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Automation Can't Save You from Broken Processes

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About the Episode

Is automation the answer to our most frustrating workplace issues? For those dealing with repetitive tasks and manual processes, it may seem like a great solution. But there’s one big catch Prabhjot Singh wants people to understand before going all in on automation. The President and CEO of Pyze knows a thing or two about the power, pitfalls, and impact automation can have. Learn from his experiences and don’t make this common automation mistake.

Episode Highlights

Meet Our Guest

Prabhjot Singh is a serial entrepreneur who has started multiple for-profit, social enterprise, and nonprofit ventures. He’s passionate about helping organizations reach maximum efficiency through automation. As the President and CEO of Pyze, he enables the world’s largest enterprises to improve business operations through AI-driven process intelligence and analytics. He has over 20 years of experience in sales, marketing, and product management, making him an expert on the intersection between people, process, and technology.

Episode Transcript

Lindsay McGuire: I have three words for you: Excel hot potato. You know exactly what I'm talking about, right? It's that Excel spreadsheet that you share with like 20 colleagues, and every time you make a change to it, you have to resend it, make a copy of it, upload it to a new drive somewhere. It's just a hot mess. It's busy work. It's frustrating. And it's one of the hundreds of little inefficiencies that add up to higher employee turnover. Although I wish I could claim that I came up with that phrase, Excel hot potato, I can't because it belongs to this week's guest, Prabhjot Singh. Prabhjot is the founder and CEO of Pyze, a low-code process intelligence company. He has years of experience in founding and scaling companies. And he's got phenomenal advice around how to practically use automation to scale your organization—AKA stop the Excel hot potato and other busywork and inefficiencies. I'm so excited to put the spotlight on this genius this week. Take a listen to my conversation with Prahbjot. 

Prabhjot Singh: We started by really looking at applications and the productivity and the business performance of applications. But as soon as you do that in the enterprise context, it's not about the application anymore, right? It's about the process because these applications help enable these sort of complex processes. That was really the impetus in getting us focused on this operational excellence space. And every enterprise we work with, they've got a focus on Six Sigma or Lean or improving their operations. And the thing that people struggle with is that visibility. How do you improve a business process? Well, in order to improve anything, you've gotta be able to measure it first. And a lot of the industrial engineering teams that we work with, or the data science teams that we work with, they spend half their time just wrangling data—trying to get it from five different systems and normalize it, and it's such a mess. We're taking that burden off their hands and integrating the different data streams, so we visualize an end-to-end process. And we've helped them quickly isolate, oh, here's a problem. And now you can do root cause, and you can start using the data to run experiments and fix things instead of just trying to make sense of the data.

Lindsay McGuire: There's just so much chaos across even similar departments within a team—or teams within a department, I should say—of we use this tool, and they use this tool, and we're doing this this way. And then it does not come together in one solid process. So what got you interested in process improvement? Why is that a passion point of yours?

Prabhjot Singh: I've always been a data guy—a data nerd, so to speak, you could say. I think data has tremendous power to help us transform our thinking. So usually when you're in the middle of something, sort of down in the weeds, you're always looking at I'm doing something this way. How could I do it a little bit better? It's possible that you could do it a lot better of you were able to take a step back and look at the end-to-end picture, to identify things that you don't know the answers to or things that you didn't even know you could ask. Like, that's one of the big challenges with data—you can use data to prove anything. And if you ask data a question, you'll get an answer back. The challenge that most people have is what questions I should actually be asking because there's so much data. If we take the example of a healthcare claim, for instance. Understanding all the different steps that a claim goes from initiation to closure. Whether it gets approved or denied, it could be touched by a dozen different people, and it might be kicked back and forth, back and forth. Escalated up. Go back to the subscriber for more information. There's so much data that it's hard for a human mind to really comprehend it, especially if you're talking about, okay, now I've got a million claims that I'm processing, not just one or two. So being able to sort of organize that data in a way that it speaks to you was really a passion of mine as we were going down this journey, and we've been able to do that to a good extent.

Lindsay McGuire: It almost sounds like you're talking about creating a good story with your data, right? And one that speaks to you and your role, your position, whatever you're trying to achieve in your business. I think that's a really powerful way to position that. And I wanna go back to something you said, too, earlier about being able to look at something from the process standpoint versus the application or tool standpoint. I think a lot of people get caught up in: What tools am I using? What tool is gonna make this work? What tool will make this better? But they forget that it really is the foundation part of that is the process. So what advice do you have for people to be able to switch that thinking away from being so tool-driven, app-driven, and more in that process standpoint?

