Turning AI Hype Into Real Time Savings in Commercial Real Estate

Bill Douglas: Hello everyone, and welcome back to this episode of Peak Property Performance. Today you're stuck with just me because Drew is on a client site and they are going way over time, but clients prevail. So still going to have a great show. The episode today is Turning AI hype into time savings through a product builder's lens on manually intensive CRE work. I know that's a, it's a long title, but, uh, Greg Achenbach is joining us. He's the VP of product at PredictAP. But first, I want to remind everybody, like, follow, subscribe, everything you do to help promote a show that you like. Uh, send this episode to a friend, tell them they should be on it.

Bill Douglas: If you think you should be on it, submit an application. We're, we're trying to raise awareness to the fact that Data and digital in commercial real estate is underutilized and undervalued. And the conversation is just sharing experiences and stories to help everybody improve in that regard. So, like I said, we're joined by Greg Achenbach, VP of Product at PredictAP. And what I like about Greg's perspective is that he is not coming in to sell us anything or sell the idea of AI, which we are all getting a little worn out about, especially in the trade show circuit, or any product or specific service. He's coming in as the person who has to turn a messy business problem into a real working product, explain it to real estate teams.

Greg Achenbach: Yeah.

Bill Douglas: And keep the conversation tied to measurable business outcomes. So Greg, welcome to the show. Excited to have you.

Greg Achenbach: Yeah, thanks for having me, Bill. Excited to be here.

Bill Douglas: Yeah, well, the, we talked about some things we wanted to get on the show and the first one was like the work behind the AI. So before we talk about AI, what world are you actually building in day to day and what makes commercial real estate accounting such a practical place to apply automation?

Greg Achenbach: Yeah, so PredictAP is an AI invoice ingestion and coding platform for real estate companies. So we've been Yeah. So we've been building AI-backed products for the last 6 years. And what makes commercial real estate like an interesting place for us to work in is just the volume of activity that's going on, especially in like the AP side of that, right? So our whole goal with what we're trying to do with the product and with the company is utilize technology to help make AP more efficient in a real estate company. AP is kind of sloppy in any business, but when you add on like the magnitude of activity that will tend to happen in a real estate company, it just like is multiple more complicated than other areas. And so we have to,

Bill Douglas: especially at the site level, like the plethora of bills and are they legitimate? And you roll that out to the portfolio, it is an arduous problem. Yeah.

Greg Achenbach: I mean, it's like there's just a lot of purchasing, a lot of repairing, a lot of services. There's just a lot of financial activity that happens at the real estate world. And, you know, it happens on a small scale like me, I own a home, I have a lot of bills to stay on top of, let alone me being a single unit in skyscraper in a suite of skyscrapers that are being managed. So it's a really interesting cross-section of, you know, like practical and complex at the same time and just volume, like very high volume of activities, which tends to be the types of thing more advanced technology has always been good at, right? Like if you can see patterns that exist, you know, many times over, you're going to be able to build technology to help solve those versus something that might only happen once or twice a year becomes harder to kind of like apply more advanced tech to. Although that's becoming easier too.

Bill Douglas: Commercial real estate owners and operators and managers get thrown under the bus a lot about being slow to adopt. I'm guilty of saying it at times, but I want to flip the paradigm and be optimistic in this regard and ask you, because you've been at it for, you said, 6 years with this company, how's the industry's willingness to adopt more advanced technology changed over the past few years?

Greg Achenbach: Yeah, I think it's been, been really fascinating to see how much that mentality has shifted, particularly over the last 2 or 3 years. So my background, I've worked in, I've been building products You know, software products, technology products for 20+ years. I've worked in the healthcare space. I've worked in education tech. I've worked in marketing technology. Now in my second go-round in real estate. And when we started this, this company in 2020, I would say that, you know, coming in and learning what the current tech stack is for a commercial real estate company wasn't as cutting edge as some of the other industries I've worked in, right? You know, I would compare it a little bit more to,

Bill Douglas: Same here, Greg. Sorry to interrupt, but I joined this industry 11 years ago. And this is the 6th company that I've done in my career. And I've been in various industries and I was, I was attracted to it because it was a laggard. And now that we're, that I've been in it 11 years, I'm super attracted to it because I'm seeing the adoption speed up. Like finally, like people are saying, yes, I'll do that. Like that being technology or digital, not yes, you're going to do a solution I'm selling. But, you know, 10 years ago they were like, nope, this is how we do it.

Bill Douglas: We rent space. We build buildings. We buy buildings. We trade buildings. We rent space. And now they're thinking about everything that technology impacts in their business process and in their tenants' lives. It's been beautiful.

