Enterprise Architecture in the Age of AI—with Fred Hennige (Jack in the Box)

If everyone in your organization wants AI, but half of them can't explain what for—where do you start?

In this episode of Futureproof, Xano CEO Prakash Chandran sits down with Fred Hennige, Director of Enterprise Architecture at Jack in the Box, to explore what enterprise architecture actually looks like inside a fast-growing restaurant company navigating AI adoption. Fred shares lessons from building EA practices at three very different organizations (Jack in the Box, Starbucks, and Alaska Airlines) and explains why the discipline must start with business outcomes, not technology inventories. Together, they unpack how AI is showing up across the organization today, why cross-functional AI value is harder to unlock than personal productivity, and how to govern AI adoption without over-indexing on hype. 

Topics covered include:

• Business-first EA over technology-first EA: Why starting from business outcomes and process alignment yields better results than cataloging application inventories.
• EA across three industries: How enterprise architecture looks radically different at a growing brand, a mature global operation, and a safety-critical airline—and what each taught Fred about the discipline.
• AI adoption at different maturity levels: Why some teams are already creating value with AI while others are still learning to spell it—and how to stack-rank where to invest.
• Cross-functional AI is the hard part: Why personal productivity gains come first, but the real challenge is unlocking AI value across departments and business functions.
• The AI uncanny valley: Why AI output still requires human synthesis, and why using your own voice matters more than copying and pasting what a model returns.

Episode ID: 19469167-enterprise-architecture-in-the-age-of-ai-with-fred-hennige-jack-in-the-box

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Transcript

SPEAKER_01 0:00

In some cases, some folks at Jack in the Box have really, really mature points of view on how on how value is created with AI for themselves. That's awesome. In other cases, we're just learning how to spell AI. Like I know it's supposed to help, but I'm not sure how. It helps you kind of easily stack rank where the most good can be and use that energy that's there to grow that perspective across other places.

SPEAKER_00 0:43

Fred has spent nearly two decades as an enterprise architect at Alaska Airlines, Starbucks, and now Jack in the Box, and his career before that spans software engineering, infrastructure, technical product management, and even a stint as a radio DJ. Maybe we'll cover that today. He's the person whose job it is to make sure that business strategy and technology can scale together. And in a world where AI is generating more and more technology faster than organizations can absorb it, that job has fundamentally changed. Fred actually has one of my favorite quotes from Andre Carpathy in his email signature. You can outsource thinking, but you cannot outsource understanding. And in this context, it means that AI can think for you, it can generate, it can analyze, it can produce, but it can understand what your business actually needs, what your data actually means, or where the real value lies. This conversation, uh, or this is a conversation about what enterprise architecture looks like when the technology starts building itself and why the person who connects business strategy to technology might actually be the most important role in the age of AI. Fred, thank you so much for being here. Thanks for taking the time.

SPEAKER_01 1:52

Yeah, thanks for coming. Nice to be here.

SPEAKER_00 1:54

Fantastic. Well, I really wanted to just get started with a little bit about your background and your career arc and how you ended up at Jack in the Box.

SPEAKER_01 2:04

Yeah, uh, so without getting into like great detail, it's a it's a few decades worth of work. Um, but but across, like you mentioned, all the frontiers. I started off as an infrastructure person, worked my way through support, wandered into architecture, software engineering, product management, did enough to be dangerous as an enterprise architect, is probably the right way to say that. I I got a good understanding. In fact, I even had a stint at Alaska Airlines as an innovation architect, which was someone who helped kind of bring that like new ideas to life with a chance to be, like you mentioned earlier, to be able to scale, which would, which is super important. And that was in the marketing organization. So I've even done a stint in marketing. So I've I've had uh, like I said, a little bit of everything.

SPEAKER_00 2:43

You've done it all. And you know, one of these things, it's like enterprise architecture, depending on who you ask, can mean different things within the context of different organizations. So I'd love to learn a little bit about what your definition is and as that relates to what you specifically do at Jack in the Box.

SPEAKER_01 3:01

Yeah, thank you. So I I I have a long title that's I've got a very short mission, I guess is probably the right way to say it. And I tell I tell folks when I meet him for the first time, Jack in the Box in other places, it's it's just my job is to make sure business and technology line up for the greater good uh at the end of the kind of the strategic cycle for a company. So it's just trying to make sure the pieces fit together um early and often.

