The Property Management Guide to Surviving the AI Shift Featuring Sabrina Rose Sadler, AI Consultant 

Co-Hosts: Ronn Ruiz and Martin Canchola, Co-Founders of ApartmentSEO.com

Martin: Welcome to The Multifamily Podcast. I’m Martin, joined by my co-host Ronn, co-founder of ApartmentSEO.com. And today we’re excited to welcome Sabrina Rose, an AI strategist and human centered design leader, who’s guided enterprise AI transformation across organizations, with more than 100,000 employees. Sabrina sits at a rare intersection. AI built from the ground up, enterprise operations and deep love for people and UX expertise. Her mission is simple but critical. Building AI systems, people can actually trust. Welcome to the Multifamily Podcast, Sabrina. 

Sabrina: Thank you. I’m happy to be here. 

Ronn: Super excited to have you. Thank you so much, Sabrina, for joining us. Let’s just jump in. I know we have a lot to cover. Super excited to hear about what you have to share and more importantly for our audience. So obviously AI is everywhere in the headlines, but most pilots obviously fail and most employees are anxious, it seems like. So today we’re trying to cut through that talk, the hype of the talk, and actually what actually works. Governance, adoption, job impact, and where leaders should really start. I’m most interested in all of the above because we are diving in ourselves. So love to hear your thoughts. 

Martin: Yeah, let’s do it. So let’s first get into your background and foundation. So you’ve guided AI transformation across massive organizations. What drew you into this work and what was your aha moment about AI’s potential for enterprise transformation? 

Sabrina: So I actually got started in, I fell into AI about a decade ago, right after graduating with my master’s degree in policy studies, where I focused on technology innovation and organizational frameworks. I was contracted by a company called Appen. And at Appen, my role was to help identify whether or not the humans that were hired to train our algorithms were in fact training them accurately and also to identify fraud or duplication of individuals. This was back when were still teaching our algorithms the difference between a cat and a dog. And at that time, a lot of companies didn’t even know that were doing that type of work yet. So as I moved on from there, I ended up going to Amazon and doing a contract at Amazon, where I led a lot of work for mobile labs, collecting biometric data in what they call a diamond security lab. So very, very Secret, very high profile. And during that time, I began to understand a little bit more in how technology and how humans are really influencing the development of technology and sciences. Went from there into a couple of other contract roles in AI development, product development for other big tech companies. And that’s where I really got my feet wet in end user experience and the world of AI. Started to move through big tech and I actually went into healthcare after that. I ended up learning a lot about large enterprise rollouts and the complexity of technology overhauls. So as I fell into this work, I started to understand that non tech companies who were trying to implement very complicated technologies into their current ecosystems really didn’t have a grasp on the end user experience or change management piece. So I took a lot of the learnings and the agile practices that I gained from working in big tech and I started to help experts in these ecosystems learn the value of people first design. I did discover that buy in for this type of work was really difficult. I saw a lot of very technically minded, intelligent people and very strong business leaders come in and really struggle to get the people in their organizations to adopt the technologies that they were throwing at them. I started to see how there was a big gap between what the business really needed to do and the problems they needed to solve and communicating that to the people who were tasked to use these products in order to get their jobs done more efficiently. And so as I came in and I started to work alongside some of the leaders in the company and I started to work alongside a lot of the tech folks, I started to build upstream processes that allowed us to identify areas of opportunity, gaps, big risks, technical dependencies, people dependencies, communication errors, content errors, the oversights. And just started building out this phased approach to really help companies implement the technologies more seamlessly and allow end users to adopt more seamlessly and to also allow the users to have their voice heard. So you’ll see in a lot of these very large technology overhauls things are moving so fast. There’s a lot of deadlines, there’s a lot of, you know, very large governing objective and key results that leaders are aiming for. And then in the layers of teams you’ll see that you’ll have other, like people managers with different metrics and you know, we’re all trying to align. You start to see very big silos. So everybody has the same goal but there tends to be a different agenda for us to get there. And somewhere in the mix the people get Lost, their voices get muddled. We start to see struggling, we start to see technologies not work as intended because people don’t know how to use them properly. We see people who just don’t simply want to use them and just a little bit rebellious. And so whenever we are looking at large technology implementations and overhauls, we really need to consider how it’s going to impact the people, how people are going to feel about it. Because if they don’t feel good about it, the tool, the effectiveness, it’s not going to provide the outcome that leaders are hoping for. 

Martin: Yeah, especially when you’re touching so many people like having such a big company, I mean it could trickle down and it could be good or it could be bad.

Sabrina: 100%. 

Ronn: Yeah, that was a great explanation. So you describe obviously your work as translating AI strategy into trusted human centered systems, which I firmly believe in and love in anything business. In practical terms, what does that look like when rolling out to thousands of employees? 

