Expert Strategies for Hybrid Workplace Success with AI: Ep 22

Jennifer Heath 0:14
Hello everyone and welcome thank you for joining us for another episode of The Hybrid Hangout podcast. I am Jennifer Heath, Director of Product Marketing for FM:Systems.

Brian Haines 0:23
And I am Brian Haines, the Chief Strategy Officer at FM:Systems.

Jennifer Heath 0:29
And today we are going to dive into a couple of topics that we are really excited about right now. They’re topics you hear a lot about, but we’re going to get into some detail on how we see AI and machine learning influencing the facility management role as well as space planning roles in the future. So before we dive in, Brian, let’s set the stage here a little bit. So in 2024 there were a number of organizations that announced back to work initiatives five days a week in the office mandates, and it generated a lot of press. These are a lot of big organizations making these announcements. How do you think those announcements have landed? How are they resonating? Do you see data to support the continuance of these initiatives?

Brian Haines 1:20
Yeah, it’s interesting. And a few weeks ago we found out that, probably, with the new administration, that the federal government’s going to be returning to work, which is going to be interesting and probably a bit of a challenge for a lot of those employees who’ve been in a hybrid workplace environment, they’re going to be required to come back. You know, it’s interesting. Jen, you know, we’ve been talking about this for a long time, and we’ve been showing a data set of real time, of real utilization data coming from kind of a large sample set, several million square feet, that we’ve been monitoring since about 2019. And we’ve got, you know, as FM:Systems and Johnson Controls, a number of different technologies to measure utilization to a very high level of accuracy within even the most complex facilities and facilities that are really experiencing kind of chaotic use patterns in the hybrid workplace environment. And we’ve been, we’ve been watching that, you know, we saw the crash during the pandemic, we’ve shown that, you know, going down below 3% and it’s been sort of this steady march upwards. We’re still not back to where we were pre pandemic levels when you look at the entire week now, if you take out Monday and Friday, which are pulling the percentages down, we’re pretty close to where we used to be before the pandemic. We’re still living in a world where, when you look at the utilization by day of the week over these millions of square feet over time, even though it’s slightly going up, the utilization rate for Mondays is much lower than Tuesday, Wednesday, Thursday and Friday is basically, almost, almost like people aren’t even going to work to the office anymore on Fridays, except that those organizations, obviously that have these mandates. So even though we’ve seen these mandates coming, we’re not really seeing, you know, we’re not seeing spikes up. It’s just still, you know, steadily climbing. Now when you drill into specific geographies, because we’ve got this data by, you know, over five years period. We’ve also got it by day of the week. We’ve got it by industry. We’ve got it by geography. We are seeing some interesting things happen in specific geographies, the UK, for instance, Tuesday, Wednesday, Thursday, like, sometime this year we saw, we saw a pretty sharp uptick in terms of the number of people returning to the office. So I know it’s a long answer for a short question, but the truth is, we’re not seeing like a massive spike up. It’s just this steady climb, regardless of mandates, it seems to be steady.

Jennifer Heath 4:01
Yeah, it’s really interesting. I think so many people have incorporated the concept of working from home into their daily lives and their daily routines, and I think it’s going to be really difficult to enforce those mandates. And it definitely raises the question of, how do you enforce a mandate? What data sets are you using? And we’ve talked on here before about the concept of coffee badging, that people come in badge swiping is a really easy way to track entrance and arrivals at a building. It’s not necessarily telling you a lot about what’s happening or how long they’re there, and there’s definitely a trend of people who go into the office swipe their badge. They get their check mark that they were there for the day, have a cup of coffee, say hi to some folks, and then they go home and resume working from home. So I think that’s going to be it’s going to be a really interesting turn to see if we do get this five days a week. And I think this is where we’re going in this conversation. I think it also really begs the question, how much more money are organizations spending to operate their facilities on Mondays and Fridays, when maybe there’s really not a corresponding productivity gain that goes along with that? And as you think about cost reduction initiatives, which is always important to every organization, as you think about sustainability initiatives, which are of growing importance to many organizations, it really does make you think, are we being as smart as we can be in how we’re operating our buildings in comparison to how we’re operating our organizations? If employees can be just as productive at home on Mondays or on Fridays? Is it worth powering up an entire building and having it operational all day? I think that’s a really interesting little contradiction in all this.