SPEAKER_00 3:23

So, what does that tactically mean, though? Are you the person that makes the decisions around what software to use? Do you set up processes? Tell the audience a little bit more about what your day-to-day looks like.

SPEAKER_01 3:35

Yeah. So so for me, um I I have a I have a small team at Jack in the Box, and there's some very crucial roles. One of them is a solution architect all around our restaurant space. So part of my role is to make sure that basically the talent is in the right spot to help. So we have a solution architect in our restaurant space. I have an information architect, particularly in the age of AI, to help make the information and data we have understandable and legible for the business needs we have. So that's how I kind of kind of shape my universe at Jack in the Box. But on any given day, uh, it could be a conversation around strategy that I'm talking with executives about, or I'm sitting down with technical product teams trying to figure out how to go from a platform to a product. Uh it varies with a small team. The day can be wildly different. And that's actually what makes it the most fun, I think, to a large extent.

SPEAKER_00 4:22

That's awesome. Uh, before I kind of get into more of the nuances there, when people think Jack of the Box, I don't think that they always think about AI and enterprise architecture. And I'm curious if you can share a little bit with the audience around when you started thinking about, you know, transformation or how you start thinking about transformation and how you when you started thinking about leveraging AI within uh Jack of the Box as an organization.

SPEAKER_01 4:46

Yeah, I th I think like many companies, the the first place it shows up for a lot of different disciplines in the organization is how does it make my day easier? Like how does AI help me be more effective and efficient in my day in and day out role? That productivity, that enthusiastic intern side of AI to help um jobs get done a little bit more easily and quickly. Um, what really gets hard to figure out is how do I have that cross-function? How do I have AI help us across many different frontiers and then really realize value in that way? And that's the stuff that we're just starting to scratch the itch on today. But but right now, it it a lot of it's just personal help. Spend a lot of time talking about that.

SPEAKER_00 5:27

Yeah, for sure. Um I would love to understand a little bit more about, you know, when we were talking before, you were saying that um there's kind of two schools of thought when it comes to enterprise architecture. There's technology first and business first. And you've worked in the context of both. Talk a little bit more about what that means, how they're different, and which one do you feel like matters more in the age of AI?

SPEAKER_01 5:53

Yeah, technology for, I think many people think that that technology first, enterprise architecture is the is the place where people start, which is all about do I have the right services? Do I have the right technologies in place to help realize e-commerce? Like, are am I doing those things well? Am I putting those pieces together? Am I am I service-oriented? Am I at, you know, is my SaaS model actually supporting what I do? Like a lot of that conversation goes on on the technology first side. Not a bad set of conversations at all. It's just kind of the emphasis is on the technology space. From a business point of view, it's what are the outcomes you're searching for? And then how does technology help enable those things and kind of sussing out some of the things that'll end up tripping up technology? Some, in some cases, it might be an edge, an edge kind of case or an edge scenario that might be that. Or in others, it might be the fact that two parts of your business actually do something extraordinarily similar. And you might be able to solve the problem with one kind of technology. You just got to get business process to line up and help help folks with business process. So it's it's more emphasizing around the processes of how you do business and what are those outcomes you're searching for from a business side. That's the side I lean towards.

SPEAKER_00 7:03

Yeah, I think that makes a lot of sense. And I think um another piece that I think is interesting to dive into is how you make an assessment when you first join an organization. Because one of the things we talked about pre-show was, you know, at Alaska Airlines, at Starbucks, and now Jack in the Box, every organization is kind of on a maturity curve around how much they've they're leaning into kind of enterprise architecture in the way that you're describing it. Talk about how you go about assessing an organization's maturity, not only from the technology side, but also from the people and process side.

