How Greenprint and MBH Turn AI Experiments Into Business Transformation

Hello and welcome to the Smarter by Design podcast. I'm your host, Christopher Parsons, founder and CEO of Knowledge Architecture. My guests today are Nicole Chavasse, president and COO of Greenprint Partners, and Hilary Thompson, principal at MBH Architects. Our topic is turning AI experiments into business transformation. Now, both Hilary and Nicole are senior leaders who are personally building agents and redesigning workflows, which gives them both a close-up view of not only what the technology can do, but also what it reveals about their firms. Again and again, they have found that building with AI exposes the same underlying questions. What does good look like? Is this actually our standard or just what we happen to do today? Is this knowledge documented clearly enough to use? And are we redesigning the work itself or simply bolting AI onto an existing process? One of the clearest lessons in this episode is that scalable transformation often means going smaller, Slower and together. Nicole describes initially trying to move quickly, work largely on her own, and use agents to redesign entire business processes at once. What she learned was that proposal development and project management are really collections of interconnected subprocesses, and that lasting change required a different approach: involving the people doing the work, breaking the system into smaller pieces, focusing on the highest impact opportunities, defining what good looks like, redesigning each subprocess for an AI-enabled way of working, and then training the firm on the new process. Hillary describes a parallel journey at MBH where rapid prototyping with firm leaders through an agent charrette surfaced her leadership team's dreams for AI-driven business transformation. Rapid prototyping that exposed the undocumented processes, conflicting standards, duplicated effort, and poor data quality standing in the way of their dreams. In that sense, AI serves as a sort of organizational mirror because when agents struggle, they often teach you something about the business. But process redesign is only part of the challenge. The other major constraint is change capacity. Nicole and Hillary are both operating in environments where the technology is improving faster than their organizations can realistically absorb change. They discuss experimenting with good enough solutions. Following the energy of early adopters, sharing successful use cases between peers, building trust with skeptics, and giving leaders closer to day-to-day project work permission to control the pace of change. The goal is to build a trusted core of people at their firms who can experiment, interpret what is changing, and act as a kind of shock absorber for the rest of the organization. This helps the firm move fast without forcing everyone to experience the full chaos of living at the bleeding edge of the AI frontier. The through line is that AI itself is not the point. It is an increasingly powerful catalyst for redesigning the business around the fundamentals AEC firms have always cared about: high-quality work teams are proud of, profitably run projects, happy clients, and engaged employees. I think you'll love this one. Okay, so Nicole Chavasse and Hillary Thompson on how Greenprint and MBH turn AI experiments into business transformation. Here we go. So Nicole and Hillary, welcome to the Smarter by Design podcast.

Thanks for having us.

Yeah, thank you so much for having us here, Chris. I'm especially excited to be here alongside Nicole.

Yes, you are 2 of my favorite people, and I'm glad that we have connected you and now we're doing this together. It should be, it should be a lot of fun. And maybe both of you could start by telling us who you are, about your firm, and then maybe even most importantly, how you ended up being the person in your organization who is not only leading AI and business transformation work, but personally building AI agents and workflows.

Great. Wanna start, Hilary?

Absolutely. I'm Hilary Thompson. I am a principal at MBH Architects. We are about 275 people spread across 6 offices in North America, India, and most recently the UK. We do a variety of different work types, anything from multifamily, retail, labs, and much, much more. And my seat covers brand communications, knowledge, and data strategy currently. And I'm, I'm not coming at this from a technology background. I'm coming at this from the perspective of watching really smart, really talented people face a, a great deal of friction in their everyday work as they do these really complex, amazing projects. And my work in brand communications in particular has led me kind of into every corner of how the firm is actually operating today. And the longer that I've looked around, the more obsessed I've become with answering that specific question of why are all these talented people reinventing work that we've maybe already done or hunting for information that already exists? So AI has turned out to be the next, next natural step in that same question.

Mm-hmm.

Watching how knowledge and work actually move through our organization.

I love that. How about you, Nicole?

Great. So I'm Nicole Chavasse. I'm president and one of the co-founders of Greenprint Partners. We are much smaller than Hilary's firm. We are 30 people, 12 years old. And so we grew from, you know, 10 to 30 in the last few years, which for us is a huge amount. We're a sustainability-focused urban planning, civil engineering, and landscape architecture firm based in Chicago. And most of our work is public work. So most of our clients are municipalities, park districts, school districts, counties, that sort of thing, which really shapes the type of work that we do and the clients that we work with and the nature of our operations. Government brings with it its own complexities, as does having 3 different practices, which are all relatively similarly sized, all working together in a very interdisciplinary way to achieve our goals. And so that brings a lot of complexity with it that I've had to navigate. As the co-founder and the, you know, effectively the head of operations, I've been building from day one. I am not an operations person by background, and I started GreenPrint relatively new to the AE industry. So I was always trying to figure things out as I went. And I built, you know, all of our systems, our hiring practices, our proposal development process, kind of every process that GreenPrint does, I had to build at some point and steward and hopefully hand off at some point. And so AI is just the next thing for me to tackle. Every year there's something new for me to tackle with knowledge management, learning and development, and now AI that I have to figure out how to— Bring our firm along with. But the benefit of being a small firm and being that sole decision maker is I get to, when I see something cool that I think can help our company, I get to be the one to say, we're going to do it. Let's do it. So that has me really excited about all the things that are at our disposal right now.

Could you tell us, Nicole, there've been some funny jokes over the last year and a half I've been getting to know you, like you're a one-woman human knowledge management system, or at one point it was Nicole LMS. You know, and you were kind of these systems before you had these systems. And can you just talk about the change from being the human knowledge and learning management system to actually building systems for the company?

Yeah.

So when you— when we started out, we were 3 and then 5 and then 8. And when I was building our systems and kind of helping design the firm, I built my own knowledge throughout all of that. So even when I had systems that I was asking people to use, they were still, I still was the most knowledgeable person about how those worked and how what they meant for the company. And so over time, I was still I had the biggest picture, the deepest knowledge about everything the firm did, and there was nothing easier than asking Nicole the question to get the information you needed. And so even as I started doing my best to document things, to try to build training, to try to pass things off, there was still a lot of friction for folks to try to figure out how to get those. Okay, which Google Drive folder was that in, which training said what? And it was still the fastest way to get information was to come to Nicole, the human knowledge management system. And that worked for a while because I had the answers. They were quick. I was very accessible. And so I was able to kind of keep doing that even as I kept trying to kind of document. But we just hit a point when we hit, gotten to like the 20-person staff, we were hiring new people who were starting from scratch, that, that was just not going to be sustainable anymore. And I really had to find a way to truly— move that knowledge into something more centralized that people could access that wasn't quite so reliant on me. Yeah.

Yeah. That makes sense. And, and Hillary, I know that your, your intranet is called Archie and there were, you know, iterations of Archie before you started using Synthesis, but how did you kind of, how did you get sucked into it? Was there like a moment where you're like, oh, I, I personally can help and help elevate the way that our company's thinking about knowledge and learning?

Oh, absolutely. You know, in my, in my day-to-day work, as I mentioned, I do a lot of digging into every corner of the firm and I realized more and more kind of how undocumented a lot of our practices and processes are, how siloed they were across studios and how lots of different teams were doing things lots of really different, interesting ways and how much value there was in bringing that all together. And the latest generation of synthesis is just absolutely supercharging that. Now with the AI capabilities built in.

Yeah, but maybe we'll kind of like start there. You know, firms getting started with AI in general, but like maybe more specifically agents, like their eyes get super big and they're like, oh my gosh, I could do everything. I can change all the ways that we're working. But then they kind of like hit a little bit of a, a downslide. It's like, oh, but we like, where do I begin? Or do I have the knowledge I need in order to be able to build these agents? And I'm, and I'm curious as you've been You know, doing your initial work with, you're both in the private beta for the Synthesis knowledge agents. Like, how did you start figuring out what problems that you were gonna go after and what, or, or opportunities you were gonna go after in terms of building, building agents?

I think the most, the most obvious answer is probably frequency times pain, right? If you're looking at a problem, like something that happens 50 times a month and wastes 10 minutes every time is gonna beat something that's mild and happens twice a year. But for me, there's also a different layer worth considering to our, to like really understand what the possibilities were and how they fit into our workflows. For me, it was really important to start with something that I knew super well.

Hmm.

I've personally touched thousands of proposals in my time at the firm. So it was one of those things where I knew the pain points, I knew the data, I knew where the information lived, and I had a team that I knew would really adopt it. And iterate on it and, you know, come up with new ideas about how it could be better. So that was a really natural place for me to start. I knew I could socialize it and also important, I knew I could analyze the results myself, which really helped.

Right. But versus like trying to streamline somebody else's work that you don't, that you don't know as well.

Yeah, exactly. And, and really quickly it became like a matter of, is it faster and better for me to do this or is it faster and better for me to build an agent to maybe do this and have it be like a repeatable piece of infrastructure for us?

What was the first one you built where you're like, oh, I see how this is gonna change the way I'm working?

I think, you know, 3 or 4 years ago when we, when we all started using the chatbots, it was like, well, yeah, anyone can do this. So like, what edge is it giving me in my work specifically? If, if everyone across every firm is doing this. And it was the first time that I built something that answered questions using our firm's knowledge rather than the public intranet that I was like, okay, this is, this is not just a productivity tool or something that's gonna give me like a one-time answer. This is, you know, as I just said, infrastructure for our team. It's gonna change the way that we actually work. And it's, you know, chatbot as helper versus like agent as infrastructure.

So, so it, maybe another way of saying what you said is, The differentiator's gonna come because your firm is differentiated and your knowledge and your approach. And that's the technology's kind of neutral, but it's what you know and think and how you talk about the world that's different.

Precisely.