Prabhjot Singh: That's a great question, Lindsay. When we think about processes, we always think about business objectives. Any engagement that we have with any customer, the first question I always ask is, okay, what are you trying to do? What does success look like for you? Do you want to be able to generate more revenue? Do you want to be able to execute a transaction faster? Do you want to cut costs? What is your goal? You've gotta be able to then measure that goal in some quantifiable way because if you can measure it, you can improve it. So if I've got applications that support a particular business process, and my goal is that I want to be able to enable execution of a process faster, now you can go from that process level to the workflows level. What are the different ways that work gets done? And you might have a dozen different ways or a hundred different ways that something flows from start to finish. Once you understand those workflows, we can easily identify which ones are your top-performing workflows and which ones are your worst-performing workflows—the ones that take the longest, in this case, versus the ones that take the shortest time. And once we segment by these poor-performing workflows, we can now see how applications interact or support those workflows. Where do you have maybe high latency? Which applications have more steps than they should? We've even gone down to fields on a form level where someone might have 50 fields that an end user has to navigate through to fill out a form to do something. And maybe 20 of those only ever get used. So you should get rid of those other 30 to help speed up that process to give some sanity back to that user of that application. So as you zoom down to the application level, and then you can even get down to the feature level to see, okay, what features are being used? Which ones aren't being used? Which ones do I need to maintain versus maybe rationalize or sunset? So once you understand the business objective, you can go through a methodical process to identify what impact did that have? Did I make things better? Did I make things worse? Are they the same? So once you have the ability to actually do that analysis, you can now start to run experiments and see the impact of those experiments. And then rince and repeat once you get there. 

Related: What Is Process Debt, and Why Is It a Problem?

Lindsay McGuire: It's funny you bring up that point of the form fields because I'd be at a disservice not to bring up the fact I do work at Formstack, and I think about these things day in and day out. And just having form fields on a form that are not necessary can really throw such a huge wrench in your process. So if someone comes into that form and says, oh my gosh, I'm not filling out all this information right now, think about how much time you're delaying that process if it was just capturing that data you needed. And, of course, if you're capturing data that's not getting used, it's probably useless data. And I wanna go back to where you're talking about those workflows that don't work well—those least efficient, least effective workflows. What are some of the issues you and your team see in those less productive workflows, or the ones that are causing the most issues?

Prabhjot Singh: It's really situational. So things that we see the most often are the ping-pong effect, where you have work that's going from one department to another department back to another department. That's something that's very common. The other thing that we see often is manual tasks that people have to do. Let's say you have this form that you have to fill out, but I've gotta go to five other systems to get data that I'm literally just copying and pasting into that form. So those types of inefficiencies can be solved by using RPA or another form of automation. Oftentimes, we'll look at these workflows that take a long time or things that get suppressed or closed out without being resolved, and we try to do business rule extraction. What about these cases makes them different than the ones that were processed normally? And if we can identify those issues and then define them as rules, now you can do straight-through processing, where no one actually has to touch those cases. And we can use automation to process those cases, or, you know, we can not even have them created in the first place. There's lots of approaches to fixing problems. The first step is really understanding what is the issue? What is the bottleneck? And then why does that bottleneck exist? Does that bottleneck exist because I need my junior people to have better training than senior people? Or people in the Florida Jacksonville data center do things much better than the Dallas center? So you can analyze these end-to-end workflows. And then our AI engine will analyze the data to sort of say, oh, hey, Dallas is 30 minutes longer in terms of processing these transactions than Jacksonville for X, Y, Z reasons. Right? And now you've got the ability to, okay, use something actionable to make changes.

Lindsay McGuire: And you bring up a really good point about how organizations can get stuck with allowing teams or different cities or different branches to process things differently and that playing a part in why these inefficiencies exist. And, somehow, it gets lost in communication, collaboration of being able to share these learnings across each other. So then you're wondering, well, why is this going so smooth and swimmingly over here? And then you look over here, and it's like, why is this a dumpster fire? Why do you think that happens? Why do you think organizations get stuck in that lack of communication? 

