Episode 8: AI, Architecture & Storytelling - What’s Really Changing? (with Grant Ecker)

In this episode of the Digital Mythology Podcast, Declan Goodman speaks with Grant Ecker about the evolving role of enterprise architecture in the age of AI - and why storytelling is becoming a critical leadership skill.

We explore:

•  Navigating AI hype vs real value 
•  The future role of architects 
•  Decision-making in complex environments 
•  Why storytelling builds trust in transformation 

Key Moments:

•  AI hype vs reality 
•  Architecting for AI 
•  The evolving role of enterprise architects 
•  Storytelling as a leadership tool 
•  Practical steps for leaders

Watch the full episode here: 

https://youtu.be/CWbpVUWAw00

Connect with Declan here: 

declangoodman | Instagram, Facebook, TikTok | Linktree

Mythology isn’t about fairy tales - it’s about the stories behind human transformation. Digital transformation is no different - it’s about people, not systems.

The Digital Mythology Podcast is here to help you bridge the gap between complex tech and human understanding, transforming your digital efforts into a narrative that truly resonates. As you embark on your digital transformation journey, remember that success isn't just about the tools or technology—it's about how well you can tell your story. By leveraging the timeless power of mythology, storytelling, and emotional connection, you can engage stakeholders, win buy-in, and inspire action.  

Join host Declan Goodman as he guides you through this journey, one story at a time.

Transcript

SPEAKER_00 0:00

Hello and welcome to the Digital Mythology Podcast. I'm your host, Declan Goodman, where Myth Meets Modern Mastery. Today I'm joined by Grant Eckar, Chief Enterprise Architect and founder of the Chief Architecture Network. Grant, welcome. Thanks for joining me.

SPEAKER_01 0:16

Thanks for having me.

SPEAKER_00 0:17

So today, Grant, we're going to talk about the role of enterprise architecture in the age of AI. And I can't resist using ancient mythology because, you know, I think it has a lot to play. So we can learn a lot from the past. And I'm going to be bringing in three myths as well today to help understand this better for our audience. We're going to bring in Athena, the goddess of wisdom and strategic judgment, bringing in Diatalus, who was the master architect of the labyrinth. And I'm going to bring in Lou, an Irish hero who approached the Irish fairies and convinced him to avail of his skills. And I'm looking forward to exploring this more with you. So, Grant, just to kick us off, do you mind just give us an introduction to yourself and your background and uh share a bit more information, please?

SPEAKER_01 1:01

Absolutely. So my name's Gran Ecker. I'm a chief architect by trade. I've been privileged to lead architecture organizations in Lowe's, Medtronic, Walgreens Boots Alliance, Danaher, and I'm currently the Global Chief Architect at Ecolab. I also spent some time growing up at General Mills, where I had my first uh full-time IT job. Uh during those travels, I created a network called the Chief Architect Network. It's a 501c3 nonprofit with 631 of my peers globally, including yourself. And uh that's been an absolute highlight in a way that I can stay connected and learn because in today's fast-paced uh technology changes and innovation, uh there's no way to stay up to speed by reading things. You have to be talking to your peers.

SPEAKER_00 1:48

Hmm. Yeah, thanks for um inviting me in. I've just joined your network recently and I was on there. I see a lot of high-quality chief architects and um advisors and people from all different backgrounds across many countries as well. Uh, there's about four or five of us based in Ireland in your network, and uh, there's people in Australia and all around. So congratulations, it's a real success. Um, I look forward to seeing it grow as well. Already I've been benefiting from being on there. So, Grant, I'm just curious about your insights into how do we move with the AI hype in a way that's sensible and I guess uh wise. And I like to refer to Athena, the goddess in Greek mythology. She was wise and she was always emphasizing leaders to look, you know, something might be shiny and new and exciting, but take your time with it because you have to give it time to prove itself. From an enterprise architecture and digital perspective, what's your thoughts on how we can manage that?