Okay. How about you, Nicole? Do, do you remember a kind of like light bulb moment for you?

Well, I went through a journey in, 'cause, you know, I'm a builder and I get so excited and I love to learn. So these agents come out and I'm like diving right in. And I want to solve, I go in wanting to solve like the biggest problems that we have, as opposed to like exactly what you said, where are the places where like pain times frequency could actually make a difference now in a simple way that people can understand. So I, I went too far and I was like, what are the biggest problems? Where are there huge gaps in what we do that are so hard that we've never tackled them that this, maybe this agent can solve that for us. So just as an example, in our proposal development process, One of the challenges that we have is that we tend to jump right in. It's like, okay, there's an RFP out, let's jump in and start writing the proposal. And we don't take enough time upfront to do the real strategy, which takes a lot of knowledge, internal and external. What is our client intel? Like, what have we, what do we know about this client? What, what research can we do about, you know, by reviewing their meeting minutes and their board meeting and pull it all together to say, here's what their needs are, here's what Greenprint has to offer, here's our win themes. And we tend to just kind of be like, okay, we gotta start writing this proposal to get it out. And so I was, oh God, I'm gonna solve that with this magical agent. It's gonna surface all of that and it's gonna create, you know, a, a strategy brief that's gonna bring us all together. And it's gonna, you know, and what I realized was that was too much to try to do with one agent, even though it is addressing a big problem. But what it really taught me was that I was trying to build a new process, create a new standard, and agentize it all at the same time. 'Cause I wanted to solve the biggest problem first. And the biggest problem was just a blue ocean of challenges that needed to be overcome. And so that's when I was like, okay, stop.

Mm-hmm.

This is not— I learned it was good for me. I learned a ton, but I needed to step back and be like, okay, I need to think more like the way Hillary's thinking about like truly where is there something that is causing a lot of pain, but, you know, could be more simply addressed. And so for example, uh, an agent I'm working with our marketing team on is, One that can simply customize our project summaries to proposals. And, you know, talking to my marketing coordinator, she's like regularly, like she does this every week. She takes her boilerplate summaries. There's an RFP, and she's the one who tries to figure out how to customize them so that we're speaking to what the client's needs are by showcasing our project work. That's simple. It's something she's doing. It's annoying. And an agent can absolutely make that a streamlined process. So I'm, I'm learning about where to focus my energy on the agents that can get like the most bang for the buck, really clear the way to better outcomes for everybody, have people feel like they understand how to use them and they're getting the benefit from it. And I'll tackle, I have other ways that I, I need to tackle those bigger problems first before I throw, start throwing agents at them.

So there's kind of like frequency times pain, maybe divided by achievability, right? Or something like that. Like there's this, To get to the right, to the right, like, score. On the achievability side though, Nicole, just to follow up on that, did you feel like that was more the achievability of what the technology can do, the achievability of like people's ability to change their processes that fast? Or like, where was the, where was the problem?

Yeah, no, the technology can do it. And I still see this future strategy brief creator agent, but the, the, The steps that needed to happen before that were, one, defining the standards for what it looks like to do that. And then 2, like actually engaging our staff, the staff who work on proposals around like collaboratively agreeing on those standards. And then, you know, creating the processes for how that fits into our proposal workflow. And so once I've done that work, which is really change management work, which has nothing to do with AI agents, although I am doing it knowing where I want to go is that an agent can help. That all needs to be done first. And then when we roll out an agent that can do this for us, everyone's gonna be like, oh my God, yes, this makes so much sense. So it really needs to be staged out, and that takes longer, but the outcomes I just know are gonna be better.

I think you have to add one more into that equation, which is repeatability.

Oh, interesting.

Because, you know, some of the, what you may perceive as the, the more low-hanging use cases that are like, just things that are easier for us to immediately solve. Those are often the things that you can repeat across many, many different types of work.

Hmm.

And you also then are, are building trust because you have something that people are telling stories about that is working well. You have people then saying, oh, this is how we're using this and this is how it's changing my daily workflow. And so you get the, the start of that kind of internal marketing piece happening.

Do you have an example in mind as you were kind of talking about the repeatability?

Yeah.

So we have really grappled with the idea of a, like a detailed history library forever and whether there's a big debate about what, what exactly we should have and what we shouldn't have as far as people copying things. And so one of the, one of the things that the marketing department has done now is, is create a precedent library for marketing work, which allows the team to explore the last 10 years of graphics that they've created so that it's not a matter of, hey, do you remember that one thing that we did for that one thing back then? It's asking an agent that will pull up the 5 different examples that we've had throughout history.

Mm-hmm.

As context. And so that's something that's now super repeatable to the detail example of if you wanted to have a library of these documents. For context and precedent. That's something that we could really easily create in the same model.

So kind of establishing some of these patterns of combining knowledge and process in an agent together.

Yeah.

Cool.

And when to use the, when to use the raw and when to use the curated.

One of the things that Nicole, kind of coming back to your example, actually, I think this is gonna kind of bounce back and forth, the kind of taking on something that's, that's too big, or at least too big for now. And I think you said, we're gonna take on, I'm gonna take on the proposal process. But I think what was implicit in what you were saying is there's actually no one proposal process. It's actually like dozens of small processes that are stitched together to go after a proposal. And it sounds like kind of teasing apart the subprocesses and trying to improve those will then add up to a better proposal, you know, process than trying to just build the Rube Goldberg mousetrap. machine that does everything on just like a one-shot, like it'll just fix everything.

Mm-hmm. Yeah, I think that's exactly right. I, I think there are some little small processes at the firm that could be really easy to tackle, but then there's these big ones that are multiple processes. And for us, I think that's project management and then it's, you know, proposal development, like the 2 things that really drive our business, winning work and managing projects well, that are really like a whole spectrum of, of processes. And so each, yeah, I really had to sit back and break down the kind of entire length of the process into these sections and the processes between them to really think about where are we doing well now, where I should just say that's a standard. We have processes in place, but did we ever really say they're the standard of good, or we just say this is our process and that's kind of what it was.

This is what we're doing.

Yeah, exactly. It's what we're doing. But I don't think we ever, yeah, had that question of let's all like, is this the best?

Mm-hmm.

You know, that we wanna enshrine as the way versus this is our process for now, 'cause we have to do proposals. So really it, the fact that I, you know, it's funny, like I, I came to this being like, I'm gonna make all these agents and I actually haven't built a whole lot of agents 'cause I actually had to step back and map out our processes.

Mm-hmm.

And help me really understand those and make sure that I had shared understanding with our team before getting to that agent part. And it's really helped me slow down a little bit. And try to be more effective in how I plan to build. So when I do build, it's gonna just be that much more valuable and I can bring my team along better in the, in the process.

How does your team feel about, I'm trying to think of how to phrase this question. I've been the person that's like, I see the future, I'm gonna go call out all the processes, then we're gonna fix all the processes, blah, blah, blah. And it's like sometimes people are like, yeah, but I'm like really, I don't want that process change right now. I, I know it like, I'm doing the thing. Can you talk about that human piece of, 'cause sometimes those are processes that you're not necessarily doing, it's somebody else's work, right? And which you're kind of like redefining what good looks like for their work.

Mm-hmm.

Yeah, that's been a learning lesson for me too, because I think for me as the chief operating officer and a builder, like I'm, I, I, yes, I love to learn, I love to build, but like most of all, I want to make other people's work easier. And so sometimes I jump into, well, I'm gonna fix these processes for you 'cause I wanna make it, I'm not, and I'm, I'm not gonna, Bother you with it. I just wanna fix it and then have it help you. And I don't wanna bother you cuz you're so busy. And, you know, that's, I, I, I'm, I'm doing it cuz I wanna save people time, but like, that's not actually helpful. I need to really understand where their time's being spent, where the real hiccups are.

Mm-hmm.

And then, you know, map that onto my understanding of where I'm seeing the gaps in the quality of our proposals or in project management, you know, places where it's not really delivering the results we want it to. So I, I do have this full vantage point of where things aren't working and ideas about how to address those and best practices from the industry that I'm kind of using to help think about how to reshape things. But then I need to come back. I need to kind of take that and then really sit with people and understand their day-to-day and where their challenges are that are maybe inhibiting them from being able to get to that next level. How can we clear that stuff out first? And then they can start thinking about these bigger picture, you know, ways to improve processes. and help them kind of connect it to all of that.

Gloria, have you had any kind of similar, like you were, you're both kind of like people I consider kind of tip of the spear people, right? But then, you know, you need to kind of like, 'cause you go first, but then you need to turn around and kind of bring people along. Did you have any similar challenges, like in terms of getting far out ahead of your team?

I've been working with a really great group of leaders here who are highly motivated in this area and really into experimentation. I think in general, bringing humans along in change management can be so arduous, especially across the length of architectural projects.

Mm-hmm.

But I feel like we have this, this moment right now of kind of like AI is still a little bit magic to people and people are really excited about it.

It's not work yet. It's just magic still. Yeah, right.

But it certainly has exposed a lot of gaps in processes and certainly a lot of places where a human has been kind of quietly patching over.

Yes.

The missing pieces, the missing standards, you know, with good judgment. But AI has really exposed, you know, if, if I'm giving, if I have instructions to follow this implicitly, what's missing?

Yes. Meaning like you could have a checklist with 7 steps. But those are just kind of like the high-level steps, but humans are filling in 7A, 7B, 7C, you know, like all the kind of other stuff.

Exactly. But you also have this rare moment, like we've all written instructions, we've all written emails, right? That you're like, maybe 50% of people are gonna read this. Mm-hmm. And 10% are gonna make it to the end of this email or instructions.

Mm-hmm.

Or follow it to the letter.

Mm-hmm.

But you also have this like motivation moment where you are writing instructions for something that is gonna follow those instructions to the letter. Interesting. So it's also, you know, a, a different moment in that way, like a different motivation in that way.

