The Multi-Family Property Management AI Playbook
The Multi-Family Property Management AI Playbook is a podcast for multi-family leaders who know AI is coming and want to understand what’s truly possible, what actually works, and what they need to know now..
Hosted by Daniel Cunningham, the show explores how AI can be applied inside real property management operations, from maintenance and vendor coordination to leasing, resident communication and other operational efficiencies. Each episode unpacks real-world implementations alongside the early questions operators care most about: where AI delivers value, where it falls short, what changes operationally, and what it takes to adopt it responsibly.
This isn’t futurism or vendor hype. It’s a practical guide for owners and operators who are evaluating AI, looking for signal over noise, and want a clearer view of both the opportunities and the tradeoffs before making real decisions.
The Multi-Family Property Management AI Playbook
Ep 4 | How AI Can Step in When Your Digital Dashboard Fails Analog Property Operations in the Field w/ Rachael Becker
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Your property doesn’t break in the dashboard.
It breaks in the field.
At 1:00 AM, when a pipe bursts or heat goes out, your system should answer one question: who needs help first? Most can’t.
In this episode of The Multifamily Property Management AI Playbook, Daniel Cunningham sits down with Rachael Becker, CEO of RB Builds, an operations expert with 20+ years across real estate, construction, and proptech, known for bridging the gap between the field and the systems that are supposed to support it.
They unpack why property management systems fail in real-world conditions, where technicians, residents, and office teams fall out of sync. The conversation reframes AI as a system of action that closes the feedback loop between the field and decision-making.
You’ll learn:
Why property teams export data just to understand what’s happening
How broken feedback loops delay maintenance and create blind spots
Why systems built for reporting fail technicians in the field
How AI can structure intake, execution, and completion into one loop
What it takes to trust AI in real operational decisions
🎧 Listen now to learn how to fix the gap between the field and your operating model.
Continue the Conversation
🔗 Connect with Daniel Cunningham on LinkedIn
🎙️ Connect with Rachael Becker on LinkedIn
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Chapters:
00:00 When Property Management Fails at 1:00 AM
00:01:00 Systems Built for Reporting vs Real Operations
00:02:00 Rachael Becker’s Field-First Background
00:04:00 The Disconnect Between Field and Office
00:05:30 AI as the Missing Connection Layer
00:07:00 Why Field Teams Are Overloaded with Admin Work
00:10:00 Why Most AI Tools Fail in Property Management
00:13:00 The Trust Gap with AI in Operations
00:19:00 Why Data Exists But Isn’t Usable
00:28:00 Fixing the Feedback Loop Between Field and Office
00:30:00 The Ideal Maintenance Workflow with AI
00:32:00 Where Operators Should Start with AI Today
00:34:00 Why the Future Is Built for the Field, Not the Dashboard
You know, one of the things I've noticed in property management is that our biggest challenges rarely show up during the calm days. They show up at 1 a.m. during a cold snap, when pipes across multiple buildings are bursting and residents have no heat, and suddenly the question becomes very simple: who actually needs help first? And surprisingly, often our systems cannot answer that question. And that's why I wanted to bring on Rachel Becker. Rachel works with ARIA Management New York and also runs RB Builds Consulting, where she focuses on operations across construction, property management, and prop tech. She spent years working both in the field and in the office, which means she sees something that most people in our industry don't. And that's where the feedback loop breaks down. In this conversation, we talk about why property teams will spend nights exporting CSV files, trying to create reports and figure out what's happening across their portfolio. We unpack some tools that are designed for the boardroom that often fail when people doing the real work in the boiler room need to use them. And we explore why AI might finally give operators the ability to connect the resident, the office, and the technician in the field through a true system of action. Because if the person holding the wrench can't scale with the business, then nothing in the business scales. Let's get to it. Welcome to the Multifamily Property Management AI Playbook. I'm your host, Daniel Cunningham. If you're responsible for running properties and trying to make sense of where AI fits, no hype, just insight, you're in the right place. This show is about what's possible, what's practical, and what delivers results. Presented by Venderoo, your all-in-one AI solution for resolving maintenance needs. Let's dive into the playbook. Hi, Rachel. Welcome to the Multifamily Property Management AI podcast. Appreciate you coming on today. How are you?
