Latest / Net Effective / Before you use AI, fix the process

Transcript

Before you use AI, fix the process · Net Effective

• 0:00
  SPEAKER_01: We're looking at AI as an easy button right now and not realizing that we need to have some things aligned internally before we apply AI to it.
• 0:08
  SPEAKER_01: I don't have a clean ROI number, and I'm suspicious of people that do.
• 0:13
  SPEAKER_01: I don't think we're at a point yet where we can talk about that.
• 0:18
  SPEAKER_01: If you have an employee that's doing the same task, the same exact task, more than three times every day, we need to automate that.
• 0:26
  SPEAKER_00: That's Josh Sidden, CEO and founder of ResiQ, and I'm Jonas Bordeaux, CEO and co-founder of Dwalzi.
• 0:32
  SPEAKER_00: And this is NetEffective, the Rental AI Show.
• 0:34
  SPEAKER_00: Josh, welcome to the podcast.
• 0:35
  SPEAKER_00: It's great to have you here.
• 0:36
  SPEAKER_00: Thanks for having me.
• 0:38
  SPEAKER_00: So Josh leads a really interesting company that's helping companies across the rental housing industry get better at AI and all kinds of technology infrastructure.
• 0:46
  SPEAKER_00: And he's recently out of MAA, where he was a technology leader there.
• 0:50
  SPEAKER_00: Josh, why don't you tell our audience a little bit about yourself?
• 0:57
  SPEAKER_01: It's always come real natural to me, but started out as a PC tech 15, 16 years later.
• 1:03
  SPEAKER_01: I found myself CIO and CISO of a$2 billion retail organization.
• 1:07
  SPEAKER_01: Um, most recently, I was the VP of IT architecture at MAA, and we were doing some cool things there with uh managed Wi-Fi and AI and automation.
• 1:16
  SPEAKER_01: Now I run RISICU.
• 1:17
  SPEAKER_01: They pay me a fee or operators pay me a fee to tell them the truth about technology.
• 1:22
  SPEAKER_01: I really pride myself on that because I don't take fees from any partners.
• 1:27
  SPEAKER_01: I want the owner operators to know that I'm coming to the table as a true advocate and somebody that's looking through these issues through their lens.
• 1:35
  SPEAKER_00: That's fantastic.
• 1:36
  SPEAKER_00: Uh so the fee for the truth is one thing.
• 1:39
  SPEAKER_00: Is there another fee if people want you to lie to them?
• 1:42
  SPEAKER_01: Yeah, I haven't gotten that request yet.
• 1:45
  SPEAKER_00: So tell me about what it means to be heading up infrastructure at a place like MAA.
• 1:49
  SPEAKER_00: What does that encompass?
• 1:51
  SPEAKER_01: You know, one is exciting, and the other, it's it's scary at the same time.
• 1:55
  SPEAKER_01: It's an enterprise IT organization, as most large companies are.
• 2:00
  SPEAKER_01: Um, but I think one caveat is you're managing people's homes.
• 2:03
  SPEAKER_01: This is where they go to live their private lives and where they should feel safe and secure as the technology is growing.
• 2:10
  SPEAKER_01: These buildings are becoming more intelligent, and integrating these networks has become a little difficult because these buildings weren't built that way.
• 2:17
  SPEAKER_01: They weren't built for network integration and modernization.
• 2:22
  SPEAKER_01: So it's become quite the challenge to keep these buildings connected when you get all of these different smart systems that we're putting in place to get that infrastructure all unified and standard and and getting internet to them.
• 2:34
  SPEAKER_01: That's that's fast and reliable.
• 2:35
  SPEAKER_01: But at the same time, it's a challenge.
• 2:37
  SPEAKER_01: And that's that's why I love it so much.
• 2:39
  SPEAKER_01: No building is the same.
• 2:40
  SPEAKER_01: So every building you go to, it's it's going to be a different town.
• 2:44
  SPEAKER_00: Yeah, no, it certainly keeps it interesting.
• 2:46
  SPEAKER_00: And you know, I appreciate you mentioned that about the residents.
• 2:48
  SPEAKER_00: It's always one of the things that I think about is it's being our business is a bit of a sacred trust, if you will, because you are dealing with people's homes, and that's a different level of care that needs to be applied versus selling shoes or something like that.
• 3:01
  SPEAKER_00: So let's talk about AI in the space.
• 3:03
  SPEAKER_00: What do you see big companies across the industry doing with AI?
• 3:07
  SPEAKER_00: You know, where are they getting value from it today?
