Rigvi breaks down what truly useful AI looks like and why it won't replace FM professionals. It will finally let them do the job they were meant to do.
Rigvi breaks down what truly useful AI looks like, grounded in outcomes like spend optimization, revenue protection, and predictive maintenance, not just convenience features or marketing buzz. He also makes the case that AI won't replace FM professionals, it will finally let them do the job they were always meant to do.
Welcome to Elevating Brick and Mortar. A podcast about how operations and facilities drive brand performance.
On today's episode, we talk with Rigvi Chevala, Chief Product and Technology Officer at ServiceChannel. With over 20 years in B2B SaaS across industries ranging from local marketing to trucking to real estate, Rigvi brings a uniquely cross-industry lens to the challenges and opportunities facing facilities management today.
Guest Bio:
Rigvi is an experienced management executive with strong leadership skills and over 20 years of experience in software and product development and has led multiple product lines with >$200M in ARR. He manages and and executes product roadmaps and organizational strategy with experience in evolving B2B SaaS products and reusable digital platforms.
TIMESTAMPS:
00:52 - About ServiceChannel
04:19 - What surprised Rigvi about facilities
08:47 - Key unsolved challenges in the industry
11:37 - Defining useful AI vs. marketing noise
15:08 - Breaking down AI types (generative, agentic, computer vision)
28:33 - Why 90% of AI initiatives fail
33:54 - Will AI replace FM roles?
39:37 - Advice for leaders evaluating AI tools
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ServiceChannel brings you peace of mind through peak facilities performance.
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ServiceChannel partners with more than 500 leading brands globally to provide visibility across operations, the flexibility to grow and adapt to consumer expectations, and accelerated performance from their asset fleet and service providers.
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[00:00:00] Producer: Welcome to Elevating Brick and Mortar, a podcast about how operations and facilities drive brand performance. On today's episode, we'll hear from Rigvi Chevala, Chief Product and Technology Officer at ServiceChannel. He'll share how AI is moving from marketing buzzword to meaningful business tool and why the FM professional isn't going anywhere.
[00:00:25] Producer: Now, here's your host, Sid Shetty.
[00:00:27] Sid Shetty: Hello everyone. Welcome to season five of Elevating Brick and Mortar. Thank you for joining us. I'm here today with our very own Rigvi Chevala, Chief Product and Technology Officer at ServiceChannel. Rigvi, welcome to the show.
[00:00:42] Rigvi Chaevala: Thanks for having me on the show, Sid. I've always wanted to be on this one.
[00:00:46] Sid Shetty: We have a lot of conversations offline today. We're doing it on a, on with, with our mics on, so I think it'll be, it'll be fun. Rigvi, for listeners who might not be familiar with your background, can you share a bit about your journey and the role you play today and, and how you got here?
[00:01:00] Rigvi Chaevala: Currently I work at ServiceChannel as the chief product and technology officer.
[00:01:04] Rigvi Chaevala: My background, I, I was an engineer at some point back in the past. I always like programming, although nowadays that that whole profession is potentially at risk. But, you know, I, I loved coding and that's what got me into technology. And since then I've, I've kind of graduated into more customer facing business strategy over time.
[00:01:27] Rigvi Chaevala: So my expertise is all B2B SaaS for, you know, enterprise level software. So I've enjoyed that for the last 20 years. And, you know, this, this place at ServiceChannel is truly bringing everything, all of my learnings together to serve this, this industry in a unique way.
[00:01:44] Sid Shetty: What kind of industries have you been part of in the past?
[00:01:48] Rigvi Chaevala: I've been in software B2B SaaS product companies that service marketing automation. Think of local marketing, for example. You go to Panera, there's a window claim, and there's offers that are specific to. That location, you know, you're in Florida versus in New York. The prices are different. The the offerings, the feature stuff is different.
[00:02:10] Rigvi Chaevala: And so we allowed these corporate brands to automate local marketing. That was my first company. And then I moved to real estate software for residential, like lease management, rental roll, and so on. And then. I moved to asset maintenance for trucking companies, completely different industry, vertical and, and you know, now I'm at ServiceChannel doing facilities maintenance.
[00:02:33] Rigvi Chaevala: So, you know, if you, if you kind of draw, like, you know, draw a line together across the board, the principles of product development and management. Are the same, like underlying, but the industries, the customers you serve, the problem space that they have are different. But you know, the, the way you solve for that is pretty similar.
[00:02:53] Sid Shetty: Yeah. So you've worked across multiple industries. What drew you into, you know, facilities and the built environment space and, and honestly, ServiceChannel.
[00:03:04] Rigvi Chaevala: It happened to be a, a happy coincidence, but also, like I said, my, my background of being industry agnostic helped me kind of pick, you know, which industry space I can work in.
