Episode Transcript
[00:00:00] Speaker A: Foreign welcome to the first episode of the AI Driven Performance Improvement Podcast. I am Anil Kumar, your host. I lead the private equity AI practice at Alvarez Marcel and oversee A and M Assist our AI Driven performance improvement platform.
Today we will explore how AI has the potential to address key challenges in field service.
Now when we think about field services, we think about the operational complexity that comes with managing a dispersed workforce, handling installations, repairs and maintenance. From home appliances to massive industrial equipment, these businesses deal with the pricing challenges, inefficient customer interactions, route management headaches and a constant struggle for better visibility into margins. Although it's still early days, we are starting to see AI make an impact, helping with dynamic pricing, predictive maintenance, real time workforce optimization and a lot more.
Joining me are two fellow Managing Directors from our private equity performance improvement practice we have Brian Long who brings over 20 years of experience in the strategic and operational consulting leadership positions with a global truck and engine manufacturer and an industrial focused PE fund. His focus for the last decade at A and M has been leveraging that background in the business services space with a specialization on field services.
Also joining me is Ben Taylor who brings extensive field services experience.
He has served as interim leader in both executive and operating roles for environment service, H vac and field distribution businesses. He brings over 15 years of performance improvement for portfolio companies and founder based businesses. Ben served in the US Navy, including as a commander in the nuclear Submarine force.
So let's get into it Brian. It would be great to get deeper into your background across business services, manufacturing and private equity. How do you see these operational complexities play out in the companies you work with?
[00:02:31] Speaker B: Sure. Thanks Anil. I work with companies that focus on business services, especially field services like H Vac, electrical, plumbing, engineering, construction, lawn care, disseminated workforces and a network of assets. Basically that's primarily been my focus at A and M. These businesses have thousands of customers, many times thousands of remote employees, and thousands of vehicles and equipment out in the field that's difficult to manage because they're remote. Companies do very well if they have handle on all the data that's available, customers, assets and people because it can be a very data intensive industry and space. But many times the data is not available and it's very difficult to manage it to make decisions and that's the crux of the issue. All this complexity with the data causes companies to more manually manage versus managed by the data and metrics and that's why this area of field services could be ripe with opportunity.
If companies manage the technology adoption with A focus on EBITDA improvements.
[00:03:49] Speaker A: Ben, given your experience leading field services operations firsthand, what excites you most about how the industry is evolving?
[00:03:58] Speaker C: Thanks, Neil. And I think I want to piggyback right off of what Brian was talking about. When you're in the field services world, whether it's landscape and or construction or industrial services, out in an oil field, for example, you know, you have a lot of distributed teams that are, that are moving about servicing your customers and the amount of data flow and information flow that happens between the teams, that happens between the regions and the headquarters, or depending on the structure of the company, leads to all types of different inefficiencies and issues around margin, around how you effectively employ your workforce, how you're making sure your customer engagement is, you know, hand in hand with your business objectives and goals. And I think one of the things that excites me most about what we've seen happening past the revolutionary, you know, 10, 30 years ago, the ERP and trying to bring together your financials in the background now is really the ability to look at near term decisions and activities that happen out in the field and be able to draw on a broader set of information quicker to make some decisions that you probably haven't had in the past.
[00:05:07] Speaker A: So let's break down the major challenges in field services. Let's explore the growing impact of automation and AI and then really discuss how companies can prepare for this new phase of AI driven transformation and automation that we expect to start increasing in momentum over the next say year, two year.
And really the way I think about it is you need to start planting the seeds now in terms of getting your data right, getting change management processes in place. You started to think about what challenges to solve. And obviously a lot of this is different depending on the company.
So let's start by looking at the key industry pain points. And when I speak with a lot of the field services executives, I consistently hear similar challenges and these are usually in three big buckets. One is workforce shortages, so I hear a limited supply of skilled technicians, which coupled with a slow hiring pipeline makes it difficult to meet demand sometimes and actually many times depending on the seasonality of the business.