Sabrina: So absolutely 100%, considering the end user experience and the voice of the end user, end to end, all the way from the very beginning of your problem statement all to the very end of post adoption and utilization. I have seen oftentimes program efforts, project efforts, companies make decisions without considering the impact that it’s going to have on the employee base solely based on technical expertise which is a critical piece and based on business need critical. And with that when we don’t consider how we are going to help thousands of people adopt a new technology whether or not it fits in with their day to day workflow, we don’t actually really understand the different business areas and the individual needs and kind of the problems happening in these pockets and we just push a technology, we see a lot of stumbling bottlenecks, risk, you know, just problems we didn’t predict. So when we take a very methodical, phased approach and we start by including the user experience from the very beginning of the strategy or you know, the, you know, the decision-making process, we’re really setting ourselves up for success. When I was working in big tech, I got the opportunity to learn, and this is again about a decade ago, I got the opportunity to learn a lot about agile user experience and rapid iterative testing and evaluation. And when I came into the more structured business world, I sought, well, I actually saw a huge deficit in considering user experience in the first place. But what I really saw was a huge lack of concern for the end user and the customer experience until the end of the pipeline. And then we would see technologies roll through. We would see so much struggle. We would see systems breaking, people unable to use the technologies, you know, workload building up, bottlenecks happening. And we would go back and we would say, you know, what happened, right? So what I was able to bring into this more structured environment was the concept of a phased approach, right? And so when I think about technology, when I think about an AI overhaul or any kind of technology transformation, the first thing I think about is breaking it up into three primary phases, where the first phase is going to be your pre-implementation or your pre-adoption. The second phase is going to be, you know, that action phase where we’re looking at actual implementation, actual adoption. And then the third phase is going to be your post-implementation or adoption. So in the first phase, this phase is critical for end user experience, again, oftentimes overlooked. So the first thing we want to do is we want to go in and we really want to understand what is the problem? Why are we implementing these technologies, right? What problem are these solutions solving? We want to understand a little bit more about each of the teams or the people who are going to need to adopt this new technology. We want to understand a little bit about the company and the goals and the mission, just to kind of get that holistic picture. In some cases, if we are building a technology from the ground up, that approach is going to be a little bit different. The tools that we’re going to need to use and the understandings we’re going to need to have are going to be a little bit stronger on the discovery side. And if we are looking at very well known, you know, out of the box solutions, also known as, you know, off the shelf solutions, we’re going to want to fine tune our approach for working with third party vendors and implementing an already existing technology into an already existing technology ecosystem, where the people actually know how to use the current technology. Right? There’s going to be a lot of change. So in this area is really critical to bring in your UX experts. Even if you don’t feel like they need to be there yet. It’s very important for us to be able to come in, hear, listen, be a fly on the wall, understand. We’re going to start putting together our strategies at this point, we’re going to start thinking about, okay, these are the gaps that I see, these are the problem areas I already see, and we’re going to start making our recommendations far upstream. A few weeks or a few steps before we get ready to move into the second phase of implementation, we are going to want to ensure that we have everything we need for the end user experience. This might include like training material, this might include videos or communications across the organization to let people know, hey, this is coming. We want to give people a heads up, you know, time to adjust, time to give feedback, time to present concerns and just time to get ready for change. Change can be really hard. The larger the organization, the larger the change, the more complex the change, the more time people need to adjust, right? Then we’re going to move into implementation. This is where we’re going to see a lot of action happening. Sometimes we have to do a big bang approach and that means it’s got to happen all at once. All of the people just have to move over to this new platform or they have to make this tech change all at once. That approach is going to be a little bit different than your staggered approach. Staggered approach, typically you’re going to go through, you’re going to have more opportunity for pilot testing, you’re going to have more opportunity for people to experience errors and report them back and more opportunity to build up champions who are saying, oh no, I got to do this early, I’m an early adopter, it’s wonderful you want to do this. And so there’s a lot of value in that, a lot of value in the word of mouth and creating positive experience all the way through. And then we have our post implementation phase where we really need to think about, okay, we put this in, how’s it going? Because this is really going to predict sustainability. It’s really going to help support continued trust. It’s really going to help support, you know, sharper functioning and continuous improvement. And this is also where we want to make sure that we have really strong feedback loops because there are going to be bumps in the road that we’re not going to be able to predict. And this is where users are going to be able to come back and say, oh, hey, it’s been going great, except for I did this one thing and you know, I got an error message or I did this one thing and it just is not good, you know, for my day to day workflow. I think that’s where when you include the people, you include the experience in the end-to-end flow, you really start to build trust with the humans who need to use these tools to support the services that the customers in turn purchase, right? And I think that’s where it starts. 

Martin: Yeah, I mean, it could be internal tools for the team, or it could be external tools for the client, right? It could be a mixture of both and each tool has to go through a similar process. So let’s talk about a little bit about enterprise AI adoption. Research shows about 95% of generative AI pilots usually fail to deliver measurable returns. What separates the 5% that succeed, and what patterns do you see in organizations stuck in pilot purgatory? 

Sabrina: I love this question. So what I have experienced, what I have seen, first of all, siloing. That’s a huge one. So when we have different teams oftentimes pushing different technologies at the same time, but not communicating, we can see major bumps in the road as far as people who are expected to utilize these technology solutions. When you’re throwing a bunch of new experiences at a group of people and it’s coming from here, it’s coming from there. And the teams implementing the new solutions are not talking, the people, the real impact is happening to the end user, right? The real impact is the person who’s expected to use these solutions. There’s been no communication. You know, things are just getting thrown at them, and it’s chaotic, right? And it creates a lot of , it create a lot  of angst, distrust and disruption. 