• 3:09
  SPEAKER_01: It's been a slow adoption, and as commercial real estate, real estate period is is usually slow to adopt technology, but it is it's beginning to take hold because there's a I use the term perceived value.
• 3:21
  SPEAKER_01: There is value there.
• 3:22
  SPEAKER_01: However, I think we're looking at AI as an easy button right now and not realizing that we need to have some things aligned internally before we apply AI to it, primarily our data sources and making sure that our data sets are clean, they're curated, and that we know what's there.
• 3:38
  SPEAKER_01: Um, on top of that, it's got to be processes, right?
• 3:40
  SPEAKER_01: And so that's where a lot of these larger organizations come in that have centralized, they do have processes in place.
• 3:46
  SPEAKER_01: It does make it a little a little easier to apply AI there.
• 3:49
  SPEAKER_01: If you have good data and good processes, AI is is, I'm not gonna say simple, but it's easier to implement.
• 3:57
  SPEAKER_01: Areas where I'm seeing it from ResiQ right now is in a couple of areas.
• 4:01
  SPEAKER_01: One is lead generation.
• 4:02
  SPEAKER_01: AI is very good at going out and finding leads in a certain demographic that you're looking for.
• 4:08
  SPEAKER_01: Very good in nurturing those leads.
• 4:10
  SPEAKER_01: So it will go out and find those contacts for you.
• 4:12
  SPEAKER_01: So you're not, you know, spending days and hours on LinkedIn and in forums and looking at people's websites and trying to find those things.
• 4:19
  SPEAKER_01: AI is very good at nurturing those leads.
• 4:22
  SPEAKER_01: And at the same time, managing systems.
• 4:24
  SPEAKER_01: You know, we're getting into the world of agents now and the agentic part of AI and being able to use those agents to help us manage things uh to alert us more effectively and to watch those because I consider agents just another employee.
• 4:38
  SPEAKER_01: We can talk about that more in a minute, but they run 24-7 and they do the job over and over and over.
• 4:44
  SPEAKER_01: So it's um it's been real exciting to see that.
• 4:46
  SPEAKER_01: And so a lot of the large organizations are going that way, right?
• 4:49
  SPEAKER_01: So they're using it to manage financials.
• 4:51
  SPEAKER_01: They want to be able to dig down into their finances and see more into how the business is performing, but they're also using AI to help manage processes and manage the business better.
• 5:01
  SPEAKER_00: So a bunch of different things I want to dig into there.
• 5:03
  SPEAKER_00: Let's let's start with the data, because I think that's really foundational.
• 5:06
  SPEAKER_00: Where are you seeing people really struggle with data?
• 5:08
  SPEAKER_00: Where are you seeing easy wins in that area?
• 5:11
  SPEAKER_01: The easy wins are PMS data that is typically curated.
• 5:15
  SPEAKER_01: I'm not going to speak about any other in one particular PMS, but they do provide those data sources.
• 5:22
  SPEAKER_01: There's a whole argument about who owns that data because you know you're talking a license fee and the and they charge you for the data you generated.
• 5:29
  SPEAKER_01: So that's a whole other conversation.
• 5:30
  SPEAKER_01: But getting that data um and being able to pull that in and curate it and build the data sets that you need to look at the data the way that you want to is pretty easy with AI.
• 5:39
  SPEAKER_01: Now it's it's all these other data sources, right?
• 5:42
  SPEAKER_01: So we have access control systems, we have smart tech in the units, uh, we have managed Wi-Fi systems, we have networks, we have all of these other systems that, you know, we need that information to get a complete picture of what's going on inside these organizations.
• 5:58
  SPEAKER_01: And finding that data and making sure that data is clean is a challenge.
• 6:03
  SPEAKER_01: Getting your hands on that data is a challenge.
• 6:05
  SPEAKER_01: Um, but once you get it, and again, you've got to do the work to clean that data up and get those data sets the way that you need them, then you can apply AI and it becomes a very valuable tool at that point.
• 6:15
  SPEAKER_00: So the PMS systems that's really interesting, that's one of the easier places because I remember my own experience there.
• 6:20
  SPEAKER_00: We had a YRD system last time I was an operator.
• 6:22
  SPEAKER_00: And one of the wonderful things about Yardy was that it was infinitely configurable.
• 6:25
  SPEAKER_00: But the net result of that meant that we had over 20 different definitions of what NOI was.
• 6:30
  SPEAKER_00: You could kind of set it up however you wanted to set it up.