SPEAKER_00I'm good, Daniel. Thanks for having me. I'm excited to talk about my favorite subject.
SPEAKER_01This is your favorite subject? This is my favorite subject too. So this is going to be a very easy conversation then. Before we kick things off, let's talk a little about your background. You're currently working with ARIA management out of New York, but you also have a consulting firm that you built that's really focused, I guess, on the built world. So let's just start out if you can just give us a little of your background, how you formed RB Builds Consulting Group, and then we can talk a little about ARIA.
SPEAKER_00Yeah, so I got into the industry and construction about 2007. So if you do the math, that was a very hard year to enter the market. You know, talk about tools and AI now, just trying to implement something back then when you were just trying to pay the bills, get people to show up to site, get materials, get jobs. I learned pretty quickly how important it was to understand the field. And I think that that's what's made my integration, no pun intended, into the AI aspect of context and prop tech so easy. It's just something that I've had hands-on experience with since the day I started. And it's exciting to see you know all the attention that the this industry is getting. So throughout the years, I've worked in subs like HVAC, successfully started a company there, grew very quickly, you know, throughout the throughout Florida and the Southeast. Got called into New York to start into tech, recruited by a tech firm for a skilled worker database that was smashingly successful. And I've kind of stayed in the New York City area doing construction management, that type of thing. And I started with ARIA about two years ago, I came on as a consultant and then spent you know some time being an operator as well, so that I could really understand the challenges, the day-to-day challenges that we're facing, you know, here in 2025, 2026.
SPEAKER_01And there's plenty of those to go around in the property management industry. If you were looking for challenges, Rachel, you you came to the right industry. So tell us a little about Aria's profile, what their portfolio looks like, where they look, where's most of their product located, that sort of thing.
SPEAKER_00Yeah, so we predominantly in the five boroughs in New York City. We've got some buildings in other major metros as well, so not very far off the business model, but multi-families, mixed use, commercial, you know, and obviously some of those mixed use, and then anywhere on the spectrum as far as the type of buildings that we're offering as well. The price points are different. And what we do is a lot of times we'll purchase a property, invest in it, upgrade it, get it efficient, get it up to code, and then you know, after a few years, then sell that asset by another one, you know, and so on.
SPEAKER_01I got it. That's an interesting point to bring up. So this is not a long-term hold play, generally speaking. It's a you know, return, acquire, do your rehab, and then three to five years you exit.
SPEAKER_0050-50. Uh, we have some properties that are valuable as is. And so I'd say there's about a 50-50 split on ones that we're acquiring, improving, and flipping, and then ones that we're keeping within. It's third-party management as well. So a lot of times when these properties get sold, we continue to manage. And so that's another revenue stream or growth lane.
SPEAKER_01Oh, okay. All right, great. So you've got that perspective as a third-party manager. That's great. So since this is your favorite subject, let's start off with a very broad question then. What about AI generally? Are you particularly passionate about that we could that we can tackle today?
SPEAKER_00I think, you know, for me personally, it's it's it's become becomes rocket fuel. I think it puts things together faster than I think a human can, connects things that would take us hours and hours to connect. And so I think that that connective tissue between whether it's the field in the office or this app or that app, you know, the different tools within these companies to me is the most exciting thing that I've seen. And I think it's gonna save the industry. I know I hear a lot of time when AI is taking jobs. If you're in the trade, you're good. But it is, I think, saving jobs because companies can't sustain the longer lead times, the higher costs, the shortages, everybody's got less money. And so without, I think AI in this industry being effective, it's not gonna make it.
SPEAKER_01So what you're saying, really, there is um properties can't really afford the size of staff that would really be required to adequately do all things that need to be done right now, since everything is done in such an inefficient way. And so AI will allow operators to kind of do more with a more limited workforce. Is that kind of the crux of what you're just saying?
SPEAKER_00Yes. Yes.