So I think what I have seen is the better the communication between siloed teams. The strategic approach of ensuring that you have the proper experts from different field expertise working together, collaborating to build out a strategy that’s cohesive all the way around. You know, between business partners, technical experts, and your people experts, all the way around, you’re going to see a much more fluid and trustworthy process play out. That also being said, I really feel that it starts upstream when we are deciding on a solution. We’ve seen a lot today that there are companies who hear about AI. Oh, AI is happening. We have to get on the AI, you know, bandwagon because we’re going to end up, you know, falling behind. So companies will just go in to get it just because. And this creates an issue. This creates an issue because if you don’t really have a purpose and you’re not really solving a problem, then you’re just creating more problems. And now we have technology solutions coming into the ecosystem that aren’t doing anything. You know, they’re sometimes duplicate solutions, which costs a lot of money. And so now we’re looking at tech waste. We’re looking at unnecessary, you know, change management needs and an unnecessary distraction. So I would say purposeful, purposeful product decisions that, you know, are going to solve an actual business problem. And you’ve done the research needed to identify the different technologies out there that might do the same thing. Because you might even have technologies in your ecosystem that can solve this problem, right?  So really think it through, do your research and lean on your researchers to provide information. And I think going from there and that’s that discovery research that I was talking about in the pre implementation phase and once a decision is made, have a very thorough strategic framework to move forward with, to include, depending on the size of your company, of course, and the needs of your company, but to include any change managers necessary, any communication and content specialists necessary within your company. Research, Research is oftentimes overlooked. Research is critical to new technology implementation and adoption. You know, if you’re building in house, you’re going to want to make sure to work with your UX designers and you know, definitely your tech experts, right? Your architects, your business leaders and you’re going to want your sponsors, right? That’s what I would say moving through to get unstuck. I would also recommend what I’ve seen work really well, especially when we’re doing large transitions. At the very early stages, we’re architecting the technology infrastructure. Let’s also lay an experience architecture on top of that. Let’s understand how the technology is impacting your sales team. How is it going to impact your finance team, your legal team. How’s it going to impact, you know, your day to day, your IT workers, right? Depending on the type of technology used in a company, you might have endpoints that differ amongst your employees. You need to understand how is this technology different, you know, on this endpoint versus that endpoint. We’re going to need to really take a deep dive prior to implementing. The more heavy lifting you do upstream up front in the discovery phase, the smoother your adoption, your implementation adoption is going to be as you move into action. When you’re in action, if you can. I always recommend little bites at a time, little bites at a time, pilot, get your proof of concept going, right? Look for the problems because when you deliver to a low risk environment or like a maybe a small group of people, small group of experts, right, that are kind of championing with you, you can identify a lot of problems before it gets to the bigger population, right, as you scale. What really, I think causes a lot of bottlenecks and what I’ve seen is when people just push too hard, leaders push too hard. They just want to see those numbers. They don’t care the impact, you know, of the end user and they’re just push, push, push. You know, we have these numbers that we have to meet, and you push it out and now you have several thousand people, and nobody can work because there’s a bug in the system or, you know, there’s a bottleneck and something’s stunted. And so now we have a really big problem. Now we’re looking at fire drills, now we’re looking at stopping, you know, the project altogether, you know, taking a big pause. Now we have a chaotic scenario where some technologies are potentially broken and we, you know, we can’t get through. And so I think moving too fast without enough information and not considering the actual users and how these technologies are impacting their day-to-day workflow, you are definitely putting yourself at risk for getting stuck in that purgatory. 

Martin: I want to be in there. 

Sabrina: No, that’s just my experience though. 

Ronn: So obviously many companies obviously start centralized and then move kind of to like a hub and spoke kind of model. I know you’ve shared some of like the organizational structure for AI adoption, but what I really want to know is how should it evolve over time? 

Sabrina: I think it really depends. It depends on the company, it depends on the need, right? So there really should be some level of central AI center of excellence or an AI board, right? There really should be some level of oversight because AI is far more complex than some of your more commonly understood technology solutions and artificial intelligence there’s a lot more risk involved. That risk has the impact to really hurt not just the company, but humans on an individual level, because we’re dealing with data. And if you are in an industry where you’re dealing with very sensitive human data, you’re dealing with demographic data, right? You really, really want to ensure that you are protecting your people. So having a center of excellence, whatever that looks like, it’s a board of oversight, is critical I think right now more than ever, when it comes to implementing AI technology. There’s a lot of models out there, there’s a lot of ways to implement technology. I think the core though, the core principles that we need to stick to ensure safe, ethical AI implementation and efficient, right, meaningful AI implementation. I think those core principles stick, you know, regardless of how we move. 

Martin: Let’s get into ethics, governance and oversight. With the EU AI act and new US employment laws emerging, how should companies think about AI governance right now? Even if they’re not regulated yet. 

Sabrina: It is my observation and opinion that even if we are not currently regulated everywhere, we will be much more regulated in the future. We will have to be in order to put guardrails and protection around people and their sensitive information. Right. So this area is a, it’s a passion area for me, especially, you know, having my background in policy. I strongly feel that regardless of what the current policies state, companies have an ethical and moral obligation to protect their client data and their employee data. It also helps us as humans, whether we be customers or employees within a company, it helps us trust that company so much more when we know, you know, you’re in it for us and you’re here to help protect our data. Right. And our sensitive information. So I’m a huge advocate for putting in your own guardrails to begin with. It starts with the data first and foremost, right? So when in the world of research, we have protocol. We have protocol that first of all, when we go to collect data, we say, this is the data we’re collecting. This is who will see your data. This is who has access to your data. This is where we’re storing your data, how we’re handling your data. This is what we’re doing with your data. And there’s always a disclaimer on whether or not you’re going to be called out, or it’s going to be anonymous. Right. So I think the same regulations should apply when using data within a company to build your AI tools or to enhance your AI tools as a solution. Right. So it’s data protection. The other thing too is that, see, in the world of research, we have to be very careful about bias. Right. The way you ask a question for data collection is critical. It’s the same when building out your AI algorithms. It’s the same when you’re, you know, implementing these tools. So the way that you’re inputting information impacts the output. Right. So I think it’s critical that we have human oversight to ensure that the data is healthy. Garbage data in is garbage data out. So we really want to ensure that we have very clean, healthy data that we are protecting the humans behind the data. Right. So we want to make sure that we’re not calling out people specifically, and we’re not revealing sensitive data for, you know, others to see that could inevitably hurt them somehow in the end. So it’s data governance. Yep. Auditing to make sure that our data is up to date. To make sure that we don’t have any kind of outliers in there, skewing the data. It’s all very critical. Creating the review boards, ensuring that we have legal security and the voice of the end user there, ensuring that the data is properly inputted or properly guarded, properly tagged. Right. Properly sorted and collected and properly utilized. 