• 6:32
  SPEAKER_00: So it became very difficult to get everybody on the same page internally on what our most basic numbers were just within that one system, let alone across the 18 or 20 different systems that a large operator might be using.
• 6:46
  SPEAKER_00: So it's interesting that you see that's an easier piece of it.
• 6:49
  SPEAKER_00: Is that because it's just such a big foundational piece for the company?
• 6:52
  SPEAKER_00: And or have those companies gotten a lot better?
• 6:55
  SPEAKER_01: I think it's really because you know, we can pull that data in now and we can create our own data set.
• 7:00
  SPEAKER_01: So we're mapping the data the way that we want to.
• 7:03
  SPEAKER_01: So taking our policies and procedures, you know, that these organizations have and then pulling that data in and mapping it so that it meets the company specifications instead of having to just download the data and use it raw.
• 7:15
  SPEAKER_01: You know, that's been a challenge.
• 7:17
  SPEAKER_01: But you know, now that we have systems in place and AI really helps with organizing that information, it really helps you organize that that data better.
• 7:25
  SPEAKER_01: Now, is it cleaner coming out of those systems today than it was several years ago?
• 7:29
  SPEAKER_01: I don't think so.
• 7:30
  SPEAKER_01: But I think it's just we've got better tools now that allow us to build those data sets.
• 7:35
  SPEAKER_00: And so the other systems, you know, I have to imagine one of the challenges is just getting them all on the same page about are we talking about the same thing from four different directions?
• 7:43
  SPEAKER_00: Is that correct?
• 7:44
  SPEAKER_01: Yeah, and that's been the biggest challenge for me with projects that I'm working on right now is unifying that data into a common data source and pulling all that in.
• 7:53
  SPEAKER_01: Now there are products out there today.
• 7:55
  SPEAKER_01: I'm partnered with one um specifically that does that for you on the front end.
• 8:00
  SPEAKER_01: It's pretty cool what they're doing.
• 8:01
  SPEAKER_01: So they're unifying these APIs and building those maps and data sets out for you so that you don't have to have a data team on the back end to do that.
• 8:08
  SPEAKER_01: But these larger organizations, they have entire data teams um that are dedicated to providing that type of access.
• 8:15
  SPEAKER_00: Yeah, that makes sense.
• 8:16
  SPEAKER_00: So it is I'm not sure what the term of ARTA is these days.
• 8:19
  SPEAKER_00: Are people building data warehouses, data lakes?
• 8:21
  SPEAKER_00: Is that what they're doing?
• 8:22
  SPEAKER_00: Or are we doing something else now?
• 8:24
  SPEAKER_01: Yeah, data lakes are a thing that seems to be the direction that people are leaning.
• 8:28
  SPEAKER_01: Data warehouses are for these smaller organizations today.
• 8:32
  SPEAKER_01: These data lakes obviously are for much larger organizations that have several terabytes or even petabytes of data.
• 8:39
  SPEAKER_00: Okay.
• 8:39
  SPEAKER_00: So you're gonna have to forgive me.
• 8:40
  SPEAKER_00: I just said that term.
• 8:42
  SPEAKER_00: I don't know what a data lake is, Josh.
• 8:44
  SPEAKER_00: What's a data lake?
• 8:45
  SPEAKER_00: What's a data warehouse?
• 8:46
  SPEAKER_01: The easy way to explain it is a data warehouse is probably a smaller data set of multiple different data sources, right, that have been curated.
• 8:55
  SPEAKER_01: You know, you've got data sets built out.
• 8:57
  SPEAKER_01: A data lake is the same thing, but on a much, much larger platform with much larger data sets, and it requires much more compute to calculate that data and to pull that data out.
• 9:09
  SPEAKER_01: It's just a much larger data warehouse.
• 9:12
  SPEAKER_00: Well, that makes sense.
• 9:12
  SPEAKER_00: So do we then give the employees at the company the ability to like query the data lake, query the data warehouse, or are we building applications in between them and those tools?
• 9:24
  SPEAKER_01: Yeah, we're building applications in between.
• 9:26
  SPEAKER_01: So I think the safe way to do AI is you're not going to query those data sets directly because of guardrails and the way that you want to set up access to that data and who can access it, who can't.
• 9:38
  SPEAKER_01: Everybody always uses the analogy, well, can I search the CEO's salary?
• 9:42
  SPEAKER_01: Those types of right.
• 9:43
  SPEAKER_01: So you want to put guardrails in place so that those types of queries can't happen.