SPEAKER_01 7:40

Yeah, yeah. And good three great examples. So, so Jack in the Box, just to kind of work backwards in time, Jack in the Box I joined to build the practice of enterprise architecture. That was the most compelling thing to me. What do you do when a company is kind of at step one and your executives say, we want this thing called enterprise architecture? You've got to help them shape that picture and then help kind of make it so, which I think is really interesting because you're assessing business maturity, technological maturity. Like you're in many cases, you're looking at how long have the employees been there? Like how firmly entrenched are they in their ways of thinking. So you're using that as both like your measure of what's it gonna take to get to kind of move the organization forward in this space and kind of where that energy is gonna have to be applied most diligently in that case. At Starbucks, very different. So Starbucks was was a place where there were literally two directors of enterprise architecture. One of them ran from field to cup, and the other ran from cup to customer. And I had cup to customer. So I had everything that once it got out of the, you know, out of the fields and roasted and packaged and sent to the stores, I had your, you know, the application that you see on your phone all the time. My teams had that, um, the information around customer and that loyalty program, some really interesting spaces. But really, what it ended up having for me to deal with was kind of how do you then plug those two pieces of architecture together, kind of the back and the front of the house together, to kind of realize some value uh with respect to how things are happening globally, literally globally for Starbucks, because that's a it was an enormous undertaking there. And then Alaska Airlines, um, again, I founded the enterprise architecture practice there, um, but a lot of that focus was around operational stability, trying to make sure the air, you know, keeping keeping aluminum tubes in the air with people safe is a very important job. And you realize that's job one. So that's really key. Uh so you you start to focus on like those aspects of the business, the operational components, the safety components. So very, very different things. If I were to sum it up, Jack in the Box, just growing, you know, just starting to grow an enterprise architecture. Starbucks was how do you make a global operation run better and much more smoothly as it's huge? And then Alaska Airlines was really like, how do you focus on being safe with respect to the technology and the people in the process that actually help those uh planes move around? And it's really interesting because each one of those has influenced my journey all the way through. Like I use, interestingly enough, a jack in the box, I we do this a lot. I talk about like, hey, what's a critical business capability we need to have? Like, what's really the most important thing we do? And I get looked at kind of like with the RCA dog kind of sideways glance, and and they're like, Fred, we sell burgers and tacos. We don't fly airplanes. I'm like, oh, yeah, yeah. But it but we still have critical business. Like there's stuff we still want to be able to do. We got to be able to sell them. So I've got to kind of dial down my safety enthusiasm, but also contextualize it for things that might be really important from a business perspective for us to um sell burgers and tacos.

SPEAKER_00 10:31

Totally. I mean, I think what's interesting there is kind of your approach and posture around going uh from the value proposition and working backwards in terms of how you approach enterprise architecture. And I'm curious what our audience can take away from this, even outside of a large organization like Jack of the Box. Let's say they don't have an enterprise architecture division uh stood up. Um, I'd love for you to maybe talk to a framework around how they should start thinking about based on what value and criteria the business wants to deliver, how do they then start standing up a framework towards good enterprise architecture, best practices?

SPEAKER_01 11:10

Yeah, that's great question. I I think that the things I would focus on to start with are really they're they're not hard because if if a company already has kind of a mission in mind, you've got you've got a starting point. But you need to figure out kind of what's most important in that mission. And I articulated like safety and loyalty and you know, just operational integrity. Like what part of your business is most important to you to run or what part needs the most energy to kind of corral, if you will, in that space. So figure out that area and figure out how to measure how good you are at it. And then by measuring, I don't mean transactions per minute. I don't mean stuff like that. I mean how do you measure the quality of life in that space? And it can be across potentially steps and processes. It could be a customer, a CSAT could be a good example of something like that. Like just how do you measure that? Because then you want to take that measure that everyone can kind of get in their own heads because you don't want to take them to a technology space. Use that measurement and then figure out how you can apply changes in process, changes in technology, changes in, you know, maybe maybe even organizational structure to help that number change and draw that line for folks. Be able to draw that line from that measurement to that change that you were making to help it. Could be a journey of a few steps. I've done that before. Sometimes there's really easy ways to make it happen.

SPEAKER_00 12:27

How are you able to visualize everything across such a large organization? You know, we we kind of opened up with it's where business strategy and technology can scale together. But let's, you know, talk about the complexities, even of what you were describing at Starbucks, from I think you said field to cup, cup to customer. There are so many things, so many applications, so many people process handoffs that need to happen. Who owns that picture? How do you manage that? How do you look at the efficiencies that you drive and the KPIs that you can rally around?

