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Welcome to Tiff Talks Tech, the podcast where we unravel
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the mysteries of multifamily marketing tech, making it not just understandable,
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but dare I say enjoyable. I'm your host, Tiffany, a
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season multi family marketer turn tech expert, here to guide
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you through your daily tech challenges with ease and a
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sprinkle of fun. Hello everyone, and welcome to another episode
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of Tiff Talks Tech. Today. I'm excited because I'm joined
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by Jacob Carter, who is the CEO and founder of
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Nurture Boss and author of the new book AI and
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Property Management. Today, I'm excited because we're going to be
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talking about the evolution of AI and multifamily. But before
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we really dive in, Jacob, could you share a little
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bit about you for anyone who may not know you
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yet and just kind of like a quick version of
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what you do in Nurture Boss.
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Yeah. Yeah, happy to Thanks for having me. I'm excited
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to be here.
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My back is professional software engineer for my whole career
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prior to starting Nurture Boss, so very tech oriented.
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I like getting in the weeds on.
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Technical stuff, so if you have technical questions, I'm probably
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your guy to answer them, but let's wait and see
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if I actually can. First, But Nurture Boss, we focus
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on AI and property management much like the book titles suggests,
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and that's building solutions for multifamily that leverage AI to
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augment and enhance all parts of the lead to lease
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and lease to renewal life cycle, as well as even
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things on the asset management side of the house as well.
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So happy to go into more detail there if you'd like.
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But all things AI and multi family Nurture Boss, we
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are your group for that.
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I love it. That's great. It did read your book
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and I think it was amazing. I think it explains
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things and an easy way to understand. One of the
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things that you do talk about is how in our
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industry we use automation, AI, conversational AI, generative agent TIC,
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all of that. We use it very interchangeably, but they're
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not all the same thing. Could you talk a little
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bit about the differences.
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Yeah, happy too.
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And I think my caveat here is that I think
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it is valuable to use the right word for the
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right meaning. I do think that there's a way to
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over index on perfect clarity.
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So I think it's really useful to know the difference.
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I think there are certain times when it is inappropriate
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to use one when the other one is the correct one.
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But I do think we also deserve a little bit
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of grace when we're trying to talk about complex topics.
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But automation at its core is really about a rules engine.
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If this happens, then do that right. It's very basic
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and compared to some of the other definitions we can
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get into around AI, and it's something that's been around
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for a very very long time. But automation most certainly
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is not inherently AI or AI driven. Generative AI is
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helpful to think of as kind of the over arching
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umbrella when we're typically talking about the kind of AI
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that we'd be leveraging in multifamily or a lot of
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other industries.
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I think of this as the brain, right.
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It knows how to produce new content based on patterns
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that it has learned in the past, and conversational AI
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is just one more step on top of that, where
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you're taking that generated outcome and then giving it new inputs.
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It's that back and forth that you're having with the
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generative AI. It very quickly turns into conversational AI.
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AI is the same as like natural language processing.
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Yeah, I mean get really like in the weeds here
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on this so we can it for the sake of
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our conversation.
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I think that that's okay.
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Natural language processing is really about taking an input right
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that somebody has done, like think chat chatbot style, and
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understanding how you can take that input and do things
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like get rough understanding of context what they're referring to,
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categorization of the question that's been asked. So it's not
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nearly natively is robust or intelligent if you will, as
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a gender AI which is going well far beyond NLP
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to actually use its learning and training to produce sometimes
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novel outcomes or outputs right based on that makes sense,
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I think augentic AI though, that's really where the magic
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starts to happen. That's when you're taking gender of AI
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or conversational AI and you're empowering it with the ability
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to interact with its environment said differently, drive outcomes actually
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do things right, and I think that's where we really
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start to get value from AI. And multifamily is on
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the agentic AI front.
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Yeah, And I think it's so important to understand the
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distinction when it comes to looking for a platform that
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you're going to use, because that will be able to
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tell the functionality.
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Yeah, definitely.
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And I think also it's helpful to know that, you know,
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we default to automation a lot because it's something we're
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comfortable with, but it is wildly different than like an
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agentic AI platform. And I think at the end of
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the day, you should ask yourselves, am I trying to
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intelligently drive outcomes in real world results based on the
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technology that I'm using? Or am I looking to automate
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a process?
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Right?
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Which are not inherently the same thing.
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Yeah, that definitely makes sense speaking of processes. I know
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in your book you talk about AI becoming this new
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infrastructure of how we work. What does it actually look
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like in our industry and how will.
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We use that? Yeah?
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I think AI will certainly be the new infrastructure and
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what I mean by that, So my favorite analogy is
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for this scenario is electricity.
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Right.
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When electricity, you know, when we harnessed electricity rights as
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a species, it didn't just replace candles, right, it rewired
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the way we do everything, manufacturing, healthcare, food, storage, communication,
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everything changed, Right. You don't you electricity? You exist in
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an electrified world? And that's what I mean when I
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say AI is infrastructure. I don't mean that we use AI.
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What I mean is that our world is powered by
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AI technology. I think that's where we're going everywhere, not
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just multifamily. When it comes to multifamily, though, we're talking
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about it showing up across the entire renter's journey, right
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from search to leasing, conversations, on site support, ongoing resident communication,
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the marketing team. Right, this is becoming more of an
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infrastructure type technology. Is we spread across those different areas.
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I think a really good practical marker for when we've
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gotten there is when AI is no longer on the
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edge of the experience, but is moving to the center
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of the experience. So rather than answering a prospect, which
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is the edges, right, we're helping teams prioritize leads, spot bottlenecks,
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drive performance right more into the center. And that's what
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I mean when I say AI infrastructure.
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Yeah, definitely, that totally makes sense. Are there things that
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we're using AI for in these kind of early stages
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that you think one day we're going to look back
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on and be like that was ridiculous?
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Yeah?
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I think a lot so I think right now, you know,
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this idea of I have a chatbot, so I'm using
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AI as something that will chuckle at down the road.
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You know, I think a lot of people now are
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hyper focused on if AI sounds like a person or not.
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I think is society at large adopts AI more and more.
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This idea that we need to somehow hide or mask
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that it is AI that you're communicating with will be
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something that you know, we look back at and again
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laugh at kind of that being a priority for us. Yeah,
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at the end of the day here, I think that
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moving more toward that infrastructure piece, as we look back
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at kind of the bolt on implementation that we have
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now across our internal workflows and external workflows, will be
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something that has changed pretty dramatically.
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Yeah, And I feel like that's one of those things
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where the context is so important, Like I want to
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know if I'm talking to an AI orf I'm talking
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to a human. I don't mind talking to the AI.
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I just want to know so that I know how
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it can practically help me. And I think with agentic
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like you're saying, it can help with a lot more
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than it used to.
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In the past.
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Yeah, definitely, I know, you wrote in your book that
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one of the biggest mistakes people make is kind of
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reducing the AI to just answering questions about the property.
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What opportunities are we missing when we think about it
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narrowly like that?
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Yeah, I mean listen, AI is capable of so much, right.
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AI can analyze documents, It can detect sentiment recognized behavioral
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patterns across the mentor journey, surface risks, operational risks, specifically
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spot trends. You know, there's so much that when we
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go into that infrastructure mindset that AI is really capable of.
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That value reaches far beyond on site teams or even
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regional managers, right, it goes on to operational leaders, asset managers, marketers.
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So reducing AI to this idea of conversation using my
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analogy from earlier is you know, judging electricity by whether
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or not it powers a light bulb. Right, It doesn't
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just help you answer, It helps you see more clearly,
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act earlier, and operate with consistency.
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Yeah, definitely, And it can you know, give you the
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next steps of where you need to go from there,
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which is so helpful.
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Yeah, definitely.
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If somebody came to you and they were like, well,
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I already have a chatbot, so I'm good on AI. Like,
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what would you say to that?
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Yeah, my question would be then what right? Okay, so
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the chatbot answer to question, So what happens after the answer?
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Is it taking action? Scheduling tours, pulling data from guest cards,
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updating records, escalating to humans when you know additional help
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is needed in passing along the context. If the bot
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can't act, then your team's still doing all the work
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behind the scenes. So chatbot's a tool, But what you
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act really want is you know, in the AI products
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that you use as a teammate that owns its own outcomes, right.
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Yeah, definitely. Are there things in our industry that you
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think are going to be standard as far as using
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AI to power it in the future that isn't necessarily
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talked about today.
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Yeah, a lot of it is that more down funnel stuff.
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I think that it's really easy for us to wrap
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our head around AI in that chatbot mindset. Even answering
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the phone and talking to perspective renters right very top
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of funnel is kind of what gets all of the
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spotlight right now for AI implementation when those down funnel
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use cases are so powerful.
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So renewals is a really good example.
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You know, being able to flag renewal risks early and
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drive consistent in earlier outreach is a really good example.
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Another one that I like a lot right now is
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the idea of leveraging AI for continuous lease audits. So
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you know, we we have all these leases, we have
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these ledgers, we have these compliance concerns around signatures and addendums.
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With AI, you can audit those things monthly, right, and
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you can get something that used to take hours and
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hours of human time done automatically powered by AI that
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not only allows you to ensure you're not missing revenue,
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but also catching you compliance risks as early as possible
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as well.
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Yeah, and I think doing things like that it gives
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on site leasing teams more time to interact with their residents,
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their prospects and just have that human element. And kind
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of going along those lines, there are these AI agents
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that we can start using to monitor, adjust respond without
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the human interaction. Where what is kind of the best
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practice when it comes to where the human should get involved,
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Where we should let AI agents kind of take it
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from there.
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Yeah, I think that the scheduled jobs, right, the rote tasks.
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That is a great opportunity for AI. Judgment is a
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great opportunity for people.
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You know.
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A way that I like to think about it is
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as an onsite team member, when you come in every day,
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you have a checklist of things that you need to
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get done right. You have that application you got to process,
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You got to reach back out to, you know, the
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resident about the noise complaint upstairs, there's a package you
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got lost in the package room. A prospect walks in
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off the street and needs a tour. So you have
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a list of things you must get done, and then
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you have a list of things that pop up out
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of nowhere that take you away from that list of
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things that you have to get done. So it's four o'clock,
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you got an hour left in your day, and none
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of that checklist is done because you've been dealing with the.