SPEAKER_01And and that probably talk a little bit. Have you seen how that relates? So, what that implies right now, which I think we both know, is that the teams in the field are really stretched in. They do a lot of things. They're expected to be psychotherapists for the residents sometimes. They've got to be great data capture analysts for work orders. They've got to turn wrenches and do things, they've got to know the law, they have to know marketing. Like, so we know they're really stretched in. Is this then an opportunity to relieve some of that administrative noise from the day-to-day and improve the quality of kind of the work environment for people on site?
SPEAKER_00Absolutely. And I wouldn't be so kind to call it noise. I think it's absolute blockers meant to, I think, keep sometimes the trades down. So, you know, you're out in the field all day, you got a hammer in your hand. Uh, at what point are you picking up a phone? At what point are you sitting down at a computer? A lot of these apps are still web-based and look terrible on their phones. So, at what point are you sitting down at a computer to get this critical data back to the office, not just invoicing, but job completion notes. Time is of the essence. I mean, take the last three weeks in New York City and we've got pipes bursting everywhere. We don't have time for them to go sit down at a computer. And gone are the days that if they wanted to work with us, they would. It's just not sustainable. We're not empowering them to give us the information we need in the office to make the decisions so that they can keep going out in the field.
SPEAKER_01Got it. So the opportunity, we'll talk here a second whether or not this is actually happening yet or not, but the opportunity to relieve the teams in the field from having to like check their device. It sounds almost like just in time delivery of information. You know, what you need, when you need it, and nothing else beyond that.
SPEAKER_00Right.
SPEAKER_01Not at all.
SPEAKER_00So the succinct, you know, it's succinct, digestible, and actionable. That's my favorite prompt in ChatGPT right now. Like, cut it, cut it. I need to know these three things so I can make a decision and get it back out. You know, and getting it back out to them is important. Not just the information in, but then the direction and directive, you know, back out.
SPEAKER_01Yeah, the your initial focus of what you're talking about here is really very maintenance. You're thinking about people in the field turning wrenches and needing to get invoices and all that sort of thing. Let's delineate, first of all, between maintenance technicians who are doing the things that you're that you're talking about, and then the managers on site who are dealing with the residents and you know, sort of the day-to-day operational piece, not separate than the maintenance piece. Um, are you are you looking for AI to play a meaningful role in making the lives of those, the people in that role, making their lives easier as well?
SPEAKER_00Well, the two go hand in hand. So I would say that the the PM in this situation is gonna be a hybrid of the field and a hybrid of the office. This is your unicorn, right? Especially now with AI, you can have somebody that's thinking it's you know in seven different lanes, can capture that information as well and get it back, but then they can receive the information from the field and process it as well. And so to me, it's twofold because they're gonna need that type of technology and understanding of the user understanding, the user experience of the maintenance guy in the field is gonna be half your PM, and they're going to be experiencing the administrative side. So that's why I think that my experience became so valuable because I'm seeing both sides, I'm feeling the pain of both sides. So I know that if I'm not getting information back to my office, we're not getting deposits out, we're not getting invoices out, we're not getting permitting out, you know, we're not getting approvals. And if the field doesn't have the information they need, it's the same thing. So you're feeling it on both sides. So they're very much that that middleman feeling the pinch.
SPEAKER_01And and have you seen yet any real material adoption of AI, either in this realm, which is sort of aspirational, I feel like. I feel like you're talking about what's what's getting you excited, maybe not what's actually deployed. But are you seeing any material adoption right yet uh of AI that's worth uh that's worth talking about?
SPEAKER_00I think the adoption is there. The hunger for it, everywhere I turn, you know, even if you get like on upwork or something, and it's it's always, you know, construction or a deathless industry looking for somebody to help implement AI into their organization. So I think that the adoption's there. I just don't think the tools suffice. I was doing a little studying before we came on, and it it there is, it takes a mid-sized contractor, mid-sized management company, three different tries, three to five tries to adopt a tool. Each time it takes about a year to either adopt or abort. And the opportunity cost, what they've lost in slower hiring, slower invoicing, that feedback loop, right, is about$500,000 a try. And if you do the numbers with 150,000 mid-sized contractors in the country, that's like$225 billion we can put back into the industry if we just give them the feedback loop, this tool that they need to make these decisions. And I think at a time like this, that's super valuable.