SPEAKER_01 13:04

Yeah, I think I think there's a giant, I'm I may make enemies of my EA brethren in saying what I'm about to say. But but but there's a giant, there's a giant constituency of EAs that are like, I've got to have all the boxes in lines so I can tell you what all the boxes and lines mean. So they want to have the massive application inventory so they can rationalize applications. I tend to flip it around and I say, where is the space that we're we're really tackling from a business point of view right now? And what are the things we need to understand about it, be it a technology processes, whatever in that space. And let's just focus on that. And in that workload that you're doing to understand the inventory of what affects that part of the business, you'll unearth other things. As an architect, though, I want to keep those artifacts so I can use them later in other conversations. And I want to build my portfolio around the most important initiatives a company has to start with, and then just enhance that portfolio as we go along for the ride and do that in a way from a visualization perspective that can bring along the audiences you need to be speaking to, be they technologists or business people. And that's really the trickiest part is how do you keep those pictures intact for different audiences? But work from the initiative back into the technology and work with it that way is the is to me I found to have the most success and not burn energy where it really doesn't yield any good.

SPEAKER_00 14:24

Yeah, that I think that makes a lot of sense. And I think uh when I think about that kind of initiative first line of thinking, I can only imagine as AI started to be kind of um, well, AI started to overtake the market, and then everyone top-down or organizations were saying we have to have some flavor of AI. So the initiative itself was like leverage AI to gain efficiency. So I'm curious, like, as much as you can share at uh Jack in the Box, how did you start saying, okay, well, AI is a thing, right? Where do we begin? Right. If you were narrowing in on a set of like, hey, these initiatives could probably benefit uh it from it the most, how did you start to think about prioritizing and stack ranking where you could get leverage with AI?

SPEAKER_01 15:10

Yeah, a lot of a lot of that started with kind of um polling the audience, if you will. A lot of that was like, we want to AI, and that's a very big term. But then folks were already honestly doing some of those things. So it was talking to those folks and figuring out where that value was for them. In some cases, some folks at Jack in the Box have really, really mature points of view on how on how value is created with AI for themselves. That's awesome. In other cases, we're just learning how to spell AI. Like that's there are some place like, like, I know it's supposed to help, but I'm not sure how. And that becomes kind of a coaching moment in that space. But then, but then using that fidelity, if you will, between those various use cases with folks, it helps you kind of easily stack rank where the most good can be because you want to gravitate towards those more mature perspectives and then enhance those maybe beyond the organization, the department they're in, and work with them and kind of grow that and use that energy that's there to grow that perspective across other places. Like for us, it's all it's all about operations and food and that type of thing. So that's where a lot of our energy is right now.

SPEAKER_00 16:14

Okay. And is that, do you feel like the biggest challenge with AI? Just kind of like there's those different perspectives that um you kind of have to kind of, I guess, harness the energy and momentum around. Like when you think about AI adoption in your company, where do you feel like this is a really big challenge for us and a really big hurdle for us to jump?

SPEAKER_01 16:32

Yeah, I I think for for for us, if if I were to break down organizationally, it it's the it are some of the softer sciences would be the way to say that. So if you're if we're not talking about financial analysis, we're not talking about operational integrity, like those type of things, if we're talking about um the reading resumes, the soft skills, those type of things. That's where it's it's harder to see value. And you get a lot of people trying to sell value from an AI perspective in that space. But the audience doesn't necessarily know how to translate what they're getting when they get pitched. Um, you know, the newest thing that'll be the resume reader that'll give you, you know, 99% success on your candidate, you know, slate if you're going through it. Those type of things become tough because the audience isn't necessarily at the maturity level to realize that value. Whereas in those other spaces like finance and operations, they're already thinking about numbers and statistics and gains and losses and safety. Um, so it makes them much more, I think, amenable to kind of using the the using AI technologies.

SPEAKER_00 17:34

Awesome. So let's talk about AI governance. Um, I think one of the things that you did was kind of stand up an AI governance council at Jack from the Box. Uh, and a lot of organizations are trying to figure out what that is, who should be in the room, and how to have a productive conversation within that group. Um, can you share a little bit more about your thought process around it, who's involved, and then the framework you use to operate uh within it?