Prabhjot Singh: It's the world we live in, especially these days. We're so engrossed in our Zoom lives that everyone's running, running, running, running from one meeting to another. My meetings this morning started at 6 AM. The last meeting will probably be at like 8:30 or something at night. In general, people have a pretty good idea of the world around them. In my office or the task that I'm responsible for—I have a really good understanding of what that is. But as you start to go outwards from there, think of the visibility as concentric circles. So the further away those circles are from the task that you're responsible for, you have less and less visibility. And, oftentimes, being able to analyze the data in near real-time, like our AI engine does, we can spot trends and make predictions much, much faster than a human would be able to because by the time data from all the different service centers gets consolidated and gets bubbled up to someone to review, it might be a month later. And now you're looking in the rearview mirror where the world's changed. My call volume might have gone up in the call center, or there's a new set of product issues that customers are dealing with. And even if you understand that trend a month later, there's not much you can do about it because the world's different. So if I could tell you right away that, hey, I'm seeing this delta, and now you can take a best practice from one area and apply it to the other, you can have immediate impact on the business. 

Lindsay McGuire: You talked about these repetitive tasks people are wasting their time on and this ping-pong effect. And, it's interesting, we actually have a report called the State of Digital Maturity: Advancing Workflow Automation. And one thing we found in this survey of 2000 people in the U.S. was that 51% of people spend at least two hours each day on those repetitive tasks. And it's taking them away from that impactful work like you were talking about. And so that's, like, a crazy stat. I mean, that's what, 10 hours each week that's at least wasted? If not more, because there were definitely people who said four hours a day, and that was scary. But for the organizations that that might alarm them or alert them to there's issues at play here, how should they start peeling back that onion and finding out where are these repetitive tests hiding? Where is all this time going, and how can we become more efficient? 

Prabhjot Singh: It starts with visibility. That's always the first step is just understanding how work gets done. People have a sense of how work gets done, and it's very rarely accurate, especially when you have processes that touch multiple people or have multiple steps. So getting a quantitative understanding of how work actually gets done within an organization is really the first step that you have to do to be able to then make changes to the way work gets done. So if you understand where those ping-pong effects are or why those ping-pong effects occur. Or maybe they only occur for certain types of orders in order management system versus other types of orders. So understanding that visibility in terms of what's high performing, what's low performing, you can now start to make changes. And then measuring the effect of those changes is just as important. You know, oftentimes, when people do these massive digital transformation exercises, these projects can go on for months or years. They'll have milestone check-ins where, okay, well, we've been doing this for three months, for six months, we put out a major release, let's try to understand what the impact of that was. My philosophy is that you've gotta be able to measure in real-time—with each release of the application, with each new feature that you're introducing, you should be able to quantify the business impact of that feature because you've got people doing work, and you can only do that if you have those business goals as a starting point. If you're clear on, hey, this is my top goal or my top two or three goals, now you can start to measure the work that you're doing against those goals. And if my goal was to reduce the cost of processing a claim, and I'm rolling out new capabilities to do that, then with each release of my application, I should be able to quantitatively measure how well did I do against that goal? Or did I just put out a new feature, and feel good about it? 

Lindsay McGuire: That does happen. I wanna bring this into the internal employee experience as well. How does bringing automation process improvements, bringing in someone like Pyze or an organization like Pyze and your tools, how can that help the employee experience? You know, what are the impacts of these repetitive tasks and these ping-pong effects on people? 

Prabhjot Singh: I think they're a huge downer, to use a technical term. No one wants to spend time cutting and pasting and doing these manual tasks or service something that you'd already pushed forward. That's not fun. People want to do new things. People wanna do things that are challenging and exciting. Being able to introduce these process improvements A) enables greater productivity. It's a huge morale booster for people because they understand that the organization has a focus on improving the way that work gets done, which has a direct correlation to their quality of work life—which has, again, a direct correlation to the business performing better and the organization running on all six cylinders. So yeah, for sure our experience has been that it's welcomed by everyone. No, one's like, hey, no, I want to go back to copying and pasting a hundred times a day, right? No one wants to do that. 

Lindsay McGuire: So true. And yet, still, we find so many organizations are resistant to making those changes that would enable that. It's just really funny. But it's nice that you bring that point up because that is actually what we found in our report is that the more digitally mature an organization, the more likely they are to have engaged, empowered, happy employees, because they're removing these barriers to their day-to-day work that at the end of the day, drain your energy, drain your inspiration, drain your creativity. And so I wanna ask you whether in your personal work experience or in a client organization you've worked with, how have you seen automation affect the employee experience? 