SPEAKER_01I'm thinking about the math behind that. So that's really a change management issue, really, isn't it? That's irrespective of the tool. The change management moving, implementing a new tool, moving from one tool to another, takes a year to do that and takes three tries before you get it right. Is that the math that we're doing here?
SPEAKER_00It's three to five. Um, you know, three's a fun, punchier number, but three to five. And so I see the change management side, but if the tools did what they said they were gonna do, if the sales guy and the customer success guy got on the same page, product guy and the engineer got on the same page, then it wouldn't take three times. Why not give them what they need to begin with? Even when they have that third tech, they're probably only keeping it because they can't afford to try another one. It's the lesser of the three evils and they move forward.
SPEAKER_01Got it. Got it. Sometimes feels a little like, you know, I have a I have a Tesla and I've tried the self-driving mode and works great most of the time, but there's a few moments where it's still a little harrowing. And so I don't really trust it fully. Are we at a trust gap here in the world of AI, or is it just functionally doesn't do what it needs to do?
SPEAKER_00I'd say that our admin and management, our back office, maybe even the financial side, still has a little concern, right? Because numbers and transparency and the human eye there, right? There might be some fear there. Watching that battery go down in my electric car is terrifying. So, you know, I get that, and I get some of the hesitations or the fear. I think we're past that. People get to play with AI now, they're making their grocery lists, I'm dragging all my buddies in, you know, into AI right now. You're relevant, let's go. So I think we're past the fear, and now it's just the cost. Is it actually effective? Is it doing what we need to do? Is it solving an issue? Are we doing it just because we're told to do it? Or is it really solving a problem?
SPEAKER_01Yeah. Well, I think in this early adopter phase, there's a lot of people who have to ask themselves that question. I I can just I can speak from my my own experience on that front. You know, if you, you know, vendor's product, for example, is um, you know, in the maintenance AI world, is meant to take on work order triage, uh, you know, receive those phone calls from the residents, and um, and then decide if it's a real work order, maybe it can be solved, maybe it's not a work order at all. If it is a work order, or maybe it's an emergency escalated, do all those things so that you remove that burden from the from the front of house staff having to do all that. And and with that, let's call that piece kind of a simple first-line implementation. The results are are surprisingly clear in terms of of the the value add there in terms of like reducing the number of open work orders by like 42% because work orders move fast, or actually solving like 9% of those work orders before they become work orders, so reducing work order volume. So there is some certainly early results in on, for example, the maintenance side. Have you looked at anything, for example, that has to do with leasing or collections or resident relations? Have you had a chance to look at anything along those lines?
SPEAKER_00Yeah, yeah. And I want to say too, Daniel, Pandero not only helps the front of the house, right? But a lot of times you're you're especially now that management companies are bringing these trades in-house, they're expected to take that first call too. So you are relieving pressure on both sides. And the, you know, the hey, did you lock yourself out of your apartment? Um, you know, do you want to pay for the locksmith or do you want us to pick the locksmith and then you pay for it? You know, those kind of conversations. And and I've tested it, and I really like the you're going in and getting information and bringing it back. Instead of making everybody behave in a certain way inside of an app, you're coming to them, asking them a question and then bringing it back in, which I think is is pivotal and critical in the in the industry. And I forgot the question. I wanted to give you an accolade there.
SPEAKER_01Yeah, oh, thank you. And I but I think what you I think what you brought up is an important, I think it's an important thing to consider, not just with respect to if you were gonna consider launching Vendruo, but in any kind of any product broadly, but I think any AI product really needs to uh meet you where you are as the as the client. What are your workflows? Yeah, right? Yeah. So sometimes that's even like regionally specific, like New York, it's operate differently than you know, probably probably in California.
SPEAKER_00Yeah.
SPEAKER_01So that customizability, I think, is is important. I mean, we talk about AI learning, but you need to get it off on the right foot to start with. And if it doesn't have that capability, you probably are setting yourself up frustration.
SPEAKER_00Yeah.