Greg Achenbach: Yeah, no, and it's, you know, we, we started, we started with this idea, we started rolling it out and folks were a little hesitant to use something. You know, we didn't even use the term like AI for a while. We were using machine learning because it's like this specific piece of tech that we were using. And, you know, and I, and I get it, right? Once you start to talk to people, the real estate reminds me a lot of healthcare where there's a lot of adversity to risk because of the pain that it can cause, right? So healthcare systems, highly regulated, lots of bad things can happen. if your ERP, like the medical records system goes down or you can't perform surgery. Like I've, I've, when I worked in that space, that was like the most stressful experience I ever had. Right. Because like you've got people's lives on the line.

Bill Douglas: Risk exposure is much higher. You have to admit.

Greg Achenbach: Yeah. It's really high. But then you think about like real estate and like, you know, trying to think on the behalf of their tenants and not having their tenants experience problems, right. Whether it's the power going out or trash collecting not being picked up, or, you know, the, the people who are managing, especially on like the residential side, have a lot of responsibility on behalf of their tenants. And so they are going to be really risk-averse as well to make sure that they're not picking some fly-by-night tech that's going to, you know, thinking about financial software, like they don't want to put the tenant experience at risk because a piece of technology is not doing what it said it would do to make things easier. So, you know, at first there was a lot of, you know, overcoming we had to do around that. But I would say definitely the last 2 or 3 years, it's just switched and people are coming to us looking for more advanced technology to do their jobs easier. And so it's been, you know, it's been really nice to see the evolution.

Greg Achenbach: Now it's just like any other industry I've worked in that's been a little bit more people want, what's, what's new, what's proven. I think there's still a lot of, you know, risk-averseness around something, you know, might seem to be temporary or not real. But I think once you've been in the space for a while, you've got a name, you've got a track record, you know, we're lucky to have some very well-known clients that can advocate on our behalf, then people are a lot more willing to be like, yeah, this sounds like it works. We need to up our game. Let's give it a shot. So it's been a lot easier talk track lately.

Bill Douglas: Yeah, we've experienced the same evolution. A young man I mentored for a decade who's a marketing guru, different industry, nothing to do with commercial real estate. He taught me years ago that the difference between a problem unaware market and a problem aware market is the problem aware market is at least a customer prospect. The problem unaware market, unless you have deep pockets and you're a billion dollar company, you cannot educate and change their mind. The market has to. So we didn't spend, you know, I told you I've been in this one 11 years, but OpticWise is 24 years old, I think. But we didn't spend any time in the problem unaware market. We went to the the problem aware.

Bill Douglas: And something happened a couple of years after the pandemic. And maybe the, maybe the AI wave, maybe it was about a year ahead of that. People started to say, ah, but the second point I wanted to bring up, and this is one that I'm passionate about, our listeners hear me vent on this all the time, but I want to hear your take on it, is the concept of don't buy AI, find the bottleneck. Look at the process first, folks. Don't just buy AI. So my question is, how should commercial real estate owners and operators think about using AI to solve a business problem instead of just buying something because it has AI on the label? Like I went to a trade show. I go to a lot of trade shows, but the last 5 I've been at, your AI partner, your CRE AI, and it's, it's point solutions using AI. I'm not saying they aren't using AI, but the owner is not actually using AI.

Bill Douglas: They're buying it. So how could they think about using AI? And I'm going to shut up, I promise, to solve a business problem from your perspective, you know, maybe digital architecture or give us a success story. I don't care whether it's around AP or not, but where somebody in some industry looked at their own process, applied AI rather than just bought it from somebody saying it included AI.

Greg Achenbach: Yeah, that's a good question. And I think, you know, being the product nerd, the thing I'm still getting used to is people just slapping a generic term to mean really specific things, right? So AI, it has been a phrase that's been around forever, and it's really just like a marketing umbrella for a suite of pretty advanced technology. And then there are specific subcomponents of it. So it's been fascinating to me that like, and it's also kind of dangerous too, because folks will latch onto, you know, certain people will think of AI purely in this like conversational experience because that's what they're used to interacting with in their personal lives. And, you know, that's like a flavor of an AI type product. There are things like, you know, really small things that folks don't think about. Like, you know, if you're typing a name on your phone, you know, you're going to get an email, it's autocorrecting it. Well, that's, you know, that's really early on machine learning pattern recognition.

Greg Achenbach: That's a form of AI. So I think people have just started to slap the marketing term on everything. And now people have like AI mandates to be using it. And so I don't think, you know, I think nothing has changed from why you would buy software products for any reason over the last, you know, 20, 30, 40 years, right? You're solving them to help run your business more efficiently. You know, I'm a similar to what you were saying around how you advise people in the product management development world is this theory that was developed by this Harvard business professor named Clayton Christensen called Jobs to Be Done. And the whole premise around this for like, if you're building a software product is no one wants to buy another product. What they want to do is have a job, right? So simpler way of explaining this, you don't need a drill.