SPEAKER_01 18:03

Yeah, so so our starting point really is the obvious ones, right? And this is no surprise. I don't think any company is like you've got to have legal, you've got to have, you've got to have your information security people in the room. There are some constituencies that absolutely um have to be there. But then you want to have uh other folks in the room who may have some opportunities um lying in the wing, so to speak, to kind of work with. So you have your HRs, you bring in the people who might have something going on there to get their perspective. Uh, and then we use that to kind of um get things started. We have a very thoughtful experimentation approach to AI, which I think is good. Um, you don't want to necessarily uh slow folks down, but you also don't want them to make bad decisions. So you want to arm and educate them through some of that governance. And we use the council to help us make that so.

SPEAKER_00 18:52

You know, I think um, well, we we met at the Gartner conference and there was so much covered. Um, there's some sometimes it could feel like there's a lot of AI hype. People are talking about AI governance very broadly, but what does that even mean? Uh in your experience there from going to the different sessions and talking to the different vendors, um, what's a takeaway that you bring back to Jack in the Box and you instill as practice within the organization through the enterprise architecture lens?

SPEAKER_01 19:22

Yeah. The thing, the thing I brought back, literally brought back, was um looking at this as uh AI, the hype is real, but it's not there yet. And then be able to kind of tempo, build your tempo, that time and tempo around that hype cycle, Gartner's hype cycle, and say, okay, in that time I have, let me bring up the education, the governance, those capabilities that are critical to being successful. Let me bring those things into the light so that when the hype kind of hype turns into material, that we're actually kind of in the right space for it. But don't try to over-index on governance too soon that because reality hasn't set in yet. And I think that's been a thing I took away from it is I want to, I want to manage towards uh the next great thing, but the next great thing's not here yet.

SPEAKER_00 20:08

Yeah. So I mean, in getting to that next great thing future, I'm curious how you think about um, you know, championing championing ideas and future state realities as an enterprise architect. Because maybe you've learned things, maybe the hype uh is we're not quite in the future state yet, but you want to be on the forefront of pushing the organization to get there. Maybe you can talk a little bit about how you as an enterprise architect leader within the company sell the vision. So outside of the tactical work that you do every day, how do you work with your contemporaries and the leadership team to sell a vision and then set a roadmap to get there?

SPEAKER_01 20:55

Yeah. So so doing doing that part of the puzzle, that's probably one of the most challenging things because you're already working to an expectation that has been set outside of your voice, right? The media conferences like we're talking about Gartner, there are things where there are some expectations that are set. So a little bit of that is expectation management, but then it becomes a conversation, I think, around what are the steps we need to take to make a vision possible before we kind of get to a vision, a vision visioning session, an envisioning session. Um, and what I mean by that is like we understand the data you have. Understand the data you need to be successful in that space. Because a lot of a lot of organizations' data estate is are not clean, I think is probably the right way to say that. There's an easy way to say it. Um, but but make sure folks understand where you are before you know where you need where you want to go. Like for us, it's it's where does AI take care of some of just the blocking and tackling work that we do today to try to transform like even something as simple as order taking? You may have read that like a company like Wendy's tried to do AI order taking and then they To pull it back because it wasn't necessarily ready for prime time. So it's like that hype being outpaced. We can see some very kind of near-term maybe business opportunities in it, but we don't necessarily know what it takes to make that a reality. So for me, it's it's tempo uh and understanding the foundational changes that need to take place, then sitting down and thinking about like what where do we want to go with with that, what what we have in that data perspective. At least use what that data perspective in mind.

SPEAKER_00 22:27

You know, I think there's another side to that around like just because you can enable and do something like the Wendy's example, it's not always clear that you should. And because there's a customer perception piece around their relationship with your brand and I guess what they expect in service. So you kind of have to balance how fast you put new uh processes and technology in front of them because they're part of that equation. That must also be something for you to juggle, I'm sure.