Martin: This next question that Ronn’s going to get into is definitely probably a pretty heavy one that I think is on a lot of people’s mind. I mean, you’re hearing the news about Amazon laying off thousands of employees because of AI, and I feel like these companies are kind of taking the wrong angle and thinking they can just replace humans with AI versus trying to augment humans with AI. I even heard Elon Musk in the interview saying companies that are just AI first and like no humans would perform better than AI with humans. Which I’m kind of like against that as well. So, I mean, this next question, Ronn, you can get into it, but I mean, I think this is like something that happens to people. Yeah, I mean, so when AI systems recommend things like hiring, firing, even pricing, because for us, you know, we’re in the world where rental prices are, you know, a lot of times influenced by AI. How do leaders ensure meaningful human oversight rather than just rubber stamping that algorithm? 

Sabrina: Humans should always be a part of the process, especially right now. So we’re in a phase of what’s called a narrow AI or weak AI. RAI is not advanced. Sometimes it feels advanced, but it’s really not. So relying on artificial intelligence tools to make decisions for you, I think is a very dangerous game right now. AI tools should help humans make the decisions that humans need to make in the end. So AI is a tool, right? I like to think about it as a baseball bat. Anybody can pick up a baseball bat. Not everybody can properly use a baseball bat, right? If you’re practiced with a baseball bat, you are practiced in baseball. You understand how to use a baseball bat to do really good things, right? You could also, a bad actor could pick up a baseball bat and do some pretty bad damage with it, right? A baseball bat does not sit there on the plate and swing itself without a human behind. Needs a human to be able to work efficiently and do its job. If we rely on AI tools to do hiring and firing, for example, my questions, my first questions would be, okay, so what data does this tool have that determines the criteria of an individual, right, or individuals that recommend these individuals for hiring or firing, right? We need people on the end of those outcomes to, who actually understand, so domain experts. So who actually understand the hiring and firing process to actually understand the company culture and you know, the work performance goals that are put in place or you know, the work, the job criteria needs put in place to actually identify whether or not these humans either meet or don’t meet that criteria. AI can help sort through some of these criteria, but it cannot make that decision for the human. The human really needs to be a part of that process. 

Ronn: The next question I had for you is obviously, it’s interesting because we are front face forward with AI adoption and building things out. And so, I worry about this next question about my own team, if they’re worried. But they’re saying that nearly 9 in 10 workers worry that AI will impact their job security. What do you tell employees or recommend telling employees who are generally afraid? 

Sabrina: I understand the fear. I understand the concern, absolutely. I would say, however, I wouldn’t worry too much. Here’s what I do feel is going to happen and also starting to happen, right? AI has come in and it’s a craze and there’s a lot of high-level leaders who have a lot of, you know, kind of pressure, right, to get on the AI, you know, wagon, right? It’s like implement, find something, implement. And what we are seeing are companies rushing to find tools, implement them, put them in the system, put them in the ecosystem. We are seeing layoffs, right? We are seeing the elimination of headcount. Whether or not that’s directly correlated. That is not, you know, every company’s different. There’s a lot of reasons for layoffs right now; however, you know, just perfectly, it happens to perfectly align with a lot of AI implementation right now. And so, we are seeing though, with this loss of headcount and this loss of human expertise that are critical to the success of a company’s operations, AI is just not meeting the bill, right? It’s just, it is not replacing people efficiently. It’s doing a great job at helping people who are experts who know how to use the AI tools to do their job better. But it’s really not good at replacing people right now. So what I really think is going to happen is we are going to start seeing more jobs open up again. Again, there are other reasons why companies are needing to kind of revamp their, you know, their employee headcount. They’re needing to revamp the way that operations are working. And I really do believe, and I am seeing, actually, I’m starting to see a lot of jobs open up. They’re just different, right? They’re just different than the jobs that were there before because some of those jobs are being eliminated because they’re outdated. So as the wheels are turning, we’re in the middle of another, you know, kind of like revolution, right? We’ve seen this time and time again and in the workforce where, you know, new, new technology is advancing society and thus society is helping to advance the new technology and jobs change, needs change. And I think we’re just in the middle of a big change. So I see it less as a loss and more as a change. And for anybody who’s really afraid of technology coming in and replacing them, I would say understand it a little bit more. Look into what your company is doing and see how you can shift your skill sets to work alongside, you know, this new AI revolution and, you know, roll with the changes. 

Martin: Yeah, it’s like in the industrial age now we’re like in the age of AI, right? So it’s like that’s kind of what you’re probably talking about. It’s not like actual machines or metal being built, but this is like our digital world is being kind of like the complete infrastructure is being built out.

Sabrina: 100%. That and also, as far as adoption is concerned, it’s coming, right? Change is happening. And I know sometimes we want to feel resistant to change, that’s human nature, but it’s coming. So I would absolutely recommend anybody who’s feeling a little bit fearful of this change, tap in, try to understand a little bit better. Don’t be afraid, just roll with the changes and look at it as an opportunity to level up, you know. So I think..