Then I hear about scheduling inefficiencies where technicians are often assigned broad arrival windows due to inefficient dispatching and a lack of real time scheduling optimization. And even then there's a percentage where they don't even show up leads to a lot of customer dissatisfaction. And then third category that I hear about is operational bottlenecks. In this case, the issues such as pricing, inconsistencies, inefficient truck deployment and these really lead to lost revenues and reduced profitability and really impact the margins. Ben, I know you've had a lot of real world examples and driven a lot of change across multiple such businesses. Can you share some real world examples where company face some of these challenges and they use automation or other ways to address them?
[00:07:04] Speaker C: You know, some significant ones that I've seen, particularly on customer service, we've had cases where no speed to get an information not only from our technician to the office, but from the office to the customer or the representative of the third party they may be representing on their behalf is always important. Usually they hold the approvals for further services or not to exceed rate for a price or an activity that they've contracted for. So being able to not only get that information and sanitize it and move it quicker will help you get to those decision rights quicker. The examples that we've seen out in the field is quite often the logs vary widely from your technician technician on what is actually seen and reported out there. And one of the ways that we had started looking at and thinking about how we can get that quick, quicker is potentially being able to use some AI to sort through the key elements and logs that we need to put into the customer facing information and streamline that activity and the time that it takes to do that to get that back, that yes, go repair this or yes, you can spend above this item. And that's really key within customer service because not only can it help your first time response rate where you can fix it in the first time, but it also can help you sequence that. You've got word back to the actual on site customer that you know, hey, we're waiting on corporate, but we've already submitted, you know, and give the actual feeling and the comfort to the customer that you're looking out for them like you should be, you know. So that's one from a customer perspective that we often see. I think another one that's really early on, but really one that we think has a lot of potential is around how do you become the most efficient in your routing? And I'm not talking about the typical route planning and the routing that happens when you set up your day that comes from some sophisticated routing software and things like that. I'm talking about the in day dynamic things that happen that may not be captured in the business rules that are typically in a system like that, such as this technician, a dispatcher may Know that a technician has this particular route or this type of capability or skill sets on particularly that person. But that also may have what's happening, what they've consumed from the previous call that they're on as far as inventory and stock and truck and having some of these other in, day, in, up to date information, how to mix that together in the broad set of all the technicians you may be looking at from the dispatch and so you can further get to the dynamic dispatch when you get an emergency call are ways that we have conceptualized and thought about cases to go after that. So certainly there are a lot of activities in a lot of areas all up and down from the margin, the speed to get in that margin, the customer interaction, you know, all the way down to how you efficiently run your techs and make their lives quite honestly easier. That is pretty promising what we're looking at.
[00:09:51] Speaker A: And I think, you know, we at A and M pride ourselves in our operational heritage. We really don't drive initiatives or technology for technology's sake or just because everyone else is doing. We really take a very pragmatic and practical approach to understanding the specific pain point for a specific business and then helping managements holding their hands to the transformation. Brian, you've had a lot of these examples and you've driven a lot of these transformation. Where we come in from A and M and say, hey, let's first understand your problem and design these problems and let's just not try to boil the ocean. Let's take one problem at a time in a way that's much more manageable and that's how what we are in the market for from an A and M perspective. Can you share some examples and how in your experience companies should be addressing these challenges or thinking about it? Just what's the mindset that they should be adopting right now?
[00:10:46] Speaker B: Yeah, I think a lot of companies are doing the simple items, the back office, the routine tasks, automating those looking at contracts, legal reviews, HR onboarding, things like that. And I think the tools are available there and AI has entered that space more quickly because that's the sweet spot of AI is automated routine tasks.