SPEAKER_01Yeah. Is is there is there any area where you find with your clients or just people in the industry you talk to that people are vastly overestimating what AI should be able to do right now, and you have to sort of like reset their expectations?
SPEAKER_00Yeah. And I see it worse in management than medical, you know, all the deathless industries that I've worked in, you know, medical, even just construction in and of itself. You know, like when you and I know enough to know that when you build, you can't necessarily unbuild and build something new, right? I get it. But I say this out of respect to all the engineers that I know. Um, but we can't afford that line of thinking right now. If it's broken and it's not going to keep working, quit adding things on top of it. Like the current platform that we use, and I don't want to say names necessarily, but there's three different ways to put in a work order. They're all three different. So they call it like a user in one, but they call it a uh an assignee in another. So the nomenclature is all confusing. I'm like, that's not structured. What's that gonna do for me? And so I go into this bot and I'm like, hey, what happened with, you know, so you know, Sesame Street, one, two, three Sesame Street this week, and it's like, oh, and it just like freaks out. Just give me a sauna. Let me pump all my stuff in there and then ask it at the end of the week what I need to know. Right now, there's so much information going into these tools, but there's no way to digest it and use it. It's a waste, I think it's a waste of energy, it's a waste of understanding how many times I've run tenant templates, you know, and they just firing left and right, sending to the wrong people, not consistent, you know.
SPEAKER_01Well, I think you just touched on one of the real things that gets that I get excited about as we think about the evolution of AI and what's happening right now, and that is the ability to pull information out of unstructured or poorly structured data. You know, if you have a PMS platform that refers to the assignee in three different ways, running a query against that is obviously problematic. But if, you know, through AI reporting, you can say, just tell me every one of these that was assigned to Bob or my maintenance technician, right? Generally, the ability to use natural language to, regardless of how the data might be structured, to pull out what you need should be a huge boon to all industries, but property management in particular, because our our teams in the field are not, you know, they're running reports, designing reports is not their strongest suit, right? They're if we can allow them to use natural language to get the information they need, you know, how life-changing is that gonna be?
SPEAKER_00You know, my favorite thing to do with uh uh property management software? Export it to a CSV and drop it into Chat GPT. It's not giving me anything, you know, that I can use necessarily. We had the other night, again, you know, I bring up the storms in New York and the record-breaking low temperatures, and I had to figure out who didn't have heat, who didn't have hot water, who didn't have water, and who had a little bit of water. Because if you have a little bit of water, that means a burst pipe. And you take 110 buildings 11 days in. I did, I just didn't sleep three or four nights each week. Like this went on for three weeks. You know, I couldn't even go into my property management software and quickly figure out those four questions. That's a shame. That is a waste of energy, emotion. People could die. It's freezing. It was you know, felt like 19 below, and they didn't have heat. And I'm sending a I'm sending the wrong plumber, you know, over to not the one to fix the boiler, but the one to fix, you know, this. And so it's critical. It's really important that we have that information. And not even that, I thought I could count on the software, I thought I could count on the tool, and I couldn't. So then I had to overcompensate with all the other work that I'm doing. And I got, you know, bosses looking at me, how do you not know who doesn't have heat? I'm like, I'm trying, you know, and I build these things, so I should be able to go in there and easily pull it out. I mean, follow the UX. I tell my kids all the time if you can order on Amazon, you can order for groceries for the house, you know, something like that. So it becomes a hindrance because you think you can rely on it and you can't.
SPEAKER_01Yeah, there's two things to unpack there I want to dive into. The first one is you're struggling because you can't pull the data that you want. The data's there. I imagine if you right, or if it's not there, then that's a different story. If you're not getting the inputs, that's a different story. But the data's there, but it's not presented to you in such a way that's actionable. Being able to pull that out, well, you're doing it right now by dumping it into Chat GPT, right? Which is a really clunky way to do that. But being able to natively ask those questions is coming. The second thing though that I think you're touching on there is you said you really need to be able to trust the application. Um what are you at a point now where you trust, how much do you trust AI right now in giving you response? Like you dump your your information to chat GPT, it gives you a response. Do you feel like that that is a trustworthy response at this point?