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People aspect all day.
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So if you let AI handle the checklist right, then
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the people get to play jazz right and adapt in
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real time to the needs of the humans that are
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living at the property. And I think that's a really
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good clear separation between the two.
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Yeah, that definitely makes sense. Along with that, as far
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as like marketing, teams. Do you think that AI will
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make small teams even more powerful or do you think
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it's going to raise the expectation of you know, being
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able to do more with less and get overwhelming.
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Yeah, it's a good question. I think my answer is both.
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You know, I think the outcome is going to depend
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on the team. In the scenario of empowerment, you know,
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agents can give marketing teams back control so you don't
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have to chase properties for data, you don't have to
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outsource routine execution because you're overwhelmed and don't have enough
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time with a small team, right, you let a small
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team operate much like a larger one. The risk case,
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I think is that efficiency without measurement creates the illusion
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of productivity, right, So faster or easier does not automatically
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mean better. The outcomes that you're driving matter a lot,
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so it's really going to depend on the team.
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And I think.
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Again, you look at agentic AIS a force multiplier for
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small teams, but only for ones that are able to
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operationalize it into a system that they're measuring right and
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ensuring that those outcomes are actually occurring, so that you
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know that more does mean better in that scenario.
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Yeah, that definitely makes sense. Switching gears a little bit.
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I do want to talk about GEO generative engine optimization.
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I know, as marketers, I mean, we talk a lot
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about SEO, but I think this is a new component
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that is going to come more into play in our industry.
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How do you think it will change the way marketers
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approach content and visibility?
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Yeah, it's a good question.
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I think the first thing whenever we talk about GEO
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that I think is really important and worth saying out
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loud is the first step to great GEO is really
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good SEO.
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So all of that stuff.
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In the marketer's brain about how to do a great
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job for the SEO for property is not irrelevant now
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it is more relevant than ever. Right, great SEO is
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step one for good GEO on the GEO side. Yeah,
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I mean we're going to start and already have but
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on changing the way we put content on our property
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websites so that when somebody's talking with Claude or chatch
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BT or Gemini, our property is more likely to be
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one of the sources referenced in the answers, right, one
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of the opportunities that's listed to the user. The end
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user leveraging that AI model, whichever one it might be.
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But I think that it's a good thing. I think
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that the kind of content that resonates well with large
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language models can also resonate well with people viewing your website.
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It's more conversational in nature, right, It's more of, Hey,
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here's a long thought out question that we often get,
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and here is a longer thought out answer that we
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often give. Right, Really robust FAQs is a great way
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to think about that. So I think in the near
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term it can benefit large language models and visitors of
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the website. I think in the long term, depending on
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what camp you're in, you know, maybe the websites just
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become repositories for data that the large language models use
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and everybody's interacting with chat GPT, right, So we'll have
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to see where the future takes us. But it's definitely
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something that we should be focusing on now.
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Yeah, definitely switching gives a little bit to operations and
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Lee saying, I know we talked about a few different scenarios,
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but are there any operational challenges that you think AI
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is really best to equip to solve right now as
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things are today?
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Yeah, it's a good question.
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I think the really immediate win that on site team
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should be excited about is reducing the cognitive load or
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constant context switching that on site teams do.
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Right. I use the word jazz earlier.
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The job of an on site team member is not
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factory work standing on an assembly line doing the same
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thing again and again.
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It really is jazz right. It's making it up as
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you go.
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It's dealing with scenarios in real time as they pop up.
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The important parts right, the human interaction, the problem solving constant.
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You know, they constantly interrupt that admin work. It still
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has to get done. So this is where AI is
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incredibly well equipped to solve immediate problems on the operation side.
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You know the I use that continuous lease auditing and
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example earlier. You know, the person that has that job
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knows how difficult that job is and time consuming it is.
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So you know, I think there's tons of opportunity to
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make on site teams lives better with AI.
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Yeah, totally. Are there certain metrics that you think people
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should be looking at to know whether their AI strategy
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is working or not?
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Think that it would be I don't want to pick
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a metric that I that I feel comfortable saying, hey,
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every property should measure this metric because every property is
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so different. What I will say, though, is that metrics
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that you choose should be tied to business outcomes, not activity.
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Right. So it's not a question.
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Of necessarily how many messages did you send, It's a
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question of.
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How many tours were booked, right.
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It's talking about you know, per lead versus cost per lease.
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It's understanding how we tie these metrics to outcomes. I
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would avoid the trap of again that illusion of practivity
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that I mentioned earlier, where you want to not focus
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on what was done, but rather focus on the outcome
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that that work drove.
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Yeah, that makes a ton of sense, and I think
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that's what people are looking at. You know, prior to AI,
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we are always looking at the business outcome. So I
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think it's just in line with that when it comes
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to your AI, continue to go down that road.
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Yeah, I agree.
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I think AI shouldn't be measured like software, should be
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measured like you would measure an employee, right, because at
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the end of the day, it's much more closely related
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to that.
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Yeah.
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Absolutely, I do want to talk about change management and
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kind of the rollout that we see. One of the
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strongest lines I think in your book is that AI
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doesn't fail, change management does. So why do you think
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it is that so many AI launches kind of fall short?
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Yeah, I think there's a few reasons.
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I think the most important aspect is adoption and follow through.
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So you know the categories I like to break it
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into is you really need that executive buy and you
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need the people at the top of the food chain
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that say, we are going to adopt an AI strategy
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at our company because we believe it will provide value
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and drive great outcomes. Right, then you need to take
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that kind of middle tier we'll say regional for an
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operations lens, and you need to make sure that they
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are bought in on measuring those outcomes. Right when you're
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having your one on one with your business managers or
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your community managers, are you referencing those AI driven outcomes?
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Right?
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Are we actually measuring those things? And then at the
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on site team level, you've got to have that champion,
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that person who's really excited, who's really bought in, and
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who is keeping tracked day to day. Right, if AI
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is driving the outcomes, if it's working correctly, servicing issues
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and those three levels of executive, regional and on site
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need to have that buy in, need to change the
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way that they work, and need to be constantly communicating
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with each other. This is not a set it and
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forget it, right. If we're by and in on the
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analogy that AIY is the new electricity, I mean that
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has been massive change and it requires constant attention and
398
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buying an adoption to make sure that it's successful as
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far from set it and forget it as you can get.
400
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I know, executive buying can be such a difficult thing sometimes.
401
00:20:17.000 --> 00:20:21.079
Is there anything that really makes the point get across
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to the executive team of like this is a thing
403
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that is going to like make the difference when it
404
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comes to us signing on with any.
405
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Kind of a Yeah, I think that you'll notice the
406
00:20:32.799 --> 00:20:35.839
repetitive themes of things I'm repeating myself on. But I
407
00:20:35.880 --> 00:20:40.559
think that that's indicative of the value of that that point, right,
408
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which is outcomes. You know, executive teams are focused on outcomes.
409
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They're focused on big number yeah, like ni right, like
410
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this is this is what our job is is graded on.
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So what you don't want to do is go and
412
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say this is going to increase NI. It's like, okay,
413
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I've heard that before, Right, you want to actually get specific.
414
00:21:00.519 --> 00:21:03.759
Here are the down funnel or downstream metrics that we're
415
00:21:03.759 --> 00:21:06.599
going to move that ultimately are going to drive the
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00:21:06.720 --> 00:21:10.160
upstream metric of NLI. So, for example, if we're able
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to increase our release excuse me, our leasing velocity with
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AI because it's doing X, Y, and Z, then we're
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going to drive down our vacancy loss, which is a
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huge detractor on NI. Right, So being able to actually
421
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paint the picture of how you're moving the needle on
422
00:21:24.920 --> 00:21:28.720
that number. But executives care about numbers. They care about outcomes, right,
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And I think your conversation should should be centered around Yeah.
424
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Absolutely. If you were giving advice to a property management
425
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company who is implementing AI for the very first time,
426
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are there a couple steps that you recommend, first thing
427
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off the bat that they should be doing.
428
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Yeah, three things stand out to me that there must
429
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have if you're going to start exploring AI adoption at
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the property or portfolio level. The first one is you
431
00:21:51.400 --> 00:21:53.799
got to name the problem. Honestly, it's the thing we
432
00:21:53.839 --> 00:21:56.400
were just talking about, like, why are you adopting AI?
433
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What are the pain points that you're trying to solve for.
434
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It shouldn't be because, as you know, the property nextdoor
435
00:22:02.480 --> 00:22:05.559
adopted AI. It should be because you see holes that
436
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need filling or gaps that need to be closed within
437
00:22:08.799 --> 00:22:11.880
your operational model that you feel AI can provide value.
438
00:22:12.279 --> 00:22:15.799
Then it's defining what success means in advance. Right, If
439
00:22:15.839 --> 00:22:18.440
you know what the gaps are, what does it look
440
00:22:18.519 --> 00:22:19.680
like when the gap is closed?
441
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Right? What is the metric?
442
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What are the numbers that you're going to monitor and
443
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measure and want to see move to know that.
444
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The AI adoption was successful.
445
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And then the last one is what we talked about earlier,
446
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is really building that internal alignment so that you have
447
00:22:33.920 --> 00:22:37.759
that executive, regional, on site team buy in going into
448
00:22:37.839 --> 00:22:40.880
it and making sure everyone is aligned on this being
449
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the right next step so that there's not internal friction
450
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that you're fighting alongside the change management process of adoption.
451
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Yeah, and I think having those metrics as well is
452
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such a critical one of like you need to have
453
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that ahead of time of what you're looking to increase
454
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or looking to improve. Otherwise you can say, yeah, I
455
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did great, but what's the proof?
456
00:23:02.559 --> 00:23:02.799
Yeah?
457
00:23:02.839 --> 00:23:07.079
Exactly, Yeah, absolutely, I know this is another thing you
458
00:23:07.119 --> 00:23:08.920
talked about in your book, but kind of just the
459
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fact that competition is it's a good thing, Like it's
460
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making everybody better at what they do. As we see
461
00:23:16.240 --> 00:23:19.079
it increase in our industry, How do you think that
462
00:23:19.079 --> 00:23:21.160
that landscape of AI is going to change over the
463
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next few years.