• 9:47
  SPEAKER_01: So what you do is you take your data lakes and your data warehouses and you create specific data sets that you want AI to hit.
• 9:56
  SPEAKER_01: So you're not going to let AI hit your entire data warehouse.
• 9:59
  SPEAKER_01: You're going to pull these data sets out, specific data.
• 10:02
  SPEAKER_01: Obviously, that data is synced ever how often you want it to sync.
• 10:06
  SPEAKER_01: Build those guardrails, uh, build access to those data sets.
• 10:09
  SPEAKER_01: So whatever specific user group you want to have access and be able to query that data, they can.
• 10:14
  SPEAKER_01: And you'll use some type of tool, whether it be Claud Teams, Copilot, OpenAI, whatever, and let them have conversations with the data that way.
• 10:22
  SPEAKER_00: This all sounds really complicated to me from an operator standpoint.
• 10:26
  SPEAKER_00: Is it something that's within reach of a mid-sized operator, or do you really need like a big technology team to be doing something like this?
• 10:34
  SPEAKER_01: You don't necessarily need a big team.
• 10:36
  SPEAKER_01: You you need somebody that's been there and done that, somebody that really understands the data, understands your processes.
• 10:43
  SPEAKER_01: So almost all of my conversations start with an assessment.
• 10:46
  SPEAKER_01: So if anybody is working with me and wants me to help them go down this path, we'll spend a couple of weeks doing an assessment.
• 10:53
  SPEAKER_01: So we're going to look at what data sources they have, what kind of shape that data is in.
• 10:57
  SPEAKER_01: We're going to see what kind of processes they have, who owns that data, who has access to that data.
• 11:02
  SPEAKER_01: And then we're going to find some quick wins, whether that be some type of automation or things of that nature, because you go into these organizations and you ask about use cases, and nobody really understands the capabilities until you automate something.
• 11:19
  SPEAKER_01: That tends to trigger some response.
• 11:22
  SPEAKER_01: Oh, that's how this works.
• 11:23
  SPEAKER_01: What can we do this?
• 11:24
  SPEAKER_01: What can we do that?
• 11:26
  SPEAKER_00: Okay.
• 11:26
  SPEAKER_00: So you need some help to be able to set these things up.
• 11:29
  SPEAKER_00: You've got to have some sort of data foundation and some sort of interpretive layer, if you will, sitting on top of that to make sure that CEO's salary doesn't get out, unless, of course, a publicly traded company, in which case you can just Google it.
• 11:41
  SPEAKER_00: Uh, much to everyone's chagrin.
• 11:43
  SPEAKER_00: But um So then what sits on top of that?
• 11:47
  SPEAKER_00: Is this the standard suite of applications that people are already using, or are people deploying new AI applications or custom-built applications that folks can use?
• 11:56
  SPEAKER_00: There's a little bit of a both.
• 11:58
  SPEAKER_00: I've done both flavors.
• 11:59
  SPEAKER_01: So I have a client that's using just straight call team subscription to talk to their data.
• 12:04
  SPEAKER_01: They haven't really built out any true data sets yet, but they at least wanted to be able to query a small data set and pull some information out of that.
• 12:13
  SPEAKER_01: In other ones, they are actually developing, you know, internal tools to have these conversations with this data.
• 12:21
  SPEAKER_01: And, you know, the different platforms that are out there, everybody talks about Claude, which I'm ResiQ is a Cloud partner.
• 12:27
  SPEAKER_01: So we work with Anthropocrit directly and uh we have some unique resources there that allow us to build these tools out a little bit more rapidly.
• 12:35
  SPEAKER_01: And then uh there's Copilot Studio with Microsoft.
• 12:38
  SPEAKER_01: That tends to be a pretty good platform for building out these chatbots, especially if you're a Microsoft customer, right?
• 12:44
  SPEAKER_01: Because it all fits in the same architecture and the same environment.
• 12:48
  SPEAKER_01: So it makes it easier to integrate all of those data sources.
• 12:51
  SPEAKER_01: And it's very customizable.
• 12:53
  SPEAKER_01: They can build these things out, they can use, well, it spans the gamut.
• 12:57
  SPEAKER_01: You can use Telegram to talk to your data.
• 12:59
  SPEAKER_01: You can use Copilot, or you can use a cloud subscription, or you can build your custom oddbot.
• 13:05
  SPEAKER_01: Some of the newer uh movers and shakers in the industry are using our conversation has been about internal access to AI, but there's also a customer facing piece here.