SPEAKER_01 2:48

Oh, absolutely. It's a it's a great thought, and the metaphor plays out perfectly. In fact, let's go back in time versus focusing on AI because it's easier to understand these lessons in the past than it is by looking at the future. So the most recent example of our journey to uh innovation where we had the difference between knowledge and wisdom was the cloud. A lot of folks had awareness or gaining awareness of, okay, we have to we have to migrate our infrastructure, we have to modernize, we have to leverage the cloud. And they would get excited about just the pivot itself as opposed to the enablement of their organization. So many organizations would simply put the cart before the horse and say, we have to get to a certain consumption level by a particular time. And ultimately that would change the incentives. Now the company needed to be consuming cloud at a particular pace, as opposed to using cloud as a way to migrate the capability and the way it was delivered. So what happened was those that kind of fell into this trap were ended up getting behind schedule and had to pivot from moving their transformational workloads to just the lift and shift. And what ended up happening is certainly they met their cloud spend commitments, but they'd moved all the easy stuff and all of the hard pieces that actually kept us in our data centers and kept all of those double run costs moving, stayed where they were. So a lot of folks look at, well, geez, what's the ROI of this cloud thing? Well, you uh you moved the stuff that didn't have a lot of uh return and actually might even cost more to run in the cloud, and you kept the anchor of the things that uh weren't really uh uh that were actually the reason we were making the pivot to begin with. So the same is true today in AI. Many folks are using the knowledge of, wow, this thing's making a big difference in industry, and we all want to lean in and see the same kind of stories um come true for our organization, but we're not actually connecting the dots. We're just following the herd. And uh that's where the challenges start to show up, right? You have to basically look at this in terms of business outcomes. What are you trying to enable for your organization? What is there to learn from the industry and what how others have been successful? How do we take the lessons that have been around forever? For every dollar you spend in technology, expect to spend two on people and process, right? Uh it's still true, right, in terms of this kind of transformation. Um, and also you want to modernize before you transform. If you're going to pivot to this new infrastructure, you have to make sure and these new capabilities that you're harmonizing your business process so that you're creating a more consistent path for that enablement. The complexity actually makes the implementation a lot more difficult. And a lot of those advantages that you saw aren't realized if you have a brownfield process uh that is uh incredibly unharmonized across the environment. You end up having to solve the same problem for each business unit, for each geography, and uh it's a death from a thousand cuts.

SPEAKER_00 5:46

I like that because I often talk about how digital transformation is not about systems really, it's more about process and people. And that's why I like mythology a lot as well, because it's about transformation. It's about what's at the heart of transformation. And AI is massively transformative. Um, but also again, there's an element to which, you know, you have to still start from where you are right now. And where you are right now isn't going to suddenly become, you know, um more performant or more rich in data if you can't get access to the data. And that brings me to, you know, the next point was really about architecting for AI. So generally, good architecture is where you build out optionality and you build out enablement. So, for example, the client I'm working for at the moment, they're not sure where they're going to be in five years' time, right? Because there's a gentic AI, there's automation, and in the industry they're in, there's a sort of uh standardization move towards how to operate with customers. So we're trying to help uh at the architecture level, you know, how do you architect out a roadmap to get there when actually you could still be in three or four different places in the future, depending on how the industry moves. So that was a good example where I I guess I I reach into mythology again. The the great architect, the Adalyst, he architected the labyrinth to keep the minotaur inside. He ended up doing such a good job that people never came out of it. He himself was afraid to go into it. And the reason I like that is because you know, we're often working in a complex environment all the time. And I don't necessarily think AI is going to make our businesses less complex. I think it's more how we design our operating models and look at our business to fix that piece. But like from an architecture perspective, putting on you know the hardware atlas architected a very complex labyrinth. But the idea was it was supposed to be complex to keep the minotaur in. In our world, like with AI coming, I often think, you know, it's we need to do something similar. We need to architect and put the safeguard rails around our data and our privacy and our customers' experience. But also, we have a wee bit of a, you know, uh we have to work with what the industry is giving us and the way the industry's going. So, yeah, from a master architect of building things, Grant, what's your view on architecting out at the enterprise level to be able to be ready for what AI can bring?