I had never put that idea together. It's like people may or may not read the instructions you give 'em on how to do the project, but you know the AI will, and that's both good and also it's also gonna read everything that you said or didn't say.

Yeah. Yeah. The instructions definitely have to be of a different quality, and your processes definitely have to patch in those places where they've missed stuff previously.

How—

I'm glad we're talking about this. I feel like inside firms, there's a spectrum of people, kind of the AI is magic spectrum. I mean, there is, I think even the most knowledgeable person that works in a foundation lab still says, we don't know exactly how this Works, like, but it's amazing, right? But there's kind of a continuum of, no, it knows everything, it can do everything, it's all-knowing, it's AI. And the other, they're like, eh, it's kind of probabilistic and we know how it works. And, and I'm curious, as you try and communicate about the need to change processes and capture knowledge and get more intentional about documenting what good looks like, like, have you— Have you found people that don't buy that? They're like, but AI should just be able to figure it out. You know, why are we spending all this time writing this down? We have the superintelligence in our company now.

Well, I think almost the opposite. Like, there's still a skeptic group that's like, AI is not going to figure this out. AI is going to get this wrong.

Okay.

And, and those people are, are right, right? AI is not getting everything right today. Those people absolutely belong in this conversation and Need to keep telling the firm that the human being must remain in the loop. You are ultimately responsible for these drawings and your work product.

Yep.

Not this system. And I think the balancing of those 2 things is, is really good. I, I recently have turned my focus to like, I guess you can call it kind of putting a fence up, right? Like when you have a dog and you don't have a fence, you have to walk the dog on a leash. And, uh, I think if we put better guardrails—

I like where this is going.

I think when you, when you put better guardrails in place, when you put a fence up, you can let the dog roam free. And I've just kind of recently turned my focus from, yeah, we have this AI policy that we've had for years and years. Let's write something a lot more practical that tells people what they can put where so that they can go wild on these tools and start using things. And that kind of balances also the worries of the skeptics a little bit with— the people that are highly motivated to do something, but are maybe need to be cautious about putting the right thing in the right place.

So in my understanding, Hilary, you're like, it's either you have more of, or you're more, do you have, it sounds like you're more, you have more of the skeptics than you have the kind of like overoptimists in the kind of population that you're talking to about this change. Or do you just have both?

I think the skeptics are just louder. Oh, interesting. So a lot of the people that are really into it are kind of like quietly working away and look at this cool thing that I did occasionally. Skeptics are maybe like, Be careful, be careful. They're the ones maybe a little louder in the room. I don't think that we have more, I think it's probably less skeptics than we do people that are excited. But I think it's the, the people that aren't saying anything, you know, that you really have to worry about bringing along.

How about you, Nicole? How are you thinking about that in terms of different, like as you're thinking about the change management that goes along with the building, the kind of different cohorts or communities of archetypes of people?

Yeah. Yeah, it's interesting. So we started engaging the staff around this 2 years ago now. We wrote our first responsible AI policy just to kind of lay down some ground rules. And I mean, it was such a hot topic, you know, but you could kind of start seeing, and this was, you know, I think AI's gotten so much higher quality too, but this was kind of like earlier ChatGPT days when everyone felt everything felt even more generic and people could really see In Slack, you know, it'd be like, oh, that's so obviously a chat. You know, you could kind of see people were testing things and people were feeling uneasy about it. There was a lot of questions. And so, we actually, we have an annual staff retreat, and so, we dedicated a day to AI so we could just surface from everybody where they were excited, where they were concerned, and just kind of get that sense of where people were at. And we absolutely, you could see the people who were super jazzed. But I agree, it was more skepticism. But I think that went a long way in building trust with the team and us saying, look, this is new, everything's changing, we're exploring it, we'll always stay committed to our values.

Mm-hmm.

We've created a responsible AI policy. Human in the loop. We stand behind our product. We'll disclose those sorts of things. And then I think as we got, as the tools got better and we got better at using them, and particularly around what Hilary said around like using it to harness our own knowledge as opposed to just create generic content.

Mm-hmm.

And then I think people started to see that where our reporting got so much better cuz it got more finely honed on like what actually matters. Our, you know, ability to better tell our story in a way that, that's compelling to clients because we're able to use AI to kind of help us make connections. I think people could start to see those benefits and realize like, oh, this could actually be useful. And so I think we had a little bit of like a natural experiment to kind of recently to test where people are at with that, where we've reused Claude and I decided to get Claude Teams for anyone who wanted it. So it's opt-in. And I would say about 35 to 40% of our team opted in to that next level. Everyone can still use Cloud free subject to our policy, but for the people who are ready to go in and like really test out like the next level, it was just like it's available. And so it's really interesting to see to me which teams were the ones to like jump on it and which teams are not. And I will say the folks who are in the most technical, like most technical engineering roles. were the least likely to jump at it. And the ones who were in the more creative roles or the more like engagement roles or like kind of broader client-facing roles were the ones to, to jump on it because they could see how it could help them with contract management, how it could help them build a plant library, how it could help them, you know, synthesize research more effectively for planning efforts. And so that— and that's helped me kind of see what outside of my own little box of how I can see how to use it, like how our teams are using it. And so I beg them to show me the things that they're building. So that I can learn from them as well. So it's very much like we're all, we're open, experimentation is welcome. We have a tool that is enterprise level now, but no one's being forced to do anything.

Do you imagine, that's, that's, that's super helpful. So I'm going back to the, what, what, where it kind of like frequency times pain times repeatability divided by achievability. This is our running, it's gonna be our running bit. There's a question I know we kind of talked about earlier, like the technology can do it, but I'm thinking about that group that kind of opted out, Nicole, and that's not unusual. Like, I've kind of heard this across other engineering orgs too, where maybe it's that those people tend to be somewhat skeptical just as a breed, or, and/or the technology in that specific instance isn't as far along as it maybe is in some of the other ones. And maybe it's those 2 things combined.

You're nodding.

For those who are listening. And I'm wondering, what's the mindset? Is it, you know what, it's 2026, we're at the beginning of this thing, who cares? We're gonna move on and we're gonna make improvements elsewhere. And maybe when we come back, people will be more excited about jumping on. Maybe the tech will be better. Like, is it that, is it you're trying to, like, I'm just curious how you're thinking about the uneven adoption of AI, I guess, within the company.

Yeah, I think that's it. I think it's about how it, Beyond just like basic chatbot stuff. I know everyone's using chatbots for something or other in simple ways, you know, honestly, just as like an advanced search. So I know everyone's doing that as a baseline, but for many people, the free version works just fine. They don't need to go, you know, but I think it's a mix of like, they're not seeing in their day-to-day, you know, how something more sophisticated than the basic search really helps them do their job better.

Yeah.

Relative to the people who do see, oh my gosh, thanks to this, I can synthesize this information very quickly.

Yeah.

In service of moving a project forward. Or, so I think it's that. I do also think, and this is, this is a challenge for me too, is like until you see someone do something cool, you know, you don't even know it's a, a possibility.

Mm-hmm.

And it's also about time and capacity too. So one of our landscape architects who's like, she's one of our like most gung-ho and she has better skills than I do. She's building out this plant library, you know, thousands of plants that we're, you know, assessing to choose for different projects. And she's got them all categorized by, you know, the sunlight they need, when they bloom, all these different facts, type of soil. And it's just this like really interactive visual library that she all built in Claude. And I'm like, I'm sure the engineers could use something like that, but they're not thinking about it. They don't know it exists. They aren't thinking about how to do it themselves. And so figuring out how to get more skill sharing across the team.

Yeah.

Where they can show each other or help each other out, you know, and maybe Ella is something I'd like to do more of is create more spaces for just that. Our team members coming together. To share their creativity and see where it can add value versus like me saying, here's a cool thing. Don't you see? Don't you want to use the thing? So that's where I hope to get as we, this become, I mean, nothing's ever going to become like, we always joke about everything's always in beta. So we'll be in beta forever, but as it becomes like less beta-y and a little bit more of the day-to-day.

Gamma, whatever comes after beta.

Yeah, right. Exactly.

It's been a minute. Hillary, like, I guess, yes. So let me, let me kind of step back. So I agree, like people, when they need to see that something's possible, and I guess the more kind of like their work it is, the example that they see and the less translation they have to do, the better. It's, but in your language, I think it was kind of, I'd like to do more of this, but it also doesn't sound like it's super urgent. Like you don't need to be in a rush. And if they're not ready yet, then that's fine. We're still learning. Is that true for you?

True.

To Hillary? Like, are you taking your time in terms of some of this transformation work?

Yes and no. I, I have been taking my time, but I am typically maybe lean towards more cautious and measured in my approach to things. And with this, if I could give my earlier months ago self some advice, it might be like start faster or start sooner, move faster. And just know that when you hit a roadblock, it's probably gonna be Solved within, you know, yeah, within weeks it's gonna be different. Like the pace of things aren't, is just moving so quickly now.

Mm-hmm.

That I am trying to be less of myself with this.

Mm-hmm.

More in experimentation mode. I think also just like the build time is so reduced now.

Mm-hmm.

That there's not so much investment in trying things and it, it getting a proof of concept out. Doesn't, doesn't require you to have everything perfect in the background. Just knowing that it could work if it is something that's gonna hold value to your work.

I think that's a really good point too. Um, just as our teams get more sophisticated, cuz I've been feeling that way too. I don't wanna release anything unless it's the perfect agent that I've tested. But when I sit down with my marketing coordinator and she like, she like gets it. And so if, if it's 80% good, but then she has the judgment to take it to the Finish line, you know, well, let's solve 80% of her job. I don't need to make it perfect for her and we can iterate on it together. So yeah, how can I be okay with rolling things out that are good enough and, and trust my team or maybe give them the guidance about it's not perfect. You should look out for these things, but here's how you can take it to that next level or we can iterate on it or yeah, enable more of that experimentation and testing collaboratively.