• 13:15
  SPEAKER_01: Some companies out there that are doing a great job, right?
• 13:18
  SPEAKER_01: And having those initial conversations and those initial contacts.
• 13:21
  SPEAKER_01: And obviously, they built their built out their own platforms tied back to the internal PMSs.
• 13:26
  SPEAKER_01: And so they're doing a good job on that side too.
• 13:29
  SPEAKER_00: So shows called Net Effective.
• 13:31
  SPEAKER_00: Let's try to get to where the profit is in all of this.
• 13:34
  SPEAKER_00: Where was MAA making money with this?
• 13:36
  SPEAKER_00: Where do you see your clients making money with AI?
• 13:40
  SPEAKER_00: How are they dropping dollars to the bottom line using it?
• 13:43
  SPEAKER_01: Yeah, I don't have a clean ROI number, and I'm suspicious of people that do.
• 13:47
  SPEAKER_01: I don't think we're at a point yet where we can talk about value.
• 13:52
  SPEAKER_00: Interesting.
• 13:52
  SPEAKER_00: Okay.
• 13:53
  SPEAKER_01: AI and AI agents, like I said before, it's just another employee.
• 13:57
  SPEAKER_01: Um, and that's exactly how I view it in that you have to manage an agent, you have to coach an agent, um, you have to do reviews on those agents and make sure that they're doing their job.
• 14:07
  SPEAKER_01: You know, the biggest difference is they just work 24-7 and work non-stop.
• 14:11
  SPEAKER_01: But sometimes they make mistakes too.
• 14:13
  SPEAKER_01: They can hallucinate.
• 14:14
  SPEAKER_01: We hear that term a lot.
• 14:16
  SPEAKER_01: AI can get outside of the box and and start uh giving you feedback that's not necessarily true.
• 14:21
  SPEAKER_01: So you've got to watch it and you've got to govern it the best you can.
• 14:25
  SPEAKER_01: And having these governance policies in place, I think, is super important.
• 14:28
  SPEAKER_01: But where I think we do see value in that it's time in the agents and creating automations and doing you know repetitive things for us.
• 14:39
  SPEAKER_01: Um it even it kind of goes back to my retail days when we were looking at mainframe systems and we would use RPA to automate processes.
• 14:47
  SPEAKER_01: And you had a merchandiser who just clicked the same button 10 times a day, and we were used RPA to automate that.
• 14:54
  SPEAKER_01: And we're doing the same thing with AI right now.
• 14:57
  SPEAKER_01: In my conversations with owner operators, you know, it's like we don't know where to automate.
• 15:01
  SPEAKER_01: Okay, well, if you have an employee that's doing the same task, the same exact task, three more than three times every day, we need to automate that and give them time back to be more productive to do their actual job, and that's to go get residents to sign leases.
• 15:16
  SPEAKER_01: I mean, that's still at the end of the day.
• 15:18
  SPEAKER_00: The department industry being what it is, there are a lot of repetitive tasks across this.
• 15:23
  SPEAKER_00: You know, there's leasing agents who it's you know kind of rinse and repeat.
• 15:26
  SPEAKER_00: I've got this many listings that need to get leased, I have this many prospects coming in today.
• 15:31
  SPEAKER_00: Each day is a little bit similar to the one before.
• 15:33
  SPEAKER_00: Obviously, residents are you know endlessly creative in terms of the challenges they come up with for us, but you know, in terms of the day-to-day job and what has to be done, it can be quite repetitive.
• 15:42
  SPEAKER_00: So does that mean a ton of opportunities to automate those repetitive tasks across leasing, across property operations, property accounting, et cetera?
• 15:51
  SPEAKER_01: Yeah, I think they are.
• 15:52
  SPEAKER_01: I think one of the biggest places where we've seen an immediate impact, and I've I've done this for a couple of clients already, is with utility billing because those bills look the same every month.
• 16:02
  SPEAKER_01: Um, they're structured the same way.
• 16:03
  SPEAKER_01: And so you can build out templates to scan those utilities and you can automate that.
• 16:07
  SPEAKER_01: You can have those emailed to a certain inboxes.
• 16:10
  SPEAKER_01: An agent can grab those bills, they can scan those bills, they can put those bills into the system.
• 16:15
  SPEAKER_01: And if you want them to without a man in the middle, which I don't suggest, you can have them paid automatically.
• 16:19
  SPEAKER_01: Needs to be somebody there to look over.
• 16:22
  SPEAKER_01: But that's one automation that is, I'm not gonna say simple, but is is easier.