Bill Douglas: You need a quarter-inch hole, right?

Greg Achenbach: So like, it's the tool is the mechanism to achieve the outcome. So when I, you know, I've talked to people, whether it's like family members who are trying to figure this stuff out or friends of mine that aren't in the technology industry, they're trying to think about how they could be applying this stuff to their professions, to take a step back and think about like, where are you or the people that you're working with spending time where you don't think they should, right? What are the lower value tasks that are just eating up a ton of people's time and stopping them from getting to the more important things, right? So we, you know, just like why we, why PredictAP focuses on invoice processing right now as like the starting point of what we, our platform does is because it's a problem that everybody has. And there at the time had been no real solid solution for it, right? There were partial technical solutions, you know, like simple OCR scanning or people just paying to ship this off to other people to deal with. And what, you know, you think about like, it's a really simple thing, but what if you don't pay your bills? Right? Like, you know, we talked a little bit about this earlier, like a real estate company, the ramifications of not getting this invoice paid on time can be huge if you're not paying attention. So very practical thing. Also the, you know, think about how do you, you know, an AI type product or AI backed product is going to work where there's real business pain and there's also data for it to consume, right? So all of the data.

Bill Douglas: We learned early on that you could automate almost everything, but there's no return on most of it. Like you only need to automate where there's legitimate gain somewhere. To just go automate is a waste of time and money. AI enables automation for so many things. And we see a lot of people spending a lot of time and a lot of hours on things that aren't really a problem. I'm like, you know, accounts payable, of course. But too, I want to inject one point because we put this in the book too, in a way, the Peak Property Performance book in the appendix, we said, what is AI? And I learned a very high level years ago in an MIT Uh, executive class about AI strategy and that AI is really a 3-legged stool. I mean, I'm going to bucketize these things, right? There's natural language processing, which is the autocorrect you mentioned, which is what lets us have transcriptions and all kinds of other powerful tools, but it lets us communicate with the machine in a way we understand.

Bill Douglas: Before we had to rely on coders, like to type a certain, you know, COBOL or whatever, Fortran, or like I'm dating myself by those languages, of course, but Python, for instance. Now we can just speak to it and it can understand what we want and go do it. So that's natural language processing. The other one's machine learning. I know that your solution is heavy in machine learning, but that has been around decades. Like that part of AI is nowhere near new. We've been using that for pattern recognition and finding anomalies and algorithms and correlations for 25 years that I know of. IBM started doing that when they did Watson that beat the chess master.

Bill Douglas: They were using machine learning then. And last one's natural, I mean, robotics process automation, RPA. We're actually seeing a lot of RPA come into commercial real estate through things like robotic cleaners, for instance, but they're collecting all kinds of data and other automation. So you can have software RPA and physical robots as well. So I wanted to interject that to the audience that maybe knew and hadn't heard our understanding of AI and keep it a high level. We're not here to talk about how to do AI. We're talking about how to apply AI and what data is needed. So I wasn't trying to steer your answer, but,

Greg Achenbach: No, all good. Yeah.

Bill Douglas: Conversation, right? But when somebody says to you, to shift it back to you, We need to do AI, Greg. Or what do you think they're actually missing? Like when they broadly say we need to do AI, like you mentioned a second ago, companies have an AI mandate. You know, we have to do AI. Management says I have to do AI. What are they missing? What are they actually seeking?

Greg Achenbach: Yeah, I think it's all like, there's a never-ending and probably will never end like journey to make your business run as efficiently as possible. Right. So if you are, you know, go back 10, 15, 20 years ago, like you need a CRM. Why do you need a CRM? Well, you need a CRM to be able to stay on top of, you know, all the data about the customer that you're managing, whether it's a tenant, a business, or whatever commercial, you know, commercial product. You need a CRM. You know, that was a big thing. I remember this was like, you know, dating myself a little bit. I remember when CRMs were the thing every business had to start adopting and then needed to go from, you know, being locally hosted to being in the cloud.

Greg Achenbach: Why? Like better connectivity and better access. Like there's, there's always been this drive, at least, you know, since you know, computers that existed to like use technology to, you know, make your business run more efficiently. I think what we're going through right now is the latest wave. I think the thing that, you know, is like, I, I was coming out of college when the dot-com era, that first dot-com bust happened. And then the rise of the internet that, you know, like came out of that. And what's going on right now reminds me a lot of what was going on then where you have like this really exciting like new type of way to make your business more effective. And people are trying to figure out what that means, right? So like everybody needed to have something in the cloud or everything needed to be online or like right now, like AI, you need to use AI tools to make your business more efficient. So largely agree with that.