Brian Haines 5:52
Yeah, and there’s, there’s a lot of different concepts in what you’re talking about. There’s sort of the, how do we measure who’s coming in, like I was saying earlier, we’ve really got utilization down to a very fine level. We can tell how well a facility is being utilized in a number of different ways. You know, by lots of different data sets and data points coming in, sensors, badge swipes, Wi Fi, booking systems, all kinds of things, even lighting systems, everything is really collecting sort of this ability to be able to see utilization, where it really gets interesting is how you begin to apply utilization to all the other problems that we’re trying to solve for within facilities. And there’s some really low hanging fruit, like energy like, why am I turning my building on on Mondays and Fridays? Or maybe, why am I turning entire floors on in buildings when one no one’s there? So the ability to be able to apply things like energy usage to utilization, you know, that’s new capabilities that we’ve got in our platform. It’s really easy to see how you can save massive amounts of energy. The other problems, I think, get a little bit more complex, is the dynamics around the workers who are coming in. The big stick that companies have and organizations have is that if we say you have to come in and you don’t come in, then you don’t get to work here anymore. That’s that’s a big stick, right? That’s a big motivator. But a lot of companies have been, you know, somewhat soft about it. Then they just say, you know, our mandate is, you’re going to be in three days a week, two days a week, whatever it is. And then they figure out, you know, how tough they’re going to be about that. How are they going to measure? Are they going to do badge swipes? And then provide a list of names. We don’t see a lot of that. We see more of, you know, department level reporting, or you may say to a manager, listen, 71% of your employees are adhering to the mandate. And you know, another percentage of them are not. They’re just, they’re not coming in. So you need to do something about that. And maybe it’s a little bit softer, but really, there’s a lot of ways of figuring that out and checking it out. I think the bigger opportunity here is when we start to take that data and we start to bring in other data sets, we can start to really explore interesting problems, I think, in a much bigger way. And you know, I’ve said this maybe before, I know I’ve said it to you, Jen and talking about this, but we as humans are really not good at synthesizing massive numbers of data inputs. That’s not what we’re good at. We’re good at a bunch of other things, but computers, and in particular, AI machine learning, is really great at taking large quantities of data and doing really amazing things with that data. So I think there’s something there that we’re going to be seeing over this coming year, absolutely.

Jennifer Heath 8:55
And that’s a perfect segue. My next point was that the only really topic in the news that is sort of outpacing the whole return to work concept is AI and machine learning. It’s absolutely everywhere. Everything is looking every organization, every app that you interact with, every software system you interact with, is trying to figure out, how do we incorporate AI to make this better, smarter, faster? How do you see new and emerging technologies being applied to these complex utilization trends? What are some other scenarios where we can find real gains in that data?