[00:03:15] Rigvi Chaevala: So the opportunity just happened and the, the coincidence was great. So, you know, I took the, the role at ServiceChannel when, when that showed up, but. Brought a question on, on the built space. Now, coming in, I had no idea what this space had from a, from a store maintenance, brick and mortar type of situation.
[00:03:37] Rigvi Chaevala: But when, when, once I got in, I think it was kind of pleasantly surprising of, of the level of sophistication that this industry has. Like I said, you know, in, in the past I've done asset maintenance on trucking that I knew trucks are complex machines and you know, thousands of parts, lots of ways that they can go wrong.
[00:03:57] Rigvi Chaevala: So that I knew going in, you know, built environments. I wasn't sure what was the set of nuances until I joined Cyber Channel got into this space.
[00:04:10] Sid Shetty: Yeah, it's a pretty fascinating space, right? And the people. What we have to do, you know? When do you think about, about someone in facilities, right? Unless something actually breaks.
[00:04:19] Sid Shetty: But when, when you first stepped into into the industry, was there anything that stood out to you? Was there anything that surprised you?
[00:04:28] Rigvi Chaevala: I think the first thing that I understood as, as the space itself is all the facilities maintenance and facilities management people in the industry, they're, they're these unsung heroes working.
[00:04:44] Rigvi Chaevala: Really in the background, you know, if, if I draw an analogy from a tech world, it's like the IT company, right? Like the IT department in your, in your company. Unless you're, you know, you, you only think of them when your outlook doesn't work or your computer doesn't do, right? And, and otherwise everything's hunky dory.
[00:05:01] Rigvi Chaevala: You don't even think about that team. It's kind of similar here is when I, you know, prior to getting into facilities industry. And when I walk into a store, I'm not thinking about the automatic door that's not working. I'm not looking at, you know, if, if the AC is on, like there's, there's this expectation as a, as a consumer walking into a store that all these things just need to magically work.
[00:05:23] Rigvi Chaevala: But behind the scenes frantically, these teams are working day and night to make sure that that store experience. Is at the forefront and it's at the, at the top of what it's supposed to be, right? So, so, so the sophistication is what surprised me is the diversity of how many, how many things exist in a store, how many things can go wrong, and how do you get those fixed, right?
[00:05:47] Rigvi Chaevala: And, and the whole workflow behind it, all the way to invoice and payment, right? That whole ecosystem and the whole workflow is pretty, pretty complex.
[00:05:57] Sid Shetty: Facilities management can seem invisible to the outside world when everything's working. Right, right. You don't think of the facilities team. When you walk into a great space, you don't say, wow, this place is maintained so well.
[00:06:10] Sid Shetty: And that means the team's doing a great job behind the scenes, but it's deeply complex on the inside. So from your vantage point. How would you describe the maturity of the industry? Like how we do things in our space compared to the other industries? You know, every industry has their level of maturity, their level of sophistication, how fast they adopt technology, how fast they change the way business is done, what's, what's your view on, on our space?
[00:06:38] Sid Shetty: And I'd love to also understand like why, what is the role of automation and why is it so critical to us to have us move forward faster as a space?
[00:06:48] Rigvi Chaevala: I think in general facilities, like from an adoption of technology standpoint, they're probably, I'd call it late adopters, right? In the, in the whole cycle. I dunno if you've read this thing about a chasm, right?
[00:07:01] Rigvi Chaevala: So you have crossing the chasm. That's right. And there's like early adopters, and then there's late adopters and, and then, then the followers mature adopters, right? So I think. Facilities kind of falls in somewhere in the right middle of, of that curve, right? And, and for good reason, right? Because you do not wanna be the leader or the first tip of the spear type of adopter into a brand new technology because if it's not proven, you have a massive scaling problem.
[00:07:30] Rigvi Chaevala: Because some of the, some of the, some of these brands have hundreds and thousands of stores, so you want it to be a little proven. You know, soaked in, into the market before they adopt it. So I think it's for good reason that that's, that's where facilities stays and, and in the new world of AI and automation and agent ai, I think that's where there's a little bit of that.
[00:07:52] Rigvi Chaevala: Tension of do we jump in now or do we wait? You know? Yeah. And there's, there's, there's that precipitation of like, do we, you know, like, what do we do now? Right. But that's the inflection point, the way I see it. But overall, I think as a, as a technology adoption kind of maturity for the space, I think it's pretty, pretty well, uh, adopted.
[00:08:13] Rigvi Chaevala: Right? There's multiple players that. Provide software, obviously, but then most facilities teams use some kind of automation, right? Could be, could be the best automation out there, or some sort of like poor man's version of it. But there is some level of automation. At least they're not doing spreadsheets and paper and pen like some of the manufacturing and other industries are.
[00:08:37] Rigvi Chaevala: Right.
[00:08:38] Sid Shetty: That's right. And we'll definitely talk about AI in a second, but you know, before we get to that, you know, you, you spend a lot of time thinking about problems at scale, right? What are some of the most important challenges in the space that you believe are still unresolved and, and what are you focused on solving right now?