Ronn: Sorry to interrupt. The adage that comes to my mind is that just with AI is always like the work smarter, not harder. Right. It’s a total like, cliche. And that’s how I am. I embrace change. Overall, I love just moving and shaking always and every time, but I know not everyone does, but I just look at it like, let’s work, let’s continue to work smarter, not harder. And so to your point. Yeah, learn new skills, adopt. You know, we have an AI engineer in house and we actually have, he’s opened up some office, open office hours for our team to get comfortable with the language and kind of what’s possible just in their day to day. He kind of curates it to their individual roles as well as the company’s needs. 

So that’s how we’re trying to approach it, without that fear of like, what are they doing, what are they building, where do I fit in, you know, kind of thing. 

Martin: Who are they automating? 

Ronn: Yeah, exactly. Yeah. And everything you’ve said so far, Sabrina, is amazing because we’ve always been or tried to be people first, right? We have to build products and services that support the end user, obviously, but without people, you’re nowhere. You don’t even retain that kind of business. So I love this approach when it comes to AI because again, it’s some unfair and inferior or, you know, uncharted territory for some. To that end, the best part about it is that so far, research has showed limited displacement thus far, while others predict a rising in unemployment. Where do you land on this debate and why do you call, I understand, why do you call it the wicked problem? 

Sabrina: Absolutely. So the wicked problem is new solutions create more problems. It’s that simple, right? So we have a, again, we’re going through this, you know, this revolutionary change, and you know, a lot of people getting on, you know, getting excited about working smarter, not harder. I love that a lot of people are getting really excited about being able to utilize AI to get their jobs done more efficiently and work faster. Other people are terrified that AI is coming in and eliminating, you know, their jobs left and right. So where the wicked problem comes in is this, right? The more that we lean in and again, without really properly understanding and just rushing into throwing AI solutions into your organization’s ecosystem without really doing that discovery in the front end and not really thinking about the impact it’s going to have on the people and not really thinking about the sentiment, the feeling of the people when you’re announcing that these products are coming in because there’s so much fear and there’s so much uncertainty and so much confusion because we’re still so new, right? Moving into this, this AI era, we are creating more problems as we are trying to solution for those problems, right? So I would say right now, as far as the research backing limited displacement, while other research is predicting a rise in unemployment, I’m going to say that both narratives are probably true. Both narratives. I do believe that right now, okay, so here’s what I’ve seen. This is hands on experience. And what I’ve seen. So I’ve had the opportunity to work on AI implementation efforts, where we wanted to ensure more efficiency in the pipeline, right, in the workflow. While doing so, we got the opportunity to build out metrics that identify things such as human productivity. This right here is a little bit of an indicator to me. Right. That there might be some underlying investigation to identify effectiveness of human and machine, human versus machine. Right. We’re not sure, but we do know that, you know, we’re monitoring some level of productivity. I absolutely understand when we approach AI like the AI implementation like this, how it could really start to build a distrust among your employee base. Right. And so, people start to see this, and they begin to get concerned. Why are we doing this? So to kind of further dig a little bit deeper into that, I have had individuals tell me who are higher up, who have been inside, you know, these decision making meetings admit that leadership are openly discussing what AI tools they are able to implement that can replace human headcount. This is real. 

Ronn: Yeah, I would imagine.

Sabrina: So this is not a myth. Right. So this fear, this validates a lot of the AI replacing headcount fears. It really does. And at the same time, again with the wicked problem, I have also witnessed that if this approach is taken and you know, those who have made these decisions don’t fully understand the full capabilities of AI, of and limitations and that we actually need your experts, we need those humans in the loop and when they’re gone, you know, we now have these really sticky problems where the solution was supposed to fix, but actually it’s made everything a lot more messy. And so that’s why I’m saying I do believe both. I think that there’s going to be a lot of like seesawing back and forth until we find the balance between machine and man, if you will. Right. 

Ronn: I think it’s very valid. Yeah, definitely. 

Martin: So we’re getting close towards the end. So now we’re going to be covering powering better outcomes around super agency. There’s a lot of talk about AI creating a super agency. Can you share a concrete example of what that looks like in practice? 

Sabrina: I can. I’m going to use something that’s very familiar as an example on our everyday. So,  AI tools and you know, products, they’re ingrained in our everyday life now, every day. Whether it be, you know, simple applications that we already use, and we, you know, we don’t even know that AI is behind it all, enhancing that experience. But something that I think we all understand is working with like Claude and ChatGPT and some of those really popular applications. I looked up a recipe the other day. It’s a simple, I didn’t want to read, I didn’t want to do a Google search. I just wanted to know a couple of things and I just typed it in very fast and the output came within seconds. I had my answer. Done. Made a great dish, right. I just needed to know a little bit. I didn’t want to read, you know, 100 stories about somebody’s experience cooking the dish. I didn’t even want reviews. I just wanted to know this one thing. And so AI really, really, I believe really makes us stronger. It enhances our knowledge base. That being said, it does not replace, it does not replace our knowledge base. So for anybody who’s an expert in any given field, you can lean on some of these tools and you can dig a little bit deeper, a little bit quicker if you know how to write or how to ask the right questions and you know what you’re looking for and that’s what’s key. Do you know what you’re looking for, right? If you see an output, are you going to be able to recognize whether or not that output is accurate seemingly, right, or if it seems a little bit off? And so, I think we really have the opportunity.  Again, I really love that, you know, that cliche work smarter, not harder. That’s what it does for us a hundred percent. We can do things that are just invaluable at this point. We, we really can, we can cut our workload down drastically just by saying, hey, can you just draft me an email with these key points really fast because I have 40 other things that I’m trying to get done over here and I just, I really can’t think, you know, about, you know what to say in this email, right? So, and we can just, we can do these like kind of mundane tasks very fast, much more efficiently now. We can just throw them in there and focus on the more complex, more creative or, you know, just more interesting things that we care about. And I think when we take that approach and when we think about how artificial intelligence works for us, right? And we bring that to the end users and we, and that’s a sentiment we create and we’re like, look, this is going to help you have a lot more fun in your day-to-day workflow. It’s going to help you enjoy your job more, right? It’s going to take all of that excessive heavy lifting off of your shoulders. It might even help you know, a little bit of advice, you know, and you know, or maybe help you have a conversation you know, and you know with somebody that you’re maybe not getting along with, that works so well. You know, there’s so many levels where AI can come in and really help us with knowledge in depth. Knowledge sharing, discovery, communication. It’s really beneficial. For me it’s like making a new friend. It really is. And I’ve got a couple of my platforms that are my sidekicks now, and I actually make a joke and I have names for them and..