SPEAKER_01 8:36

Well, this has been a really good conversation among the chief architect network in terms of the AI reference architecture, which are the tiers of functionality from the data foundations and the platform foundations and the cognitive layer as well as the user interface layer. And it's been a really helpful conversation because effectively we are designing that labyrinth, right? And and trying to create the componentized pieces that helps us understand how these things evolve. If we try to create this as a monolith, uh it's going to be very difficult to pivot, as you said, the industry is changing and moving so quickly. Additionally, we look outside of the environment where we're connecting to our enterprise systems. And it's incredibly important that we think about things in terms of loose and tight coupling. Where do we want to be loosely coupled? Maybe the LLMs and the models we might be using today might be changing tomorrow. We don't want that to be something that's fixed and very hard to change. However, if we're connecting to our back-end ERPs and things of that nature, even though the interface might change over time, there's really less of a question of the source of truth for our financial data to pivot that quickly. So that's a little safer to be tightly coupled on those things. So I think between both creating a thoughtful reference architecture, the patterns and the solutions that you're going to be deploying for each of the capability areas up that stack, and then for each solution you're deploying, being thoughtful around how long is the future state changing or how quickly is the future state changing for these particular areas of the architecture of the solution, of the enterprise, and designing with that kind of nimbleness in mind so that this can meet those changing business priorities, those changing technology landscape components and the uh innovation that we're seeing out there, being able to leverage it without having to create something that is either so monolithic that it's difficult to understand where things exist, or so ad hoc that you can never actually build anything. If you don't tightly couple to anything, it's way too hard to actually create a solution. So being thoughtful about both those dimensions, I think really helps you be positioned for agility, which is the name of the game in today's pace of change.

SPEAKER_00 10:59

I like that. It's a trade-off, isn't it? It's this like this whole best of sweet, best of breed debate that's been going on forever. Um it's often a hybrid model, I think, is what the optimal outcome is. And it kind of comes back to what's your core business and where, you know, where should you be differentiating and maybe going a bit best of breed versus where is your business sort of commoditized, just jump on the standard platform or whatever uh is out there. It's a really interesting one. That's why I always love architecture as a it's sort of when you move from the craft of architecture, which is all of the methodologies and the frameworks, to the art of architecture, which is, you know, in my view, I know everybody talks about, you know, you have to have a target state and you have to tell us where we're going and what's the blueprint look like. But the art of architecture is really the fact that we're not always certain at all. We we can't be. So we have to be agile. That's a key word you used there. So it's actually about architecting for change and architecting for agility rather than architecting for certainty. And especially with the way AI is moving and the way businesses have been business models have been completely disrupted. Um I think that's uh a nice analogy there is enterprise architects, the the analyst of the world. Today the myth manifests itself into architecting something that can actually move or plug and play differently as it needs to, which is pretty exciting. Um the other uh the the other item I wanted to just uh uh tap into your uh uh insights on Grant is around the skill sets now, as in the role of what enterprise architects and digital leaders is kind of changing a bit, right? Because you know, traditionally we had, if I can refer to the likes of the TOGAP domains, BDAT, or we had other frameworks where they separate out business architecture, information architecture, all the different domains. With the way AI is going and the way automation in general is going, it's it's sort of blurring those lines a bit because we see a lot of services coming out of the even like which we spoke about uh before when we were talking about this podcast, you were talking about agentic AI and how powerful that is in terms of you know, it's like a different paradigm again. And everything as we go forward and make things more uh sophisticated in tech, we always kind of abstract up, you know, we go up and up. So now you don't have to worry about detailed entity relationship models, it's rolled up to a conceptual data layer of some kind. And similarly, in application space, we don't have to worry about components or modules anymore, we're more up into the platform or enterprise capabilities. Everything's always been rolled up, but the risk with that is uh you lose the devils in the detail, right? So, one thing I wanted to mention was or talk about was the skill set. So a nice myth I use is Lou in Irish mythology approached the Tuit the Danan, which is the Irish fairies, and he wanted to join forces with them. This is the original uh mythological peoples of Ireland. And um he came and said, Look, I have a lot of skills that I can help you with. I have, you know, I'm a warrior, I'm a strategist, I'm a craftsman, and he pitched himself to the council and they took him on and eventually he became a hero. But what I liked about that mythology is, you know, what does the modern AI future-facing enterprise architect skill set look like? What do you think that is?