SPEAKER_01 22:55

Yeah, yeah, yeah. Customer perception is real. That's a that's a very big deal. I think for us, um that that moment it had two hands to it. One is the customer perception, the other is kind of like what's going on in the kitchen behind the scenes, and what do you have to do to train those people up to work in that space? AI can't make burgers. Well, there's a robot that makes French fries, but we're not going to go into that one right now. But but AI can't make burgers for us today. And and nor do we want them to. That human element's awesome, but there are certain things that that human beings, um, you know, that that having AI to kind of augment that space would certainly be helpful. Um, but the perception of both employees and customers, I think, is equally important, was kind of where I was heading with that.

SPEAKER_00 23:37

Yeah, that makes sense. You know, I think one thing that's unique about enterprise architecture specifically is you always have to think about things at scale. Um and I think that, again, means different things to different people. So I'm wondering if you can talk about what you mean when you talk about scale uh and handling scale uh when it comes to an organization like Jack of the Box, Alaska or Starbucks.

SPEAKER_01 24:05

Yeah. So three very different things. Um so for Jack in the Box, uh, scale really it just comes down to units, right? Like number of units you have. And I guess there's to some extent Starbucks works the same way. How many units do you have? More importantly, where are those units at? Because that scale, there's there's nuance to geography, right? So what's going on in Canada or what's going on in Mexico is not what's going on in the United States. I think compliance, all of those wonderful things. So scale means different things to Jack in the Box, but basically it boils down to number of units and the number of people we affect in those units. That's that's pretty straightforward. Starbucks being a global, like a global entity, um, scale's vastly different. Uh, the United States is enormous in what it does. Um, but there you you have to kind of do much more work before things see the light of day. So your experiments have a different size. So in in Jack in the Box experiments, maybe 20 units or so. In Starbucks, you may be talking a few hundred in different places. So that scale, even the experiments have scale. So, but that really depends on the on the size of the operation between um Starbucks and Jack being very alike. Um, Alaska Airlines, the the interesting part about Alaska Airlines, getting to scale on that one, does fall right back to safety. It comes back to, okay, our are you have to kind of button up a lot more, particularly in the operational space, before you actually um let something roll out to the public. Um, because that network, that literally that network is so tightly tied together that if something goes wrong in one place, you're grounding airplanes in another one. So there's a very different thought process when it comes to scale there. From a customer perspective, much more iterative, much more, you know, the same things you would expect, like any customer-facing organization trying to create a great customer experience, great loyalty program, that type of thing. Much more iterative. So you've got the dynamics of tension at scale in two different parts of the business, which is really interesting. I'm in the airline space. One is worried about safety and the other's worried about a great customer experience, compelling customer value. And that's that's a really exciting space.

SPEAKER_00 26:10

Yeah, for sure. You know, I think I mentioned uh up at the top in your email signature, you had that quote that, you know, I resonated with the you can outsource thinking, but you can't outsource understanding. Why did you put that there? Why do you feel like that's an important takeaway for people uh today, especially in or especially through the lens of yourself and others as an enterprise architect?

SPEAKER_01 26:37

I yeah, the the reason that ended up my signature line was was a was kind of a uh response to this AI forward. It can, it is the best thing sent slice bread and it can make peanut butter and jelly sandwiches at the same time. It's awesome, it can totally do that. And it was kind of a response like you've got to watch, like when you're prompting and you're working with with AI, particularly through, you know, the lens of Gen AI, um, that you know, is that output you're getting really what you are what you want to see? In other words, you have to understand the subject matter. Um, I don't, you know, I have a ton of times where I've seen like AI slop in an email where someone just copies and pastes what they get out of a out of an AI prompted answer. And I'm like, I know that's not a human being doing that. And now I have to spend some cognitive load to kind of weed my way through it. Much better if people kind of go through the process of, let me ask the question and then let me synthesize my understanding on top of that to get good output from it. So that was really what a point of my signal on that one.