Greg Achenbach: But if you're just scattershot trying things, which, you know, we've, I've seen some folks do versus take a step back and be like, okay, well, like what, what are the actual bottlenecks? Which is something you brought up, like where are the big bottlenecks in my company? Let's start with that because ultimately you're going to spend money and these things are not getting cheaper to run too, right? That's a whole other... conversation we could have around like these more widely available AI products. All of a sudden the price tag's starting to significantly increase on them.

Bill Douglas: They gotta pay for those data centers. Yeah. Oh yeah.

Greg Achenbach: You gotta pay for the data centers. And so, you know, it's gonna end up costing more than you think, right? In a lot of these implementations of tech. So like, what do you... start small, start practical, start with something you can actually measure the result, right? So if you're just slapping it on, you know, I'm gonna throw a chatbot on my website so people can interact with me and I don't have to personally answer it. It's like, okay, great. But like, why? Why would you want to do that? Is that the biggest problem that you have? Like, you, you know, whether you're a CFO or a CTO or a PM or whatever you are, like, try to think local, right? Like, what is the thing within my world that's causing me the most pain that I could probably apply technology to? And then actually measure the impact that it's having. I think that's something that a lot of folks kind of jump and just assume it's going to happen, right? Instant ROI. I'm going to slap this piece of AI on my whatever, and then, you know, ROI.

Bill Douglas: Check the box for being AI-centric. Yeah.

Greg Achenbach: And, you know, then you get hit with a $100,000 bill from this tool that like, you know, cause, you know, like surprise, you know, surprise token usage and like, what, what am I actually trying to achieve with it? I think that's the first thing is like, you know, start small, start simple. And also start with, you know, once you narrow, once you think about that, I think it also will help you think about, you know, the type of tool or service that you really should be looking to latch onto. Like there are general purpose tools that do general purpose things really well. There are hyper-specific tools that do specific tasks significantly better than general-purpose tools. So, you know, not only will thinking about the pain, like, more locally and to how you're going to measure the results, it's going to help you select the right type of tool too. Like, these things are fascinating. I've been using this stuff for years. You know, not everything's a multi-tool.

Greg Achenbach: Not everything is going to perform to the way that you want it to, or the cost of trying to alter a general purpose tool to be something more hyper-specific is going to be wildly more expensive than buying something that was purposely built for that purpose.

Bill Douglas: I couldn't agree more. So why do you think the commercial real estate industry gets so tripped up by terminology like AI and machine learning and conversational AI, agentic AI, and chasing titles and handles versus actually solving problems?

Greg Achenbach: Well, I think there's a lot of marketing dollars being spent from technology companies to put a lot of these things out in the world for people to latch onto. It's also, you know, I think I said this before, like we're in a really, really interesting time and like the pace at which technology is evolving right now is unlike anything I've ever seen.

Bill Douglas: It's meteoric. Yeah.

Greg Achenbach: And you don't want to be left behind, right? Like, not like, you know, you don't want to be left behind on your business and even personally, like, you know, you don't want to fall behind for whatever you are trying to achieve personally over the course of your career, you want to make sure that you're staying current with this stuff as well, right? I think that's a being in the SaaS, you know, business world for a while. I think the personal drivers that people have for making business purchases are often forgotten, but, you know, you don't want to get left behind personally. You don't want your company to get left behind. Everyone's got competition. Everyone's worried about the people they're competing with getting ahead of them. So I think just the volume of activity, like you can't go onto a news source today and not read something about And so I can, I understand like why people are fascinated by this, right? Like I can open up LinkedIn right now just to go check on, you know, if there's something, you know, interesting out there. And 9th, you know, my feed's going to be very biased because it's a lot of technical people, but like 9 out of every 10 posts, regardless, even like all the real estate folks I'm connected with, it's all about AI right now. So, you know, no one wants to be left behind.

Greg Achenbach: And I think that's why there's just like a driver to, you know, in a rush to make sure that you're not. Yeah.

Bill Douglas: Drew and I both have a newsletter we publish on LinkedIn. And the whole conversation is not AI. It's about translating technical or market impacting market impacts from technology to owners and operators and managers. Like it's how we generally are not allowed to sell anything. It's how, okay, this acquisition happened. Why is this good or bad for the clients? This company might be saying this, but this is what we believe is actually happening. So this trend is not, may or may not be good for you. So I try to translate a lot more than pour gas on that.

Bill Douglas: fire of, AI is so cool. I, do I love AI? Absolutely. Do I use it? All the time, but only when I know the process or the bottleneck to go back to your question. So how do you help clients, employees, friends? It doesn't matter. Take off your PredictAP hat. How do you help anybody else outside of the room you're sitting in right now focused on features, uh, excuse me, on outcomes instead of features so they don't fall prey to the AI marketing hype that you just mentioned?