464
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Yeah, I mean, I think competition is an incredibly healthy signal. Right,
465
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when something becomes infrastructure, Like I'm talking about category forms
466
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and competition flows, and that's good for everybody. Everybody's driven
467
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to be better, build better products, have more affordable products. Yeah,
468
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it means that the problem that it's solving is urgent, right,
469
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and valuable enough for multiple companies to try to run
470
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a business solving that problem. I think the flip side
471
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of that coin, or the risk, is that language starts
472
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to get really uniform. AI is in everything, Right, what
473
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product do you use today that doesn't claim to have
474
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an AI component? So it becomes tempting to choose based
475
00:24:04.839 --> 00:24:08.839
on convenience or price instead of outcomes, which we were
476
00:24:08.839 --> 00:24:12.400
talking about earlier. If everybody offers AI, you're going to
477
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find free AI, cheap AI, right, You're going to find
478
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AI that does this, AI that does everything. So It
479
00:24:18.759 --> 00:24:21.000
really makes the job of the buyer a little bit
480
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harder because you have to be diligent and you have
481
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to drive the sales process when you're purchasing you products.
482
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And again, just reiterate focusing on outcomes. What are the
483
00:24:31.480 --> 00:24:34.720
outcomes that you're after, and make sure the products or
484
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companies that you're talking to are actually driving positive results
485
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towards those outcomes.
486
00:24:39.759 --> 00:24:42.160
Yeah, I remember, I know I've talked about this on
487
00:24:42.200 --> 00:24:44.400
the podcast before, but when I was on the property
488
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management side, I was tasked with finding a chatbot for
489
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and this was like, I don't know, more than five
490
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years ago, so it was before chat gbt really got big.
491
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But I had this whole spreadsheet of like, this is
492
00:24:55.519 --> 00:24:57.880
what I need the bot to do. These are the
493
00:24:57.920 --> 00:25:01.440
companies that have these exact functional and their cost Like
494
00:25:01.559 --> 00:25:05.200
I was crazy on my spreadsheet about what each company
495
00:25:05.279 --> 00:25:08.200
had because you really do have to do that due diligence.
496
00:25:08.480 --> 00:25:10.599
You can't just settle for the first thing. Make sure
497
00:25:10.599 --> 00:25:11.720
it has exactly.
498
00:25:11.319 --> 00:25:12.920
What you need. Yeah, that's incredible.
499
00:25:13.000 --> 00:25:15.440
I love to hear that, and I think what I
500
00:25:15.440 --> 00:25:18.680
would encourage everyone to do not only to follow your
501
00:25:18.720 --> 00:25:21.440
model and do your diligence and keep your records and
502
00:25:21.680 --> 00:25:25.440
internal decision making matrix is to share that with the
503
00:25:25.519 --> 00:25:28.200
vendors you're talking to, Like, there's no reason to keep
504
00:25:28.279 --> 00:25:31.079
it a secret. What you need in order to say yes,
505
00:25:31.279 --> 00:25:33.480
you know, if you let the folks you're talking to
506
00:25:34.039 --> 00:25:36.519
that you're getting demos from, know, Hey, this is how
507
00:25:36.559 --> 00:25:38.279
you win my business, right, This is what I need
508
00:25:38.319 --> 00:25:40.559
you to be able to do. This is what matters
509
00:25:40.599 --> 00:25:43.000
most to me. It'll create a better experience for everybody.
510
00:25:43.079 --> 00:25:45.559
It won't waste time going down a road with a
511
00:25:45.640 --> 00:25:47.599
vendor who will never be able to support that. And
512
00:25:47.880 --> 00:25:51.480
it doesn't make you listen to pitches about features.
513
00:25:50.880 --> 00:25:52.079
That you don't care about.
514
00:25:52.319 --> 00:25:54.880
The other piece and I'm curious if you had a
515
00:25:55.000 --> 00:25:56.599
row for this on your spreadsheet.
516
00:25:56.680 --> 00:25:58.160
Is the people aspect? Right?
517
00:25:58.200 --> 00:26:01.279
Whatever company you get married to, you know you're going
518
00:26:01.359 --> 00:26:03.720
to have to work with them moving forward. And I
519
00:26:03.720 --> 00:26:07.720
think feeling like you're with good people also matters a lot.
520
00:26:08.319 --> 00:26:11.039
Yeah, Like for me, it was the responsiveness. I mean,
521
00:26:11.079 --> 00:26:12.720
I can't even tell you how many vendors I had
522
00:26:12.759 --> 00:26:14.680
where I would send an email and wait a week
523
00:26:14.680 --> 00:26:17.640
and a half for response. If somebody responded to me
524
00:26:17.720 --> 00:26:20.440
within like a day or two, it was like bonus
525
00:26:20.480 --> 00:26:21.200
points for you.
526
00:26:22.359 --> 00:26:22.559
Yeah.
527
00:26:22.559 --> 00:26:25.519
And the funny thing about that is that's also true
528
00:26:25.519 --> 00:26:29.039
for prospects looking for apartments and why products like this
529
00:26:29.160 --> 00:26:31.440
make sense, right because are you going to be the
530
00:26:31.480 --> 00:26:33.200
property that takes a week to get back or are
531
00:26:33.240 --> 00:26:35.279
you going to send them back an email thirty seconds later?
532
00:26:35.359 --> 00:26:39.079
Because it does drive decision making right, Yeah, totally.
533
00:26:39.160 --> 00:26:41.640
And I will say this too as being on the
534
00:26:41.680 --> 00:26:44.960
property management side and the vendor side, I have always said, like,
535
00:26:45.000 --> 00:26:46.920
when I was on the property management side, I would
536
00:26:46.960 --> 00:26:49.839
love for a company to give me a comparison list
537
00:26:49.880 --> 00:26:53.640
of like them versus their biggest competitor. But after being
538
00:26:53.640 --> 00:26:55.680
on the vendor side, I can say that that is
539
00:26:56.319 --> 00:26:59.079
a little bit difficult as a vendor for us to
540
00:26:59.119 --> 00:27:03.680
produce because we don't know what the competition has verbata.
541
00:27:03.480 --> 00:27:05.359
You don't work there, yeah, yeah, And.
542
00:27:05.680 --> 00:27:09.559
It's always changing. Everybody always has new developments. So if
543
00:27:09.640 --> 00:27:12.079
you're an operator and you're asking for that, take a
544
00:27:12.160 --> 00:27:14.480
bee and kind of come up with that list of
545
00:27:14.519 --> 00:27:16.799
what do you want, what's important to you, and then
546
00:27:16.839 --> 00:27:18.920
go to the vendor and ask if they have those things.
547
00:27:19.039 --> 00:27:22.640
But asking for a verbatim comparison can be kind of difficult.
548
00:27:23.599 --> 00:27:27.200
Yeah, yeah, Listen, I don't work at the competitor's company, right,
549
00:27:27.240 --> 00:27:29.960
and any information I have about them, I got from
550
00:27:30.000 --> 00:27:32.279
a website or from a story somebody told me, and
551
00:27:32.319 --> 00:27:33.680
I don't know if it's true or not, you know,
552
00:27:33.799 --> 00:27:36.440
So don't ask me to make to make you something
553
00:27:36.480 --> 00:27:37.960
that perjure myself or what.
554
00:27:38.000 --> 00:27:40.680
You know what I mean. So, yeah, I'm sympathetic to that.
555
00:27:41.400 --> 00:27:43.880
Yeah, Okay, final thoughts, What are some of the biggest
556
00:27:43.880 --> 00:27:46.720
opportunities in multifamily we could be missing right now when
557
00:27:46.720 --> 00:27:48.240
it comes to AI and leasing.
558
00:27:48.400 --> 00:27:51.119
Yeah, I think it goes back to that chatbot analogy
559
00:27:51.160 --> 00:27:53.920
of really just thinking too small, right, if you treat
560
00:27:54.000 --> 00:27:57.960
if you treat AI as just another tool, then you're
561
00:27:57.960 --> 00:27:59.680
going to use it like one, right, and you're going
562
00:27:59.759 --> 00:28:03.440
to be chasing these really small efficiencies or these really
563
00:28:03.519 --> 00:28:07.200
isolated use cases. And I think that the real opportunity
564
00:28:07.240 --> 00:28:10.319
is so much bigger than that, and it's rethinking how
565
00:28:10.920 --> 00:28:14.799
the business operates. And you know, intelligence is no longer
566
00:28:14.839 --> 00:28:17.400
a limiting factor when you bring AI into the fold,
567
00:28:17.839 --> 00:28:21.119
So don't let it be a constraint when you're building
568
00:28:21.160 --> 00:28:25.160
out how operations is going to work, you know, within
569
00:28:25.240 --> 00:28:27.880
your business or within your property. So it's really a
570
00:28:27.920 --> 00:28:30.880
mindset shift of stop asking whether something can be done
571
00:28:31.240 --> 00:28:33.119
and start asking why it hasn't been done yet?
572
00:28:33.200 --> 00:28:35.440
Right? What are we missing that's stopping us from doing
573
00:28:35.440 --> 00:28:37.039
the thing? Yeah?
574
00:28:37.119 --> 00:28:39.359
Exactly. Okay, I have one final question that I ask
575
00:28:39.440 --> 00:28:41.279
every single one of my guests. It does not have
576
00:28:41.359 --> 00:28:43.720
to be related to this whatsoever, but is there tech
577
00:28:43.759 --> 00:28:45.839
tool that you are personally loving right now?
578
00:28:45.920 --> 00:28:46.119
Yeah?
579
00:28:46.160 --> 00:28:48.279
I mean for me, it's easy. It's Claude. I think
580
00:28:48.319 --> 00:28:50.920
the thing I would really emphasize those It's not like
581
00:28:51.319 --> 00:28:53.200
the claud app on my phone that I talked back
582
00:28:53.240 --> 00:28:56.079
and forth with. Claude is capable like a lot of
583
00:28:56.119 --> 00:28:59.640
the models are of so much, and it's really taking
584
00:28:59.680 --> 00:29:01.920
the the spirit of what I said earlier, what are
585
00:29:01.920 --> 00:29:03.720
the things that I'm doing every day all day that
586
00:29:03.759 --> 00:29:06.079
are repetitive that take up my time, to take up
587
00:29:06.079 --> 00:29:09.799
my team's time, and leveraging Claude to build out those
588
00:29:09.880 --> 00:29:14.000
really bespoke internal solutions that do it the nurture boss way, right,
589
00:29:14.119 --> 00:29:17.480
so that we can really get operational efficiency in there.