SPEAKER_01 14:53

Well, I I think that you're bringing forward a very interesting question. And one of the components that um I like to zoom out far enough that we can kind of say where things are the same before we go into where they're different. So if you think about the challenges we're solving, the things that are unchanging are there's a business-centric outcome or challenge we're facing, and there's an information outcome. The rest is the how, right? Around having an adaptable solution that is secure, that consistently addresses the challenges in a standardized way, um, curated in terms of how we're gathering and and and finding the right inputs to provide the output. Those things didn't change at all, right? And there still is the basics of the TOGAF, a business challenge, your visioning, your understanding the um the information landscape, the, and then you get into the application and the technology, and then the actual detailed data. So I think all of that still remains, and it's important that we don't lose the detail. I think we have to think about it as how are we delegating the detail? Are we delegating the detail to another organization where we have federated technical and data architects and technical leads and subject matter experts and business analysts that might be on the business side that understand the nuance of this particular space? And where are they delegating to agents and to other technology assistants that can help them close gaps, find maybe themes in the data? Uh, there's an interesting uh study that's been put out there that the level of the answer you get from an AI solution depends on the quality of the question you ask it. Ask a PhD question, get a PhD answer. Ask a uh middle school question, get a middle school answer. So I think that it's important we kind of zoom out and realize like the the breadth of our scope didn't change. We're still doing the same thing at the furthest out edge, and we're still actually doing the work to drive from problem and vision into the ultimate architecture that we deploy. We just have more help and we have a different how that we can step into both across the people in the organization as architecture starts to have to shift to things like causal intelligence to understand the things that actually are more like the needles in the haystack that help us understand what we should be levering on to optimize solutions. And we start to think about decision intelligence. Dr. Lorian Pratt's work is now extremely relevant. It's the 3D chess of the work that is now really important because we have to teach agents how to think like us. They need to be our agent, right? They need to be our delegate on the edge, representing us both in the thinking of how to interpret data and in the guardrails of the um the company's um perspective on what's right and wrong and how are we, what are our values in representing that. And I think that that's a lot of work that we have to do to actually put those guardrails there in terms of the solutions we design. And then similarly, if somebody wears a stop sign t-shirt, a Tesla will stop in front of them, right? If it's in self-driving mode, or at least some of the models. Like we've certainly seen some of that um uh on the internet. And I we also have to redesign the highways. The highways are not set up for the agents to be effective, they might have the right answers. The Navy recently spent a bunch of money um to be able to figure out what's happening in the supply chain, what are the recommendations? But if it goes back to a roadway and that agent's trying to drive, and there's a web of who gets to decide and different silos of our organization, and no one is empowered to act on that information in a holistic way, ultimately we just find out we're going over the cliff sooner. We don't actually have the organization pivot and make changes. So, really, the decision architecture, the decision intelligence, and the ultimate agentic architecture of what's needed all the way down to the depth of the data, to the outcome it's driving and the how the organization wants it to behave, I think are the skills where the enterprise architects need to be keeping their eye on the ball and empowering the rest of the organization to tree up those insights and that's um sort of subject matter expertise to ensure that we don't lose um the forest through the uh, or the I should say, uh, with an eye on the forest, we lose sight on the trees, kind of the other way around. Yeah.

SPEAKER_00 19:32

Yeah, I like that metaphor of the getting the highway right, you know, getting the main because there's a lot of distractions where, you know, there's all of these niche AI cool stuff coming out. And um I see it in organizations like they they might satisfy some functional need or business need, but the the customer information behind it is disjointed or it's not aligned to your downstream processes, or so you end up spending more and more money building uh track, I guess, little small by roads or cycle tracks around the edge of the motorway just to join up information instead of saying, actually, let's get the let's get the highway right, let's get the main cornerstones of our information agreed. Let's make that consistent so that when we're out looking at AI or agentic AI or whatever, we're working off the organization's best interest, not not that of the vendors or that of the industry. Yeah, it's a nice one. I like that one.

SPEAKER_01 20:43

What's kind of cool about this is it isn't my ideas, right? I'm learning from others in the network. Mike Carroll came up with a lot of this causal intelligence along with Soma Soma Venkat and a number of other thought leaders have shared that path. And Jesper Lauren out of Australia really kind of helped us break down the ontological, the semantic, the compute, and the agentic layers. With the we've seen uh even more recent evolutions on this. So I think it's really helpful that we have a group of our peers that can help us see uh the line of sight of not just uh as a Minnesota metaphor where I grew up, uh where the puck is, but rather traveling to where the puck is going. And I think uh these these conversations really help us uh keep an edge on on where the conversation is going.