Greg Achenbach: Yeah. I mean, I honestly sit down, you know, and I try to talk to them about like, what's, what are the things that are really getting in the way from you or your, your company, your, your department, whatever, like being more efficient? Like, what are the things that, like, where are your, where are your, where's your, where are you or your team like spending more time than they should? Right. So what's the thing that you wish you could just make easier? You know, it kind of starts with some simple questions like that.

Bill Douglas: Love that question. Yeah.

Greg Achenbach: And then, and then boil it down. Right. So if I am talking to someone who is a product manager and they're trying to release a new product and they're getting hung up on synthesizing product research, right? So like, I went out, I talked to 100 people, I've got all this stuff, it's just an insane amount of information. How do I look at it all? How do I retain it? How do I categorize it? How do I find a through line between all this stuff? Like, okay, great. Well, have you thought about using something like this that can speed up that kind of process? Or whether it's somebody who's having a hard time, you know, like One of the things we hear a lot about from our customers is like really finding valuable information from the mountain of data that they sit, right? So every real estate company is sitting on a mountain of data, like whether it's like the financial data.

Bill Douglas: Oh, I know. We help them get their arms around it. And instead of hugging Jell-O, they actually turn it into a data repository that's valuable.

Greg Achenbach: Yeah. And so like, how do you act on that? Like, what are the things that you, like the questions that like you want to be able to answer that typically might take like a human looking at, you know, like if I want to know how many of my invoices has had late fee on it. Like, okay, like, how do you do that other than sit down and literally look at everything one by one? It's like, all right, well, you can use a piece of technology specifically designed to help with that type of thing. And so, you know, I talk a lot about that. And I think it's really good for people to, you know, switch gears from like your personal realm to your business realm. Like, if, you know, once you kind of, you know, wrap your head around where's the big time sucks, where do I wish the problems could just go away, you can try some very simple off-the-shelf things, you know, like I I'm a big fan of like prototyping or trying things before you kind of jump into the deep end. So, you know, I'll usually give folks advice on, you know, try this, try that, you know, try it this way, see if it's more efficient. And then once it locks in, like, yeah, they're like, technology can solve this problem for me.

Greg Achenbach: If you're thinking about it in like more of the business realm than the personal, then you got to do the really fun thing and start thinking about stuff like data security, right? So as great as a lot of these tools are, what are the guarantees that you have in place that your data, which you are obligated to keep secure on behalf your clients is going to be kept secure and it's not going to necessarily benefit somebody else from the use of some tool.

Bill Douglas: That's a perfect segue because in my brain I have the whole idea of talking about how to use, actually use AI without losing control. Like, well, I guess the question is, what does safe AI adoption look like for commercial real estate teams that handle sensitive operating customer, vendor, financial, uh, it could be LP, it could be all kinds of things, information? Like, I'm outside of just the PMS system, of course, because you have tenant leases and tenant information on there and very deep PII. Let's talk about it from the, the rest of the operating or the property or the portfolio data. Like, what does safe AI adoption look like? And tell some people what they should be asking their team or their vendors about, like just some high bars without diving in deep about standards and specifications. Don't geek out. Keep them at 100,000 feet.

Greg Achenbach: Yeah. Well, I think so. When you, when a commercial real estate company makes the decision to sign an agreement with a property management system, right? So I'm not going to name names because I, you know, I don't want to get myself in trouble for promoting or underpromoting people we might work with. But there's a lot of, you know, everyone's got a PMS system in place. There's a bunch of them out there. Part of the procurement process for that is like, I'm obligated to keep my company's financial data secure and my clients' data secure, right? Whether I'm a,

Bill Douglas: Absolutely.

Greg Achenbach: You know, an asset manager, a data center, You know, multifamily residential. Like I've got a lot of data that some of which is relevant to me, some of which is relevant to my customers. I need to make sure that's secure and that can't get leaked out for all the, all the right reasons. There's a very methodical purchase process that goes with purchasing one of those systems. That same type of mentality needs to get applied to any of these ancillary tools that you're using. How do I know that common name off the shelf, you know, AI tool that I'm seeing commercials for when I'm watching baseball games isn't going to, you know, like, yes, there are terms and conditions. Of like keeping your data secure, there's you know there's the silos and stuff like that. But like everything you're doing is training their model.

Greg Achenbach: Like everything that you are doing, all of the interactions that you are you are having with these tools are training those tools to be better. So in an essence, like you know yes you're going to connect it to your data, but also like the you know the secret sauce of you and how you work is going into that product. And your competitors might also be using that same fancy name off-the-shelf general purpose AI tool. And there's a lot of blowback starting to happen within the technical space right now. There's a lot of, a lot of news articles coming around this where people are getting a little hesitant to start using, to continue their company's widespread usage of these big name products because there's a fear that like, what is the benefit that we're giving up by giving all of our IP into this? Even if the data is secure and all that, the questions that I'm asking it are going to allude to things that are hyper-specific to my business and how my business operates to make these general purpose tools better.