Brian Haines 9:37
Yeah, so interestingly enough, I mean, I’ve been talking about AI a lot this past year, and at conferences and presentations and things I’ve been doing about its value and what it’s going to be bringing to us, and what it’s already bringing to us. I think if you go back, you know, AI is not new. It’s been around for a number of years. It seemed like a distant concept due to probably most people, especially in the facilities management industry. Now we’re really seeing the advent of AI right in front of you. We’re seeing a lot of AI chat bots. We’re seeing a lot of the ability to be able to simply ask for answers. Whenever I’m giving a presentation, whether it to be if my or any other conference that I’m at, you know, I encourage the audience, if they’ve never experienced an AI type environment, to just do, get a free chat GPT or Copilot, log in and just start doing it. It’s kind of fascinating. I love it. I use it for work. I use it for, you know, home stuff. Sometimes, if I want to do some interesting data correlations and things just simply asking, and now you’re seeing things like Google Now returning when you’re Googling. You’re not just getting a list of a bunch of links, but you’re getting an AI overview or answer to the question. So it’s right in front of us now, and it’s actually highly usable. You know, it’s pretty it seems to be pretty friendly. So that’s one thing that I think is going to absolutely change how it’s perceived, because it’s accessible, and I think many of us are already using it, and most people probably don’t even know. What’s been really fascinating for me when we look at our specific industry and our specific jobs. One of the things that our data science and AI teams have been doing on for our own products is really looking especially during like hackathons. You know, we do these hackathons, our development team pair up with product managers and anybody within the organization for a couple of days that just try cool stuff. They’ve been doing things during those hackathons, and I’ve been collecting sort of the results, because it’s fascinating. Because if you ask a space planner, you ask somebody like me who’s been in the industry for a gazillion years, you know what type of problem I want to solve. I’m going to give you something that sounds very pragmatic and probably something that you may hear from any space and occupancy planner, but what they do is interesting things that I’ve not thought about. So one of the projects that the team worked on again is two years ago, and I just looked at the results, and I looked at their write up was, how does weather and demographics affect utilization over time, and they looked at it to your data set, and they overlaid it, and basically they came up with some really simple things, like, guess what bad weather snow or rain affects utilization. It just does. You see it just it goes down on really nice days, utilization goes up. One of the things they wrote up, which I thought was fascinating was for every one degree increase in outside air temperature, utilization goes up. Like I’m what I never would have thought of that. But, you know, basically nicer days where you might think people want to go to the park or go to the beach, actually, utilization within the workplace goes up. So I think the fascinating thing for me as we start taking additional data sets, right, not just utilization in and of itself, but we start to add additional data sets, we start to get insights into the way space has been utilized, in the factors that are affecting choice, you know, the way humans choose to use space really starts to reveal itself in ways that I think only AI and massive data analysis can reveal. So that’s new, and I think that that’s fascinating.

Jennifer Heath 13:33
So let’s talk a little bit more about that, because you have alluded to that before, that you anticipate some big changes in terms of the way we plan for and manage our space. How do you see AI and machine learning driving that change forward? But also, do you see this as being something that is evolutionary, something that was naturally going to happen over time, or is this a major sea change in how we think about operating. Is it a revolutionary change?

Brian Haines 14:05
Yeah, this is an absolute hockey stick moment, right where it just kind of shoots up because, you know, my early career, and what I focused on a lot during my career is space and occupancy planning and the analysis of space. You know, there’s a lot of traditional data inputs that we looked at as space planners. And we continue to look at things like space types, space use, space classifications, adjacencies between departments and teams, assigned versus unassigned, pace reservable, and we did a lot of stacking and blocking. You know, most of the people on this call probably understand what that means, but it’s kind of a rudimentary way of looking at how we can maximize utilization on a floor, maybe in a building, or make sure that departments that collaborate with one another are next to one another, and those were the traditional data inputs. Now we’ve got phenomenal ability to be able to add all of these additional data sets. We’ve got things like indoor air quality. We’ve got temperature, humidity, access to light, access to noise, or lack of access to noise. We’ve got energy usage. We’ve got trends. We’ve got the hybrid workplace, which has completely changed things. We’ve got things like security and we’ve got things like amenities, neighborhoods, commute times, social interactions, cohort groups, resiliency, carbon footprint, all of these things are data inputs that are now potentially available to the space and occupancy planner to do really robust analysis of space. You know, one of the stories that I’ve, I’ve talked about many times, is that we’ve got a client that did, you know, they were moving into a hybrid workplace environment, and they did very cool space that really helped collaboration. And they did the exact same large open space with touchdown spaces and meeting areas and really ad hoc, a lot of hoteling stuff, phone booths, and they did the exact same space on two different sides of their building, and one of them was being used heavily, and one of them was a ghost town. And the only thing that was different was access to daylight. That’s what having people’s behavior. They wanted the south light. They wanted light coming through the windows. They wanted access to the light. The other one was really kind of dark, right? It was, it was on the north side of the building. So that’s interesting, right? We could start to take these kind of really interesting data inputs instead of just saying, you know, Jen needs to sit next to Brian, because we work together often. It can be much more robust thinking about how we’re thinking about space, and it requires, it really does require AI and machine learning and software tools, I believe, to do that, because when I’m talking to the market, when I’m talking to our clients, they’re not hiring a ton of people right now, we’re not seeing facility teams going, oh, gosh, yeah, we just got approval to hire 100 new people. And that’s just not that’s not happening. You know, you know, as our coworkers and the experts within our industry sort of get older, like such as me, and they sort of start aging out of the profession. They’re taking all of this knowledge with them, and they’re not being backfilled at the rate that they really need to be. So we need to look for new tools and new capabilities and new ways of maintaining that institutional knowledge. So there’s another aspect Jen that’s really, I think, really kind of fascinating as well, is this ability to be able to make really, really highly informed decisions without having to have a data analyst sit with you and comb through data that may take weeks, if not months, to figure out you know what the answer is, you can just simply ask and get amazingly high quality results for your planning purposes.