SPEAKER_00 21:30

Yeah, and it's it's kind of back to the myth around the mythology of lived experience, you know. It's like um the network you've built and this IP you're bringing together has hundreds of lifetimes of experience in there. And um if without that wisdom, like Athena advising, you know, wisdom is a critical component of strategic thinking, and you're talking about decision architecture. Which I love that idea of a decision architecture, decision governance, it's around there are so many decisions we can make about AI. And then secondly, on top of that, AI itself will start making decisions on behalf of our customers, etc. So it's a sort of a implicit and explicit reference to decision making. It's gonna be really interesting to see how this, you know, comes together. I also I noticed on your Chief Architecture Network you have these regular sort of uh think tanks, maybe, or or sort of you know, information sharing ideas. There was only one last week. I I missed it. It was about AI and I seen afterwards. So to our people listening, please have a look at the Chief Architecture Network. Uh, we'll share information at the end or in the in the bio on the back of this. But it's also as well important to have that sort of spearheaded thinking as well, with people who have real experience from all different industries and all different aspects, because sometimes enterprise architecture or some disciplines can take a bit of a more industry explicit sort of approach. And you have uh reference models, say for higher education. I use the coded one, which is uh a very good one for higher education out of Australia, but it's now going more international in finance, insurance. You have a core with loads of different reference models, but we kind of need to transcend those really to understand how AI can help us because uh it's so transferable into different industries. But yeah, I've seen that um talk was very interesting. So you're kind of living the myth there a little bit, Grant, with the Athena being the Greek goddess of uh wisdom. You're bringing your elders together from around the globe and having that real thought-provoking piece. And also you're producing frameworks or at least blueprints and guidelines on how to navigate it, which is the the other part of how do we how do we get to where we need to be with all this hype and all this like I was talking to someone the other day about um there's lots of roles have popped up in organizations, you know, AI strategist or AI architect, and I'm thinking uh that's pretty new. And as far as what AI really means, um I would say uh it it's more your I'm it's more on the strategy kind of decision making side we have a bit of certainty on, you know. Like, I mean, if we were to say what will the AI stack look like in three to five years time for say customer experience or for um automation or manufacturing, it's like, well, it's pretty it's pretty incredible the rate it's moving at. But at the same time, the real challenge is how do you take your organization on that journey? Because you know, no no organization has the really opportunity to just throw away their old kit and move to all the new kit. In general, there's a brownfield challenge. So it's a nice analogy to get from where we are to where we need to be in the limitations of what we have.

SPEAKER_01 25:24

And I think that the AI architect roles, you're gonna certainly have the strategist, as you spoke about, but I also think the technologists, it's the same as a cloud architect back in the day. Certainly, some of the folks were focused on the what and ensuring that the platform met the myths and uh security framework, right? Um, I think that that's incredibly important. Um, but there were also folks that were focused on the individual components of the and services within the cloud stack and mastering them and localizing them to make sure you didn't create an unsecured S bucket. Remember when that news was out there? So I find the same is true with here with AI, that there's folks that are helping with the decision intelligence and the codifying solutions. There's also a data engineering that's bringing the bottom of that pipeline together. And then the technology architects that understand the different LLMs, the uh the different solutions out there. Um, and it could be everything from integrating something that helps with a three-way match that's a buy versus a build of looking at how we create um chatbots to start and then sort of grow that into agents that can reason and act on our edge on our behalf. And uh again, there's a lot of technology enabling that as well. And I think it's important that that center of excellence is created where this can be the center of incubation of what might become eventually embedded in our organizations. Now anyone in our companies can create a rag search or restorative augmented generative search, um, where a year and a half ago only a small group could. And I think we're seeing the same here, right? Is that the center of excellence of that AI team, my peer Naveen inside Ecolab runs that, and it's an incredible shop. And they're staying on the edge looking at the agentic pieces. Also, now the infrastructure. You need to have an observability layer, right? You need to have an agent catalog. You need to define the MCP, agent-to-agent communication protocols, and how all that's going to work. So there really is a top-to-bottom in every one of these and a growing specialty in the AI space that at each layer, from the data, the tech, and all the way up to the business uh architecture and uh problem design.