Ronn: That’s awesome. 

Sabrina: They’re my, I call them my robot besties, my robot best friends. 

Ronn: We use that same term like oh, I asked my best friend, like the trusted best friend source, right? 

Sabrina: Yeah. 

Ronn: And they said I should do, you know, both, you know, 

Sabrina: 100%, 100%. 

Ronn: That’s how we also tell on each other, that it’s like hey, this is AI generate what I’m about to say. 

Martin: And it’s actually unique because Sabrina brought it up actually where like each AI, like whether you’re talking with ChatGPT Claude, it’s like each one has their own AI entity or like soul or like almost like their own personality, you know, Pretty unique.

Ronn: When based on how you use it and what you give it. Right. It’s gonna even more land. 

Sabrina: You know I really, I would love to just slightly touch on that topic because from an end user experience there really is that concern about AI taking over the world, you know, or AI being able to predict future events and sometimes, you know, we see a lot of social media influencers who might not really understand how it works from a technical perspective. And so, there’s a lot of fear mongering around, you know, what AI knows and can tell us and that misinformation I think is really dangerous and it’s really, there are a lot of dangers utilizing the tools. These dangers are more around your privacy and your data though, right? They’re less around AI being able to predict, you know, end of the world scenarios. Right. So I see that a lot and I definitely like to be able to work with people to help them understand these tools really, they’re just a reflection of yourself, they’re just mirrors. So if you’re mean to, it’s going to be mean to you, right? Because that’s what you’re teaching it, right? If you’re nice, it’s going to be nice to you. So on that sentiment, Martin, I was asking two different tools, the same question, and one of them was being very nice to me and the other one was not. And I thought it was really funny. So I had reached out, you know, to a group of folks and I was telling them about the experience, but knowing what I know, I ended with, you know, so this tool that’s not being very nice to me, I ended with, well, I guess. I guess there’s a part of me that must not be very nice because it’s just a reflection, right? And so, since then, I have been actually a lot more bubbly and that tool is reflecting that sentiment right back at me. That tool’s like, you know, very happy to talk to me now.

Ronn: That’s a great observation. That’s a takeaway for many to listen, right? What you get out of, what you put into it, you get out, right? 

Sabrina: 100%. 100%, yeah. 

Ronn: That’s amazing. So transition a little bit to our, a lot of our audience is obviously Multifamily. So if I was a Multifamily property management company and they say, we want to start using AI, but don’t know where to begin, what are step one, two and three? If you could break it down.

Sabrina: Excellent. In a nutshell, I will say, one, understand why. Okay. Why do you want to use it? What problem are we solving? Right. How does it help support your business goals and your business needs? That’s number one. Number two, I would say, right? Oh, okay. So this is critical. All right, so along with understanding, so we want to know the problem, we want to understand the business goals, the business needs. We want to understand the experts that you already have within your organization that have knowledge. This is very important, right? Listen to your experts. And experts can be engineers and developers, they can be AI experts at some level, they can be HR people, experts, other business experts. They can be, again, like content development, research, design experts. Just listen to your experts. Listen to what they have to say because they know their areas and they’re going to be able to help you as the decision maker, better identify where the problems are, where the blockers are in your pipelines, where the risks are, where they’re struggling, you know, where communication is lacking. And that’s going to help you be able to make better decisions about what solutions you might want to implement in the first place. As you are considering your solutions, you also want to understand what is your current ecosystem, right? What’s existing. This is really going to be important because you are going to need to understand are we working? Do we need to, do we need to bring on additional expert, right. Do we need to work with third parties who might have a different knowledge base? Because we don’t have that here. No problem. Do we need to hire someone, right. Look at your processes and identify, okay, this is something that we’ve done in the past, and it worked really well, right? Really, really well. We tried this over here and it really didn’t work well for our company, right? So hold on to those. Think about what works well, what doesn’t work well. For the things that work well, I say keep them. For the things that don’t work well, you know, build on that supplement, right? What, you know, see what other deficits that you have. So once you understand the issues, the problems that you’re trying to solve, once you understand the solutions that you want to implement and how you’re going to implement them, you want to develop a process that will allow you to create small wins at first. Because what you’re going to need to do is you’re going to need to have a proof of concept and you’re going to need to validate the solution and ensure that it does indeed solve the problems that you’re aiming to solve. And you’re going to need to get buy in, right? You’re going to need to work with stakeholders and champions, and you’re going to need to get buy in not only from other executive staff, but you’re going to need to get buy in from the people. So they want to utilize the tool building metrics, designing metrics that are meaningful, purposeful. There’s a lot of vanity metrics out there and people don’t really understand or you know, maybe leaders don’t really understand that they’re really good for a business scenario, but it doesn’t really help us identify and understand utilization success by the people.
So you want to work with your internal experts who understand how to assess value, engagement metrics, you know, user experience metrics, so and so forth, whatever. Customer experience metrics are really important as well. Do it in small chunks and then you’re going to want to see what works, keep moving forward. If we fail, no problem, let’s fix those, you know, patch those little issues up and then move forward. And you want to scale slowly but surely. That’s my recommendation. 