[00:08:56] Rigvi Chaevala: I, I think the space, you know, as, as old as it is, like brick and mortar has been the way, even before e-commerce, right? The way you sell goods, right? So if you think of the, the maintenance part of that, like anything like you have, you own a house, you think of maintenance, but you kind of have a mental budget around that, right?
[00:09:16] Rigvi Chaevala: Say, I'm not gonna spend more than. Or 2% of whatever the cost of the building or whatever, right? Like, so there's, there's that mental budget, but in, in corporate world, there's a hard kind of a business limit of how much you can spend on maintenance. So that budget. Is one of the key challenges, but also an opportunity for these companies to get better.
[00:09:39] Rigvi Chaevala: So spend optimization, I would say is probably the biggest, biggest business outcome that all of our customers and facilities are thinking of because their budgets are. Fairly static and, and, and you wanna make sure that your facilities work well within the, the budget you have. So spend optimization, I'd say is, is one of the key outcomes.
[00:10:01] Rigvi Chaevala: They, they focus on. There's also revenue protection, which is very important. Like, you know, if you're a pizza store and your pizza oven goes down, well, you can't serve pizza, you lose revenue, right? Yeah. So the, the criticality, the mission criticality of. Making sure everything's working in order is up there, right?
[00:10:20] Rigvi Chaevala: Very high. Same thing on the coffee shop. Barista, the, the espresso machine breaks down. You can't do anything with your customers, right? So that's an important aspect for these teams. So that's another outcome that they look for is can you make sure that my revenues protected store experience, you do not, you do not want customers to not ever come back again because.
[00:10:40] Rigvi Chaevala: The, the roof was leaking or your toilets were bad, or you know, just, just, it was hot. The store was hot because the h HVAC wasn't working. It's, you know, those are important. Those are soft software outcomes, but very important outcomes and, and goal of these at scale. If you don't plan for the future, you are just punting this problem down the street, right?
[00:11:01] Rigvi Chaevala: So, so planning for the future in a more data-driven way is another key outcome. So if you think of these four. Outcomes at a high level, that's what every facilities management, you know, leader is, is thinking of that we every FM is thinking of. And so we wanna make sure that our product and, and the space that we're solving for addresses those outcomes and, and actually drives those outcomes for them.
[00:11:27] Sid Shetty: So Rigvi AI is everywhere right now, right? But there's a real difference between real meaningful and useful AI and marketing noise, right? So in your review, like how would you look at what is, what does good ai, what does useful AI look like, especially in the context of facilities management. So if you strip away all the buzzwords, what are the core capabilities people should really try to understand that we need to solve for?
[00:11:54] Rigvi Chaevala: You are so right. I mean, there's so much marketing noise out there that you can't really tell if, if that AI is actually useful, right? I mean, the fact that we're, we're having to use the word useful ai. Kind of puts it in context is, I mean, technically AI is supposed to be useful, right? But what does that actually mean?
[00:12:14] Rigvi Chaevala: I, I think it kind of ties back to the, to the outcomes I was just talking about. I mean, AI can be useful in a way, in that it's probably a delight feature, right? In, in the sense that, oh, look, instead of me clicking three things, I can, you know, ask a chat bot or ask Alexa kind of thing, right? So that's, that's kind of the, the delight piece of it.
[00:12:35] Rigvi Chaevala: And. Really, it doesn't drive outcomes. I mean, it makes it a convenience thing, it makes it more efficient. Sometimes it makes it fun, but really from a business standpoint, it, it may not drive an outcome. And, and I wanna preface that with, you know, there's, there's typically questions on, well, what, what does it mean when a company just slaps the chat bot onto their platform?
[00:12:57] Rigvi Chaevala: Right? I mean, it's not necessarily a bad thing as long as it drives the outcome. The customer's expecting. Right. So, so going back to your original question, the, the big difference in my view between real, meaningful, useful AI to just marketing buzz is, is it grounded in driving those outcomes and is there proof points that have been validated to get to those outcomes and have customers themselves validated?
[00:13:30] Rigvi Chaevala: Like in a, in a early adopter phase or potentially even through the road mapping stage, like it has to be a true partnership to get to those real meaningful AI features. Right? So, so for example, in in, in facilities there's, like I said, spend and optimization. If an AI feature. Actually reduces spend for you, right?
[00:13:54] Rigvi Chaevala: Meaning it avoids a truck roll, it, it, you know, makes sure that the repair is done the first time correctly, that the parts are available at the time of the fix. I mean, these are things that add up right over time at scale and actually drive the spend optimization goal. And that to me is useful. Ai, everything else.
[00:14:16] Rigvi Chaevala: As long as it's driving one of the outcomes that the customer's expecting is still good, but everything else would be fluff, right? And then just convenience or a perceived efficiency, right? I mean, when people say, yeah, I can do this now in two minutes versus 10 minutes before, what are you doing with those eight minutes?