590
00:29:17.559 --> 00:29:19.960
So I'm a really really big proponent of Claude. I
591
00:29:20.000 --> 00:29:22.839
recommend everybody give a shot here and try it.
592
00:29:22.880 --> 00:29:23.079
Out.
593
00:29:23.640 --> 00:29:25.759
I think Claude is having a moment right now because
594
00:29:25.880 --> 00:29:29.559
I have always been a chat GBT girl, and all
595
00:29:29.599 --> 00:29:31.440
of a sudden, in the last few months, I have
596
00:29:31.759 --> 00:29:34.559
just been loving Claude as well, and I just think
597
00:29:34.599 --> 00:29:35.400
it's amazing.
598
00:29:36.559 --> 00:29:39.880
Yeah, I mean, and they put out really incredible Stuffy
599
00:29:40.359 --> 00:29:43.079
Fable five is the new model they put out that
600
00:29:43.319 --> 00:29:46.559
was in the Mythos family of models, And without getting
601
00:29:46.559 --> 00:29:49.519
too detailed about it, it's just very incredible what it's
602
00:29:49.559 --> 00:29:54.599
capable of. Its ability to understand giant chunks of context
603
00:29:54.720 --> 00:29:58.400
across many disparate sources has really been a game changer
604
00:29:58.480 --> 00:29:59.680
for what you can do with it.
605
00:30:00.160 --> 00:30:02.359
Yeah, it's just crazy to see how far we've come.
606
00:30:02.440 --> 00:30:06.000
Like when I was initially looking at those different bought options,
607
00:30:06.039 --> 00:30:08.440
it was like you have automation and you have NLP
608
00:30:08.599 --> 00:30:12.000
and that's it. And NLP was just not there at
609
00:30:12.039 --> 00:30:14.160
the time. And so now all of a sudden, it's
610
00:30:14.200 --> 00:30:18.240
like we're having full multi context conversations with the AI
611
00:30:18.359 --> 00:30:21.400
and it's just insane how quickly that happened.
612
00:30:22.200 --> 00:30:24.559
Yeah, I think that you know the phrase you're using,
613
00:30:24.559 --> 00:30:27.440
it's so important as context right, context is king, and
614
00:30:27.799 --> 00:30:30.680
that's what's making the difference between something that's, you know,
615
00:30:30.880 --> 00:30:34.119
pretty cool versus mind blowing is its ability to manage
616
00:30:34.119 --> 00:30:37.319
and maintain and organize that context. Yeah.
617
00:30:37.359 --> 00:30:40.400
Absolutely, well, thank you so much for joining me. This
618
00:30:40.440 --> 00:30:42.880
has been such a great conversation. Where can people find
619
00:30:42.920 --> 00:30:45.359
you and connect with you and learn more about nurture Boss.
620
00:30:45.960 --> 00:30:50.160
Yeah, I mean our website, nurtureboss dot io has tons
621
00:30:50.200 --> 00:30:54.160
of resources. I spend most of my time talking on LinkedIn, right.
622
00:30:54.240 --> 00:30:57.440
I love to let people know what I'm thinking about
623
00:30:57.480 --> 00:30:59.640
in case maybe you find it interesting what I'm getting
624
00:30:59.680 --> 00:31:02.440
excited about. I'm always creating content and putting it out there.
625
00:31:02.480 --> 00:31:05.000
It's always free. It's not behind like a Hey give
626
00:31:05.039 --> 00:31:07.480
me your email address and I'll send you the pdf.
627
00:31:07.079 --> 00:31:07.640
Right, none of that.
628
00:31:07.839 --> 00:31:10.279
I just want everyone to kind of, you know, see
629
00:31:10.279 --> 00:31:14.319
what I'm thinking about. And yeah, it's been fun for me.
630
00:31:14.400 --> 00:31:16.079
So that's definitely the place i'd follow along.
631
00:31:16.559 --> 00:31:19.640
I love it, and I do highly recommend anybody who's
632
00:31:19.640 --> 00:31:23.720
curious to learn more go check out Jacob's book AI
633
00:31:23.799 --> 00:31:26.720
and Property Management because it really does break it down
634
00:31:26.759 --> 00:31:28.880
in a way that you can understand. There's tons of
635
00:31:28.920 --> 00:31:32.319
research I know you did behind that as well, so yeah,
636
00:31:32.440 --> 00:31:35.200
go give that a read if you haven't already. Thanks
637
00:31:35.200 --> 00:31:39.079
for joining Tip Talks Tech. Tune in next time for
638
00:31:39.200 --> 00:31:39.799
more candidate
1
00:00:01.679 --> 00:00:05.679
Welcome to Tiff Talks Tech, the podcast where we unravel
2
00:00:05.759 --> 00:00:10.640
the mysteries of multifamily marketing tech, making it not just understandable,
3
00:00:10.759 --> 00:00:14.679
but dare I say enjoyable. I'm your host, Tiffany, a
4
00:00:14.759 --> 00:00:18.679
season multi family marketer turn tech expert, here to guide
5
00:00:18.719 --> 00:00:21.600
you through your daily tech challenges with ease and a
6
00:00:21.640 --> 00:00:29.719
sprinkle of fun. Hello everyone, and welcome to another episode
7
00:00:29.719 --> 00:00:32.600
of Tiff Talks Tech. Today. I'm excited because I'm joined
8
00:00:32.600 --> 00:00:35.039
by Jacob Carter, who is the CEO and founder of
9
00:00:35.200 --> 00:00:38.399
Nurture Boss and author of the new book AI and
10
00:00:38.479 --> 00:00:41.119
Property Management. Today, I'm excited because we're going to be
11
00:00:41.200 --> 00:00:46.119
talking about the evolution of AI and multifamily. But before
12
00:00:46.159 --> 00:00:48.880
we really dive in, Jacob, could you share a little
13
00:00:48.880 --> 00:00:50.719
bit about you for anyone who may not know you
14
00:00:50.799 --> 00:00:53.479
yet and just kind of like a quick version of
15
00:00:53.479 --> 00:00:55.560
what you do in Nurture Boss.
16
00:00:56.039 --> 00:00:58.359
Yeah. Yeah, happy to Thanks for having me. I'm excited
17
00:00:58.399 --> 00:00:58.840
to be here.
18
00:00:59.000 --> 00:01:03.479
My back is professional software engineer for my whole career
19
00:01:03.520 --> 00:01:06.519
prior to starting Nurture Boss, so very tech oriented.
20
00:01:06.640 --> 00:01:08.359
I like getting in the weeds on.
21
00:01:08.680 --> 00:01:13.120
Technical stuff, so if you have technical questions, I'm probably
22
00:01:13.200 --> 00:01:15.120
your guy to answer them, but let's wait and see
23
00:01:15.120 --> 00:01:18.400
if I actually can. First, But Nurture Boss, we focus
24
00:01:18.480 --> 00:01:22.439
on AI and property management much like the book titles suggests,
25
00:01:22.879 --> 00:01:27.000
and that's building solutions for multifamily that leverage AI to
26
00:01:27.319 --> 00:01:30.319
augment and enhance all parts of the lead to lease
27
00:01:30.480 --> 00:01:33.719
and lease to renewal life cycle, as well as even
28
00:01:33.799 --> 00:01:36.519
things on the asset management side of the house as well.
29
00:01:36.599 --> 00:01:38.879
So happy to go into more detail there if you'd like.
30
00:01:38.959 --> 00:01:42.719
But all things AI and multi family Nurture Boss, we
31
00:01:42.799 --> 00:01:43.640
are your group for that.
32
00:01:44.400 --> 00:01:46.480
I love it. That's great. It did read your book
33
00:01:46.480 --> 00:01:48.599
and I think it was amazing. I think it explains
34
00:01:49.120 --> 00:01:52.079
things and an easy way to understand. One of the
35
00:01:52.120 --> 00:01:54.519
things that you do talk about is how in our
36
00:01:54.560 --> 00:02:00.920
industry we use automation, AI, conversational AI, generative agent TIC,
37
00:02:01.000 --> 00:02:03.480
all of that. We use it very interchangeably, but they're
38
00:02:03.519 --> 00:02:05.879
not all the same thing. Could you talk a little
39
00:02:05.879 --> 00:02:06.840
bit about the differences.
40
00:02:07.799 --> 00:02:08.639
Yeah, happy too.
41
00:02:08.719 --> 00:02:11.680
And I think my caveat here is that I think
42
00:02:11.759 --> 00:02:15.159
it is valuable to use the right word for the
43
00:02:15.240 --> 00:02:17.800
right meaning. I do think that there's a way to
44
00:02:18.080 --> 00:02:20.159
over index on perfect clarity.
45
00:02:20.199 --> 00:02:22.240
So I think it's really useful to know the difference.
46
00:02:22.240 --> 00:02:24.960
I think there are certain times when it is inappropriate
47
00:02:25.000 --> 00:02:28.199
to use one when the other one is the correct one.
48
00:02:28.360 --> 00:02:30.240
But I do think we also deserve a little bit
49
00:02:30.280 --> 00:02:33.639
of grace when we're trying to talk about complex topics.
50
00:02:33.680 --> 00:02:37.319
But automation at its core is really about a rules engine.
51
00:02:37.319 --> 00:02:41.719
If this happens, then do that right. It's very basic
52
00:02:41.840 --> 00:02:45.680
and compared to some of the other definitions we can
53
00:02:45.680 --> 00:02:48.280
get into around AI, and it's something that's been around
54
00:02:48.360 --> 00:02:51.319
for a very very long time. But automation most certainly
55
00:02:51.439 --> 00:02:56.000
is not inherently AI or AI driven. Generative AI is
56
00:02:56.080 --> 00:02:58.599
helpful to think of as kind of the over arching
57
00:02:58.719 --> 00:03:01.719
umbrella when we're typically talking about the kind of AI
58
00:03:02.240 --> 00:03:04.560
that we'd be leveraging in multifamily or a lot of
59
00:03:04.599 --> 00:03:05.560
other industries.