Bill Douglas: How would you advise an owner-operator, commercial real estate, of course, to think about the difference between a general purpose AI tool, like you said, the one you see during a baseball game and a purpose-built secure solution? And then I'll add to that versus building it myself. Because we help a lot of clients build their tools without having to do the gory AI piece, but build it in a way that the LLM never gets their data. You used to use the LLM for what they're good at, but it sits here in a brain, right? In the property brain or the portfolio brain. So that is where that like they could look, you, you would have a repository of all the AP you need to run your function and you as Predict AP could still use it. That would be a specific tool leveraging the strengths of an LLM without giving up the weaknesses. So how would you think differently? How would you advise people differently?

Greg Achenbach: Yeah, I think it gets into the, the problem that I'm trying to solve. And is there a tool out there that can, it's really focused on solving that specific problem and solving it in a way that I can sleep well at night knowing that all of the things that my team, like I've hired really good people to do, you know, really good jobs. I don't want that, you know, like the, wherever you are, I think a lot of the secret sauce of what makes a business successful is the people that are actually in there doing the work, right? Like there's a lot of, you know, you hire really good people, really good things happen. How you interact with each other, how these people perform their jobs is like really important to the success of every business. So how do I protect, you know, protect the data, protect the uniqueness of like my team and their skill sets in a way that like it's in my house, it's never going to leave my house. How do I get guarantees Yeah.

Bill Douglas: And if you need that, we can absolutely do that, but it's a matter of need first.

Greg Achenbach: Yeah. And then the whole like build versus buy conversation, it's a fascinating one because there are times where I feel like you've got, you know, a one-off kind of simple use case that's not, you know, extremely complicated. Like you can probably in a safe and secure way find a general piece of purpose technology and go ahead and do that, right? So if I want to know, you know, I'm big baseball fan. So if I want to have like, you know, some daily alerts kind of sent to my phone about the teams that I care about or the players that I'm watching, or I follow minor league baseball too, like general purpose information that I can, you know, like that was a fun little hacky thing for me to do on the side. There's no risk involved in doing that. That allowed me to, you know, have a little fun side project with my high school-aged son. Now that's a one-off thing. If I am trying to think about solving a daily problem that my team has, I need to think about like building it in-house.

Greg Achenbach: Do you have the team and the expertise to actually, you know, building software products is easy today, right? Like these tools that are out there that allow non-engineers to be engineers. Like you can put together something real quick and it's going to work, but are you doing it in a secure way, right? Like if I'm not a software engineer that's used to having to go through the security checklist as part of my code deployment, like,

Bill Douglas: Or PC deployment.

Greg Achenbach: Yeah. Like, how do I know I'm not leaking stuff out to the world that I don't know about? And the other thing is like, can I keep up to date? These tools are evolving rapidly. And so if I spend a month building something that's really good and the underlying LLMs that I'm using have like totally been overhauled 2 weeks later, how am I staying on top of that? How am I going to make sure that the underlying tech's not getting depreciated? Because now that's the other thing is these, these providers are depreciating the LLMs much more rapidly than they used to. So you need a software development methodology behind all this stuff. And also, is it worth my time? Like, Like, is the best use of me and my time for my company to be building these one-off things? Can I do it securely? Can I do it at scale? Like, if you think about, you know, invoice processing, how am I going to continually train a piece of technology with the hundreds of thousands of invoices that come in every year? Is that something I can just do on my MacBook Air that I'm sitting on right now versus, you know, a cloud solution that was built to handle some of this stuff in a safe secure way. So there's times and there's places for all of these things. But I think when you start to, start to think about the stuff that's touching critical information that is, you know, going to be using technology that's going to evolve pretty quickly, is that something that your team is equipped to be able to handle in the same way? Or is it a more efficient, managed, go buy a subscription to a tool that was purpose-built for these types of things?

Bill Douglas: Some of that, I can keep bringing up the book, but it's a process. And I think it applies whether you're buying technology or not. But when you engage vendors as a commercial real estate owner-operator, we highly recommend you understand where the vendor's coming from. Who owns the data that my system's generating, right? I'm buying a service from you. Does that include the system? Who owns the system? Is it a software license, whatever? But more importantly, where's the data? Are there any physical devices? Who owns those and who manages and maintains those, supports them, et cetera? But Mr. or Mrs. Vendor, who owns you? Like you brag about these customers. Do they own a piece of you? Because I see a lot of that happen in commercial real estate.