• 16:26
  SPEAKER_01: And the other, I was having a great conversation with a gentleman last week about maintenance.
• 16:31
  SPEAKER_01: The maintenance guys, um, some of these systems are just strictly ticketing systems, they're not maintenance ticketing systems.
• 16:38
  SPEAKER_01: And so they're not very efficient.
• 16:40
  SPEAKER_01: So it leaves it up to the maintenance professionals on where they go throughout the day and what tickets they close or try to address.
• 16:47
  SPEAKER_01: So he and I had a conversation about ways to, you know, when the maintenance staff came in in the morning, they would have a list of where they needed to go and what they needed to do so that they can go from building A and then go all the way to building C.
• 17:00
  SPEAKER_01: Oh, well, I got to go back to building A.
• 17:02
  SPEAKER_01: We could prioritize those tickets and prioritize them by location so that they're being, you know, as efficient with their time as possible.
• 17:10
  SPEAKER_01: So that's another way that we've been looking at it.
• 17:12
  SPEAKER_00: That's fantastic.
• 17:13
  SPEAKER_00: I've seen so many applications on the leasing side, but fewer on the maintenance side.
• 17:17
  SPEAKER_00: So that's interesting.
• 17:18
  SPEAKER_00: Although I had a conversation recently and uh we have an upcoming podcast episode about uh robotics.
• 17:23
  SPEAKER_00: I'm curious if you explored that area at all, if that's been something that you've got the chance to play around with.
• 17:29
  SPEAKER_01: So, yeah, interestingly enough, I'm an FRC First coach.
• 17:33
  SPEAKER_01: I don't know what that is.
• 17:34
  SPEAKER_01: What does that mean, Josh?
• 17:36
  SPEAKER_01: That's first robotics competition.
• 17:38
  SPEAKER_01: So very cool.
• 17:40
  SPEAKER_01: A national organization called First, and they have uh worldwide high school team uh robotic competitions.
• 17:49
  SPEAKER_01: Uh it is the Super Bowl of high school robotics.
• 17:51
  SPEAKER_01: It is so much fun, and I really like it because you have to set your team up like a business.
• 17:55
  SPEAKER_01: So you actually have CEOs, COOs, CTOs, um teams.
• 18:00
  SPEAKER_01: Um, it's it's real fun to be a part of that.
• 18:03
  SPEAKER_01: So see a lot of AI come into that because of the coding piece that's really moved that type of competition forward.
• 18:09
  SPEAKER_01: But you know, I live in Memphis, Tennessee, one of the largest distribution centers in America, and robotics is a very big topic of conversation here, and AI is is playing a big role in distribution automation.
• 18:22
  SPEAKER_01: And so in the circles I run in with my peers and mentals, that comes up quite a bit.
• 18:26
  SPEAKER_00: Yeah.
• 18:26
  SPEAKER_00: Do you see that making the leap to apartment technology?
• 18:30
  SPEAKER_01: Yeah, and you know, we're about to see it with DoorDash and with with the streets is with the drones.
• 18:36
  SPEAKER_01: So they're working on tech right now that you know it's gonna be an interesting conversation with owner operators to say, hey, you've got to create a landing zone now for the drones that are going to be bringing your residents' food to them and discussing how that works.
• 18:51
  SPEAKER_01: So yeah, I can see that coming over.
• 18:53
  SPEAKER_01: And you know, and we already see some of it with with some of our HVA systems.
• 18:57
  SPEAKER_01: There's a little bit of that built in now with maintaining these systems and alerting systems and trying to fix themselves.
• 19:03
  SPEAKER_01: And we definitely see it with our managed Wi-Fi networks because those are actually self-healing now, and the AI is doing most of that work.
• 19:11
  SPEAKER_00: That's fantastic.
• 19:12
  SPEAKER_00: It's amazing the passage of time.
• 19:14
  SPEAKER_00: I think it was 2016.
• 19:16
  SPEAKER_00: I remember talking to a venture capitalist uh in the Bay Area who had a company uh and they were interested in leasing space on our rooftops for delivery of packages, delivery of food.
• 19:27
  SPEAKER_00: 10 years later, maybe it's actually going to happen now.
• 19:31
  SPEAKER_00: Yeah.
• 19:31
  SPEAKER_00: Yeah, it's coming.
• 19:32
  SPEAKER_00: Yeah, that's pretty exciting.
• 19:34
  SPEAKER_00: Faster Barritos, everybody wants that, right?