SPEAKER_00 27:44

Yeah, it's um it's the whole gambit, really, isn't it? It's becoming uh putting pressure on all the layers really. Um and then we have the other aspect of, you know, AI nowadays is writing code and producing all sorts of, you know, efficiencies in the development side, and and but then there's the aspect around where, you know, how far do we go in terms of um, you know, um not setting guardrails into that so that there could be vulnerabilities within. So, you know, it's almost like the more powerful our technology gets, the more we have to play that um governing or or oversight role. And I guess from an architecture perspective, it's always been important to do that, but I think it's becoming more the rate of change now. It's uh it's quite even more prominent the role. Grant, what would be your advice to say early stage and enterprise architects and you know, beginning of their career in enterprise architecture versus mid to late old fellas like myself in architecture, what would be your advice to that sort of, you know, those starting out in the career versus those uh established?

SPEAKER_01 29:03

I think the advice for all of us is uh really find a network of people we can talk to and benchmark and learn from. I have a frame I've always been putting out there in the architecture career, which is if you're solving a problem by yourself in a room, um, erase the whiteboard, get some friends, and start over. Uh, I really believe that we is bigger than me and that that insight just continues to grow in its importance as you grow in your career. That's number one. Secondly, I think it's really important that we stay focused on business outcomes, understand business problems we're solving, understand the language of the business so we can communicate in words that are meaningful to them. And then also really try to help others, right? Um, the value that we're providing to the organization is generally consumed through the technical experts and the business relationship manners managers of the IT organization, helping them translate their message. And as we do that, we get invitations to join additional tables, especially when we bring the business relationship managers along and they understand and respect our help. And they we also understand their position, what challenges the the business is placing on them. That helps us enter room with them or even without them, and not give a quick yes to something that sounds good coming from the business, where your uh BRM would tell you, actually, they've been telling that story for two years, and here's why it isn't viable. You don't want to be the um ask the other parent scenario or the one that's helping the business kind of talk about how IT isn't really doing what they need. Those are destructive to the organization, right? So I really find that in each of these, the trust equation reigns supreme. How are you maximizing your trust? And I didn't make this equation up, it's another thing I've learned from others. Um, but we'll just throw it out there. Trust is your intimacy, your consistency, and your reliability of providing solid information, right? Valuable information, divided by your self-orientation. So if you're creating those top three things and maximizing those, you're going to increase your ability to drive change across an organization because you have a lot of trust. And the biggest inhibitor for that is being confused and thinking that you're the smartest person in the room or looking for your own self-interest and outcomes, not the outcomes for others. I think if you can take that approach in all the dimensions you're engaging, because architecture really exists to serve broader organization, not itself. Um, so how do you take that forward and make it real for people so you can create the trust that builds a virtuous cycle of engagement that brings us into more problems, that activates the solution uh-minded folks across the organization to help, that provides a good outcome, that invites us into the next challenge?

SPEAKER_00 32:05

Yeah, it's really good advice. It's uh not technical at all, you know. It's um filling out the partnerships and taking the time to bring in different points of view, different perspectives. It's really powerful.

SPEAKER_01 32:21

I mean, I'll honestly say that I don't think, you know, and when you get into the architecture profession, particularly enterprise architecture, even at the first level, IQ got you there. From where you're gonna go from here, it's all EQ. Um, you don't need to generally be more technical after you've entered the enterprise architecture function and profession. You already have those skills. You need to become super curious so you can learn them faster from others and go along the journey and ask the right questions, not have all the right answers. Lots of talks about that, right? Um, having the right questions is more important. It is. And um, that comes from a root emotion and thought of curiosity, um, which pays itself back as you're able to convert that curiosity into something that's insightful, that you can drive into wisdom that is shared at the right moment at the right time in the organization, and the reference back to the person who shared it.