Jennifer Heath 18:01
Absolutely, and I want to go back to your point about people wanting to be on the side that is more brightly lit. I think one thing that is maybe a bit evolutionary in this whole conversation is the idea that what employees want and what organizations want to try to create for their employees is much more experiential than it was in years past. 10, 20 years ago, the only requirement would have been, I work with Brian. I sit next to Brian. There would have really been no other consideration of, you know, what is the best environment for Jennifer to work in? What’s going to enable her to be the most productive, the most creative, you know, the most focused. It just wasn’t really a consideration. And I think now we’re in a place where people are so much more tuned in to what does make us productive, what are our individual strengths, or what things are going to be very distracting to another individual? I think we have moved into a place where space planning has gone so far beyond just allocating departments, you know, and just assigning people to their desks to how do we create an experience? And that’s one of those areas where facilities really ties into HR, because you want to have this environment and this culture so that you’ve got the top recruits, you’re retaining your top talent. It’s really expanded into this much broader concept.

Brian Haines 19:29
Yeah, it’s interesting, because we talk about hybrid a lot like it came out of nowhere. But the truth is, before the pandemic, people were experimenting with alternative workplace strategies. They were called, that’s what the term was then. And also activity based working, which was, you know, depending on the activity that you were doing, you would need a different kind of space. And the days of the cubicle farms, at least in terms of people who were thought leaders, were short lived, right? We were thinking about space in a kind of a different way and the need for that. The pandemic forced that, and with the kind of chaotic nature of the way people came back and continue to come back, like I said that, you know, it’s still kind of just creeping up. It’s not like just everyone just came back in. The other thing that brought into it is just really interesting use patterns, the way we come in, and that’s one of the things that, you know, I think, affects the decision of Brian sitting next to Jennifer. We work in different locations. We do get together. We’re going to be get getting together for some meetings next week in Raleigh. The requirement for us to come together and then go apart is very different than it used to be. FM:Systems used to hire exclusively in Raleigh. I’m talking a decade ago. That’s the way we did it. Now, less than half of our employees are in Raleigh, and the other half are all over the world, and so it’s requiring us to think about space differently. We don’t need hundreds of cubicles. We don’t have that many people coming in every day. What we need is flexible space that allows us to be able to do, you know, focus rooms, but also kind of small rooms that could get bigger and big rooms that can get smaller, kind of flexibly, as well as better access to technology within those spaces as well. Because, you know, many times we’re going to be not only in a hybrid working environment, but the way people are working is hybrid. So some people dialed in. It used to be, if you go back 10 years ago, I never turned my camera on. People just didn’t even when you were dialing into a conference call. Now it’s like cameras on all the time, and the line between where we’re working and how we’re working is really blurred. Often I forget that, you know, I’m not in the same room as the people that I’m operating with. And so that that puts pressure on creating a different kind of space. And I will tell you, because we’ve done this ourselves, we did an entire new corporate headquarters for for FM:Systems as part of Johnson Controls. And, you know, we use data. We use the data because we used to, we had the sensor technology and everything in our old office. We use that to understand that the number of workstations that we needed in this new environment was going to be much lower. But we needed this kind of, really, much more flexible, ad hoc space for us to be successful. And that has, that has been successful, and but it takes data. You need data to plan so that you get it right, and you need data ongoing and continual use of data now and into the future, so that as you because no one ever gets it right the first time, you’ve got to reconfigure. You’ve got to keep refining and redefining the space that you have to really get it right and needs evolve over time. Acquisitions happen, different teams get built. You know, different capabilities come along. You need different kind of space, and the way you do that, right, I believe, is scenario planning and data with AI on the back end of that, so that you’re just making really sharp and smart decisions as you reconfigure your space over time.