Companies are dipping their toes in that water, as it should, as those tools are more mature and what they're starting to do now and will over the coming months as AI tools mature and we're all in this together. This roadmap is thinking about it, I think in a, in the opposite way of what are their pain points and starting from that perspective, how can we solve the big needle Movers of EBITDA by using AI, but not just a software solutions ploy, but thinking about streamlining your processes along those main points. What is it that you're trying to achieve? Probably streamline the process in, in that process of going through that and then applying AI to be faster, bigger achievement and more sustainable.
And I think when companies think of it that way, still using the processes that they all have and change management and such, and then applying AI, much more dramatic and it'll be a holistic, pragmatic picture at the end of the day versus a series of disparate systems that they're installing.
[00:12:25] Speaker A: That's really well said, Brian. I think we've been now at this for multiple years and we'd like to think we're at a cutting edge. But still what we see, the way we see AI's role in operational improvement, it really makes us faster, better, smarter by taking away things that are generally grunt work, repetitive things, information gathering that technology is better at, which frees our time to solve more of the challenges around scheduling, creative problem solving, dealing with managing customer interactions, improving our customer service and so forth. And that's really the role of AI. It's not just I implement it because it's going to try automate everything, it's really to make us better at providing client service and providing our customers more of what they need.
So in terms of I think the role of AI and how we see it evolving, we think from a field services perspective.
Field services generates vast amounts of data a lot with the route optimization, with the trucks on the ground and moving around, you got the pricing, you got seasonality, lots and lots of data, but a lot of it is underutilized. And AI has a potential to change that by transforming how companies optimize their operations and enhance decision making by freeing up management to do more of decision making and less of information gathering. And in our experience what we're seeing is some of the key applications that AI is already starting to make an impact and it's still there's a lot of room to grow. Is one is AI powered pricing models. We think technicians can start receiving more close to real time pricing recommendations based on demand fluctuations, margin targets, competitor pricing. And this will allow companies to reduce revenue leakage and improve profitability. Second, AI driven customer support. That's really a huge advantage for companies who are implementing intelligent chatbots and virtual agents. They not only solve the customer issues more efficiently in terms of the simple customer issues and directing more of the complex customer issues to agents, to real agents, but they also identify upsell opportunities driving additional revenues. Then we are seeing dynamic workforce scheduling. This is a huge opportunity. AI can optimize technician dispatching by factoring in real time traffic conditions, technician expertise and skillset enlargement cancellation, thus improving efficiency and customer satisfaction. And one other area we're seeing AI starting to make and a lot of this is dependent on the data, the quality of the data that the company has. But predictive maintenance AI driven analytics can assess equipment performance, enabling companies to identify and address potential failures before they occur. Early warnings early warnings is a concept that we at A and M drive quite a bit is to have a sense that something would go wrong before it goes wrong so you can address it proactively and significantly reduce costly downtime. So overall, I mean that's the potential and that's generally clear. But the reality is that many AI applications in this space are still evolving, it's still early days and not all of them deliver measurable roi. It's just let's take it and we'll talk a little bit more about Crawl Walker and the process we have. But I think we are still in the early days of AI. So Brian, in terms of, you know, where you see all of this evolving, how should companies think about adopting new technologies in a way that drives real value and does not distract them from real operational problems that they need to solve?
[00:16:17] Speaker B: Yeah, very good. I would say my advice would be not still understand the roadshow of all the software suppliers that are coming in and selling a certain solution and then just install ABC software solutions because of the sales presentation. But think of it from the reverse way. What, what are your problems in your business? A lot of these field service problems are very similar with common threads. Thinking from your business back, how are we going to solve the problem inherently within our four walls of our business?
Fix the processes, fix the people, fix the capabilities as much as we can and assets, streamline that as much as we can and then look to the market for what AI applications might help our specific need.
Don't try to jam software solutions into your business. Think about your business and then try and augment your business in your change program with the AI solutions as they evolve. As, as things evolve over the summer and and into the fall this year things are going fast and they will mature into very reasonable like high roi, low installment cost products, but don't end up with the Frankenstein of solutions. So with your business EBITDA back and make sure that you're doing things that will drive double digit EBITDA percentage change.