[00:14:34] Rigvi Chaevala: Is that driving a business outcome for you? Right? And if not, then you know that eight minutes is kind of meaningless, right?
[00:14:42] Sid Shetty: Yeah. You know, it's interesting because AI has been there forever, right? If you've played on a video game or Atari, right? You know, AI was there, you're playing against a computer.
[00:14:52] Sid Shetty: What changed, I think in the past three years is AI got democratized. Like everyone had access to ai and the AI that most of us when it first came out began getting exposed to was generative ai, like chat, GPT and open AI and everything, right? And, and that's. Many times people think that that's the only AI that that, that you know, is being referred to when you say ai, but there's so many different types, right?
[00:15:17] Sid Shetty: There's generative ai, there's agentic ai, there's computer vision. But for most operators, right, who are not dealing with this every day, that can feel abstract, right? Help us understand this better. How would you break these down in terms of what these different types of AI are and in a, in a practical way, and maybe give us some examples of each and how that applies to our space.
[00:15:38] Rigvi Chaevala: AI is only good when you are dealing with unstructured data. When we have structured data, and when I mean that is, let's say I have a, a database table containing all the addresses of all my users, right? That's a structured piece of information I can query and say, how many users live in New York? Right?
[00:15:58] Rigvi Chaevala: And boom, you get the answer. And that's a structured way of getting that information. There's no AI there. But if, if I have that information and. I don't know, SMS texts and, you know, un like unstructured, like just text files and maybe some databases, and it's a mix of those. And I asked the same question.
[00:16:18] Rigvi Chaevala: This is where natural language text needs to be parsed, understood. Take out the extraneous stuff, come up with the the meaning of the question and the answer, and provide that answer. That's what generative ai, for example, excels. Because LMS are created for natural language right now. Agent DKI takes it one step further is where you can, instead of a human clicking a button or doing something like from an action standpoint, AI can a first recommend that action and then potentially in the future do the action for you.
[00:16:54] Rigvi Chaevala: Right? So that's where agent it comes in. Computer vision is. Different modality. It's not text-based, it's picture based, it's video based, and you know, it's kind of what your eyes do, right? And so that's a different way of interpreting data. It's still unstructured because it's still pixels, right? And you're interpreting pixels and saying, well, this looks like a cat versus a dog kind of thing.
[00:17:18] Rigvi Chaevala: So, so to me those are like different modalities. Off dealing with unstructured data and, and then there's the traditional ai, because now we have all these types of ais, we have to call the traditional ai. Traditional ai, right? And, and that that is basically you have tons of data and in, in different formats.
[00:17:37] Rigvi Chaevala: Again, unstructured, right? You, you might be getting it from a database, a bunch of documents, PDFs. Could be, you know, notes from like another place. When you are combining all of those, if you wanna mine patterns and classify things and create clusters, that's traditional ai, right? You're, you're talking about clustering and classification algorithms.
[00:17:57] Rigvi Chaevala: So there's all these use cases that you can leverage. You know, for example, the posturing or classification, that's where you find outliers. I didn't say anomaly detection and everything fits this bill except this one. Right? Here's an outlier and that's, that's a key piece of information that from a business standpoint, from facilities, for example, you could say, oh, this, for example, this work order is supposed to be this trade.
[00:18:21] Rigvi Chaevala: It seems like this is completely outside in this trade. Worth taking a look at this,
[00:18:27] Sid Shetty: right? As an example.
[00:18:28] Rigvi Chaevala: So, so you have. These different types of AI all dealing with unstructured information, but with different modalities. And so that's why it, it can feel overwhelming, but once you break it down into these practical applications, you know, it's, it's funny, like.
[00:18:44] Rigvi Chaevala: The, the, the most powerful ais can be, the most unsexy ones on the, on the screen could be a little number on a field that's powered by a very powerful AI model, like estimated type of completion, right? That's a very complex problem and solve, but all you see on the field is 9 25 ai. That's it, right? And so that is the power of ai.
[00:19:06] Rigvi Chaevala: And, and you know, sometimes it may not feel like, oh, that's all you're doing, but there's a lot of signs behind that.
[00:19:13] Sid Shetty: You know, with, with, with, with computer vision, I mean, Rigvi, it's really fascinating, right? Because one of the things that happens in facilities is it's very difficult to bridge the gap between being in the corporate office and what's out in the field.
[00:19:26] Sid Shetty: And, you know, help us understand like what, what advancements have taken place in, in, in computer vision where now, because you have that. Capability and the technology, you're able to bridge the gap on assets and condition assessments and make model serial numbers. Like how, how is it helping us get there faster?
[00:19:46] Rigvi Chaevala: One of the key areas. Ities managers pull their hairs out is because they don't have the asset information in the system. And if you, if you don't have a full registry of assets and a long of repair history on each of those assets, then you are shooting in the dark essentially every time there's a reactive repair work order, right?