Yeah. Different approaches for different people though.

Yeah.

You also do have your people where like trust is a ledger and you really need their first experience with agentic AI to be positive and valuable.

Totally.

And then you have the people that like understand it, have the vision for it, and yeah, you can, you can show them 80% and they will take it to 150%.

This feels like a little bit of a tangent, but I, I've been hearing in some of our clients talking to their leaders, uh, uh, kind of a theme over the last 6 or 12 months. That I just want to test by the two of you. And it may be that you're not seeing this at all and that we can move on or maybe even cut it. That the pace of change just keeps accelerating. Hillary, you shared, like, even for you personally, it's like, I need to let go of some of my ways of working in order to be in this moment and take advantage of this moment. But there's some pushback at some of the firm leaders I've heard back from some of their staff that there's too much change. Things are going on too fast. We need things to slow down, like no more change. And they're like, I don't know what to tell you. Like, this is the least disruptive it's ever going to be again. I mean, we don't know that. I mean, but like it, it could very well just be continuing. And so like people's ability, willingness to kind of pick up their pace or become a little bit more good enough versus perfect. Is that, are those conversations that are happening in either of your firms around like who we need to kind of become as people? In this new era?

That is 100% a challenge we're having. And we talk about it every day about how much change there is and how challenging it is and how our team is feeling the pressure of all that change. But I also, it's the same thing, like it, we can't control. I think we could do a better job. There's some things that we could, like, you know, I think we need to prioritize where the change needs, truly needs to be. But, you know, we're all in the pressures of change. The world is so different. And I think, It's gonna be like a hiring criteria in the future for staff to be flexible and be able to manage change. And I think we've lucked out in that that's always been something we've looked for because the nature of our work, like working in sustainability across these different practices, like things change a lot. Our clients are changing a lot. Working in government in this current environment, things are changing a lot.

Mm-hmm.

And so if you can't be flexible with the, how the winds change, It's hard to work in this field. So we've always kind of looked for that, but now it's like times 100. So even our most flexible people are like, you know, feeling this whiplash. So yeah, we have to— I don't have the answer, but it's top of mind. And I love to get the community helping figure out like what it looks like to address this in a sustainable way and bring teams along.

I think here at MBH, it's maybe more about segmentation and platform fatigue, having to know which platform to go to, to get information. Or there's this new platform where you have to enter all of your milestone dates or, you know, something else where you have to do your resource planning. There's just so many different spaces where you are now entering data. And I think the wonderful thing about what AI will do for us is It allows us to like actually unite all of those sources in the future and use all of the information that is already in disparate places. So I think connectivity will help with that. But also the wild thing about AI right now is that when we're talking, when we're talking about the future, we're talking about 3 months from now.

Right. Yeah.

Not years from now. And so it, it's kind of like, it's here, grab on because we have to go at this speed now.

Hmm.

I was just gonna say, it's really hard for me cuz when I see a better solution, I wanna put in the better solution, you know? And we have this conversation where I'm like, I think we should do it the, you know, yes, we were doing it this way and we were testing that out. That's not working. I think we should do it this way. Or we've learned, I shouldn't say it's just not, I don't wanna make it sound like it's just me and my whims, but we learned something. From trying it this way that are telling us we should actually do it this way. And then it's like, yes, that would be better, but is it gonna be too much change to then tell people we're changing it to that? So that there's, there's actually this weird thing of we know that will actually be better because we learned so quickly by doing, but then we're asking people to change again. I mean, it's silly stuff, but meeting cadences. We keep trying to figure out the right meeting cadence to get the right people together in the room to share the knowledge. When it's needed, you know, and we keep trying to figure out what's the best way that we all, when should we engage on Slack versus in person? We keep kind of changing things around and we learn every time better ways to engage with our team, but that shapes the way we think about doing it differently. But then people just have no idea what's going on.

So it's like if this change could make us 30% more efficient, but it's gonna cost us 25% to do the change management, is it really worth putting people through it?

Yeah. Or do we like lose? Lose some trust that it's like, oh, they're changing it again, even though the— so yeah, exactly. It's like this calculation that we have to do about—

So frequency times pain times repeatability times—

Loss of trust.

Loss of trust. Yeah, carry the 2. All right. Hillary, you did something kind of like in the nexus of things we're talking about, about doing kind of a design charrette with your leadership team around starting to help them envision how agents could work and what that would do for them. Can you talk about that, Charette, a bit?

Yeah. So we had really that full spectrum of AI knowledge in the room from skeptic to people that are using it at a pretty impressive level. Everybody brainstormed individually what agent they would want as part of their work. I think I gave them 5 minutes to do it so they could just come up with as many as they could, and then I put them into smaller groups to discuss those things together and come up with what they thought would be the most valuable for the firm. And reading through people's individual answers, it felt like a little bit like people like putting their work dreams on paper without really any expectation that they would ever become a reality in many cases.

Right.

And it surfaced a lot of like really real problems that people are facing in their daily work. A lot of like Really good trends for knowing where to build. So that was week 1. And then between week 1 and week 2, I built 3 of the, the agents that different groups had come together and agreed on. And that was what really changed the conversation. It became not this like abstract thing or some distant promise or far away work dream. That was when people were Like, whoa, that quick?

Hmm.

That came into reality that quickly. So the tone really flipped. And then people in the room, instead of kind of like debating the value of AI in, in an abstract way, started talking, like asking really specific questions about how they could apply it to their work. So it was like they suddenly went from spectator to co-author.

Hmm.

At that moment.

Hmm.

And if there's one thing that I could stress to anybody doing this, it's bringing those real working examples as fast as you can to show that, like the kind of like the prototype of the work dream kind of thing. Yeah, yeah, absolutely. Like it doesn't have to be fully baked. That said, I do think that having one thing that is pretty, pretty well along, pretty fully baked.

Yeah.

At least is good to show them so that you do get that trust going.

How, how much context did you need to give them to be able to have those conversations about like what an agent is? And how it works, and so that they're. Did you give them any, or did you say there's this technology that can do make your work dreams come true? Go think about what your work dreams are. Like, how did you get them talking?

It's like maybe a ten to fifteen minute spiel about it introducing like the difference between an AI chatbot, agentic AI, synthesis AI search, synthesis knowledge agents. Gave them a brief overview, and then also like a like a kind of a check chart of this does. This does this.

Yep.

This doesn't do that. So that it was easy to follow along. And then I, like, I intentionally left it kind of open cuz I wanted the creativity and the possibilities to come out.

Like you didn't try and give 'em like, these are known limitations of the tools today. You said just go, go do it. Write down your dreams. Yeah.

Yeah. Cuz also that's how we're gonna figure out what the future is also.

Mm-hmm.

Without the, without the limitations in place. What would you do?

When you went away and, and prototyped the WorkDream pilots, did you I'm guessing that you didn't have kind of back to Nicole's part from the very beginning, like necessarily all the standard, well-documented processes of how things work, or you didn't have all the knowledge or documentation you needed to build them. So did you kind of fake some of that in order to show them the prototype of how it would work? How did, yeah, how did you, how did you navigate that?

So let's see, in one case, I, I approached one of the teams And said, you know, I've been hearing that you have this document where, like, you've gone through this set and written down everything that you look for when you are checking, like, this particular set of drawings. Could I have that document? And then can I also have some documents that you've checked previously?

Hmm.

Um, and so in some cases I just got a very small sample set. In another case, I already had a good-sized sample set, and it just really worked out that that was what people wanted, that particular topic. Because I work across the entire organization, I do have a lot of like knowledge of where different things live, who is doing what. So it wasn't terribly difficult for me to source the things that I didn't have. And then in the case of that last agent, I made it, made sure it was something that I had already been working on for a considerable amount of time.

Yeah.

Like perfected and I could show them like many, many different conversations with the agent to show them like, this isn't smoke and mirrors, that like I'm using this every day.

Yeah. Yeah.

And this is how it's working.

Did showing them those prototypes build any kind of buy-in for the change management you'd have to do around documenting their processes more thoroughly to build some of those other dreams? Did that connect the dots for people that's like, oh, I want that, but in order to get that, We have to do the knowledge and process piece.

At the end of the second session, someone in the room actually said, sounds like we need a lot more documentation to make these work. And I think I stood up at the end and I was like, there it is. So yeah, like building, building the agents became an, an organizational mirror. It, it has exposed the undocumented processes and undocumented knowledge. The conflicting standards.

Mm-hmm.

That's actually a fun, a little fun agent now that I've built is to compare standards across the firm.

Fantastic.

To show everyone how different they are. Duplicated work, poor data quality, and, and helping people understand that if an agent is struggling, it's teaching us something about our business, not about the AI, probably.

Hmm.

That is so profound. I feel like the idea that we had to go to AI to learn about our own, our own like gaps in our own Yeah. Way of doing business. And I took that idea of like the AI is the organizational mirror, like that just really resonates with me. 'Cause it really, if when you, because you, to try to tell the robot what to do, you have to be so precise. And then when you realize like you don't actually have the right work to tell it what to do and you're projecting your own things onto it or whatever, Mm-hmm. It really does highlight all those failures. And if you keep going, it, the agent itself will fail. So yeah, I just, I did not expect that on this journey when I thought I was just gonna be building all these cool agents that was gonna solve everyone's problems, that it would actually just like completely turn around and revisit the business and the way it's set up and designed from like back to the beginning, you know, to take us to that future.

Yeah. And, and it's funny cuz even in the things that we already had, like I mentioned that like the set that had everything outlined for that someone is using when they're checking the set. It was like, are humans using this? Are, are we using this? But it goes back to the idea of, you know, like AI is gonna follow the directions. It's gonna follow them meticulously.