Bill Douglas: To. It's a spinout or they'll be on a panel and this vendor is bragging about their largest customer. Well, their largest customer owns 30% of them. They don't sit there and say that, right? So they just, they, of course they have great success stories, but it was not an arm's-length transaction by any means. I'm not saying all of them. I'm just saying I see it very commonly that the people on the panels have the deepest pockets to pay to be the thought leaders versus the people that are just actually sitting down with a customer and solving a real problem and not spending much marketing dollars. So there's, there's a whole page in the book about questions. It's supply chain management.

Bill Douglas: It's, it's not technology. Ask like, what's your vendor's roadmap? Like, what if I want this feature? Do they fit into it? Rather than ask them to build it, you know, tell them this is where you're going. How can you meet me there? You know, instead of following their roadmap, steer the roadmap. Even if you're a small customer, the right vendor will say, yeah, we do that, or we can do that, or give me 3 months until I do. So we are a big advocate for driving the bus versus just buying Now, that is a Jim Collins analogy, right? Right butts, right seat. But I think that what you just said a second ago piqued my interest about it because yes, the large LLMs are getting very expensive, but if you build your own tool, it's really arduous to keep up with it because the person who built it's probably getting promoted in your own organization and they're not going to sit in that seat anymore. And then they have to train somebody. So then the problem can get exacerbated.

Bill Douglas: And the largest clients we consult with, maybe we, maybe we do recurring services, maybe we don't, but we consult with. They have their own teams and it's interesting how some of them stay completely away from certain functions. Data science team here might be 15 people deep, large, large company, of course, Anomalies, one of the top 10 in the world. And over here, they're not developing the software, they're buying it. And then you got a medium-sized firm that's developing the software and has no data science team. Yet they both have exquisite results because they looked at the bottleneck, because they asked the right questions, because they know where their data is and it's not leaking and it's secure and all the PII is locked down. and compliant. So there's, there's not one way, build versus buy.

Bill Douglas: There's a million ways, but just know which one you want and don't bend any corners because of somebody's marketing is my advice.

Greg Achenbach: Yeah, no, I would 100% agree. I think, you know, do what makes sense for your business. You know, it's these, the ability to spin up these purpose-built agents to perform specific tasks is fascinating and actually saves people time. What happens when you've got 40 of these agents doing, like, who's monitoring them? Who's keeping them up? Like you said, like the person that enabled their the team to be more efficient is likely the one that's moving up and beyond or out because of their skillset. So this thing that was built 6 months ago, is it still working? Is it still adding value? Is it still secure? You know, those are big things that, you know, folks need to keep on top of. There are horror stories around people trying to apply some of these general purpose things to like real-world ramifications, like real-world scary things like legal, debates and things going awry. So, you know, it's like the Spider-Man quote, like, great power comes with great responsibility. Just because you can do it doesn't mean you should.

Greg Achenbach: And, you know, everyone's beholden to certain standards and regulations. So you should experiment with them safely, figure out the things that are safe to do, you know, on your own, and then partner up with people that can do this stuff efficiently, securely, and stay up to speed rapidly. And I think your point around Making sure that the people you're partnering with are actually listening to their customers is super, super important. There are, you know, people who are letting the technology tail wag the dog versus actually sitting down and, you know, consistently talking to their customers around the pains that they've got to make sure that the things that they're evolving are actually continuing to solve those pains more efficiently rather than just flashier.

Bill Douglas: I think I could sum it up by saying, buy the purpose and the objective and the methodology, not the sizzle and sexiness of the acronyms that are marketing deep and marketing intensive. It's easy to fall in love with, oh, I'm going to buy that and we're set. But I will point this commercial real estate audience back to pre-pandemic where PropTech was huge. PropTech was going to solve everything, you know, and then the pandemic came along and now you don't need leasing agents anymore. You know, now you don't need this and there's these smart thermostats and yada, yada, yada. Well, all the promises didn't come true. Some of them did, like the ones that did are still around, but just like you mentioned the dot-com boom, we had a PropTech tech boom. I mean, talk proptech bust is the consolidation and failures and not funding of VC rounds 3 and 4, you know, and PEs buying them up for tool solutions instead of strategic acquisitions is happening because it was overdone and people fell in love with the idea of, oh, I can buy proptech.

Bill Douglas: Proptech is still needed, mind you, but it's, they may or may not use AI, but if you buy proptech, you're not using AI to better your business. You're buying from a vendor that should better your business. They're using AI.