Jennifer Heath 23:17
So, I want to go back to your point about how tools today allow particularly facility managers, space planners. It allows them to comprehend these huge data sets without having to be a data analyst, without having to be, you know, a real techie, IT, you know, kind of a person that they have access to this now. How do you think this is going to change the profession? How do you see the face of facility management and space planners changing over the next 10 to 20 years?

Brian Haines 23:51
Yeah, I had somebody at a conference recently, a space planning conference, after I gave my how AI is going to revolutionize space and scenario planning, raise their hand and go, are we all going to lose our jobs? I always get asked that question. And the truth is, well, certainly not for the foreseeable future, not because of AI. I really see it as this tool to help you up level what you do and you’re going to be providing, and you can provide much more well thought answers to very complex questions really quickly. I think back to the beginning of my career, Jen. I used to when I was in space and occupancy planning at the University of Arizona, we had a data analyst on our staff, and she would do these reports, and they were complex reports, and she had to bring into like spreadsheets from all over the place and get data out of a old data system that we had. And my boss had to walk the space and bring back color coded floor plans with markers and pencils, and she would take that and then once per year would produce these reports. Once per year, you take six months to aggregate all of that data. I was thinking, if I had the tools then that I have now, I could have done that in seconds. I’m not talking like a long time. I could just simply ask what the answer is. And because of the data, if the data is available and it’s curated good, I’m going to get the answer. And so that’s the truth, you know that is that’s completely revolutionized the way we could do things. So what I said to this person who asked me that question, I said, if you’ve got a giant wall and you’ve got little stickies and you’re moving it around, doing stacking and blocking and writing notes on the wall, I said, you might be in trouble, because someone could do it 10 times faster and better, with probably better thought out results using technology, which is at your fingertips now, you can do it. So I think Jen, what it’s going to do is take our profession and up level it right, not eliminate, but up level and give us the ability to be able to- CEO, or a senior executive walks by you in the hallway and says, ‘Hey, I’ve got this problem. Why isn’t why doesn’t it look like this building’s being utilized?’ It’s not, ‘Oh, I’ll get back to you as in a couple of weeks with an answer’, it’s like, ‘I’ll have an answer. I’ll show you what’s going on. It’s going to be in your Inbox by the time you get back to your desk.’ I mean, that’s how you get that’s how you up level your career. That’s how you become more important, more informed, and a better producer. In my opinion, by using the tools like this, it’s good. I’m excited about it. I’m not afraid. I’m excited.

Jennifer Heath 26:43
Yeah, and it makes you more influential. Like facility managers and space planners are going to be in such a position to influence the direction of their organizations in a way they never could have before, because they are able to access that data so quickly, make those informed decisions, and at the same time, they can weigh it against that experiential component of what’s best for our employees, what’s going to make them most productive, because ultimately, that’s the end game, right? Like we want our buildings to be efficient, we want them to be well operated, but ultimately they have to create an environment where your employees are doing their best work, because that’s what drives your organizations forward. And I think it’s really exciting to think about how much more influential these roles can be now in the success of their organization, they;re so much more empowered to have an impact on an employee’s day to day experience? So my last question for you, what are some of the other use cases that you see that can maybe change the way we think about not just the performance of our space, but how we plan for and design it? What other use cases are you keeping an eye on right now?