Martin: Love that. Now, what would you say, speaking of metrics and proof, what metrics should companies actually track to know if AI adoption is actually working? Not vanity metrics, but real measure of Success to the one.

Sabrina:  I’m going to say it depends. And I always say this. So I’ve had the opportunity to generate and design metrics from the ground up. We always, you know, we take a look and we want to benchmark, we want to design very purposefully, very meaningfully. So when people come to me and they say productivity, for example, it’s my favorite one. It’s my favorite one to dig into. We need productivity metrics. And I say, great. How do you define productivity, right. So whenever you’re building metrics, we need to ensure, right, can you define the metric first of all, ambiguous metrics are not helpful. They don’t help us really understand. I would also say qualitative and quantitative mixed method approach is key. So I come from an interdisciplinary mixed method research background and I have learned that when you use only, I’ve seen, pardon me, that when you use only quantitative metrics to try to understand a problem and then explain it, you struggle to understand the why. And I have also seen that when you use solely qualitative metrics to try to understand a problem area and solution for it, you might get a big why, but you don’t necessarily get that statistical significance behind it to determine whether or not this is a problem that we even need to pay attention to. Right. Or that it’s widespread. Right. So mixed method approach, critical, quantitative and qualitative. When you’re doing your metrics, I’d recommend thinking about both. You’re going to have your numeric, probably more like telemetry metrics and that’s great. And then you’re going to have your qualitative, you know, your feedback. You’re going to want to get in there. You’re going to want to get some real meaningful verbal feedback from the people who are impacted by these technology changes. And then we’re going to create stories; we’re going to create a storytelling with our metrics. Data visualization is critical. I highly recommend that when you do your quantitative metrics that, first of all you employ one of your experts who are educated in statistical analysis and data visualization to do the metrics. These are critical, right? There is a right way to build and analyze metrics and there is a not so right way. And it’s very easy when you are not experienced in quantitative metrics to accidentally do data skewing, and you didn’t know or to not really make meaningful stories with your data analyzation. And that’s really on leadership to make sure that you have the right people doing the right job. Right. You’re qualitative. This is critical as well. There are right ways to ask people questions about their technology experience, and there are not so right ways to ask people about their technology experience. That creates bias in the data. And now that data is useless. You can accidentally ask double questions. So not so useful, right. And you can accidentally sway people to answer one way or another. Another critical piece when it comes to qualitative data collection. I highly recommend that you have a researcher who is skilled in qualitative data collection, to go talk to the people. And I highly recommend that you do not send leadership managers or other people with hierarchical influence to go talk to your employee population. Because in those scenarios, you know, you have to build trust with your people that you’re speaking with. You have to build trust. You have to let them know, hey, what you tell me is safe. It’s not going to impact your job. Nobody’s going to know what you said or nobody’s going to know that you said it. Right. There’s a level of protection that the people deserve to have when they’re providing you with this feedback and this data. So if you send in, you know, people with authority to go ask these questions, you might not get honest answers back because people don’t, you know, they want to protect their jobs or, you know, they don’t want to sound, you know, like they’re complaining. Right. 

So to get that honest feedback, it’s very important that you use the right experts who know how to collect qualitative data, who can ensure trust and, you know, data security with the people providing feedback. And then we got to put it all together. Right. Then we get our mixed method data storytelling. 

Martin: Yeah, those are some great points. And I think you really kind of buttoned it all up together with kind of just thinking about the whole organization. So yeah, spot on. I love that we’re finalizing the rapid fire question, so we can kind of keep these short and sweet and Ronn, go ahead. 

Ronn: Yeah, so I know one of the fun ones is how do you make, how do we make AI literacy accessible to our frontline workers, and not just executives and the data scientists that we’ve been talking about?

Sabrina: Training. Right. You were mentioning earlier that Ben hosts open office hours. 

Ronn: Yeah.

Sabrina: Love it. Absolutely love that approach. My door is open, I’m an expert. Come on in, let’s talk. Right. Love that. Encouraging your employees and even, you know, I want to say, like, I’m going to use the word like motivating or rewarding. Right. Individuals to go and seek internal training opportunities. Some companies will provide external training opportunities. I think that’s such a win. Smaller companies maybe don’t have the resources to do that. That’s okay. Knowledge sharing though, right? Hey, team. I just watched, you know, the coolest, you know, episode or heard the best podcast on AI implementation. Check it out, right? Knowledge sharing. I’ve been on a couple of teams as, you know, research heavy teams, right? So where we actually have repositories where we all share not like knowledge pieces. Right. So I’ll say, oh, I read this, like really great article on xyz. Here it is. Take a look if you want. This is my favorite book. Take a look, right? So it’s just knowledge sharing all the way around. Individuals have to have the desire, though. There’s a lot out there, right? There’s a lot out there, and it can be overwhelming. And so it’s create the desire. Create the excitement around learning AI and training up and provide training opportunities and just talk about it. 

Ronn: Perfect. 

Martin: So much to learn. So much to learn and not enough time. And I think Ben drove it home a lot too, where it’s like, you got to be careful not to get overly, you know, into too much, you know, going down the rabbit hole and give yourself some time for like, your own humanity and your own peace of mind as well. So go ahead. 