SPEAKER_00 33:18

It kind of um screams to me uh storytelling, really. So, you know, it's about um a lot of business people or non-technical people who in organizations um they're more interested in the story of what digital is going to do for them rather than the how it works. It's more why and what's the outcome. And I think, yeah, that piece around bringing other people into the room, uh, you're not the smartest one in it, and getting more context to what the proposed journey looks like and telling it in the form of a story. That that was one of the reasons why I set up this podcast. It's about I think enterprise architects it is a key skill that should be always developed is how to turn all the technical narrative and complex jargon into something that our stakeholders can clearly understand. But it's also a case of empowering our non-technical stakeholders with an understanding of what tech can do and can enable, and in cases where it can't, explaining why, you know, it could be legacy or it could be something else in simple language, and that's uh it's probably gonna get more and more demanding now because uh even I've been in tech for a long time and I'm finding it hard to keep up with understanding what the new tech capabilities are able to do. Um and so uh yeah, kind of keeping yourself grounded, I guess, and and making sure you can understand it yourself. And if you can't explain it to you know someone who uh uh has no technical jargon, if you can't explain it well enough to them, then you're you're you're simply not clear on how to make it a a story worth hearing. So leaning on other people's experience as well. Great. So just to wrap up, Grant, uh what would be your top takeaways to uh leaders in organizations who are a wee bit nervous or a bit unsure of what AI is going to mean to them and in terms of what how you can reassure them putting an enterprise architect or AI sort of hat on how it can uh how they're gonna find their way.

SPEAKER_01 35:46

So this is gonna be a little different advice than we've talked about. I think it's important to share that. So the first thing I'd say is use AI before you start to do and build AI. So start with the basics of um some of the uh co-pilots that are out there, right? And then start to buy and use where you have use cases like three-way match where you can implement a third-party solution that can take away a manual non-value add work and push it into something that's built for it. LLMs were made for fuzzy matching and things of that nature. So I should leverage those. And then as you start to step into building AI, you really have to understand the architecture foundation. You have to understand what the tiers of the architecture are. And you also have to spend the energy to build horizontal, not just vertical AI. And that usually starts with horizontal process harmonization, horizontal understanding in terms of the business problem, and then building your solutions in a way that trees that up, starting with the data foundations that you need to curate and build the semantic and ontological understanding layers so that we aren't solving a problem in just one little slice of our business or one physical location or one geography. Uh, we're taking a business problem that's a hotspot that has significant value to the organization if done differently. And we're creating the data foundations that are actually going to help us interpret that both improve the quality of that data and then interpret that data and then codify how we make decisions in the cognitive type of layer, whether that's understanding causal in terms of what factors help us make pivots, or it might be um looking at decision processes today and standard operating procedures and applying them into this kind of lens. And not everything needs to be AI. Sometimes you can actually put in RPA, robotic process automation, and that can solve it. So also look at it's not all the things have the same hammer. Now, once you've built those foundations, you have the ability to start to do buy versus build on the edge. If you can understand your data and how it how to pertain and make how it pertains and helps to make decisions, now you can really put a lot of different solutions on top of that. That's where you can do some buy versus build analysis. As we mentioned earlier, the complexity isn't going away. In fact, it's increasing and it always has, right? From the days of just doing our own farming to having farming equipment to now vertically integrated supply chains that we don't even know where the food comes from the world. It's the same thing happening in technology. So you can't lose sight, though, of those foundations that are actually holding up the entire infrastructure. If we don't know and have an understanding of how our food is grown, all the infrastructure in the world doesn't help when uh that that part of the architecture fails. So you have to make sure you're grounded in that. Um, we did talk about translating um technology for business leaders and speaking in the terms that are important and meaningful to the business. And cross-domain understanding is important as well. As we start to think about how we actually piece these macro level puzzles together, we need to understand the complexity of the business and how it crosses domains and the complexity of the technology. And the best way to do that is with others. Bring a friend along, bring 10. Because if we try to understand all of it to every depth, we're never going to get anywhere. And then lastly, I think the decision speed is one of the things that we absolutely have to focus on. Um, if you are spending tons and tons of cycles to be able to make each decision, a company that can more fluidly make those decisions is going to win out in the end because they have more opportunities to pivot to the right outcome. So I think we have to be looking at decision speed. And we talked about re-architecting our highways. Uh, some of the work that Mike Carroll has been sharing uh in the network. I think it's absolutely critical that we take that piece and play that forward as well.