Mm-hmm. That's another thing that I've noticed too, is where we have had some standard, they're very important standards and they may be completely unintelligible to anybody but the person who wrote the standard. And so just one simple example of that is like our writing guide. Which our marketing team is like, you know, the way we write needs to look exactly like this AP style. It has this voice and they spend too much of their time going back and fixing all of that, you know? And now it's like, and so, but they had on, on the commons, we have our, our detailed writing guide and now anybody can do it. That was like one of the simplest agents I made was just review this against our writing guide and fix it. And now like marketing will never have to do that again. And so it's like we had the standard, but it was unusable. For most people, and now it is completely usable and no one actually needs to know the details of AP style to make sure we're aligned with the standard.

Hmm. That's really interesting. So better writing for the AI means better writing for the humans, but not just humans in general, but like the humans who aren't the subject matter expert that own the thing, right? So you need to make it more democratically, you know, accessible. I, I'm curious about this process, like one of the things that we've talked a lot about in the community is not only what good— defining what good looks like being important for AI, but also for doing learning and development. So like, if you don't understand how we do project management here, how could you teach it? Well, I mean, you— the way you could is you would teach the way Hillary does project management or the way Nicole does project management, but not the way that your firm does project management. And I'm curious if that convergence has hit your firms yet where you're thinking about learning at the same, like learning and development alongside AI and just like the kind of, there's a, a Venn diagram that connects all of it. I am, I am certain of.

I love a Venn diagram. Yeah. One of the, one of the things that I think AI is going to do most for us, we're not, we're not there yet by any means, is like connecting the just-in-time learning and the, hour-long training and learning, actually allowing people— yes, they may have completed the training, they may have taken the test, gotten an A+, but 3 months from now when they need that information, are they gonna remember or surface it or remember where it is? And now that content doesn't have to be separate from our L&D content. AI has united them and allowed people to find those snippets of information in their day-to-day real work. That they can connect back to the learning development. And that's going to be pretty amazing when it comes into a reality here.

Yeah, I think for us, so project management obviously is really important. And I guess the ability to think about higher quality project management processes and higher quality learning development, someone's got to be accountable for that. And as we've heard, I do too much and I can't I, you know, I'm, I'm an inch deep, a mile wide. And so I think at the end of the day, to get to that, I, it can't just be me.

Mm-hmm.

And so one of the things we did last year was actually tap one of our practice leads to actually have a hat where she's the vice president of practice operations. So over our 3 practices, she is responsible for project quality and our setting our project management standards and making sure everyone's adhering to them across the practices. 'Cause that's exactly what's happening. Each practice had their own way of doing things. This is the challenge of 3 co-equal practices that are kind of doing different things, but there are things that we should all be doing.

Mm-hmm.

And so that step 1 was just having someone who was responsible for that and felt accountability for it and committed to making a change. And then there were sort of 2 sets of what could look like that had to happen to get to better project management processes that people are trained on and actually adhering to.

Mm-hmm.

And so first was having her just again revisit our standards, our processes, and refresh them. And she did that first by interviewing all the staff, collecting feedback, back and then, you know, kind of doing that refresh. And then the second was a training, which also has a what does good look like for how training is designed and delivered, which is also new to us. Right.

Very meta. Thank you.

So the idea of what good looks like for a process like that, I feel really confident in, but what good training looks like was very new to me. And it honestly wasn't until joining this community, having the idea of like this great LMS at my disposal, And learning about this idea of adult learning principles, you know, and hearing how other people were structuring trainings in terms of length of course content that made us also completely rethink what a good training is. Historically, our trainings have been very much, you know, slide decks, here's the process, screenshot, do the thing. Okay. You know, now you've been trained. And so, right, exactly. You know, I, I learn, I learn so much every year. And so, so we had to do the same thing. What does it, what does a good training look like?

Mm-hmm.

It's based on these adult learning principles. Here are our ground rules. A course should be this long. It should have this mix of, you know, optional and required content. There should be a fun homework item in the middle. It should have a quiz. There should be expert interviews in it that keep people engaged.

Mm-hmm.

And it shouldn't be more than an hour. You know, those sorts of things that are our new standards that she, along with some team members, was able to build on. And the output is so good, and I had nothing to do with it. Maybe that's why it's so good. But so, yeah, it was just this 2-step, what is a good process to get to these higher quality trainings that we are starting to see as leading to higher quality project management effort. It's really exciting to see how that all plays out. And it's all related. The processes alone aren't enough. The training's not enough. It has to be like a fully thought out approach to improving. The quality of our work and getting to better budgets, better schedules, better happy clients.

I mean, the, the phrase that's running through my mind as you're telling this story is, you know, just even for your example, Nicole, at Greenprint, it's like, well, redesigning proposal process, redesigning project management, redesigning the way that we learn, you know, in a couple years, will there be anything left from, do you know what I mean? It's like, are we redesigning our businesses from the ground up during this transformation?

Uh-huh. Yeah, I think so.

And, and it's interesting that like, I'm sure, I'm curious on the project management piece, cuz we talked about the proposal one of trying to redesign the subprocesses versus the whole thing. And when you were focused on project management, which is also like proposals, it's like, that's a big, that's a big bucket of things. And did you, from a training perspective, did you go after it on specific subsets of project management? First, are you tackling kind of like sub-processes first?

That's, yeah, that's exactly right. So, you know, when you actually map out all the phases of project management, there's the contract scoping and contracting, there's the launch process, there's the monthly project management, you know, maintenance and processes, and then there's closeout. And we decided to focus on monthly project management cuz that's the bulk of the effort. That's what people are spending most of their time on. Mm-hmm. And that's where we just saw a lot of opportunity for improvement, a way to better Get higher quality data, better elevate problem projects that need to be addressed, and get to real consistency in the client experience.

Mm-hmm.

And really focus on that. And then from there, I think it'll be project launch and contracting, I guess, to an extent. Although contracting's, it's good enough now. I'm just not as worried about that as, you know, just the, the monthly client experience and how well we know our budgets and those sorts of things. So, it was basically looking at all the subprocesses and focusing on the one that was the highest impact. People spend the most time on, the most repeatable. It happens every month as opposed to every time a project launches. And yeah, it just felt like the most necessary to focus on.

It looks like Hillary's about to say something, but I can't tell if she is cuz I have a follow-up question, but I could see you calculating, so I didn't wanna jump in.

No, please ask your follow-up question.

Okay. I wanna stay with the redesign. So this is like outside in what I think I'm hearing, like on one stream there's like We're rethinking proposals kind of like piece by piece for an AI world. And another stream, we're rethinking project management piece by piece, but more from like a how do we train people world. But are, is the opposite also happening? When you're thinking about the proposal piece, are you also thinking about how we're gonna train people how to do proposals? And when you're doing the project management piece, is there like an AI component? How do we identify some of this project management work?

Yeah.

So were you thinking about learning and AI at the same time in both streams?

Absolutely. So we approached them from different locations on that spectrum, because I think we kind of started with learning on the project management, and then we're like, wait, we need to clean up these standards first. And we used AI obviously throughout the whole way to write clean, clear standards, SOPs, and design the training and all that sort of stuff too. So AI was used all the way. But now because we have this like refreshed set of key templates, key processes, I see the, the future plug where the AI agents will help.

Mm-hmm.

And so then I think with the proposal development, I kind of started with agents. I was like, oh yeah, agents are gonna fix all of this. And then I was like, oh wait, we need to address what good looks like. And so now I'm like, all right, I'm, I'm nailing down what good looks like. That's gonna help refresh our processes. And now I'm gonna do the same thing I did with project management. We're gonna get those trainings and now I've got a template that really worked for project management that I'm now gonna bring to proposal development to bring the same perspective. So we'll also have that same. So I, I think I entered both of those streams from a different part of it.

Yeah.

But the end result is gonna be the same. Clear what good looks like, clear standards, really good processes and templates, and then AI agents that can plug on to improve those processes for everybody.

And then potentially training. Yeah.

And then more training. Yeah. It's constant.

Yeah, yeah, yeah. That's it. But then what good looks like is the, is the kind of the gateway to have those, to have those options.

If that's not there, the whole thing falls apart.

Yeah.

I, I got stuck on the word redesign.

Okay.

There for a second.

Is that where you went? Is that where you went before when I saw you? Yeah.

Yeah.

Yeah.

I was really thinking about the word redesign. Mm-hmm. Because I, like, the way that we're getting there is redesigned for sure. Overhauled. Like we were swimming there before and now we're in an airplane, but I don't know that the destination is that different.

Mm-hmm.

I think that the possibilities have unlocked what we always knew to be true, who we always wanted to be, what we always wished we had time for is suddenly coming true with L&D. Like all of a sudden we have all of these capabilities at our disposal that makes it easier for people to impart that knowledge without so much effort and to make that super scalable across a much larger organization than it used to be. And like, I, I think we always knew that we wanted to have these standards. We wanted to teach people these standards. We wanted to be the best project managers that we could possibly be. We wanted everybody to do it roughly the same way with their own twist. We just never had the time and resources before now with these new possibilities unlocked to like— board the plane and get there in 2 hours instead of having to, you know, walk or swim across the ocean.

That's so interesting because you're right. At the end of the day, what we're building these on are industry best practices. We're not reinventing how project management is done. We're just actually making it work for Greenprint, have it be clearly communicated, make it easy to actually execute it, train people on it. You know, I think about some of the parts of the process that AI is now helping us with that have always been best practices. So in public procurements, like again, you've got all sorts of publicly available data to you that could help you better understand the clients. But in the past, some human was hunting through municipal board agendas to try to find insights into where their money's going. And now AI can get that for me in 3 minutes. And like all of a sudden, this thing that is best practice, but no one except big firms with lots of resource— human resources could do it. We can do that. And it, so we're actually able to do the best practice because, you know, with the limited resources that we have.