60
00:03:06.199 --> 00:03:08.240
I think of this as the brain, right.
61
00:03:08.400 --> 00:03:12.240
It knows how to produce new content based on patterns
62
00:03:12.240 --> 00:03:15.439
that it has learned in the past, and conversational AI
63
00:03:15.560 --> 00:03:18.280
is just one more step on top of that, where
64
00:03:18.280 --> 00:03:23.319
you're taking that generated outcome and then giving it new inputs.
65
00:03:23.360 --> 00:03:25.280
It's that back and forth that you're having with the
66
00:03:25.319 --> 00:03:28.719
generative AI. It very quickly turns into conversational AI.
67
00:03:30.680 --> 00:03:34.000
AI is the same as like natural language processing.
68
00:03:34.159 --> 00:03:36.719
Yeah, I mean get really like in the weeds here
69
00:03:36.800 --> 00:03:39.759
on this so we can it for the sake of
70
00:03:39.800 --> 00:03:40.599
our conversation.
71
00:03:40.840 --> 00:03:42.080
I think that that's okay.
72
00:03:42.520 --> 00:03:47.439
Natural language processing is really about taking an input right
73
00:03:47.520 --> 00:03:50.759
that somebody has done, like think chat chatbot style, and
74
00:03:50.960 --> 00:03:53.680
understanding how you can take that input and do things
75
00:03:53.719 --> 00:03:57.240
like get rough understanding of context what they're referring to,
76
00:03:57.639 --> 00:04:01.479
categorization of the question that's been asked. So it's not
77
00:04:01.599 --> 00:04:06.719
nearly natively is robust or intelligent if you will, as
78
00:04:06.719 --> 00:04:10.319
a gender AI which is going well far beyond NLP
79
00:04:11.120 --> 00:04:15.199
to actually use its learning and training to produce sometimes
80
00:04:15.240 --> 00:04:18.920
novel outcomes or outputs right based on that makes sense,
81
00:04:19.879 --> 00:04:23.399
I think augentic AI though, that's really where the magic
82
00:04:23.439 --> 00:04:26.160
starts to happen. That's when you're taking gender of AI
83
00:04:26.360 --> 00:04:29.720
or conversational AI and you're empowering it with the ability
84
00:04:29.720 --> 00:04:34.279
to interact with its environment said differently, drive outcomes actually
85
00:04:34.319 --> 00:04:37.360
do things right, and I think that's where we really
86
00:04:37.399 --> 00:04:40.800
start to get value from AI. And multifamily is on
87
00:04:40.879 --> 00:04:42.600
the agentic AI front.
88
00:04:43.560 --> 00:04:46.040
Yeah, And I think it's so important to understand the
89
00:04:46.079 --> 00:04:48.560
distinction when it comes to looking for a platform that
90
00:04:48.600 --> 00:04:51.279
you're going to use, because that will be able to
91
00:04:51.319 --> 00:04:52.279
tell the functionality.
92
00:04:52.879 --> 00:04:53.439
Yeah, definitely.
93
00:04:53.480 --> 00:04:57.040
And I think also it's helpful to know that, you know,
94
00:04:57.079 --> 00:04:59.639
we default to automation a lot because it's something we're
95
00:04:59.639 --> 00:05:02.360
comfortable with, but it is wildly different than like an
96
00:05:02.360 --> 00:05:04.480
agentic AI platform. And I think at the end of
97
00:05:04.519 --> 00:05:06.680
the day, you should ask yourselves, am I trying to
98
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intelligently drive outcomes in real world results based on the
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technology that I'm using? Or am I looking to automate
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a process?
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Right?
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Which are not inherently the same thing.
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Yeah, that definitely makes sense speaking of processes. I know
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in your book you talk about AI becoming this new
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infrastructure of how we work. What does it actually look
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like in our industry and how will.
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We use that? Yeah?
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I think AI will certainly be the new infrastructure and
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what I mean by that, So my favorite analogy is
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for this scenario is electricity.
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Right.
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When electricity, you know, when we harnessed electricity rights as
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a species, it didn't just replace candles, right, it rewired
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the way we do everything, manufacturing, healthcare, food, storage, communication,
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everything changed, Right. You don't you electricity? You exist in
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an electrified world? And that's what I mean when I
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say AI is infrastructure. I don't mean that we use AI.
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What I mean is that our world is powered by
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AI technology. I think that's where we're going everywhere, not
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just multifamily. When it comes to multifamily, though, we're talking
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about it showing up across the entire renter's journey, right
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from search to leasing, conversations, on site support, ongoing resident communication,
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the marketing team. Right, this is becoming more of an
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infrastructure type technology. Is we spread across those different areas.
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I think a really good practical marker for when we've
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gotten there is when AI is no longer on the
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edge of the experience, but is moving to the center
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of the experience. So rather than answering a prospect, which
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is the edges, right, we're helping teams prioritize leads, spot bottlenecks,
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drive performance right more into the center. And that's what
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I mean when I say AI infrastructure.
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Yeah, definitely, that totally makes sense. Are there things that
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we're using AI for in these kind of early stages
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that you think one day we're going to look back
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on and be like that was ridiculous?
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Yeah?
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I think a lot so I think right now, you know,
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this idea of I have a chatbot, so I'm using
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AI as something that will chuckle at down the road.
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You know, I think a lot of people now are
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hyper focused on if AI sounds like a person or not.
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I think is society at large adopts AI more and more.
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This idea that we need to somehow hide or mask
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that it is AI that you're communicating with will be
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something that you know, we look back at and again
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laugh at kind of that being a priority for us. Yeah,
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at the end of the day here, I think that
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moving more toward that infrastructure piece, as we look back
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at kind of the bolt on implementation that we have
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now across our internal workflows and external workflows, will be
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something that has changed pretty dramatically.
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Yeah, And I feel like that's one of those things
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where the context is so important, Like I want to
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know if I'm talking to an AI orf I'm talking
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to a human. I don't mind talking to the AI.
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I just want to know so that I know how
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it can practically help me. And I think with agentic
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like you're saying, it can help with a lot more
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than it used to.
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In the past.
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Yeah, definitely, I know, you wrote in your book that
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one of the biggest mistakes people make is kind of
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reducing the AI to just answering questions about the property.
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What opportunities are we missing when we think about it
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narrowly like that?
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Yeah, I mean listen, AI is capable of so much, right.
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AI can analyze documents, It can detect sentiment recognized behavioral
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patterns across the mentor journey, surface risks, operational risks, specifically
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spot trends. You know, there's so much that when we
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go into that infrastructure mindset that AI is really capable of.
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That value reaches far beyond on site teams or even
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regional managers, right, it goes on to operational leaders, asset managers, marketers.
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So reducing AI to this idea of conversation using my
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analogy from earlier is you know, judging electricity by whether
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or not it powers a light bulb. Right, It doesn't
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just help you answer, It helps you see more clearly,
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act earlier, and operate with consistency.
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Yeah, definitely, And it can you know, give you the
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next steps of where you need to go from there,
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which is so helpful.
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Yeah, definitely.
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If somebody came to you and they were like, well,
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I already have a chatbot, so I'm good on AI. Like,
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what would you say to that?
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Yeah, my question would be then what right? Okay, so
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the chatbot answer to question, So what happens after the answer?
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Is it taking action? Scheduling tours, pulling data from guest cards,
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updating records, escalating to humans when you know additional help
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is needed in passing along the context. If the bot
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can't act, then your team's still doing all the work
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behind the scenes. So chatbot's a tool, But what you
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act really want is you know, in the AI products
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that you use as a teammate that owns its own outcomes, right.
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Yeah, definitely. Are there things in our industry that you
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think are going to be standard as far as using
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AI to power it in the future that isn't necessarily
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talked about today.
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Yeah, a lot of it is that more down funnel stuff.
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I think that it's really easy for us to wrap
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our head around AI in that chatbot mindset. Even answering
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the phone and talking to perspective renters right very top
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of funnel is kind of what gets all of the
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spotlight right now for AI implementation when those down funnel
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use cases are so powerful.
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So renewals is a really good example.
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You know, being able to flag renewal risks early and
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drive consistent in earlier outreach is a really good example.
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Another one that I like a lot right now is
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the idea of leveraging AI for continuous lease audits. So
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you know, we we have all these leases, we have
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these ledgers, we have these compliance concerns around signatures and addendums.
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With AI, you can audit those things monthly, right, and
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you can get something that used to take hours and
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hours of human time done automatically powered by AI that
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not only allows you to ensure you're not missing revenue,
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but also catching you compliance risks as early as possible
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as well.
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Yeah, and I think doing things like that it gives
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on site leasing teams more time to interact with their residents,
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their prospects and just have that human element. And kind
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of going along those lines, there are these AI agents
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that we can start using to monitor, adjust respond without
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the human interaction. Where what is kind of the best
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practice when it comes to where the human should get involved,
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Where we should let AI agents kind of take it
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from there.
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Yeah, I think that the scheduled jobs, right, the rote tasks.
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That is a great opportunity for AI. Judgment is a
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great opportunity for people.
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You know.
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A way that I like to think about it is
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as an onsite team member, when you come in every day,
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you have a checklist of things that you need to
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get done right. You have that application you got to process,
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You got to reach back out to, you know, the
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resident about the noise complaint upstairs, there's a package you
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got lost in the package room. A prospect walks in
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off the street and needs a tour. So you have
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a list of things you must get done, and then
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you have a list of things that pop up out
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of nowhere that take you away from that list of
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things that you have to get done. So it's four o'clock,
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you got an hour left in your day, and none
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of that checklist is done because you've been dealing with the.
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People aspect all day.
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So if you let AI handle the checklist right, then
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the people get to play jazz right and adapt in
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real time to the needs of the humans that are
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living at the property. And I think that's a really
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good clear separation between the two.