[00:20:08] Rigvi Chaevala: And so capturing these assets. Information, for example, as an example, is, is a time consuming thing, right? You have, you have faded name plates right up on the roof for an HVAC system. You have grills that you have to move to find the labels on the side versus, you know, just knowing the model and make.
[00:20:30] Rigvi Chaevala: Potentially giving you, like taking a picture of that and saying, Hey, this is the grill I have, and it might say, oh, this looks like a model X, y, Z. Right? And, and so that's where computer vision can be super effective, right. To in, in asset capture, for example, there are other areas in the workflow, for example, when the invoicing happens, right?
[00:20:51] Rigvi Chaevala: Well, the work's done, the invoicings, you know, happening at, at a provider and they use a. Their old ERP system could be QuickBooks or anything else. Now they have to turn around and re-key that information into one of the CMMS platforms. So when they, when they have to do that, there's potential for error missing out on line items and the totals not matching up.
[00:21:14] Rigvi Chaevala: All these things that cause like kind of busy work, right? And so this is where you could leverage computer vision and say, upload the PDF, let it recognize the whole OCR using computer vision and just. Populate right what it's supposed to instead of someone keying it in. So that's another computer vision kind of use case, right?
[00:21:34] Rigvi Chaevala: There's compliance workflows where if, if you have insurance requirements. No one's gonna read through a 30 page compliance document to say, oh, they have coverage. They, they can show up at my store. Right. Versus having computers kind of do that work for you. So it's in, in my view, computer vision as, as, as a science.
[00:21:56] Rigvi Chaevala: Is actually fairly mature. It's even, it kind of predates obviously LLMs, right? So E, even before that we had computer vision. So it's a fairly mature area, but I think it's very underutilized, like in business context and, and I think companies that leverage that have already seen amazing results, right? In terms of data accuracy, efficiency, cost reductions.
[00:22:17] Rigvi Chaevala: So. I feel that that is like, that's a low hanging fruit, you know, in terms of AI is just to pick that and, and apply that across your workflow
[00:22:27] Sid Shetty: as an industry. Do you think AI has the power to change how we operate in the sense that, you know, we're a very reactive business, right? Because things break when you go with salt, you know, we go fix it.
[00:22:40] Sid Shetty: That's been the, this kind of quicksand that many companies get stuck in, which is you only fix things when they're broken and then you kind of are always chasing fires and, and it, that's not a good spot to be. It's very reactive. Do you think new technologies and AI particular will give us the ability to get to a more proactive state, and it may be even predictive state.
[00:23:07] Rigvi Chaevala: Absolutely. I think that's, that's actually a mind shift that is long overdue in this industry. Right. If you think of, like you said, right, it's, there's a reactive mindset today is let it break and then I'll go fix it. And even worse is I'm gonna keep fixing it until it fails. Right? You know, run to fail mentality, right?
[00:23:29] Rigvi Chaevala: And so both of those, I need, I think AI has, has the opportunity to flip the entire industry on its head to say now instead of, you know, reacting you, you only kind of plan for it right ahead of time. And so then you don't have those reactive issues to begin with. And then the prediction part is you have enough data.
[00:23:51] Rigvi Chaevala: You have all the acid information, you have all the repair history, and now you're talking millions of records of, of information, right? And once you have that data density, now you can simulate how long is this? Grill gonna last is how long is this HVAC gonna last this reefer, right? I mean these, so there's, there's, there's so many ways that this data then can be leveraged to predict and simulate and you can even plan for your 2028 budget right now, right?
[00:24:18] Rigvi Chaevala: Like if, if you have that information and, and I think that's where FMS would want to spend their time. They're not able to spend their time there. They don't have time to plan. They don't have time to come up with a strategy, right? Because they're constantly firefighting on the other side. And I think that's where AI has a power to plug that gap.
[00:24:41] Rigvi Chaevala: Create like an always on FM type of situation where. AI is watching for all these reactionary. I mean, it's not like you would eliminate reaction, right? To, to repairs. Things still break down. But if you are able to manage by exception and not get overwhelmed by 20,000 alerts a day, right? That's, that's a win, right?
[00:25:03] Rigvi Chaevala: So you stop that and then you pivot and say, okay, now let me go plan for the future. I think if they're able to do both, and that's where AI has the power to kind of let them do both going forward.
[00:25:16] Sid Shetty: Yeah. And you know what's interesting is iot and energy management's been around forever, right? I mean, for some time.
[00:25:21] Sid Shetty: But it's always been challenging to put the capital up front to get all your locations and assets censored up. And there were a lot of sensors needed, right? I think AI has changed that too. You don't need as many sensors now 'cause you can do so much with the data and the, you know, and, and doing, create inferences and algorithms and, and the right logic to say based on.