Sabrina: I just wanted to, I wanted to piggyback off of that because, you know, leaders who really want to influence and motivate other workers within their company to learn more about AI having that transparency when you can. Right? When you can. Hey, this is an AI tool that we’re planning to implement and, or, you know, we’ve implemented and go take a look, you know. Tell me what you think or, you know, let’s talk about it. Having just these luncheons or these coffee talks or these open forums where all employees can come in and, you know, not be kind of, you know, pushed away from the, the word I’m looking for. And not be pushed away from the innovation, right. That’s coming forward, like communicating the innovation get people excited. People will be excited if you’re, if you include them. If you’re excited, like get on board. Let’s go. 

Martin: Bring it on. 

Sabrina: Yeah, right. Yeah. 

Martin: Now there’s a ton of hype with AI, right? It’s like everyone wants AI. They don’t know why they want it; they don’t know what problem they want to solve a lot of times, or even the clear definition of AI as a whole. What do you find is the biggest gap you see between AI hype and actual reality right now? 

Sabrina: Well, one of them we’ve kind of covered. AI is going to take everyone’s jobs. Okay. I feel that it is changing everyone’s jobs. Absolutely. I do feel that there is fear and there is some valid fear, and with that, it’s not going to, you know, come in. I don’t believe it’s not going to come in and, you know, just do a wide sweep and nobody will be able to work, and we’ll just have, you know, robots running the world. Well, not yet, at least. And we’ve got some time, if at all. Right. So, I think the other, again, hype is that AI can predict, you know, very dangerous things, or it can, it’s prophetic, you know, it’s a tool. That’s all it is. And any tool in this world can be very useful and very helpful. And any tool in this world can be very dangerous. Hammer is a really good example. You build a house with a hammer. Right. You can also do very destructive things with a hammer. AI is the same. So if you think about your own personal safety, how you use it, educate yourself and others in how to use a given tool, make sure you understand how to safely use the tools, and make sure you understand what the tools are used for, use them properly, you know, and then we have a really great opportunity to build incredible things, you know. So I think the hype is really around a large misunderstanding of the dangers. Are they dangerous? Absolutely. But are we talking about the right dangers? 

Ronn: So I have one last question. I know you’ve completed the MITX Programs. What was the most valuable takeaway from that experience? 

Sabrina: So I’m currently a few weeks shy of the executive certification, which includes a couple of the MIT Expo programs. 

Ronn: Awesome. 

Sabrina: And the program that I’ve completed is one of the programs already, and it’s an AI strategy leadership program. So I loved that course because there was a lot of validation, all about AI leadership and strategy, and moving AI into your ecosystems. But I also learned a lot from that program that was just a little bit deeper, you know, and layered than some of the knowledge I had before. So that one, I would say the gap that I’m seeing. And so, and with this course as well, with the current course that I’m in right now, which is more, it’s based more on product development and trying to build products from the ground up, which I’m really excited about, because this one is not my area of expertise. And I get to get my hands, you know, dirty a little bit, right. And get in there and build some products and, you know, pardon my corporate speech, but, you know, really see how the sausage is made. And so, I’m making sausage and I’m loving it. So, so I would say, though, I am seeing a continuous theme with hundreds of experts that are already deep into AI product development and AI implementation. They keep asking the same question, how do we get people to use it? How do we get people to adopt? And it’s exciting for me because I’m sitting back here and I’m like I can tell you I have these expertise. I’ve done it, you know, and I’m doing it now. And I’m helping companies big and small with headcount adopt these technologies into their current systems. And so, I think right now what I’m getting out of it the most is, well, I’m getting a lot out of all of them. But I think what I’m getting the most is really seeing the future of AI need, which is people are building, they’re building fast. Companies want it; they want it. They don’t always know why, but they know they want it. There’s a lot of technical experts that are getting hired, a lot of data scientists. There’s a lot of people coming in and setting up the right environments. But I believe the next phase is, oh, shoot, now we have to think about the people. And so, I’m really looking forward to the continued evolution of AI implementation because that’s where I come in, and I get to help a lot of people do great things. 

Martin: Yeah. Exciting times. 

Sabrina: Yeah. 

Ronn: That’s amazing. Well, Sabrina, this has been an amazing conversation. I love how you just say it, how it is. Less hype, more reality. We can definitely, there’s many nuggets and takeaways. So thank you so much for joining us. Hopefully you enjoyed yourself as well. 

Sabrina: Oh, a hundred percent. You know, there’s so much, there’s always so much to say. There’s so much more. We’re in such uncharted territory right now. 

Ronn: 100%. 

Sabrina: There’s so much opportunity. So thank you so much.

Martin: Sabrina, how can people get in contact with you? What’s the best way for them to reach out? If they have any questions or maybe they’re a company that wants to have you join the fun and help them with their AI strategy. 

Sabrina: Right now, I would say… 

Martin: Okay.

Sabrina: Just very quickly, March 1, 2026, I am launching my own consulting business and I’m launching some other special nuggets that go along with that. Martin, I know that you know a little bit about it, but I’m gonna go ahead and surprise the world as soon as…

Martin: A lot of good stuff is coming. So thank you again, Sabrina. 

Sabrina: Very excited. 

Martin: Yeah, I’m happy to kind of be along this journey with you and kind of being a part of the cohort. So I’m really happy and grateful to be able to meet people like you and really expand my relationships and learn more from, you know, people that have been doing it for so long. So thank you so much for taking the time. 

Sabrina: Thank you so much. Thanks for having me. Wonderful to meet you, Ronn, and wonderful talking, Martin. 

Martin: All right, everyone, and make sure to check out ApartmentSEO.com for your free marketing analysis. And if you have any questions or we need help turning renters into prospects and getting those residents, then make Apartment SEO your top choice. Until then, I’ll talk to you next time. Bye, Ronn. Bye, Sabrina. 

Ronn: Cheers. Bye, bye.