SPEAKER_00 27:38

It's so true. I I I mean, I've definitely been guilty of this and I've had to adjust my usage. I also think that it damages credibility. Like when you read something that's so uh obviously generated by AI with all the M dashes and hey, X, uh, this Y is better than this. It's foundational, it has like these patterns that it uses. You kind of stop reading. And what was interesting, I was talking with someone else the other day around a really well-written PRD before AI. And the value there was it was a synthesis uh uh of thought um from an individual who brought their muscle memory to a uh a product and an approach. And when you would read it, it would be like this is from this person and everything that they bring to the table. Where now AI is writing pretty much all of them to where you don't even take it seriously anymore. You don't even read it anymore. It's the same thing with linear tickets, like people are kind of completely skipping that. And it it it it's straight, it's a strange kind of time we're living in where yes, AI can help you generate, but it also kind of removes like the seriousness that you take these artifacts and the output of everything. Do you do you see that as well?

SPEAKER_01 28:58

Yeah, very much so. I I feel like I'm I'm in that, you know, early days of I'll my my parallel, early days of like um, you know, 3D animation, um, like the uncanny valley, now we're in, we can see it. We can't cross it, but we can definitely see it with respect to what, you know, like you're talking about your PRDs, a lot of the output we get from these things. You can definitely tell um when that problem exists uh very easily. I I don't think um, I don't think I've seen a good, I'm just in my head rattling through some examples, a really good case where just straight from the, straight from the response, you can do anything about it, really takes that understanding right now, because it is the uncanny valley. You're trying to span that uncanny valley of response.

SPEAKER_00 29:41

Yeah. And I think it's like the most important thing that we can do is if we're leveraging it, we have to be able to infuse our taste and our humanity into it. And this is what I'm finding, especially with my own personal usage. And sometimes I want, I'll want to uh, you know, leverage it to kind of fix an email that I'm writing or something. But I found myself saying, look, I'm I'm losing that muscle of the creative thought that I would normally put into this. So it's a balance. And I'm wondering, you know, as you uh have obviously a big responsibility in introducing AI to individuals within the organization, do you also have training around how to think about it, how to, when to like fully trust it versus when you should infuse a lot of that understanding against it?

SPEAKER_01 30:26

Yeah, it's like that inverse slope, right? Like if there's more on the line, trust it less. If there's less on the line, trust it more, kind of kind of thinking. And and helping people understand that. I was, I was literally just having a chat with someone today about that particular kind of problem. It's like, how do you, how do you know how much of it to put out there? I just tell folks to use their own voice. You made the great point. It's like make sure it's your voice. I think that implicitly brings along for the ride some degree of editing in what you're gonna see and some degree of bringing that understanding um into the output that you're gonna try to share as you kind of take that output and send it wherever it needs to go in your organization.

SPEAKER_00 31:04

So I want to talk about like your uh role specifically in your use of AI. Like, do you use it as an enterprise architect in practice to help you at all or help you evaluate software or solutions? What do you use AI for in your day-to-day um that makes you a better enterprise or a more productive enterprise architect?

SPEAKER_01 31:26

Yeah, in a couple of places. So in some in some spaces, I use it to do analysis and comparisons, um, software packages, um, various approaches, different engineering thoughts. Like I can use it to do that. And it's a very helpful friend from that point of view. Um, but I again, you have to be very specific about what you want it to do. You have to give it some context, give it some constraints, give it the right information to look at. Um, but it can do a lot of that work without me having to go through and and generate, you know, a ton of content, visit a bunch of different websites, and do a thing. That's super helpful. Um, on the other side, and I've had some very frightening experiences where I've kind of analyzed like deep content um with respect to like some of our uh software tickets and that type of thing, um, and noticed that it just that the its analysis smelled wrong. Like it was just not, that wasn't right. And then of course you go back into you to to prompt it again and you say, you know, that wasn't right. Can you re-categorize it? And then of course, you're, you know, AI is super friendly. It's like, hey, yes, oh, I see you're absolutely right. So thanks for telling me that. I reanalyzed it and I came up with this, and now I'm only three quarters as wrong as I was before. So so we go through that process, that refinement process and analysis quite a bit as an EA to really understand um the depths, I guess, of some of the benefits that are that's in the information we're trying to get through. And in my case, it was help desk tickets. Um, and then and then the other spot is it sure does a great job with um trying to wean down, you know, data that's pretty much unstructured and trying to put it into a structured format for me, particularly like around just inventory of software, like the hundreds of versions you may be running with, like using AI to help dial that down. Boy, that's my best friend. So that's where it's do, it's really doing me some good in that space.