[00:25:43] Sid Shetty: These sensors, this is what we predict as the outcome and that changes how much customers will need to invest even in, you know, putting sensors up in their locations. And so. You know, do you think that AI is changing even that part of the business, which is now how, what do you need to do rather? What you need to do to get to a proactive state is also different and the, there's more chances that a customer can think about getting to a predictive and proactive state than ever before.
[00:26:18] Rigvi Chaevala: A hundred percent. Because if you think of like, I'll, I'll take that example you just mentioned, right? There's so many types of sensors. There's temperature pressure sensor, there's fluid sensors, there's vibration sensors, right? In the, in the past, the mentality was I need a time series data for each of these parameters, and then you need an expert analyst to look at that and say.
[00:26:41] Rigvi Chaevala: Oh, I see a spike there that's gonna break down in the next couple of days, and you needed that expertise. In the past, the game's changed. Now AI is basically using inference layers to say, well, I don't need a temperature sensor. I can still use the vibration sensor and just predict that the temperature is gonna go up because the motor.
[00:27:01] Rigvi Chaevala: You know, the motor's going on for longer than it's supposed to be. 'cause of the vibration that I can capture. So, so there's different ways to infer and AI is actually unlocking those capabilities. There's, I've heard of cases where you can, you can, uh. Kind of estimate how hard your HVAC's working based on how many times your door opens.
[00:27:20] Rigvi Chaevala: There's a, there's a correlation to that, right? And so, you know, you don't need a HVAC sensor. You if you have a door sensor already use that, right? Use that information. And so that's, AI is changing the game on IOT. And couple that, I think there, there's a secondary problem is most of these iot systems in the past, and maybe even now, are not integrated.
[00:27:43] Rigvi Chaevala: They're sitting on the side. And they basically, you know, have that information siloed away from the rest of the transactional work order history and everything. The, the, you know, and, and the power of integrating that back into the workflow will make it much, much more powerful. Right. In addition to just using better sensor data and better inference.
[00:28:08] Rigvi Chaevala: Also embedding that back into the workflow. I think both need to happen. And, and then, you know, you, you solve for this with the power of ai.
[00:28:18] Sid Shetty: Now, you know, there's always this tension of like moving quickly and, and actually being responsible with ai, right? There's, there's a lot of things to consider, especially around data and privacy and trust.
[00:28:30] Sid Shetty: And then you add on top of that, the layer, you know, MIT did a study last year that said 90% of AI initiatives are failing. Keeping all of these in context, like how should organizations think about this balance and, and how do you do it in the right way?
[00:28:46] Rigvi Chaevala: Yeah. I mean, you know, the 90% of the initiatives failing is, is.
[00:28:51] Rigvi Chaevala: Most likely because all the experiments are also being counted as initiatives, right? And, and there's nothing wrong with experimentation, but if they're not grounded in the problems worth solving, customer validation, the business case justification, then they are just that. They're experiments. They're more like art of possible.
[00:29:08] Rigvi Chaevala: And I think that's where the distinction lies, is that, again, going back to our useful AI comment, you know, is. The 90% of the initiatives are failing because they're more experiments. They're not like the 10% that are actually succeeding, most likely have that line of sight to, to the problem we're solving, right.
[00:29:27] Rigvi Chaevala: For the customer and driving those outcomes. But having said that, I think in general, like for, you know, to your, to your other question about how do companies need to think about the data privacy and the trust, right? I think that's. Again, we're, we're in kind of the learning phase of that as, as a whole industry, right?
[00:29:47] Rigvi Chaevala: Not just in facilities even outside of that, but. I think there are some signals of stability emerging, right? I mean, there's some legislation, there's regulation already in place in both EU and us. Then that's helping guide some of the, some of the discussions on data. And then there's also the, the unknown.
[00:30:06] Rigvi Chaevala: Nervousness that that was there before, like especially in financial services, that was huge. They were like, don't, I'm not gonna give my data to anyone, right? It's gonna stay in my cloud or my data center. I think that mindset is also kind of easing up a little bit, as long as you can show that the data is handled.
[00:30:24] Rigvi Chaevala: Correctly the privacy controls are in place, right? You know, the data scaling and making sure that you know, data security overall, like it's not just data security, it's application security is taken care of. So it's the same i key principles that have already existed. We just need to kind of extend that to all the data elements that that come along with it.
[00:30:46] Sid Shetty: So here's a scenario, right, that many of our listeners are probably going through right now. I'm a facilities leader. My business is telling me that I need to leverage AI and do more with less and ensure that my, my partners are leveraging ai. But my IT and legal teams are telling me to be careful and make sure that, you know, I'm not sharing data that I shouldn't be sharing and that, you know, are there other third party applications that are involved and how do we ensure that everything is secure?
[00:31:16] Sid Shetty: I. If you were talking to a customer right now and if you were in their corner trying to like help them navigate this, this whole situation, what advice would you give them? How would you say they need to go and handle those conversations individually to ensure that they're the ball's moving forward, but without introducing, you know, risk and, and creating anxiety within the organization?