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Yeah, that definitely makes sense. Along with that, as far
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as like marketing, teams. Do you think that AI will
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make small teams even more powerful or do you think
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it's going to raise the expectation of you know, being
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able to do more with less and get overwhelming.
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Yeah, it's a good question. I think my answer is both.
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You know, I think the outcome is going to depend
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on the team. In the scenario of empowerment, you know,
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agents can give marketing teams back control so you don't
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have to chase properties for data, you don't have to
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outsource routine execution because you're overwhelmed and don't have enough
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time with a small team, right, you let a small
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team operate much like a larger one. The risk case,
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I think is that efficiency without measurement creates the illusion
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of productivity, right, So faster or easier does not automatically
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mean better. The outcomes that you're driving matter a lot,
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so it's really going to depend on the team.
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And I think.
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Again, you look at agentic AIS a force multiplier for
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small teams, but only for ones that are able to
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operationalize it into a system that they're measuring right and
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ensuring that those outcomes are actually occurring, so that you
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know that more does mean better in that scenario.
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Yeah, that definitely makes sense. Switching gears a little bit.
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I do want to talk about GEO generative engine optimization.
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I know, as marketers, I mean, we talk a lot
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about SEO, but I think this is a new component
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that is going to come more into play in our industry.
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How do you think it will change the way marketers
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approach content and visibility?
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Yeah, it's a good question.
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I think the first thing whenever we talk about GEO
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that I think is really important and worth saying out
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loud is the first step to great GEO is really
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good SEO.
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So all of that stuff.
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In the marketer's brain about how to do a great
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job for the SEO for property is not irrelevant now
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it is more relevant than ever. Right, great SEO is
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step one for good GEO on the GEO side. Yeah,
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I mean we're going to start and already have but
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on changing the way we put content on our property
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websites so that when somebody's talking with Claude or chatch
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BT or Gemini, our property is more likely to be
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one of the sources referenced in the answers, right, one
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of the opportunities that's listed to the user. The end
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user leveraging that AI model, whichever one it might be.
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But I think that it's a good thing. I think
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that the kind of content that resonates well with large
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language models can also resonate well with people viewing your website.
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It's more conversational in nature, right, It's more of, Hey,
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here's a long thought out question that we often get,
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and here is a longer thought out answer that we
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often give. Right, Really robust FAQs is a great way
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to think about that. So I think in the near
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term it can benefit large language models and visitors of
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the website. I think in the long term, depending on
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what camp you're in, you know, maybe the websites just
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become repositories for data that the large language models use
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and everybody's interacting with chat GPT, right, So we'll have
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to see where the future takes us. But it's definitely
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something that we should be focusing on now.
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Yeah, definitely switching gives a little bit to operations and
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Lee saying, I know we talked about a few different scenarios,
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but are there any operational challenges that you think AI
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is really best to equip to solve right now as
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things are today?
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Yeah, it's a good question.
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I think the really immediate win that on site team
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should be excited about is reducing the cognitive load or
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constant context switching that on site teams do.
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Right. I use the word jazz earlier.
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The job of an on site team member is not
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factory work standing on an assembly line doing the same
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thing again and again.
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It really is jazz right. It's making it up as
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you go.
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It's dealing with scenarios in real time as they pop up.
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The important parts right, the human interaction, the problem solving constant.
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You know, they constantly interrupt that admin work. It still
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has to get done. So this is where AI is
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incredibly well equipped to solve immediate problems on the operation side.
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You know the I use that continuous lease auditing and
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example earlier. You know, the person that has that job
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knows how difficult that job is and time consuming it is.
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So you know, I think there's tons of opportunity to
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make on site teams lives better with AI.
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Yeah, totally. Are there certain metrics that you think people
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should be looking at to know whether their AI strategy
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is working or not?
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Think that it would be I don't want to pick
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a metric that I that I feel comfortable saying, hey,
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every property should measure this metric because every property is
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so different. What I will say, though, is that metrics
345
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that you choose should be tied to business outcomes, not activity.
346
00:17:40.759 --> 00:17:42.400
Right. So it's not a question.
347
00:17:42.279 --> 00:17:46.079
Of necessarily how many messages did you send, It's a
348
00:17:46.160 --> 00:17:46.640
question of.
349
00:17:46.599 --> 00:17:48.559
How many tours were booked, right.
350
00:17:48.799 --> 00:17:54.680
It's talking about you know, per lead versus cost per lease.
351
00:17:55.319 --> 00:17:59.279
It's understanding how we tie these metrics to outcomes. I
352
00:17:59.319 --> 00:18:03.319
would avoid the trap of again that illusion of practivity
353
00:18:03.720 --> 00:18:06.839
that I mentioned earlier, where you want to not focus
354
00:18:06.880 --> 00:18:10.039
on what was done, but rather focus on the outcome
355
00:18:10.079 --> 00:18:10.960
that that work drove.
356
00:18:11.119 --> 00:18:14.319
Yeah, that makes a ton of sense, and I think
357
00:18:14.359 --> 00:18:17.559
that's what people are looking at. You know, prior to AI,
358
00:18:17.680 --> 00:18:20.160
we are always looking at the business outcome. So I
359
00:18:20.200 --> 00:18:22.240
think it's just in line with that when it comes
360
00:18:22.279 --> 00:18:24.880
to your AI, continue to go down that road.
361
00:18:25.359 --> 00:18:26.200
Yeah, I agree.
362
00:18:26.400 --> 00:18:28.880
I think AI shouldn't be measured like software, should be
363
00:18:28.920 --> 00:18:31.200
measured like you would measure an employee, right, because at
364
00:18:31.200 --> 00:18:33.480
the end of the day, it's much more closely related
365
00:18:33.519 --> 00:18:33.759
to that.
366
00:18:34.440 --> 00:18:35.039
Yeah.
367
00:18:35.079 --> 00:18:38.440
Absolutely, I do want to talk about change management and
368
00:18:38.519 --> 00:18:41.079
kind of the rollout that we see. One of the
369
00:18:41.079 --> 00:18:43.279
strongest lines I think in your book is that AI
370
00:18:43.359 --> 00:18:46.319
doesn't fail, change management does. So why do you think
371
00:18:46.359 --> 00:18:49.960
it is that so many AI launches kind of fall short?
372
00:18:50.079 --> 00:18:52.880
Yeah, I think there's a few reasons.
373
00:18:52.920 --> 00:18:56.960
I think the most important aspect is adoption and follow through.
374
00:18:57.039 --> 00:18:59.559
So you know the categories I like to break it
375
00:18:59.599 --> 00:19:02.000
into is you really need that executive buy and you
376
00:19:02.039 --> 00:19:03.680
need the people at the top of the food chain
377
00:19:03.799 --> 00:19:06.640
that say, we are going to adopt an AI strategy
378
00:19:06.720 --> 00:19:11.200
at our company because we believe it will provide value
379
00:19:11.200 --> 00:19:14.039
and drive great outcomes. Right, then you need to take
380
00:19:14.119 --> 00:19:17.640
that kind of middle tier we'll say regional for an
381
00:19:17.680 --> 00:19:22.160
operations lens, and you need to make sure that they
382
00:19:22.240 --> 00:19:25.480
are bought in on measuring those outcomes. Right when you're
383
00:19:25.519 --> 00:19:28.160
having your one on one with your business managers or
384
00:19:28.200 --> 00:19:32.160
your community managers, are you referencing those AI driven outcomes?
385
00:19:32.240 --> 00:19:32.359
Right?
386
00:19:32.400 --> 00:19:34.880
Are we actually measuring those things? And then at the
387
00:19:34.920 --> 00:19:37.160
on site team level, you've got to have that champion,
388
00:19:37.200 --> 00:19:40.240
that person who's really excited, who's really bought in, and
389
00:19:40.279 --> 00:19:43.400
who is keeping tracked day to day. Right, if AI
390
00:19:43.519 --> 00:19:46.920
is driving the outcomes, if it's working correctly, servicing issues
391
00:19:47.240 --> 00:19:50.400
and those three levels of executive, regional and on site
392
00:19:50.599 --> 00:19:52.799
need to have that buy in, need to change the
393
00:19:52.839 --> 00:19:55.480
way that they work, and need to be constantly communicating
394
00:19:55.480 --> 00:19:57.960
with each other. This is not a set it and
395
00:19:58.000 --> 00:20:00.559
forget it, right. If we're by and in on the
396
00:20:00.599 --> 00:20:02.920
analogy that AIY is the new electricity, I mean that
397
00:20:03.000 --> 00:20:06.759
has been massive change and it requires constant attention and
398
00:20:06.839 --> 00:20:10.359
buying an adoption to make sure that it's successful as
399
00:20:10.359 --> 00:20:12.680
far from set it and forget it as you can get.
400
00:20:12.960 --> 00:20:16.920
I know, executive buying can be such a difficult thing sometimes.
401
00:20:17.000 --> 00:20:21.079
Is there anything that really makes the point get across
402
00:20:21.119 --> 00:20:24.000
to the executive team of like this is a thing
403
00:20:24.079 --> 00:20:27.039
that is going to like make the difference when it
404
00:20:27.039 --> 00:20:29.079
comes to us signing on with any.
405
00:20:29.000 --> 00:20:32.480
Kind of a Yeah, I think that you'll notice the
406
00:20:32.799 --> 00:20:35.839
repetitive themes of things I'm repeating myself on. But I
407
00:20:35.880 --> 00:20:40.559
think that that's indicative of the value of that that point, right,
408
00:20:40.599 --> 00:20:45.279
which is outcomes. You know, executive teams are focused on outcomes.
409
00:20:45.279 --> 00:20:50.000
They're focused on big number yeah, like ni right, like
410
00:20:50.079 --> 00:20:53.200
this is this is what our job is is graded on.
411
00:20:53.279 --> 00:20:54.960
So what you don't want to do is go and
412
00:20:55.000 --> 00:20:57.599
say this is going to increase NI. It's like, okay,
413
00:20:57.720 --> 00:21:00.440
I've heard that before, Right, you want to actually get specific.