SPEAKER_00 33:08

Yeah. It's uh it's it's kind of that understanding pieces uh helps you kind of realize, hey, that doesn't smell right. And that only comes from like doing it yourself for sure. Um, you know, I uh as we start to close, you obviously have worked in these different environments from coffee to flying people around the world with Alaska and now uh, you know, delivering food with um Jack in the Box. If there is a through line or a thread that you feel like is a truth or a framework that you've established that you can share with the audience around what it means to practice good enterprise architecture or just really a framework that they can leverage uh outside of being an EA, uh, what would that be?

SPEAKER_01 33:54

Yeah, the the biggest thing that has shown up in every one of those adventures I've had so far has been really, really be clear about value. And that's not just about business value. It's it's about the the time and energy you spend in doing something. It's about the technologies you're using and what you click you gain from it. Be really clear about value and try to be as objective as possible with it. You can't be completely objective, it never happens, but try to be as objective as possible with it so that when you go and try to litmus test it for improvement or maybe some sliding backwards, that you're you're getting a good solid kind of representation of what's going on. And again, that that's be if you're trying to improve something from a business point of view or you're trying to compare technologies, just be really crisp on the value that you're trying to kind of understand out of what you're trying to do. And that's really hard to do. I gotta point that out. That's really hard to do at times because you know, even even I as an EA, I fall in love with things and I tend to get biased towards something or biased away from something. Um so getting crisp and objective about value as much as possible, huge thing to do.

SPEAKER_00 35:00

Yeah, and that durable value too, right? And I think when there's so many shiny objects and things, like trying to figure out what is kind of the North Star that you are constantly marching towards. And that can evolve, uh, you know, for sure. But I think it's that is something that's like a constant practice for all of us and a good reminder. Um, and then my final question to you is what are you excited about and looking forward, whether it be AI related or otherwise, when you think about, for example, the next uh six to 12 months, what's something you are um really excited about in terms of evolution of the market, space or otherwise?

SPEAKER_01 35:38

Yeah, I I think I think the next year or so and it using AI in particular will be really telling. I I think there's some there's some things that as the understanding of the environment um and the environment itself moves forwards and matures, I think there'll be some great um recollections around like where AI does great things for a business. I think the really interesting part is particularly like where I am now, it's as we mature our understanding, I think more doors will open. And I think those doors will open faster. And I think we're experiencing it right now. Like, like, how do you actually get on top of those things and understand them at the speed they're gonna come at you? As an EA, we're so used to wanting to plan things out and get a good target state or a good ideal state. That game's got to pivot. And I'm so looking forward to that, into that more iterative like, what's the ideal state that's six months from now? What's the next ideal state look like for us? And building systems that actually fit into that model, I think that's super compelling from an EA point of view.

SPEAKER_00 36:39

Totally. I I resonate with that so much. I mean, we're we were even just talking about um the concept of a roadmap and how that has evolved. Like before you would say, okay, well, within 12 months, we're gonna look like this. But now with how fast you can iterate, you can't really do that anymore because you're shipping things quickly. So you have to have to your point an objective, a north star, and then kind of categorically themes that you kind of iterate against and the, you know, kind of the path reveals itself and AI kind of helps you get there.

SPEAKER_01 37:08

Yeah, yeah. And you said that north, that North Star keeps getting tested and you keep and you have to move it along this the horizon, right? You've got to, because the market will change in that way, regardless of the technologies you pick. I think that's really interesting. Um, a lot of people want to have, in my role, want to have a lot of structure. The world ahead of us doesn't have that level of structure. It's got a lot of iteration and a lot of, and you use the word a lot today is a lot of frameworks that you're gonna need to work within. You've got to build your own to make that that uh ideal state possible as it iterates over time.

SPEAKER_00 37:40

Yeah. Um well, that's a great place to close, Fred. Thank you so much for your time today. If people want to learn more about you or what kind of Jack in the Box is doing to innovate in this area, where might they go?

SPEAKER_01 37:52

Yeah, for me, yeah, I I uh LinkedIn, you'll find me on LinkedIn. That's the place to find me. And I'm I'm pretty active there, so that's great. Um, Jack in the Box, just visit one or just visit www.jackin' the box.com