Mm-hmm.

Because we can get, just do it so much faster. And I feel the same way about Deltek reporting and, you know, all these things where it's just, as a small firm, we just, it would just be frustrating that we couldn't get high-quality reporting, we couldn't do high-quality research because we didn't have the resources. And now we do and we can actually do it. Yeah.

And, and maybe the piece you're unlocking really is people can now use that time for More creativity, more innovation. Maybe that's the real redesign is like where the, all of that, all of the other stuff that we always, that we always had to do can now go to a greater purpose.

That's so inter— I, I love this. And my mind was going the opposite. I got stuck on this. The destination's not that different. And I think in all the examples you gave, that's true. But then, of course, I started trying to think of devil's advocate of that. And one place my mind went, which I could get spun around into arguing the destination's not different. I'm thinking, Hilary, of the story that you shared with our community around your retiring CFO and capturing— that was the person that reviewed nondisclosure agreements. And we can get into the details of that. But the kind of— the thing that happened is you guys were able to rapidly do critical knowledge transfer from someone that was leaving the company. Yeah. Which is something that, you know, at K Connect 2017, we did this whole thing where we brought in an expert in critical knowledge transfer, and she did a 6-month project with 4 of our clients, and we all shared what we were doing. But some firms did some things, but generally it was like, man, that looks hard, you know, and it's hard to scale and hard to sustain. And but it was the best available technique at the time. And I look at what's going on in our community over the last few months. And I'm like, oh, this is critical knowledge 2.0. We're finally gonna be able to do these things that we talked about doing. And it could be possible that the firm doesn't have to keep learning the same lesson over and over and over again as the people switch. We can actually scale some of, not all of it, obviously that people have 30 years of experience and reps and tacit knowledge, but the amount of knowledge that we could potentially hold onto as organizations just seems like a step change different. Otherwise, we would've had to relearn how to do these things all over again from the ground up when somebody leaves the company. Are you feeling that? Are you, I guess, Hilary, your company is older than Nicole's. So Nicole, I don't know if you've been worrying about critical knowledge transfer with people leaving. I guess it's been more like scaling things from experts.

Yeah, we have a strong demand for a robot gym. So I did, uh, we didn't do a, a, uh, charrette, but I did on a, just like a monthly call, ask people to put in the chat, if you could have an agent, what would it do? And we had multiple people Multiple people. Jim is our most experienced, 30 years of experience engineer. Mm-hmm. And multiple people said, I want robot Jim so I can get into his brain.

Like what, what do they want out of his brain?

I, they just wanna hear all the stories. Okay. All the, you know, every project risk he's addressed and how he thinks about different challenges and how he works with clients and, you know, all the things that they know he has and that they get to experience sort of like You know, on the job, but not necessarily in this really focused way. And it, and it, that's on my mind too, is that, you know, time is always a challenge, but at some point we need a structured interview series with, with Jim that can help share that knowledge with the team.

Have you told Jim that people want a robot version of him?

Oh yeah. He laughs.

Does he like the idea? What's he like? No.

He's so modest. You know, the answer is nobody wants that. And it's just like people are crying out for a robot Jim.

How about you, Hilary? Are you thinking about robo versions of your employees?

Oh yeah. I've, I've also approached our someone about being a, a robo Kevin.

Okay.

I think he's coming around to the idea. One of the, one of the interesting things was, so after Nicole and I had the session with our fellow beta testers of, of knowledge agents and heard about robo Marty for the first time.

Yeah.

I think that sparked a lot of ideas. And then a— as I was talking to Kevin about Robot Kevin, it occurred to me that, that Robot Kevin could actually help Kevin be Kevin faster.

100%.

Yes. Because he's not typing the answers to the questions, you know, hundreds of times over history. And perhaps these things could actually help save our experts some time also in being themselves and answering questions throughout the firm. But I also think the really cool thing about the capabilities now goes back to the idea of We could have 10 hours of Kevin talking, and in fact, we have recorded all of his lessons from our corporate university over the past 8 years.

Hmm.

So we actually do have hours upon hours upon hours of Kevin imparting his, his knowledge on people throughout the firm. We don't have to now go through 200 hours of footage, come up with the exact questions, and then expect people to remember everything from them. We can put them all into a library. They're transcripted. People can ask it questions. We can have Agent Kevin answer those questions. And Kevin luckily will still be here for a long enough period of years to make sure that Robo Kevin is answering those questions correctly.

Right.

Yes.

For people. So I, I think it's super exciting. Probably a little creepy if you're the person that is scared. That's gonna be agentified.

I've gone through the experience, like there was a new, there's a tool that came out called Delphi, which is meant for doing this more public-facing. It's for like authors and those kind of people that they get tons of email and they're like, you know, this thing could answer email for me. I was just curious to see how it works. So I gave it all my podcasts and newsletters and a bunch of stuff I'd written. And it was like really good, but I spent a lot of time with it trying to find the holes.

Yeah.

You know, and I'm like, I don't think I've ever talked about this. And so I'm like trying to like trip up Robo Chris, you know? And it's a really interesting experience. I, I've heard that AI Marty, and shout out Epstein Yuan for this, like he's using it a lot to try and break it and figure out where there's holes in its knowledge. And so, yes, if the person could be the— they realize it would help them if it were to work, but also I think they'll have fun trying to trip themselves up with it. So critical knowledge transfer is like, we're giving that a whole new rethink. As you were talking, Hilary, I seem to connect some dots that did Kevin also run the Lessons Learned program at MBH? Yes. Am I right? That's Kevin?

All of our, all of our retrospectives. Yeah.

So the, the kind of, we had a, we had a, actually, was it the same conference? It was right in that neighborhood. It was either 2017 or 2018. We had a person, Nick Milton, who is well known for Lessons Learned and his like thinking on that kind of broadly in knowledge management, not just for AEC. And one of his key slides was lessons learned. Databases are where knowledge goes to die. For the exact reason that you just shared, Hilary, it's like, who's gonna think on their project to go look at things in the exact right question, like pull the retrospective. And it's like, that was so true. Except all of a sudden I'm like, is it? You know? Oh my gosh, you've got a database of that and you can search it in an unstructured To geek out for my knowledge management people, semantic way. So you don't even have to use the right term to go back and pull the lesson. Maybe that wasn't a big waste of time after all. Maybe that was just, we didn't have the right technology to make that knowledge available.

All those shoeboxes under the bed.

All the shoeboxes all of a sudden look really good right now. Yeah. Are you, have you talked, is that, have you been thinking about lessons? Because I know you have a lot of lessons learned content, Hillary. Have you been thinking about, does this give that a new lease on life?

Yeah, I think particularly around, well, lots of different ways. I think across projects, people, if we can socialize it properly, will actually start to query those to understand how we've solved problems before. But I also see that more broadly, it can help us Recognize greater trends across our projects.

Mm-hmm.

Even cultural things like, you know, where are designers feeling pain points? Where are job captains feeling pain points that we need to adjust in our processes?

Yeah.

Or, you know, technical issues that are repeats. Okay. That informs our L&D strategy for technical content. It's gonna be so much more easy to identify those trends across all of that content. And also then reuse the, reuse the things that are reusable on a project-by-project basis and, and take them out of silos. I don't, I don't know anybody currently gets the lessons learned outside of their team, much less studio.

Mm-hmm.

So, mm-hmm, making them more broadly available across the firm is sky's limit there too.

Right. And you don't, cuz like the old thing with the lessons learned database and why it was a broken idea. is they were usually like a separate thing. So like as a knowledge seeker, you have to think, well, is there a lesson learned on this? Is there something in our learning management system? Is there something in our project archives? And I think the world we're building towards now is I don't have to think about where the knowledge lives. I just have to have a good question.

That's right.

And bring the right intelligence to me, which also makes me think the adoption of such an approach feels a lot more realistic than it's ever been.

Yeah, I completely agree.

Where do you think that for the two of you, you know, what's next in the future? And by the future, I mean 3 months, but like, what are, what are kind of like you gonna be working on for the rest of the year?

Yeah, continuing to, so, you know, I, I can't mention this year was the year of focusing on our sales process and our project management process and just trying to get them to the next level. And that starts with the, what does good look like? What, you know, what is the process and then what's the training? So, I think that's going to be a continual process that we continue to, you know, as you said, we've got subprocesses. So, it's kind of creating a plan for what a reasonable rate of rollout is for attacking each of those subprocesses and rolling them out. That's probably, you know, with the change management side of things, that's going to take a while, probably another year, to be honest. Because really, yeah, that's what's slowing it down is the change management to actually get people trained, adopted, all that sort of stuff. And then I think just actually really starting to measure the results, cuz you know, I, I keep, I'm building and building and building and I don't want to build for the sake of building and I don't want people to constantly feel like something's being rebuilt. So I do hope that, you know, we get things to a place of good enough where people feel like they understand it and then we start to see, are projects more profitable? Are we getting better client satisfaction? Are we increasing our win rates on our proposals? Actually really looking at the data to see if the investments we're making are getting the return we hoped they would.

Mm-hmm.

And then I think the other thing is I, you know, I've been so focused at the business level, business development, project management. I want to get into the practices and start getting into what can we do for the engineering team that could really, like, what are your challenges that I could really jump in? And I, and I've had it, one, it's what I know, but two, I think we have to get that company foundation right. Everybody has to be doing the same thing. The sales has to be, has to be bringing the work in. And once I feel like that's in a good place, then I really want to jump into the practices and be like, what are the things In how you deliver the work that I can help use all these tools to create new standards, develop better processes, and figure out how to create the kind of training that can support you all.

Is, is one of the metrics you're like looking to measure is employee engagement or satisfaction with the way that their work is being done? Is that something you're thinking about too?