414
00:21:00.519 --> 00:21:03.759
Here are the down funnel or downstream metrics that we're
415
00:21:03.759 --> 00:21:06.599
going to move that ultimately are going to drive the
416
00:21:06.720 --> 00:21:10.160
upstream metric of NLI. So, for example, if we're able
417
00:21:10.160 --> 00:21:13.960
to increase our release excuse me, our leasing velocity with
418
00:21:14.200 --> 00:21:16.920
AI because it's doing X, Y, and Z, then we're
419
00:21:16.920 --> 00:21:19.160
going to drive down our vacancy loss, which is a
420
00:21:19.240 --> 00:21:22.240
huge detractor on NI. Right, So being able to actually
421
00:21:22.480 --> 00:21:24.839
paint the picture of how you're moving the needle on
422
00:21:24.920 --> 00:21:28.720
that number. But executives care about numbers. They care about outcomes, right,
423
00:21:28.759 --> 00:21:31.599
And I think your conversation should should be centered around Yeah.
424
00:21:31.640 --> 00:21:35.160
Absolutely. If you were giving advice to a property management
425
00:21:35.200 --> 00:21:37.799
company who is implementing AI for the very first time,
426
00:21:38.160 --> 00:21:40.680
are there a couple steps that you recommend, first thing
427
00:21:40.720 --> 00:21:42.440
off the bat that they should be doing.
428
00:21:42.599 --> 00:21:45.079
Yeah, three things stand out to me that there must
429
00:21:45.079 --> 00:21:48.720
have if you're going to start exploring AI adoption at
430
00:21:48.759 --> 00:21:51.359
the property or portfolio level. The first one is you
431
00:21:51.400 --> 00:21:53.799
got to name the problem. Honestly, it's the thing we
432
00:21:53.839 --> 00:21:56.400
were just talking about, like, why are you adopting AI?
433
00:21:56.559 --> 00:21:58.759
What are the pain points that you're trying to solve for.
434
00:21:59.039 --> 00:22:02.400
It shouldn't be because, as you know, the property nextdoor
435
00:22:02.480 --> 00:22:05.559
adopted AI. It should be because you see holes that
436
00:22:05.599 --> 00:22:08.759
need filling or gaps that need to be closed within
437
00:22:08.799 --> 00:22:11.880
your operational model that you feel AI can provide value.
438
00:22:12.279 --> 00:22:15.799
Then it's defining what success means in advance. Right, If
439
00:22:15.839 --> 00:22:18.440
you know what the gaps are, what does it look
440
00:22:18.519 --> 00:22:19.680
like when the gap is closed?
441
00:22:19.759 --> 00:22:20.920
Right? What is the metric?
442
00:22:21.240 --> 00:22:23.519
What are the numbers that you're going to monitor and
443
00:22:23.599 --> 00:22:26.720
measure and want to see move to know that.
444
00:22:26.640 --> 00:22:28.359
The AI adoption was successful.
445
00:22:28.680 --> 00:22:30.880
And then the last one is what we talked about earlier,
446
00:22:30.920 --> 00:22:33.839
is really building that internal alignment so that you have
447
00:22:33.920 --> 00:22:37.759
that executive, regional, on site team buy in going into
448
00:22:37.839 --> 00:22:40.880
it and making sure everyone is aligned on this being
449
00:22:40.880 --> 00:22:43.400
the right next step so that there's not internal friction
450
00:22:43.480 --> 00:22:47.000
that you're fighting alongside the change management process of adoption.
451
00:22:47.680 --> 00:22:50.799
Yeah, and I think having those metrics as well is
452
00:22:50.880 --> 00:22:54.039
such a critical one of like you need to have
453
00:22:54.119 --> 00:22:56.279
that ahead of time of what you're looking to increase
454
00:22:56.400 --> 00:22:59.440
or looking to improve. Otherwise you can say, yeah, I
455
00:22:59.519 --> 00:23:01.720
did great, but what's the proof?
456
00:23:02.559 --> 00:23:02.799
Yeah?
457
00:23:02.839 --> 00:23:07.079
Exactly, Yeah, absolutely, I know this is another thing you
458
00:23:07.119 --> 00:23:08.920
talked about in your book, but kind of just the
459
00:23:08.960 --> 00:23:12.119
fact that competition is it's a good thing, Like it's
460
00:23:12.160 --> 00:23:16.200
making everybody better at what they do. As we see
461
00:23:16.240 --> 00:23:19.079
it increase in our industry, How do you think that
462
00:23:19.079 --> 00:23:21.160
that landscape of AI is going to change over the
463
00:23:21.160 --> 00:23:22.279
next few years.
464
00:23:22.880 --> 00:23:27.319
Yeah, I mean, I think competition is an incredibly healthy signal. Right,
465
00:23:27.359 --> 00:23:31.440
when something becomes infrastructure, Like I'm talking about category forms
466
00:23:31.920 --> 00:23:35.200
and competition flows, and that's good for everybody. Everybody's driven
467
00:23:35.559 --> 00:23:40.400
to be better, build better products, have more affordable products. Yeah,
468
00:23:40.480 --> 00:23:43.119
it means that the problem that it's solving is urgent, right,
469
00:23:43.160 --> 00:23:46.759
and valuable enough for multiple companies to try to run
470
00:23:46.759 --> 00:23:51.160
a business solving that problem. I think the flip side
471
00:23:51.200 --> 00:23:55.039
of that coin, or the risk, is that language starts
472
00:23:55.039 --> 00:23:58.519
to get really uniform. AI is in everything, Right, what
473
00:23:58.960 --> 00:24:01.160
product do you use today that doesn't claim to have
474
00:24:01.440 --> 00:24:04.799
an AI component? So it becomes tempting to choose based
475
00:24:04.839 --> 00:24:08.839
on convenience or price instead of outcomes, which we were
476
00:24:08.839 --> 00:24:12.400
talking about earlier. If everybody offers AI, you're going to
477
00:24:12.480 --> 00:24:15.519
find free AI, cheap AI, right, You're going to find
478
00:24:15.599 --> 00:24:18.720
AI that does this, AI that does everything. So It
479
00:24:18.759 --> 00:24:21.000
really makes the job of the buyer a little bit
480
00:24:21.039 --> 00:24:23.880
harder because you have to be diligent and you have
481
00:24:24.000 --> 00:24:27.000
to drive the sales process when you're purchasing you products.
482
00:24:27.200 --> 00:24:31.400
And again, just reiterate focusing on outcomes. What are the
483
00:24:31.480 --> 00:24:34.720
outcomes that you're after, and make sure the products or
484
00:24:34.720 --> 00:24:38.359
companies that you're talking to are actually driving positive results
485
00:24:38.400 --> 00:24:39.279
towards those outcomes.
486
00:24:39.759 --> 00:24:42.160
Yeah, I remember, I know I've talked about this on
487
00:24:42.200 --> 00:24:44.400
the podcast before, but when I was on the property
488
00:24:44.400 --> 00:24:47.039
management side, I was tasked with finding a chatbot for
489
00:24:47.200 --> 00:24:49.640
and this was like, I don't know, more than five
490
00:24:49.680 --> 00:24:52.319
years ago, so it was before chat gbt really got big.
491
00:24:52.759 --> 00:24:55.480
But I had this whole spreadsheet of like, this is
492
00:24:55.519 --> 00:24:57.880
what I need the bot to do. These are the
493
00:24:57.920 --> 00:25:01.440
companies that have these exact functional and their cost Like
494
00:25:01.559 --> 00:25:05.200
I was crazy on my spreadsheet about what each company
495
00:25:05.279 --> 00:25:08.200
had because you really do have to do that due diligence.
496
00:25:08.480 --> 00:25:10.599
You can't just settle for the first thing. Make sure
497
00:25:10.599 --> 00:25:11.720
it has exactly.
498
00:25:11.319 --> 00:25:12.920
What you need. Yeah, that's incredible.
499
00:25:13.000 --> 00:25:15.440
I love to hear that, and I think what I
500
00:25:15.440 --> 00:25:18.680
would encourage everyone to do not only to follow your
501
00:25:18.720 --> 00:25:21.440
model and do your diligence and keep your records and
502
00:25:21.680 --> 00:25:25.440
internal decision making matrix is to share that with the
503
00:25:25.519 --> 00:25:28.200
vendors you're talking to, Like, there's no reason to keep
504
00:25:28.279 --> 00:25:31.079
it a secret. What you need in order to say yes,
505
00:25:31.279 --> 00:25:33.480
you know, if you let the folks you're talking to
506
00:25:34.039 --> 00:25:36.519
that you're getting demos from, know, Hey, this is how
507
00:25:36.559 --> 00:25:38.279
you win my business, right, This is what I need
508
00:25:38.319 --> 00:25:40.559
you to be able to do. This is what matters
509
00:25:40.599 --> 00:25:43.000
most to me. It'll create a better experience for everybody.
510
00:25:43.079 --> 00:25:45.559
It won't waste time going down a road with a
511
00:25:45.640 --> 00:25:47.599
vendor who will never be able to support that. And
512
00:25:47.880 --> 00:25:51.480
it doesn't make you listen to pitches about features.
513
00:25:50.880 --> 00:25:52.079
That you don't care about.
514
00:25:52.319 --> 00:25:54.880
The other piece and I'm curious if you had a
515
00:25:55.000 --> 00:25:56.599
row for this on your spreadsheet.
516
00:25:56.680 --> 00:25:58.160
Is the people aspect? Right?
517
00:25:58.200 --> 00:26:01.279
Whatever company you get married to, you know you're going
518
00:26:01.359 --> 00:26:03.720
to have to work with them moving forward. And I
519
00:26:03.720 --> 00:26:07.720
think feeling like you're with good people also matters a lot.
520
00:26:08.319 --> 00:26:11.039
Yeah, Like for me, it was the responsiveness. I mean,
521
00:26:11.079 --> 00:26:12.720
I can't even tell you how many vendors I had
522
00:26:12.759 --> 00:26:14.680
where I would send an email and wait a week
523
00:26:14.680 --> 00:26:17.640
and a half for response. If somebody responded to me
524
00:26:17.720 --> 00:26:20.440
within like a day or two, it was like bonus
525
00:26:20.480 --> 00:26:21.200
points for you.
526
00:26:22.359 --> 00:26:22.559
Yeah.