• 19:36
  SPEAKER_00: So, Josh, I'm curious, you know, as we think about vendor evaluation in this space, there just seems to be a lot of AI washing.
• 19:44
  SPEAKER_00: I'm not quite sure what to call it.
• 19:45
  SPEAKER_00: Everybody is talking about how their product is AI infused in some way, shape, or form.
• 19:50
  SPEAKER_00: Obviously, people aren't buying AI for the sake of it, but how do you evaluate a good vendor in this space?
• 19:55
  SPEAKER_00: What should folks be looking for?
• 19:57
  SPEAKER_01: There's so much noise right now.
• 20:00
  SPEAKER_01: I feel like it did back in the dot-com boom.
• 20:03
  SPEAKER_01: Just that much noise out there right now.
• 20:06
  SPEAKER_01: And it's even difficult for me to filter that noise.
• 20:09
  SPEAKER_01: Trying to stay on top of AI is is a full-time job.
• 20:13
  SPEAKER_01: There's just so much coming out, there's so much change.
• 20:17
  SPEAKER_01: And being a network professional, that's my bread and butter and infrastructure.
• 20:20
  SPEAKER_01: You just kind of want to settle on one thing, and that's what you focus on, and you just make it better.
• 20:25
  SPEAKER_01: And we can't do that with AI because everybody is getting better.
• 20:29
  SPEAKER_01: And with these integrations and with the tech that's coming out, it's just become a challenge.
• 20:34
  SPEAKER_01: So, a couple of things that I would be watching for are especially with demos.
• 20:39
  SPEAKER_01: I would ask um some very important questions on demos, and one of them is what's failed.
• 20:44
  SPEAKER_01: They never talk about what's gone wrong, right?
• 20:46
  SPEAKER_01: They don't tell you everything that's the greatest.
• 20:49
  SPEAKER_01: And the other are when you're seeing demos and they don't show you the demo and it's on a PowerPoint.
• 20:54
  SPEAKER_01: They're hoping that you agree to sign a deal with them so they can actually fund building out the software.
• 20:59
  SPEAKER_01: But in seriousness, they need to have a proven background.
• 21:03
  SPEAKER_01: They need to be able to show what they have done and let you talk to both sides of the fence.
• 21:09
  SPEAKER_01: Hey, who went with you?
• 21:10
  SPEAKER_01: And who's somebody that didn't go with you?
• 21:12
  SPEAKER_01: I want to talk to them.
• 21:13
  SPEAKER_01: I want to know why.
• 21:14
  SPEAKER_01: It could be something totally unrelated to why I'm choosing why.
• 21:17
  SPEAKER_01: But and vendors don't really like to put those people in front of you.
• 21:21
  SPEAKER_01: But I will openly ask for things.
• 21:24
  SPEAKER_01: But yeah, everybody's coming out of the woodwork right now, and it's a very hard time.
• 21:28
  SPEAKER_01: So I, you know, I would go with some trusted people that have been in the trenches that have been there, done that.
• 21:33
  SPEAKER_01: I'll tell you what messed up.
• 21:35
  SPEAKER_01: I'm not afraid.
• 21:36
  SPEAKER_01: I tell people all the time I know way more about what not to do than what to do.
• 21:40
  SPEAKER_00: Those lessons are pretty valuable.
• 21:41
  SPEAKER_00: We all pick them up along the way.
• 21:43
  SPEAKER_00: Right.
• 21:44
  SPEAKER_00: All right, a couple of quick questions to wrap things up, Josh.
• 21:47
  SPEAKER_00: Did you have an aha moment with AI?
• 21:48
  SPEAKER_00: And if so, what was it?
• 21:50
  SPEAKER_01: So I I went down the wormhole uh of YouTube one night and I came across this guy who said to build yourself a chief of staff.
• 22:00
  SPEAKER_01: And so I did it.
• 22:02
  SPEAKER_01: And that was really my first agentic type experience.
• 22:08
  SPEAKER_01: And so I built myself a chief of staff.
• 22:10
  SPEAKER_01: I was a VP of IT architecture, so I made sure it understood Azure architecture, it understood the architecture of our actual network, and then it understood about me.
• 22:22
  SPEAKER_01: And the next day when I walked in, I had my entire day lined out for me.
• 22:26
  SPEAKER_01: Like, here are the emails that came out last night, here were the tickets that were open on systems that haven't been closed yet.
• 22:32
  SPEAKER_01: You need to look at dot dot dot.
• 22:34
  SPEAKER_01: And I was like, oh my gosh, this is crazy.