Brian Haines 28:00
Yeah, so four of them come to mind that I’ve actually seen some results from. One is demographics and weather data versus utilization. That’s one I was talking about earlier. But we were also looking at, they were also looking at things like, like commute times as part graphic, the density of neighborhoods and how long it takes to commute, and whether or not those things affect it. Also the ability to be able to look at energy use is, I think, probably really close in, and we’re able to do that now making big decisions about actually connecting that utilization and energy usage to the way the building actually operates, in terms of connecting directly to the building control system, bringing the lighting down, bringing the heating and cooling down when no one’s there, knowing when they’re going to be there, bringing it back up in a way that’s most energy efficient, reducing energy use and cost, reducing carbon footprint. That’s a that’s an absolute no brainer that’s here. And I think this data is providing just such significant insights into that. Um, the other one I I’m kind of fascinated about is how indoor environmental factors affect space selection choices, and really what we as humans are drawn to in terms of our space selection. And I think measuring that over time, we’re going to be probably thinking about our space in terms of the way we provide it as more like air being, being our corporate space, which I know sounds kind of funny, but I was thinking in one of the things that we’re looking at as like, five star ratings of space so that employees can say, ‘Yeah, I sat here today. It was awful, because I was close to a door that kept opening,’ or ‘I sat here today, and it was amazing. It was quiet, it was the temperature was just right, and it was really nice sunlight coming in’, and then providing access to other employees to say, ‘Wow, that looks like a really great place to sit’, or maybe ‘I’m going to want to sit there again next week, because I really had a good experience.’ And being able to see those kinds of trends over time is really fascinating, because it starts to take into account human choice. And then I think the other one really comes down to maximizing, sort of the runway for our existing assets. And what I mean by that is, you know, as we look at the way buildings are utilized, and we look at things like reducing energy costs, we’re also talking about doing things like maximizing the useful life of really expensive capital expenses that we put into our buildings, like heating, ventilation and air conditioning systems, all of the stuff that goes in, lighting, security systems. Can we extend the useful life of a lot of this stuff, reduce the carbon footprint having to reproduce it over and over again because we’re only operating our buildings when they’re needed, instead of 24 hours a day, seven days a week, 365, days a year. So it starts to look at things like maintenance costs, deferred costs, extending the use of our you know, of our assets. We’re even we’re even applying it to things like, why clean workstations that no one used, why clean floors that no one was on? You know, that makes perfect sense to me. And these use cases are all starting to reveal those kinds of insights. And so all of those begin to stack up to this ability to be able to make really robust, I think, space based and AI based scenario planning, decision making for our end users, Jen, in ways that they never had available to them before. So that’s just four right there that I’ve been thinking about. I asked an audience recently if they had additional ones, and there was a woman in the audience, and she raised her hand and she said can you tell me when I’m going to run out of parking? I was like, That’s fascinating. That’s absolutely great. You should be able to look at your utilization and predict when you’re going to have enough or too little parking. We’re already seeing things like in like at the airport, where it shows you how many spaces are available parking that because they’ve got a sensor there that’s, you know, telling you if there’s cars in spots. That data made available to people for informed decision making is groundbreaking. I think it’s fascinating.

Jennifer Heath 32:30
It really is. And it’s interesting to me to think that for any business challenge, if you’re worried about not having enough desks, not having enough parking, there is a technology that you can plug in and just give it a little time, and now you have a whole data set that’s going to reveal to you what you need to do, where you have gaps, where you have challenges, and the other aspect of those data sets. And again, this is something we talk about, I think every time, the idea that it can be a little bit overwhelming when you’re thinking about your smart building strategy, and you know, where do you start with all this technology? Start with whatever your current problem is. If you’re worried about parking, get sensors, get some data on parking, see what you need to do, and then those data sets, over time, are going to inform your AI models, your machine learning models. It’s just this sort of wonderful snowball effect of data that the more data you have, the more data you can gain and aggregate and analyze. And it really does. There’s so many interesting opportunities for us to be very practical, very efficient, very pragmatic, and how we go about making our decisions, and it’s all right there in the data.

Brian Haines 33:47
I’ll tell you, Jen, every time I talk to you about this time I get pumped up. But I’m really pretty excited. It’s wonderful it really is.

Jennifer Heath 33:58
Awesome, same here, all right, Brian, it’s been great chatting with you today, as always. Thank you to everyone who joined us, and we will see you next time.

Brian Haines 34:07
See ya.