SPEAKER_00No, not even close. So what I what I do with it, it doesn't take away the human aspect where that that instinct, the analytical skills, the thing that used to get in trouble, me in trouble in school, you know, I'm focusing on like seven different things, can't sit still. That becomes the superpower, I think, with AI because you still have to check your work. And I heard it's gonna sound silly. Um, this is how far removed from like the game of AI that I am, but it was one gentleman who has a very large stake in AI, the big guy, I forget his name. But he said that the new skill set, this new thing is that analytical, that person that like sees what's coming around the corner can read uh interpretations of something and build their own. And so that's what I use, that's what I use AI for. So even if property management software spit out some data sets, which is really give me a table, run a report, you know, I would have a conversation with it. You know, typically what what I'm seeing is it comes back with an answer, and I'm like, how'd you get there? Show me your work. And that also strengthens, I think, the the asset. And I think that also the property management software that I am using right now, you can say, like, you have to tag certain events or or inputs, right, to get the information to come back. I get that. So you got to add it and it'll pull it up. And then it says you can hit a button and says, Show me your work. 99% of the time it's totally off. Um, but at least they're trying, you know? They're trying. But I just assume drop it into something that's really learning me and learning these buildings. You know, I've just set up projects for each building, dump it in there. It's growing with me as I'm learning and my understanding develops and how I need to hear it. How how many times I've been told I'm not administrative, you know, I need to pay attention to detail. You know, that's where AI comes in and learns with me, and I can build on their administrative, I think, strengths.
SPEAKER_01Yeah. The trust factor for AI is really to me comes down to how it handles the edge cases, the things that you don't expect. And you know, I think I think one of the things that Adventure would have to do to accommodate that and to bridge that trust gap is a human in the loop contingent. You know, there's an AI that's kind of watching the AI and you know, making a judgment call, like, I'm not so sure that was exactly the right decision, but let's kick it up stairs and have a human review this. And so if you have that, and I I think this is maybe this is just an interim step, but it feels like this is an important long-term component of any trusted AI system, is that you must have this human in the loop, like Van Drew does, that ultimately can step in so that you can handle all of the edge cases, maybe not completely through AI, but at least the system doesn't break down when AI, you know makes an error or comes up against something that it's not exactly sure what it needs to do. Because if you don't trust it, you you haven't really relieved yourself of any work. If you have to do, like you haven't relieved yourself of any work having to get into the PMS platform, export the Excel file, and dump it into ChatGPT, you've made more work for yourself.
SPEAKER_00Right.
SPEAKER_01I mean, how much time does that take of yours to do that? That's the opposite of efficiency. So we need to make sure we're not making things worse for people by introducing AI.
SPEAKER_00Well, right. And what relationship that you have strong trust in doesn't take work. So there's nothing that's ever going to replace the human. Everybody can relax. But like there's there is it's a tool, it's not a replacement. And you have to prime a tool, you have to switch blades out of a tool, you have to do maintenance on a tool, you gotta plug a tool in sometimes. They're not just these, it's not just there and it's never, you know, it's supposed to be perfect, you're never supposed to mess with it. This is something that we get to use. It's active, it's not stagnant, something that we get to use to increase productivity. The telephone, the computer, the next help, you know, the beep beep, you know, everything we've got so far is scared people, you know. But if we use it in the right way, that's why I've told anyone that's scared of it. I'm like, then own it. Take control of it. You can't be afraid. It's coming. Infrastructure's either gonna break in half and there's no energy or electricity at all, or we're gonna have AI. You know, it's just a matter of understanding it, regulating it, getting ahead of it.
SPEAKER_01And the interesting thing about AI is it's the first tool. Like, I like your analogy about, you know, you've got to change the blades out, that sort of thing. You know, the AI is the first tool that is sort of sharpening its own blades as it goes. I think it's probably wise to make sure you're paying attention to how those blades are getting sharpened, but but it does allow us the tools get better somewhat on their own as things go. Or they should. I mean, I think that's one of the promises of AI: is like the more you use it, the more it's exposed to your procedures, your residence, the better, the more it understands and accommodates. And that is where you build a system of action. Once it really understands how Rachel wants the business to be run, then it's a it's a system of action that can then replicate your own decision-making process in the day-to-day operations of the of the company.