• 22:37
  SPEAKER_01: And began to modify that even more to where it actually became a partner.
• 22:41
  SPEAKER_01: And so it was assisting me in my day-to-day function.
• 22:44
  SPEAKER_01: So when network changes came in, it would advise me how you got an email on this network change.
• 22:49
  SPEAKER_01: This is what I think about it, based upon previous conversations I've had with you and the way that you've architected the network.
• 22:55
  SPEAKER_01: Here's what I would suggest.
• 22:56
  SPEAKER_01: So yeah, it's become a very valuable tool.
• 22:58
  SPEAKER_01: And that was my very first like, wow, this this is real.
• 23:02
  SPEAKER_00: That's fantastic.
• 23:02
  SPEAKER_00: I have a chief of staff myself.
• 23:04
  SPEAKER_00: I'm a huge fan, makes my life much easier.
• 23:06
  SPEAKER_00: Just got me checked in for my flight tomorrow this morning, and uh, you know, does all kinds of other great stuff.
• 23:11
  SPEAKER_00: So it's super helpful.
• 23:12
  SPEAKER_00: How about in your personal life?
• 23:14
  SPEAKER_00: How do you use AI in your personal life?
• 23:16
  SPEAKER_01: So personal life is interesting because I'm a techie and I'm always playing with things.
• 23:22
  SPEAKER_01: And so I've become a big fan of Hermes Agent, which is an AI harness, as the techies like to call it.
• 23:29
  SPEAKER_01: And basically, at the end of the day, all it is is a very intelligent Unix cron job schedule.
• 23:37
  SPEAKER_01: That's literally what it is.
• 23:39
  SPEAKER_01: Now people use AI agents to drive all of these different things.
• 23:42
  SPEAKER_01: And people are, you know, you can go on YouTube and oh, I'm running my business and made a million bucks because I used Hermes Agent.
• 23:47
  SPEAKER_01: But what I I do is I have a hobby.
• 23:49
  SPEAKER_01: Um, I have my own media server.
• 23:51
  SPEAKER_01: I have a lot of Docker that runs at home, and I'm using AI to manage all of that infrastructure for me.
• 23:58
  SPEAKER_01: Where in the past, you know, even though it's a hobby, it would still take a good 45 minutes every day to make sure that all my systems were running and things were going we're supposed to.
• 24:06
  SPEAKER_01: Now AI just runs it for me.
• 24:08
  SPEAKER_00: That's fantastic.
• 24:09
  SPEAKER_00: Super fun.
• 24:10
  SPEAKER_00: And a little more technical than most of our guests' answer on that question, that's for sure.
• 24:15
  SPEAKER_00: I'm not familiar with Hermes, but I'll have to do a little research and check that out.
• 24:18
  SPEAKER_00: Josh, this has been fantastic.
• 24:20
  SPEAKER_00: How can our listeners get in touch with you?
• 24:22
  SPEAKER_01: Yeah, so you can reach me at my website.
• 24:24
  SPEAKER_01: That is r-es-i-q.co, resiq.co.
• 24:30
  SPEAKER_01: And email address is josh at resiq.co.
• 24:33
  SPEAKER_01: Um, I'm on LinkedIn.
• 24:34
  SPEAKER_01: I post almost every day, but my baby is techscribe.org, t-cscribe.org.
• 24:42
  SPEAKER_01: Um, that is my blog.
• 24:43
  SPEAKER_01: That is what got me where I'm at with my business.
• 24:46
  SPEAKER_01: I started blogging and people started responding, and that's how it all got started.
• 24:51
  SPEAKER_00: Well, Josh, thank you.
• 24:52
  SPEAKER_00: Uh it's been a real pleasure to chat.
• 24:53
  SPEAKER_00: Really appreciate the insights you're able to share.
• 24:55
  SPEAKER_00: Very much obliged.
• 24:56
  SPEAKER_00: Yeah, thanks for having me.
• 24:58
  SPEAKER_00: Appreciate it.
• 24:59
  SPEAKER_00: That's Net Effective.
• 25:00
  SPEAKER_00: New episodes weekly.
• 25:01
  SPEAKER_00: If you're running rentals and figuring out AI, hit like, subscribe, follow, or whatever your app makes you do to get more of the show.
• 25:08
  SPEAKER_00: And if you'd like to come on as a guest or subscribe to the weekly updates, go to neteffective.show.
• 25:13
  SPEAKER_00: I'm Jonas Bordeaux.
• 25:15
  SPEAKER_00: Thanks for listening.