mission.

Angela Sheffield:

I encourage business leaders, decision makers and this doesn't just have to be you, this can be among your team but to have a really firm understanding of that catalog and the things that affect that prioritization, both in terms of what's really important to your mission and the sorts of resources that it takes up to execute it. Because when something like cloud migration or AI is introduced to you, because it is new capability that is now available, you're assessing what's that catalog of the things that I do?

Angela Sheffield:

and does adopting this capability address one of those high priority things or maybe the not top priority thing, but you have again an understanding of the gazins and the gazouts. As someone I know used to say to know if AI is going to make a big change, will that knock that off my list? That is the core of how you make those digital modernization journeys. Another thing I really want to empower business leaders and enterprise leaders is that understanding that list and the gaz in the gets outs and the priorities. That doesn't require that you understand AI or that you understand cloud. It understands that you really deeply understand your mission and your businesses.

Angela Sheffield:

So when I was finding bad guys, I had my list of pain points or really hard missions or things that we couldn't get done, critical challenges. I'm not like, oh, is AI a solution for this? Because you don't have a question now that AI or quantum or whatever's coming next is a solution, for I know these are my gazins and my gazouts for this mission and then maybe I talk to an expert. These are the things that matter. Does AI change that?

Pamela Isom:

Does AI work?

Angela Sheffield:

for this. Does AI change the way I get a gazelle out of this? If yes, then it's a good candidate to put on your AI adoption roadmap. If not, it's not going to matter. And lots of mistakes are made, adopting AI because you think it's going to help, but you haven't done that mapping of. Does it really change one of those inputs or change a process or change an output. So I always encourage enterprise leaders, business leaders, government leaders I need you to deeply understand your missions, your business cases and how they go and how they don't go, so that then you know we can have a conversation about what's in the trade space in terms of what we can tackle with AI. So don't take another AI course, you know. Just really dig deep. And if you're talking to a consultant or somebody who can't help bridge that gap or is selling you something and it doesn't sit right with your understanding of your mission, they're not giving you that, they're not the right person and their answer isn't going to be the right fit for your adoption journey, your modernization journey.

Pamela Isom:

So you're saying that, but then you also know that there is room to experiment, to really take a good look at their business mission and understand that mission so that with all of the information that's out there, so that that doesn't become overwhelming, it's a strategy for not becoming overwhelmed and staying true to the mission. Right yeah, staying focused.

Angela Sheffield:

Yes, helping to see through the noise. And again, you mentioned experimentation. But another thing that even when we had the nation's resources to do experimentation and research at a scale that your listeners can't imagine, to solve very, very hard problems, there's often low hanging fruit. That again, if you start with the mapping of this, it's like either cut away at the complexity or the cost of one of those inputs, you could make a lot of progress. You know why not start there. If you've totally exhausted that, then perhaps it's time for experimentation. But I think there's so much to learn again from this looking ahead, looking at those crystal balls, perhaps consulting someone, but on the basis of your understanding of what your inputs are. And then how does AI or how does cloud slice away at the cost or complexity at one of those inputs or the process that you take to get your mission done?

Pamela Isom:

That's really great. This has been a really, really good discussion and I hope to see you real soon. I'm so glad that you were able to partake in this podcast. This is the inaugural week of recording, so you're one of the first ones because you're near and dear and special and with a great mission and I just love what you do and that's not going to stop. So I appreciate you working with me and joining the show. Your insight has just been very, very helpful and invaluable. Thank you again and I hope to be in touch with you soon.