SPEAKER_00 39:48

I really like that decision. I mentioned it already before, the decision speed, because you know there's a general sort of I won't say the word anxiety again, but there's a kind of an insecurity building around an organization that's sometimes are so big and slow moving that they take them six months to a year to make a decision. Absolutely. When it comes to AI and things moving forward, you you sort of snooze you lose sort of thing. And then at the same time there has to be a balance, right? Because there's the leaders versus the followers, and uh it's it's sort of balancing that uh pros and cons. Look, it it it it's nice to bring in the um for me, it's nice to bring in the sort of learnings we can have from the Greek mythology around Athena. She kept getting pressured by leaders at the time to, you know, make decisions, and in many cases she was advising what you say is very wise, actually reach out to other lived experiences, and then when you make that decision, you have a nice bit of de-risking taking place because you got different lived experiences behind it. The second one was the Atlas, the great architect of the labyrinth. Again, don't architect yourself into knots. Try to keep decoupling and and uh agility in there, and then the last one was Lou, which really around the skill set. I think that's a big one as well, is um making sure that there's still a need for specialised architects and specialized domain knowledge, but there's also uh a greater need, in my view, for that r raising it up into the storytelling piece and making sure that we embrace all of the complexity and break it down into something simple that our stakeholders can understand. I mean, that's for me the key. I mean, I think that's what one of our main roads are as enterprise architects is to build the bridge between what the business are talking and what tech can deliver and make sure it it's like a highway, make sure it flows well. Yeah. Well, Grant, look, thank you so much for your uh insights. Really enjoyed chatting to you. Uh, before we before we finish up, uh do you mind me asking what you do outside of all of this wonderful tech stuff? Do you have any hobbies or things to take your mind off this uh exciting as it is, it's uh you must need a bit of what do you do to get away from it all and relax?

SPEAKER_01 42:22

Well, I I love spending time with my family. Um I've got my uh my wife and a couple dogs up on a farm in North Illinois. And um we absolutely uh just uh find ways to stay busy here on our little ranch. Um and I also get back to Minnesota where I have a lot of family. And luckily there's uh the other the other main eco lab campus is up in Minnesota, so I can go work up there for a week or two and get to see folks uh on the uh in the Minnesota side as well, which is really nice and enjoy some of the lakes and uh things to do out there. So yeah, very fortunate uh to uh have the opportunity to kind of um be so focused in this industry, but also get to unplug once in a while.

SPEAKER_00 43:05

Yeah, it's uh it comes through as well. I think you know, your your knowledge is very grounded and earthy, which is why you're on your farm out there. You you're close to the ground, just like your your knowledge, you know. So it's been very, very uh insightful listening to you. And lastly, um if people want to get in touch with you or they want to learn more about uh Chief Architecture Network, how can they find out more information, Grant?

SPEAKER_01 43:34

Oh, sure. I've got uh sticker here to kind of help you find remember the name, ChiefArchitectnetwork.com. If you're a chief architect managing a billion plus uh scale of uh organizational revenue as the architect leader for that function, or you manage 10 or more architects, um apply to join us. Or if you're just interested in what architecture leaders are discussing, follow the Chief Architect Network on um on LinkedIn. We're also building YouTube and a lot of other places to follow us. We also have a new concept I'm really excited about, which is called the Digital Expert Network. So, what this is about is you might notice the red dots there, those are still the chief architects. And that allows us to connect our organization. So the uh AI conversation last Friday wasn't just uh the chief architect network. It allows folks to bring experts in from their organization, digital experts, to really broaden that conversation in areas where we're trying to go a little deeper. So if that's something that interests you, we'd love to have you either following us and joining a conversation with chief architects or join us on stage, join us at dinners, and join us at events around the world. We're going to have about 60 of us together in London, maybe even as many as 70, April 15 to 17th. Not too late to join if you're uh an architect leader. And uh, we do these at no cost. We just have a sponsor that promotes our community, supports our community, and we're able to come together without a lot of uh commercial activity because uh we're a 501c3 and all volunteers. So there's always uh um just that kind of through line of giving back and learning from each other and not having to have that typical commercial focus because it's no one's full-time job, which is really nice.

SPEAKER_00 45:19

Fantastic. It's a great network. Look, I'm only I'm only in a couple of weeks or so, and I found it very insightful already. So my recommendation, yeah, fully endorse that. Uh look out for the Chief Architect Network, C A N on LinkedIn and link in with Grant, and uh you won't be disappointed. It's uh it's a fantastic opportunity to meet fellow minded leaders and uh uh look forward to collaborating. Great. Grant, thank you so much for your time. Really enjoyed it.

SPEAKER_01 45:48

I did too. Great to spend time with you.