[00:17:48] Speaker A: Great, that's very promising. Ben, can you share any additional examples of where this is going and how AI could make an impact in the near future?
[00:17:58] Speaker C: Yeah, I think as you look across, and again, we've already talked about how a lot of the field services and the different ways the branches operate and integrate together can widely vary by region state, whether it's rules, whether it's the customer set, or whether it's the actual service offering that's happening in there. And so as we think about the, the capabilities of AI, right. I think companies need to be specific around what they need to fix and then they need to think about, okay, when I, when I have something as optimized as I think I can do, where are there areas where there's either I'm synthesizing a lot of data to make a decision, or I need to increase the speed of my decision in order to deliver or capture some other revenue for a customer. If you can get to those points as you're working through your kind of your journey on there, those may be really good areas to apply the AI type capabilities and think about the problem set that you have out there. Ultimately, at the end of the day, right, we're all trying to grow the ebitda. We're all trying to make it better for the working conditions in the workforce that are there so that they can keep servicing and be excited about what they're doing. And we're all trying to do that to make sure that the customers actually get their needs met. And if you could do those things and there's other ways they had to get there with that, then those are the areas that we need to continue to double down on. So it's a mix of what I would say, you know, traditional performance improvement activities, the ways that you would attack a problem, and then applying this other lens, this other capability on top of how do I get this extra capability or extra thought that I may not have either been able to have before or that I didn't have the bandwidth to get to on a daily basis for like decision making.
[00:19:36] Speaker A: That's, that's very well said. And just I think for companies thinking about AI, you know, based on my experience, I think start small. And this really, this is just capturing what Ben and Brian, you said, let's start small, focus on ROI and scale from there. And this is what we at A and M call it the crawl, walk, run approach. Start with high impact, low effort. Don't go for 15 use cases, go for two use cases. Create early wins, think about change Management takes time to change behavior. So from a crawl perspective we start with low risk automation like AI powered routing, scheduling which are just slightly bit more advanced and the more mature software is available. Then move into the walk phase which is AI enhanced pricing, workforce optimization, customer engagement. Then once you have that the organization has gone through some of the acceptance of new changes and new way of doing things then you can go into the run phase which is really use AI for full scale predictive analytics or more of the decision making. That's the way we think about it. Ben, in terms of any final words of advice as we close this out is when I look at companies on one extreme I see executives and field services who are still in wait and watch more. So they're saying well let's see where this goes and then we'll address based on that. On the other extreme we have folks who have complete full and fear of missing out and they are starting to implement technologies just mostly so that they don't feel like they don't get left behind. Any final words of advice?
[00:21:15] Speaker C: I think one of the things I've always said when out in the field particularly is a lot of times you run into places and companies will, will face something. They kind of admire the problem, right? They'll look at it, they'll talk about it, they'll think about it, they'll hit it from a bunch of different angles. I think in particular here, back to our crawl walk, run protective. You know, it's more than admiring the problem, it's let's go ahead and tackle it. Let's tackle it in a smart way that's not going to sink the boat but also can prove out where we can need to go and point is quite honestly in areas that we, we may be able to grow into and, and increase the EBITDA in the ways that we're working in it. So I think the, the message I would have and, and what we would be seeing out there is you know, start small but start and, and, and, and watch the productivity. If it's not what you're getting and the way you're looking at it, then adapt.
[00:22:01] Speaker A: Great. That's great. Really appreciate your insight. Thanks Brian and Ben for, for sharing all of your insights from driving a lot of the change across field services and similar businesses to our listeners. The AI journey in field services is still unfolding. It's really very early stages, but companies that take a structured and data driven approach will certainly gain a competitive advantage. Stay tuned for future episodes as we continue exploring AI's impact across different industries.