SPEAKER_00It frees up time. You know, I have a really, really one of the gentlemen I work with, he's one of the owners, the properties as well as and you know, helps with the company itself, the management from with it within itself, one of the smartest guys I know. If I come to him with like a fully formed thought or like I do the, I got the best advice, and it was like burn the calories first, right? So like when I finally get a chance to talk to this person, I get to really utilize what what I need him for, right? We don't have to figure things out necessarily. I can be like, hey, this or this, you know, rapid fire. And he's like, uh uh. So like I think that that is AI itself, too, is it it gives us more opportunity to answer the bigger questions than roll around in you know, data sets and and simple deductions, you know, from a data set.
SPEAKER_01So I'm gonna guess I know the answer to this based on what you were just saying. But if you could wave a magic wand right now for this industry, what would you change first? You could do anything with that AI could accommodate. What would it be?
SPEAKER_00I think it's I think it's strengthening that feedback loop. It's critical. Without it, there's nothing. If the tenant can't get the complaint in fast, if that can't get out to the you know, decision maker, then to the field person, then back to the office to say that it's been fixed. There's we literally have nothing. So it's it is the feedback loop and taking away the latency that we can't afford.
SPEAKER_01Yeah. That's that's reducing efficiency, that's making people do things they don't that they're not good at, introduces errors, all of those things are just administrative work pieces that that should be able to go away. Yeah. Feels like we're we're not that far away from that one. I think it's a reasonable expectation. How's that?
SPEAKER_00I think that's why I get so frustrated. You know, I've just seen the skilled worker, the field worker. We don't have a financial office if we don't have somebody out in the field. You know, none of that matters unless we've got those crews thought about and taken care of. And if they can't scale with us, I'm gonna say it a thousand times every chance I get. If the field, whether it's your PM or your maintenance person or a vendor, if they can't scale with us, you know, we've got nothing. And so focusing on, they're not the buyer historically, right? The buyer is the GC, the buyer is the management company, but they are the diamond that we are mining for. They are the you know, the product, they're the ones that keep these buildings going, their expertise, their skill sets, which aren't administrative, is what we're trying to find and interpret, integrate, you know, into our decisions.
SPEAKER_01So AI should handle the intake of the issue, process that, provide the right information to the person who needs to perform the work so they can go perform their work, anticipate maybe when that person needs more information or there's new information to share with them so they can give that to them, gather from that person the results of the work that was done, materials used, time that it took, that sort of thing, synthesize that into an invoice, a PO, distribute that invoice, check to make sure that the invoice that's returned is correct against the work that was done, and then process that payment. Is that the whole life, happy life cycle that you're looking for?
SPEAKER_00Yeah, and heavy on the on the proof of completion, right? Because that's potential, that's your second blocker, right? So it's not just I say the loop ends with the payment. Everybody wants to get paid, everybody needs to get paid. But in between that, you've got, okay, here's the problem. I'm from Florida. So working in New York, I didn't know you sent plumbers to no heat calls, right? So it's identifying the issue, knowing what that next step is. We're all property managers, we're not construction workers, we're not maintenance guys. So I might need to draw on my experts in the field just to get where do I go from here? Who am I sending?
SPEAKER_01You know, or AI is that expert, right? You you bring a brain in that is the expert. Yes.
SPEAKER_00Yes, yes, yes. So all that's so that's the point, is that there's so many just to even get to the invoicing piece that has to move quickly, especially when pipes are bursting, people are cold, don't have hot water, don't have water. We've got to move fast.
SPEAKER_01Yeah, if you can build or inherit a brain that has the expertise on all these sort of things, then you can operate real estate without being the expert yourself. Like right now, you're at some risk. If you don't know what you're doing and you buy a piece of real estate, like you're at some risk if you don't know what you're doing. If if AI can step in and be the expert on your behalf, then all you have to do is focus on the execution.
SPEAKER_00And look at us just ignoring the field worker sometimes. You know, it's like we are trying to understand and empower them because they know the next right step. We're counting on them to know the next right step.