Greg Achenbach: It's a No, it is. And you go back to like the, was it the adage that like VCs used to use, which is like you invest in the people and the problem and not the technology. So if you're out there evaluating a piece of AI tech and you get a salesperson that tells you something you want to do isn't on the roadmap or it can't do it, that's a really good signal that that person's being honest and actually cares about what you're doing and what you're trying to solve for versus someone that's just like nodding along and then you buy a piece of vaporware. So I think you can learn a lot about, you know, like the actual validity and stability of these things that you're buying just from the quality of the experience you're having, like getting your foot out the door with these people. Because high-quality, honest salespeople are going to point to a really good company culture, and that's going to bleed into the leadership team and the thought that goes behind how the products are built and all that stuff.

Bill Douglas: Yeah, I tell our team here all the time, the last thing we want is a customer That bought a promise we can't do. Like, so if we can't do it, be the first to say no. Like, introduce the solution to them to a company that can. Like, even if it's a competitor, say, no, we don't do that. They do. Like, we don't want to, if it's not in our core, of course. But ancillary business might look good for revenue, but that's vanity. It doesn't look good for the strategic growth of a business.

Bill Douglas: And I know that because I've been a lifetime entrepreneur. Like, you go chase the revenue versus chase the solution.

Greg Achenbach: Yeah.

Bill Douglas: The customers teach us what they want, but we still have to stay focused in our lane. Like nobody can do everything. Yeah.

Greg Achenbach: Yeah. And commercial real estate in general is a really, it's really large market, but it's very tight-knit, right?

Bill Douglas: So very tight.

Greg Achenbach: Yeah. Word spreads very rapidly on people who are burning bridges or people who aren't living up to their promise.

Bill Douglas: Well, it's large by asset class. It's small by number of companies and people in the industry. So, and that was one of the things that attracted me to Good too, because we fit right in there. So I like that. Greg, this has been awesome. And I'm sure we could talk another hour, but we might lose some people if we do. So at this point in the show, we shift from talking about the industry or technology or data and shift it to you. So we call this the extra floor and the lightness of commercial real estate.

Bill Douglas: And it's just 3 questions, answer it. And it's, it's just you, just off the cuff. Doesn't have to be conversational. Number 1, what's the best piece of career or life advice you've ever received?

Greg Achenbach: So I get, I've gotten asked that a couple of times. I always say I had a boss years ago that told me that the key to being successful is to be, to start to get comfortable being uncomfortable. So, you know, you're going to get put in situations that you don't know how to address or what to do and just put your head down and go through them. And then at the end of it, you kind of realize what I did right, what I did wrong, but the next time that gets thrown at me, I'm going to do it better. So I've spent a lot of, you know, the early part of my career, like seeking out uncomfortable situations for me to put in just to see what I can learn out of them.

Bill Douglas: Yeah, I love it. I won't chime in, but you nailed what I was going to say, so I'll stop because I said it's not conversational. Number 2, what's one habit or practice that consistently makes you more effective?

Greg Achenbach: I think particularly as I've gotten older and iffy, and I've never been an agile or flexible person, I find But like, I have a lot more clarity and energy when I like exercise the first thing in the morning. It's a really stupid, simple thing, but the older I get, the more I realized that that's kind of like a reset for my body and my body and my mind. And like, I attack the day much more efficiently, like after I've done some level of exercise in the morning.

Bill Douglas: Sounds stupid. I think it's perfect. Fitness is the fountain of youth. All right. This one's really hard. Are you an early bird or a night owl?

Greg Achenbach: I would say I would lean, like, I really hate to get up, right? Like, waking up is like the hardest part of the day. Once I'm awake, I'm like good to go. So like, I hate getting out of bed, but once the feet touch the ground, I feel like the first, you know, between when I get up and lunchtime, I'm probably the most effective, uh, in whatever it is I'm trying to accomplish that day, whether it's, you know, yard work or work work or, uh, you know, doing something with the kids. Like, I get it, I get it done a lot more efficiently in the morning than if I'm up Well, how can our listeners contact you?

Bill Douglas: They'll be on the show notes too, but for those that aren't looking at the show notes, how would you prefer if somebody wants to reach out?

Greg Achenbach: Yeah, I think LinkedIn's the best way to find me. You know, you can go to the PredictAP website, find my LinkedIn info there. I don't know if it'll be in the show notes here or not, but that's probably,

Bill Douglas: Oh, it'll be on there. Yeah, we'll put that there.

Greg Achenbach: Yeah, LinkedIn's probably the best way to get ahold of me because I get way too many emails to keep up with. So that's usually the best way.

Bill Douglas: Every episode has its own page and all the contact information, the highlights, slides, the full transcript, everything's up. You'll have your own show page.

Greg Achenbach: Hey, cool.

Bill Douglas: Thank you, Greg. And thanks to our listeners. Again, like I said at the beginning, be sure to like, follow, subscribe. We're always looking for quality people to be on the show. So put your hand up or tell your friend to put their hand up if they would like to be on the show. And we look forward to seeing you on the next episode of Peak Property Performance.