[00:31:39] Sid Shetty: Right.
[00:31:40] Rigvi Chaevala: Let's just say if you have a vendor that's supposed to. Deliver X, Y, Z outcomes. I mean, that needs to stay done, right? So that, that's one pillar that I would advise some of these IT and legal departments to say, you know, just make sure that your outcomes don't change. Right? That's number one. Number two is on the data security side.
[00:32:01] Rigvi Chaevala: If, if. A customer is already leveraging a SaaS platform, right? There's already kind of, they've embraced the fact that you have a trusted, vetted partner that has all the security controls in place to handle your data, right? Which even prior to ai, they still have work order transactional data, right? And so if, if, if they've already embraced that fact, I think this to me is more of an extension to say, okay, where is my data stored within your.
[00:32:32] Rigvi Chaevala: Premises and is it being shared outside of your premises? And if it is, show me the controls that, you know, a third party that you are leveraging is also doing. But if, if I wanna to pick between vendors, I would prefer vendors that. Can commit to that security all in one place. Right. Versus me, me having to handle different data vendors behind the scenes and having, like, making sure there's governance across all of these.
[00:33:01] Rigvi Chaevala: It's, it's a lot of overhead. So my advice to these ITM legal teams would be to take a look at card, look at the vendors that you already have today, and see how they're sharing data. With their third party vendors. And if that's necessary, and if you need to govern that, right? And if you don't want to, or if you don't have the bandwidth to do it, then you should pick vendors that kind of take care of all of that for you and, and obviously that you trust already.
[00:33:29] Rigvi Chaevala: Right
[00:33:31] Sid Shetty: now, every professional in every field is, is thinking the same thing, which is. Is AI coming for my job? Is my job gonna exist, you know, in a few years? And, you know, it's an interesting thought and it's a, you know, in many ways, yes, the jobs are gonna change and jobs are gonna evolve and roles are gonna evolve, careers are gonna evolve, right?
[00:33:54] Sid Shetty: When you put that lens on, on our industry, do FMS and facility coordinators and you know, folks, you know, that are at different levels in our, in, in, in our world and operators. Should they be worried about AI and the impact it'll have on their job? Can they put their head in the sand and think that, that they don't wanna think about it and not worry about it?
[00:34:15] Sid Shetty: What should they do? How should they think about where the world is going now?
[00:34:21] Rigvi Chaevala: It it's, it's an inevitable, inevitable question that every industry's facing, not just facilities, right? But, but I would flip that question, especially for Fens to say, Hey, when you took this job and you look at your job description.
[00:34:35] Rigvi Chaevala: Did it ever tell you to go look at 2000 more orders a day? Right and firefight it every day. I don't think any job description has that. Right? I think the, the, the original intention of FMS was to make sure that you have a predictable spend across your portfolio of, you know, whatever, if you're regional FM versus global fm, right?
[00:34:59] Rigvi Chaevala: That's your scope. And you need to make sure that your, your spend optimization across your portfolio is consistent, predictable. Right. That's typically the job of these folks, but they're not doing that today on a day-to-day basis because. 'cause of the tools that they have today, don't let them do their actual jobs.
[00:35:19] Rigvi Chaevala: So the reason I'm flipping that question is, are you even doing your job today the way it's supposed to be? Right. And if AI is gonna let you do your job that you were supposed to be doing, then why wouldn't you embrace that? Right. So to me, it's not like. Chucking fms out, it's actually making fms do what they were supposed to do to begin with.
[00:35:40] Rigvi Chaevala: And AI is actually helping them to get there, right? Because they're not, they've kind of skewed away from their real, like planning and prediction, predictability and, you know, financing and that that's their goal, right? That's their. That's your bread and butter and that's, that's what they're supposed to be doing and not firefighting and picking up a phone call and saying, Hey, why is this technician not here at the store?
[00:36:02] Rigvi Chaevala: Right. That needs to, that needs to be managed by exception. Right. And not by vote.
[00:36:08] Sid Shetty: The roles are not going anywhere. It's just that the roles are gonna evolve and you're gonna be able to do the things you've always wanted to do. To your point, you just got a, a massive superpower or massive tool in your tool belt.
[00:36:18] Sid Shetty: Right. Before we talk about the future. If you look back five or 10 years, what's possible today that just simply wasn't before?
[00:36:27] Rigvi Chaevala: From a consumer standpoint, you know, every one of us have seen the evolution, fast evolution of LLMs, right? So that absolutely is, is a game changing moment. But LLMs, like I said earlier, it's just one of the ways AI can be used.
[00:36:41] Rigvi Chaevala: It's for not natural language, it's for summarization, kind of use cases, writing text, and so on or, or drawing insights from text. But LLMs kind of acted as a catalyst for. Of the common man to, to know and embrace ai. Right. But to me, there, there are several things that happened in the last five to 10 years.