SPEAKER_01So let me wrap up by asking you this question. Then knowing, knowing kind of where the state of the industry is, what can be done right now, what's still maybe beyond on the horizon, where where would you advise operators right now to start their AI journey? What delivers Good Bang for the Buck right now that can be done today?
SPEAKER_00I'm gonna assume that 99.9% of them are already using some type of property management solution for accounting. So I'm not even and work orders, yeah. I'm not right, right. So, and that sometimes is not even an operator's decision, right? That's the finance department. And then we get to pick, you know, we gotta work with what we've got, what they've chosen. So I think it's extensions. I think that those extensions, like the maintenance extension, the compliance extension, which is not unique to New York City, but definitely a bigger issue, I think, or a bigger problem to solve in New York City. But those types of extensions, but those extensions that understand the importance of making it easy, that user experience for the field, whether it's the PM, the compliance person, or your maintenance person, and getting that information back in. I would pick, like I'm doing now, you know, partners that understand the importance of that piece.
SPEAKER_01Vendor partners, software partners.
SPEAKER_00Software, software partners, yeah.
SPEAKER_01Then last thing, we have it. Let's this is our our our our time capsule uh check here. Think uh let's just a year is long enough for things to change. Think a year down the road, Rachel. What do you think will look different about property operations in a year thanks to AI than it does today? Let's make it two years, just so you can maybe push the boundaries a little bit. Two years from now, what's different about how we operate properties to than we do today, thanks to AI, do you think?
SPEAKER_00If I have anything to do with it, yes, you have everything to do with it.
SPEAKER_01You're in charge. Go.
SPEAKER_00It would be that focus. So I'm working, you know, with you guys as well. I don't take no for an answer. And I push and push and push until I understand. And so even just the tools that you guys have built, I've been like giggling at how cool and how close we are. It's almost to the point where I can hack it and get what I want out of it, which just means I need to talk to your engineer a couple times. And I think we've got it. But getting that, I think what we're gonna see is the, and I hope that I say it enough to guys like you, men and women like you in these positions, that it doesn't have to break before we build it because I think it's gonna be too late. But what I'm seeing, and what I think that I will see, is the urgency and understanding of the UX for the field and understanding their unique experience because they're going to be in hypothetically several different solutions, and that is still a massive drain that the industry can't afford, these companies can't afford, and the buyers, the property management companies, the building owners, can't afford. They don't want to pay for wrapped vans and large administrative staffs anymore. They want to use AI so that their partners, vendors, field people are efficient, just like everybody else in every other industry.
SPEAKER_01The improvement in the user experience for folks in the field is the biggest opportunity that one of the biggest things you think will change within the next year.
SPEAKER_00Think of Uber. Think of Uber. I was a fourth hire. The fourth hire at Uber went out with a tablet and passed tablets out to cavvies and said, do it this way. The cabbie's not the buyer. Yeah, cabbie's not spending the money, but the cabbie's got to show up and he's got to wait, and he's got to trust the system. They built for the field worker. And they're, I mean, we don't need to say how successful they are, you know? It's the same concept.
SPEAKER_01Yeah. Well, Rachel, one of the reasons we want to have you on the show is we we got I got to know you a little bit as you were thinking of deploying Vendoro as a solution internally. And I really I recognize kind of the advanced way you're thinking about AI and and how to deploy AI. And so I knew you'd be great to have here in one of the earlier episodes here of our new podcast, and you haven't disappointed. So thank you so much for taking the time to share some of your wisdom with us, and uh, we we will try not to let you down on your big picture projections.
SPEAKER_00Yeah, and thank you that I get the opportunity to come and you know talk about this stuff. So it means a lot to me. So thank you so much.
SPEAKER_01Yeah, we should check in a year from now and see see uh see how good how uh how accurate your projections were.
SPEAKER_00Love it. Thank you.
SPEAKER_01Thank you so much, Rachel. Take care. Thanks for listening to the Multifamily Property Management AI Playbook, where AI stops being theoretical and starts improving how multifamily operations actually run from processes to people to performance. Learn more at vendoroo.ai. Until next time.