527
00:26:22.559 --> 00:26:25.519
And the funny thing about that is that's also true
528
00:26:25.519 --> 00:26:29.039
for prospects looking for apartments and why products like this
529
00:26:29.160 --> 00:26:31.440
make sense, right because are you going to be the
530
00:26:31.480 --> 00:26:33.200
property that takes a week to get back or are
531
00:26:33.240 --> 00:26:35.279
you going to send them back an email thirty seconds later?
532
00:26:35.359 --> 00:26:39.079
Because it does drive decision making right, Yeah, totally.
533
00:26:39.160 --> 00:26:41.640
And I will say this too as being on the
534
00:26:41.680 --> 00:26:44.960
property management side and the vendor side, I have always said, like,
535
00:26:45.000 --> 00:26:46.920
when I was on the property management side, I would
536
00:26:46.960 --> 00:26:49.839
love for a company to give me a comparison list
537
00:26:49.880 --> 00:26:53.640
of like them versus their biggest competitor. But after being
538
00:26:53.640 --> 00:26:55.680
on the vendor side, I can say that that is
539
00:26:56.319 --> 00:26:59.079
a little bit difficult as a vendor for us to
540
00:26:59.119 --> 00:27:03.680
produce because we don't know what the competition has verbata.
541
00:27:03.480 --> 00:27:05.359
You don't work there, yeah, yeah, And.
542
00:27:05.680 --> 00:27:09.559
It's always changing. Everybody always has new developments. So if
543
00:27:09.640 --> 00:27:12.079
you're an operator and you're asking for that, take a
544
00:27:12.160 --> 00:27:14.480
bee and kind of come up with that list of
545
00:27:14.519 --> 00:27:16.799
what do you want, what's important to you, and then
546
00:27:16.839 --> 00:27:18.920
go to the vendor and ask if they have those things.
547
00:27:19.039 --> 00:27:22.640
But asking for a verbatim comparison can be kind of difficult.
548
00:27:23.599 --> 00:27:27.200
Yeah, yeah, Listen, I don't work at the competitor's company, right,
549
00:27:27.240 --> 00:27:29.960
and any information I have about them, I got from
550
00:27:30.000 --> 00:27:32.279
a website or from a story somebody told me, and
551
00:27:32.319 --> 00:27:33.680
I don't know if it's true or not, you know,
552
00:27:33.799 --> 00:27:36.440
So don't ask me to make to make you something
553
00:27:36.480 --> 00:27:37.960
that perjure myself or what.
554
00:27:38.000 --> 00:27:40.680
You know what I mean. So, yeah, I'm sympathetic to that.
555
00:27:41.400 --> 00:27:43.880
Yeah, Okay, final thoughts, What are some of the biggest
556
00:27:43.880 --> 00:27:46.720
opportunities in multifamily we could be missing right now when
557
00:27:46.720 --> 00:27:48.240
it comes to AI and leasing.
558
00:27:48.400 --> 00:27:51.119
Yeah, I think it goes back to that chatbot analogy
559
00:27:51.160 --> 00:27:53.920
of really just thinking too small, right, if you treat
560
00:27:54.000 --> 00:27:57.960
if you treat AI as just another tool, then you're
561
00:27:57.960 --> 00:27:59.680
going to use it like one, right, and you're going
562
00:27:59.759 --> 00:28:03.440
to be chasing these really small efficiencies or these really
563
00:28:03.519 --> 00:28:07.200
isolated use cases. And I think that the real opportunity
564
00:28:07.240 --> 00:28:10.319
is so much bigger than that, and it's rethinking how
565
00:28:10.920 --> 00:28:14.799
the business operates. And you know, intelligence is no longer
566
00:28:14.839 --> 00:28:17.400
a limiting factor when you bring AI into the fold,
567
00:28:17.839 --> 00:28:21.119
So don't let it be a constraint when you're building
568
00:28:21.160 --> 00:28:25.160
out how operations is going to work, you know, within
569
00:28:25.240 --> 00:28:27.880
your business or within your property. So it's really a
570
00:28:27.920 --> 00:28:30.880
mindset shift of stop asking whether something can be done
571
00:28:31.240 --> 00:28:33.119
and start asking why it hasn't been done yet?
572
00:28:33.200 --> 00:28:35.440
Right? What are we missing that's stopping us from doing
573
00:28:35.440 --> 00:28:37.039
the thing? Yeah?
574
00:28:37.119 --> 00:28:39.359
Exactly. Okay, I have one final question that I ask
575
00:28:39.440 --> 00:28:41.279
every single one of my guests. It does not have
576
00:28:41.359 --> 00:28:43.720
to be related to this whatsoever, but is there tech
577
00:28:43.759 --> 00:28:45.839
tool that you are personally loving right now?
578
00:28:45.920 --> 00:28:46.119
Yeah?
579
00:28:46.160 --> 00:28:48.279
I mean for me, it's easy. It's Claude. I think
580
00:28:48.319 --> 00:28:50.920
the thing I would really emphasize those It's not like
581
00:28:51.319 --> 00:28:53.200
the claud app on my phone that I talked back
582
00:28:53.240 --> 00:28:56.079
and forth with. Claude is capable like a lot of
583
00:28:56.119 --> 00:28:59.640
the models are of so much, and it's really taking
584
00:28:59.680 --> 00:29:01.920
the the spirit of what I said earlier, what are
585
00:29:01.920 --> 00:29:03.720
the things that I'm doing every day all day that
586
00:29:03.759 --> 00:29:06.079
are repetitive that take up my time, to take up
587
00:29:06.079 --> 00:29:09.799
my team's time, and leveraging Claude to build out those
588
00:29:09.880 --> 00:29:14.000
really bespoke internal solutions that do it the nurture boss way, right,
589
00:29:14.119 --> 00:29:17.480
so that we can really get operational efficiency in there.
590
00:29:17.559 --> 00:29:19.960
So I'm a really really big proponent of Claude. I
591
00:29:20.000 --> 00:29:22.839
recommend everybody give a shot here and try it.
592
00:29:22.880 --> 00:29:23.079
Out.
593
00:29:23.640 --> 00:29:25.759
I think Claude is having a moment right now because
594
00:29:25.880 --> 00:29:29.559
I have always been a chat GBT girl, and all
595
00:29:29.599 --> 00:29:31.440
of a sudden, in the last few months, I have
596
00:29:31.759 --> 00:29:34.559
just been loving Claude as well, and I just think
597
00:29:34.599 --> 00:29:35.400
it's amazing.
598
00:29:36.559 --> 00:29:39.880
Yeah, I mean, and they put out really incredible Stuffy
599
00:29:40.359 --> 00:29:43.079
Fable five is the new model they put out that
600
00:29:43.319 --> 00:29:46.559
was in the Mythos family of models, And without getting
601
00:29:46.559 --> 00:29:49.519
too detailed about it, it's just very incredible what it's
602
00:29:49.559 --> 00:29:54.599
capable of. Its ability to understand giant chunks of context
603
00:29:54.720 --> 00:29:58.400
across many disparate sources has really been a game changer
604
00:29:58.480 --> 00:29:59.680
for what you can do with it.
605
00:30:00.160 --> 00:30:02.359
Yeah, it's just crazy to see how far we've come.
606
00:30:02.440 --> 00:30:06.000
Like when I was initially looking at those different bought options,
607
00:30:06.039 --> 00:30:08.440
it was like you have automation and you have NLP
608
00:30:08.599 --> 00:30:12.000
and that's it. And NLP was just not there at
609
00:30:12.039 --> 00:30:14.160
the time. And so now all of a sudden, it's
610
00:30:14.200 --> 00:30:18.240
like we're having full multi context conversations with the AI
611
00:30:18.359 --> 00:30:21.400
and it's just insane how quickly that happened.
612
00:30:22.200 --> 00:30:24.559
Yeah, I think that you know the phrase you're using,
613
00:30:24.559 --> 00:30:27.440
it's so important as context right, context is king, and
614
00:30:27.799 --> 00:30:30.680
that's what's making the difference between something that's, you know,
615
00:30:30.880 --> 00:30:34.119
pretty cool versus mind blowing is its ability to manage
616
00:30:34.119 --> 00:30:37.319
and maintain and organize that context. Yeah.
617
00:30:37.359 --> 00:30:40.400
Absolutely, well, thank you so much for joining me. This
618
00:30:40.440 --> 00:30:42.880
has been such a great conversation. Where can people find
619
00:30:42.920 --> 00:30:45.359
you and connect with you and learn more about nurture Boss.
620
00:30:45.960 --> 00:30:50.160
Yeah, I mean our website, nurtureboss dot io has tons
621
00:30:50.200 --> 00:30:54.160
of resources. I spend most of my time talking on LinkedIn, right.
622
00:30:54.240 --> 00:30:57.440
I love to let people know what I'm thinking about
623
00:30:57.480 --> 00:30:59.640
in case maybe you find it interesting what I'm getting
624
00:30:59.680 --> 00:31:02.440
excited about. I'm always creating content and putting it out there.
625
00:31:02.480 --> 00:31:05.000
It's always free. It's not behind like a Hey give
626
00:31:05.039 --> 00:31:07.480
me your email address and I'll send you the pdf.
627
00:31:07.079 --> 00:31:07.640
Right, none of that.
628
00:31:07.839 --> 00:31:10.279
I just want everyone to kind of, you know, see
629
00:31:10.279 --> 00:31:14.319
what I'm thinking about. And yeah, it's been fun for me.
630
00:31:14.400 --> 00:31:16.079
So that's definitely the place i'd follow along.
631
00:31:16.559 --> 00:31:19.640
I love it, and I do highly recommend anybody who's
632
00:31:19.640 --> 00:31:23.720
curious to learn more go check out Jacob's book AI
633
00:31:23.799 --> 00:31:26.720
and Property Management because it really does break it down
634
00:31:26.759 --> 00:31:28.880
in a way that you can understand. There's tons of
635
00:31:28.920 --> 00:31:32.319
research I know you did behind that as well, so yeah,
636
00:31:32.440 --> 00:31:35.200
go give that a read if you haven't already. Thanks
637
00:31:35.200 --> 00:31:39.079
for joining Tip Talks Tech. Tune in next time for
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more candidate