[00:37:04] Rigvi Chaevala: Compute, scaling, you know, is like, we didn't have data centers that had GPUs in the past. Right? And, and so without that, there's no way you can scale or leverage or use ai, right? So that that changed. Inference speeds changed because, because now we're embedding it into workflows. You need it real time. In the past it used to be batch mode, right?
[00:37:24] Rigvi Chaevala: So we that that completely shifted and changed the gate talent availability. We didn't have that before. Right? Now we have folks that have been in the AI space for almost five, 10 users. That's a good enough. Lead time to, to leverage that talent. A number of developers, right? Like that, coming outta colleges, there's courses now on AI and ml, so now you're getting new talent, new developers into the marketplace.
[00:37:50] Rigvi Chaevala: And finally, the commoditization was right, like the, the whole space, because there was. Revenue potential. And there was way to monetize this. A lot of players came in, right? And so that competition actually helped everyone in democratizing this and making it open source free and, you know, cheap, right? So all of these changed in the last five to 10 years that, that we, we couldn't be talking about leveraging AI and
[00:38:17] Sid Shetty: business workflows, if.
[00:38:20] Sid Shetty: These didn't happen. Right. So when you look ahead now, Rigvi, what does the modern facilities organization look like in an AI enabled world? I,
[00:38:29] Rigvi Chaevala: I, I think we touched on it, but I think kind of summarize that piece. Right. I, I think two main things that the modern facilities would look going forward is they would focus more on planning.
[00:38:42] Rigvi Chaevala: Then reaction. Right. A reacting. And, and that would be a, a major shift of like mindspace and, and talent and people and their motivation to do this. Uh, I would wanna plan in advance, I would wanna make sure that something is fixed before it breaks and creates a catastrophe. Right. A, a small plumbing fix that you could have done, you know, as, as part of.
[00:39:08] Rigvi Chaevala: Planning ahead would kind of avoid a whole store getting flooded, right? That's a massive cost. That's a massive disruption. And so those are small things, but that's, that's where I think facilities will spend more time on going forward and then running away from the run to fail mentality and going into predictive maintenance.
[00:39:28] Rigvi Chaevala: I think that that probably will take a little more time, but eventually that's gonna happen in my view.
[00:39:35] Sid Shetty: Agree completely. Final thought, if you are advising folks that are listening into our conversation right now, and they're in facilities, or they're in operations or they're a tech company, right? What advice would you give them about just making decisions, you know, on the technologies that they focus on and being able to evangelize the impact it can have on their business?
[00:39:58] Sid Shetty: What would you tell them to focus on?
[00:40:02] Rigvi Chaevala: I think I would focus, like, I would tell them to actually challenge any tool internal third party provided or commoditized any, any one of those to always draw that line of sight to, to the ultimate outcome. And if you are not able to do that, or if it's a stretch, then you're trying to make it work and not, you know, it's not gonna work for you.
[00:40:26] Rigvi Chaevala: Right. So, so to me, I, I think just. This is back to basics kind of thing, is as an operations leader or a facilities leader, what is, what is it that you deliver to your business? And can AI be an enabler and a tool and not like it? It cannot be the reason why. Like you can't, you can't switch because there's an AI tool.
[00:40:51] Rigvi Chaevala: Switch your process or change your outcomes. It should be the other way around. AI should be enabling you to get there, right? So just make that slight distinction. Make sure that, that, that is clear in your head when making these technology decisions.
[00:41:06] Sid Shetty: I love it. Well, Rigvi, that was a fascinating conversation.
[00:41:09] Sid Shetty: Thanks so much for joining me today. For listeners who wanna learn more about you and you know, I would love to connect with you, where can they find you?
[00:41:17] Rigvi Chaevala: Well, one fascinating fact about my name, if you search for me, I'm the only guy on Google, so that's an easy way. But I'm on LinkedIn and you know, I'm pretty active there.
[00:41:27] Rigvi Chaevala: Of course, you can reach me through through my work email as well.
[00:41:32] Sid Shetty: Love it. Well, with that, a huge thank you once again Rigvi, and to everyone in our audience, thank you for joining us, and I'll see you on the next episode of Elevating Brick and Mortar. Well, that was Rigvi Chevala, CPTO at ServiceChannel.
[00:41:47] Sid Shetty: What stood out to me today is how important it is to move beyond the hype and really understand where AI can drive meaningful impact. This isn't about adopting technology for the sake of it. It's about solving real problems, making better decisions, and giving teams the tools they need to operate more effectively.
[00:42:08] Sid Shetty: And as Rigvi shared, the opportunity in front of this industry is significant. For those who approach it thoughtfully. One thing is also abundantly clear. The FM professional isn't going anywhere. They're just gonna become a lot more equipped and powerful. With that, I'm your host Chei, and I'll see you on the next episode of Elevating Brick and Mortar.