We do, we do an annual employee engagement survey. Okay. But I don't know that it really reflects on this question of like speed of change or AI. You know, there's, I think there's new questions maybe that we need to start asking them that gets at the modern workplace, the modern learning organization. Yeah. To maybe better get a sense of where they're at with that.

Right.

So yeah, I think that might be due for a refresh in how, what kind of questions we're asking around that.

Yeah, 'cause I was thinking about specifically your proposal process one, and I mean, Hillary can speak to this in much more depth than I can, but from what I hear, that's not always the most fun process to work in. And it can be like deeply frustrating spending lots of time hunting for information. And even if it didn't necessarily move the dial on the win rate, if your people that work in marketing actually enjoy their jobs more, like that feels like we've done something helpful.

Yeah. Well, even just so much of the marketing team's job is like bugging PMs for information and like nobody's happy, you know?

Correct.

Like marketing just feels like a nag. The PMs, feel bad that they can't be helpful, but they don't have time for this. It's not a priority. And they're probably not really, you know, the information they're giving them is probably just like, yeah, that's fine. As opposed to really thinking about what's the best way I can tell you what, what's the best information I can tell you about this project that you can use for this proposal? And so those are the kinds of things that an, an efficient AI process that can like pull all the knowledge we have about the project before even having to bother the PM, Hmm. you know, could make, I think, empower our marketing team to feel like they can get the information they need. first and understand it and, you know, write it in a way that's meaningful and just get a check from the PM. And the PM can save time and only be brought in when we really need them to bring like unique perspectives. So yeah, that's a really good point. How can it truly make people's jobs better and have them stop having to do the stuff that makes, you know, that just doesn't make anybody happy, but is a requirement of, you know, being able to do proposals or being able to do project management. How can we make things easier for them?

Quality of life improvements.

Yeah, quality of life. Yeah.

What are you thinking about, Hilary, for the rest of 2026?

Well, and, and on that note, quality of deliverable too.

Mm-hmm.

I thought it was gonna take us a long time to get to that place, but you know, a week ago I used someone's notes from a site visit, a project description from an RFP, and then a bunch of past approaches that that person had written and could provide a pretty dialed-in starting place to say, here, go. Hopefully that saved you 2 hours of figuring out where to start.

And the other neat thing about that is it makes the PM feel seen. Oh, you, yes. You know, I like, that is me. Yeah. You know, and try and support both. You guys have been listening to me and I feel seen. You know, it's a little bit validating to feel, you know, you're doing the work and you don't need to repeat yourself.

Yeah. I mean, and, and like, you are our client. We, we want to make things as easy on you as possible. I think for me, and I don't know whether this is, well, I guess I'm gonna go like maybe 3, 6, 12 months here.

Great.

So 3 months, you know, I talked about like building the fence. It, I didn't know that I needed a fence until recently, which is why I was talking.

All I can hear is When Harry Met Sally. Am I the dog in this scenario? As soon as she started talking about the dog, I have to tell you, I really love my dog. Okay, good.

I, I didn't know, I didn't know how much I needed this until recently, which is why I keep coming back to it. But like, you can't, you can't have people go free unless you've provided this safe zone to go free.

Yeah.

And particularly within my firm, we have a lot of really sensitive information. Mm-hmm. And a need for guardrails. And I think we are maybe moving more cautiously than we should through this process because it's just not super clear, like what the parameters are.

Mm-hmm.

So I think for me, the first, the next 3 months is saying fence is built, go buck wild. Let's see what crazy creative things people can start working on. With that newfound freedom.

Hmm.

In the next 6 months, I really want to have use of our knowledge become, take less intention.

Hmm.

I think with what, how we're building things right now, there's still a great amount of intention involved in saying, we've built this agent around knowing jurisdictional nuances, but I need to know that it exists and I need to go to this place. I, I want ultimately to feel like our knowledge is really deeply embedded in the workflow.

Mm-hmm.

And agents to not just be somewhere that people go visit, but something that becomes able to surface and embed things in real time.

Mm-hmm.

I think that will be the ultimate success. I don't, I don't think that's actually realistically gonna happen in 12 months, but like at the speed of AI, why not? Right?

Yeah, I like that though. I mean, I think I think, but you know, it's not a zero-sum game. You can move in that direction towards knowledge being easier to use.

Yeah, exactly.

Which I would argue we've been, that's the trajectory we've been on for the last few years as a community too. It's, I mean, I keep calling it the golden era of knowledge management. This is stuff I don't, at K-Connect last year, there was a funny moment, Nicole, where there were a couple firms who were brand new to the community and somebody spoke up and was Talk about how they're so glad to be here. And somebody said like, man, it's really like, you're so lucky. You didn't have to go through the dark ages of knowledge management. And you know, just really, you're, you're just coming in. It's, it's, it's just like after everything's been figured out, like lucky you. And I kind of get it. But I also feel like there's something in what you said, Nicole, and I wanna test this with you, Hillary. The last few years have also been pretty wild. I think for people that like a lot of change. It's been exciting, but even, I mean, just even for me personally, it is, the pace has just gotten pretty insane. And there's something I like the way that you were saying, Nicole, it's like we now know enough about the work that needs to be done that we can kind of slow it down and be a little bit more intentional and not as chaotic around just like whipping around trying to figure out what do we do in this environment. I love the way you planned out, yeah, and when we go after this process, we know we have to establish this and then we have to do the training and then we have to do the AI piece and then we know how to Could 2027 be the year that we see a little bit of calm restored and we can get more intentional about how to build in this new era? I don't know. Am I making that up?

I've been, I've been working with a group of people to put everything into a tracker that lets us measure, is this, is this an idea that we should hold onto for when the technology is developed? Is this an idea that we could build now? Is this something that's gonna have impact for our client? Is this something that's gonna have impact for us? And A, it allows visibility across multiple teams for us to see what each other are working on. And there's been like iteration that's already occurred at a wild pace from that. That's really cool. But the other thing is it's allowed us to kind of like hold both things at once. It's like if we only reacted, We're like, we're gonna make today's processes and things slightly less annoying. But if we're too focused on the future, we're gonna either build things that people are not ready to adopt, or we're gonna build things that the technology's just not quite there yet. And we're maybe gonna create some distrust that's gonna hurt us down the road. So I think, I think you can be, I think you can, I think you can have the wild and free and also the logical and organized.

Right.

At the same time.

Right.

And you might think something is the greatest idea one week and, you know, seeing it in the grand scheme of things 2 weeks later, you're like, eh, it's not really that good of an idea.

There's a, I think what you're also hinting at is you're, if, if I understood what you're doing with this tracker, is it's also making things visible that we've said this is a good idea, but not today.

Yeah, exactly.

There's a, one of my favorite books is a book called The Creative Habit by Twyla Tharp, who's a choreographer. And the kind of central conceit, the part, the part of her book that stays with me is that she in her mind is working on choreographing like 25 dances at the same time, but she's actually only working on 2. She's getting paid to work on 2. And so she built a wall in her studio of banker's boxes for all of the future projects that she's not actively working on. So if she has an idea on one, she pulls it down, puts it in the box, and puts it back up on the wall.

Interesting.

And so, like, she knows it has a home. She knows that we're, like, thinking about the thing, but, like, I don't have to work on it today, and I can actually stay focused on the thing I'm working on.

And if it is valuable enough, you'll find it and pull it out.

Valuable enough. And then it's— so I'd, like— that led me— I tried doing it with Evernote, and I ended up using Trello. So I've kind of built her thing of mine in Trello, and I've got projects going out into the future, but I've got a home to log all those things. And when I decide to work on it, I open it up, and there's, like, all these notes. of these ideas. Sometimes it's the same note 6 times over a period of months that I thought was original each of the 6 times that I wrote it down.

Right.

And then, yeah. So I feel like that part of that, like, I like what you're saying, Hilary. It's like we can also start to say we don't have to do this all this month. Like, there's time.

Yeah. And also with the speed of AI, like, you don't have to choose between, like, your reactive and proactive things. So like, yeah, like budget and time-wise, you used to have to very much choose because making things was so slow. But now, yes, we can focus, but we can also like, we can do a couple different things at once. We can work on the future and we can work on the now at the same time, which is cool.

Yeah.

Something we just, that was kind of an aha for us again, and, and team structure is a big part of this too, is so we have a 4-person management team. It's myself, our CEO, our now Vice President of Practice Operations who we just elevated, and then our marketing director. So we've got practice operations, sales and marketing, and then CEO and COO. And all of it, we're all jazzed about AI. We're all excited about all this work. But April, my CEO, and I are the ones who are a little bit higher where everything's exciting. We see bigger picture down the road about what can be. Whereas my marketing director and, and practice lead are the ones who are like, they see that, but they also have to deal with the day-to-day and they're managing the teams who are managing the change. And so—

Mm-hmm.

We kind of had this agreement that going forward they can be the gatekeepers. So we'll keep on innovating. We'll, we'll, we'll build the boxes and put them all out there, and they can be the ones to decide how to stage the rollout and change of these things.

So you're the gas and they're the brake a little bit?

Yeah, exactly. So, 'cause they, they have the best insight into like where the team is and their ability and capacity to absorb the change, facilitate the change, whether they're ready to put, you know, let, let us push the gas a little farther and people, you know, or we need to stop for a little bit and So, so yeah, giving them that permission to be able to say, you know, I'm gonna stage it out this way. And, you know, and it's a collaboration. We might push one way, they may push back, but having, feeling like they have ownership and a kind of, you know, the ability to say, yeah, I wanna stage it out this way. We can't force this too fast. And the insights from the day-to-day work that can help shape, help us. And I don't have that visibility anymore, even going from 15 to 30 people. I'm like losing touch with what's going on in the day-to-day.

Mm-hmm.

And so I think that's, Kind of setting those rules for ourselves is gonna be really helpful in properly staging out the work going forward.

I love that. Maybe to kind of start closing on a what's next theme, like you, you are both aware that we have a Synthesis MCP server now that we just put into private beta. And Nicole, I know you've been doing some stuff with it, and I wonder if we can just talk about that for a minute, cuz I think there's something very connected to the conversation we were just having about, about that.

Yeah, so I've been dying for this, so I'm so excited that it's live. Kind of, we, we've been talking about now with AI, we now have access to every knowledge container that we have. So with Claude, we've got, we, we're connected to SharePoint, we're connected to Outlook, we're connected to our voice transcripts, we're connected to Slack. So like really both our semi-organized SharePoint folders are accessible, our ethereal conversations are accessible. But until now, all of our standards and all of our like most curated, highest quality knowledge, shared knowledge amongst the team has been in the commons. And I've had to copy and paste that in if I want to kind of take that as part of the bigger knowledge picture. And this is kind of the missing piece of now I can look at my entire knowledge base all at once. And so, I think the 2 highest value things that I'm pulling out of the commons now through my central I don't even know what to call it. My—

Yeah. Command center. I don't even know what it is.

Yeah, it's the command center. Exactly. From the command center is our standards.

Mm-hmm.

So before I'd been copying and pasting our standards into things, and that's not good. Now I can just be like, follow the standard from the commons for this and our project directory, because the projects that we have going from Deltec to Open Asset to the commons are the highest. They're our best projects. Mm-hmm. They all have project summaries, they all have data. And now I can just point to that and grab that all. Whereas prior to that, I was using kind of a poor man's version of that where we'd like— it would search SharePoint, which also has all sorts of data about our projects. But now I have the good stuff.

Yeah.

And that's helped me build agents that can help kind of traverse multiple of those knowledge bases and the internet to answer questions or get to deliverables and to, to be able to have our standards and our projects. and our employee directory really connected to all of that has been the missing link.

You told me this use case in prepping for K Connect, and luckily this podcast will come out after K Connect, so there's no spoiler alert here. That's going to be in your presentation around updating project descriptions by connecting all those systems. And I wonder if you'd be okay to share that.

Yeah. So our marketing coordinator, as I mentioned earlier, just the bane of her existence is trying to get PMs to review project summaries, update them, you know, get the right content in. And I think even just from a quality perspective, it's fine. You know, like we have a boilerplate, we have a standard for what a good project summary is, you know, how many words it is and the basic content it covers, but it doesn't have the wow. You know, the marketing team can't find those wow things about a project that really sell it. You know, those are in the PM's brain. And when you ask the PM, hey, can you please review this? Is this up to date? And, you know, they're not thinking about, well, what's the really exciting stuff? And so what I'm building with her now is an agent where it can pull all of our current project summaries for our projects and it can compare them against project notes that are in SharePoint, against Slack chatter, and elevate where things might have changed or maybe some really cool things came up that could really take it to the next level. And it's catching things. And then, and then the final output is for each PM, it'll just be a list of projects that have changed and a redline recommendation. So our marketing coordinator can review that herself and see what she wants to accept or if she felt like it captured it. And then the last piece is the PMs just do a final review and say, yes, that's right or not. And so it's capturing things like, for example, an affordable housing site development project that currently, you know, previous to this just said, you know, we did this project, we did these, this site work, the project amount was this much, here's the roles we had. But we discovered through a Slack conversation that we had actually Helped save the developer money and found a more sustainable solution to their drainage challenges. And like, that's the wow stuff we want showing up in our project summaries. We saved the developer money and found a more sustainable solution, and just the current process wasn't even elevating that. And we found it through Slack chatter.

Yep.

And now it's in a project summary.

Because they had that conversation in the flow of work kind of thing. Exactly.

Exactly. They were just talking about it. And so it's— yes, it is streamlining the process. Reducing the nagging, making things happen faster. The PMs aren't bothered as lot— as much, but it's also surfacing things that just the previous way wasn't surfacing because they happened ethereally.

Yep.

Cool.

So I wanna wrap by asking you both as kind of two hats, right? One is leaders in your company who have also been building. What kind of advice to other leaders of AEC firms In terms of why this isn't just something that the innovation team or the IT team or some of the emerging professionals who are super kind of quote unquote good with technology, you know, should be doing. What have you learned by being in the seats that you're in leading this work that you think would be useful for other leaders to hear about?

I think the first thing is do not outsource your understanding of this. You do not need to be an AI engineer to build something for yourself. And even if it's something small, you'll learn a great deal in the process. And, you know, as I, as I said, like the, the more I approach AI problems, the more I learn about the business. And for leaders, like how valuable is that? The AI tool itself is very easy to learn. It's, it's really not that complicated. It's essentially the same steps as giving directions to a human being. But what you will learn about your firm firsthand and where to go is so valuable. And I think it allows you to ask, start asking better, harder questions about your firm, about where you're losing energy and where you can supercharge things. It's, it's like it is a tool to redesign how your work gets done.

Mm-hmm.

And AI itself is not the strategy.

Right. I've been saying like, I don't think this is AI transformation. This is business transformation powered by AI. Right.

100%. And I guess the other thing I would say is like people, as far as the people that you should pull in.

Yeah.

Pull in the people that are already asking, why do we do it this way? Yeah. Like AI doesn't need to be in the conversation. You already know who those people are. And then like you have people that have been practicing this craft for 30, 40 years.

Mm-hmm.

And are super adaptable. Like, you know who those people are too. Pull them into the conversation because they have the knowledge to transform. And even if AI scares you, the willingness to change is a great indicator.

I like what you just said. Like, okay, there's something bigger there. Is it possible that we don't need everyone to be as adaptable to change in the organization? Is it possible if you get kind of a core in the middle that can absorb some of it be a shock absorber for the organization, take the first hit, right? And figure out how things kind of like settle down. Maybe not everyone has to see all of the chaos all of the time. I think that was for myself.

Yeah. I, I, I know who that shock absorber is on my team.

Yeah, yeah, yeah, yeah.

Yep.

Yeah. But I like, like even that man, that 4-person team you talked about, Nicole, like that feels like that's partly the role of that team, right? And like you've got people on the team that are like, No, this is fine. People will be okay with this. And they'll be like, no, this is gonna be really disruptive. We can't pull on this lever, you know, right now. Yeah.

And it's built on a lot of trust because we have to trust that they like our ideas or, you know, are excited about us having ideas. They may not like all of them.

Yeah.

As it, it's not just like a, oh, that's too, you know, don't do that. Don't do that. It's like they genuinely think it's cool, interesting, see the value and not right now. Or, you know, so it, it is built on trust to be able to, have your big ideas and have people gatekeep it in a respectful, trust— trusting, trust-grounded kind of a way.

Do you have advice for leaders about kind of business transformation powered by AI, Nicole?

Yeah, you know, it's interesting. This conversation has really had me reflect on, at the end of the day, it still comes back to the same business fundamentals that we understand if our business is doing well, you know, it's still the same metrics that we look to to tell us, are we meeting our sales goal? Are we, you know, hitting our utilization goal? Are we profitable? What are our multipliers? The metrics are the same and they tell us how we're doing. And the best practices haven't changed. The industry looks to the same groups for best practices in project management, for best practices in proposals. But for me, I've known those things, but I've been hampered by a couple of things. One is, as an operations person, I'm like an inch deep, a mile wide. And so I kind of I know what I'm trying to get to, but I didn't have the skills to develop spreadsheet models, to, you know, build reports or to navigate Deltek in the way that I really needed to, to understand how to get the most out of our data or to do the kind of research that was needed. So, there was just like, it was just too, too complex for me to feel like I could get from the work we're doing up to, you know, driving those metrics. And now I do. And These tools are helping me now get from like where we're trying to go and business fundamentals and do it faster, better, you know, in the Greenprint way of doing it. So, and I think that grounding is really helpful for me as somebody who gets excited by everything to be like, what's the point? We're trying to, you know, do good by our clients, bring in business, keep our employees happy. That's what everything we do should be in service of. And there are decades of work saying what good work looks like, and we should build on that.

So are you saying— I like this. I think what you're saying is you feel empowered to kind of be able to get answers to the questions that you really wanna know, because I don't have to go through— someone has to do a SQL export, then they have to build a Power BI thing, and then I hope that the product is able to actually visualize things I wanna see it. Now you're like, I've got the data, I've got a question, let me just do my thing.

The decision has not already passed us by.

Right. And it isn't 3 months later when I finally got the thing built and I missed the window.

Yeah.

Yeah.

Awesome. Yeah. We can make decisions faster and we can get insights faster than ever before. And those are the 2 things that really hampered us, like struggling to get enough information to have insights that can lead us to make decisions. And I finally feel like I have the tools and the insights to be able to do that.

Yeah. Decision quality, not just speed. Hmm.

That's great. Well, Hilary and Nicole, thank you so much for your generosity and time and sharing in very much in-progress works and thoughts publicly on YouTube, Spotify, Apple Podcasts, and wherever you get your podcasts. So we appreciate you both.

Thanks, Chris.

All right.

You got it.

Awesome. Thanks so much.

Hey, thanks for listening. If you like what you heard, you can subscribe for new episodes on YouTube, Spotify, Apple Podcasts, really anywhere you get your podcasts. And if you've got comments, feedback, or ideas for future shows or guests, you can email us at smarter@knowledge-architecture.com. Thanks for listening, and we'll catch you next time. We took the 5th route well to 16th Street.

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My favorite transportation, no matter what's your station. Wherever I need to be, I take the BART. We took the BART.

To conquer our daily city, my favorite transportation.

No matter which station, wherever I need to be, I take the BRT.

Which is the best? Which is the best? Which is the best?

Because wherever I need to be, I take the BRT first.