Prabhjot Singh: Automation, in general—and, of course, there's lots of different types of automation, whether it's sort of robotic process automation or implementing a BPM solution that enables better orchestration of a business process versus Excel hot potato—is immensely liberating for employees and enables better work productivity. As I said, we're working with a customer in the manufacturing space where they have this master Excel sheet that had to be updated by 17 or 18 people for an order to be processed into the ERP system. And you can't enter the order unless every one of those fields has been filled out, and you gotta have a dozen odd people that are trying to modify this Excel sheet to actually get that order inputted. And they replaced that with a BPM solution that orchestrated this work across these 14 or 15 people, and you can't move to the next step unless you fill everything out, right? So it just streamlined that process where, okay, I've got something I need to do, it gets assigned to me, I have to do my task, and that promotes it to the next person. And I never have to think about it again, versus, you know, having to go to that Excel sheet five or six times, or someone saying, hey, that's not filled out. Is that yours? Or is that the other guys or the other girls? It was immensely liberating for the employees and cut down the time to get those orders into the E P system by, I want to say, more than 60%. It was something like 65% improvement. So just imagine the impact of that on the business itself, in terms of being able to turn around more things and then improving the overall predictivity and morale of customers. 

Lindsay McGuire: Excel hot potato. This might be my new, like, favorite phrase. So, listeners, I have a challenge for you. If you want to make an Excel hot potato meme and either tweet it or put it on LinkedIn, tag me and Formstack, and I will send you some Practically Genius swag for the best one. So, please, let's see some Excel hot potato memes out there in the wild

Prabhjot Singh: Right on. The other thing I'll say is these types of process reengineering exercises are great to do when you're modernizing your system. We see there's a huge focus on taking these legacy systems, mainframe systems, old school apps, and then migrating them, whether it's to the cloud or onto a low-code platform like Pega or OutSystems or Mendix, right? And as you're making these changes, whether moving to a react or mobile apps or whatever modern framework, it's a great opportunity to actually look at the process and not just address your technical debt, which typically these exercises do, but also understand process debt, which people often don't understand. So if you don't understand, you can't do anything about it. And then you take this poorly performing process from a legacy system and migrate it to a new system. And you still have those process issues that you had in the old system. 

Lindsay McGuire: Let's dig a little further in there because I don't know if I've ever even thought about the term process debt. So, please, do tell us more. Dig in a little. 

Prabhjot Singh: Yeah. So this is actually coined by my colleague, Scott Ritchie, who heads our partnerships teams at Pyze. If you think about technical debt, we all understand that, It’s I've got end-of-life applications that are hard to maintain. Every time I need to have a new feature added, everyone's walking on eggshells because I might break something. Or it's really, really hard to utilize an old system, right? So these are all kind of things that we understand to be technical debt. And when people are looking to modernize or digitally transform their system, you take these applications that run on these old systems and you move them to new systems. So process debt is very similarly understanding the inefficiencies of that legacy environment or even your existing environment. Where are those ping-pong effects? Where do escalations happen? Where are the bottlenecks or the hotspots in your process today? Because remember, like, as I said, the process is enabled by the applications. So just by modernizing the applications, you don't wind up changing the process. And if I want to improve the process, just like we are focusing on improving the technical capabilities of the application or the technical stack that we have in place, you want to do the same thing with processes in terms of understanding the current process. And then, just like we don't address all the technical debt at once, you don't have to attack all the process debt at once. There's a roadmap that you can create and understand that, okay, as we move forward, you know, maybe we will do a lift and shift and understand that these are the process improvements that we have to do. Or here's a process improvement that we can introduce in version one that's going to significantly increase the outcome of this modernization exercise because these modernization projects run for a long period of time. They can cost millions of dollars, and no CFO in the history of the world will ever wanna write a seven-figure check for an upgrade. Right? They're looking for business process efficiencies, and this is a great way of introducing those business process efficience.. And when it comes to identifying which processes to improve, to make better, to automate—insert whatever adjective you want there—there's a lot of teams that are competing for resources. There's a lot of different goals, different mindsets. So how, as either senior leadership or even as an entire organization, do you prioritize what you work on first? I think it's gotta be driven by what's the most important thing to the business. Those are the projects that will always get the attention or should get the attention in terms of resources, in terms of funding and approval to forward. It's gotta be business objective and goals driven and not necessarily squeaky wheel driven. 

Lindsay McGuire: Yeah. And I think a lot of people get caught up in that part of it—of, well, this is my issue, and this is making my life terrible, and listen to me, help me! But you have to see that greater picture somehow and at some point or else it's just gonna keep being an issue.

Prabhjot Singh: If it's data driven, those decisions become easier to make for leadership. The hard thing is getting to that apples-to-apples comparison so that you can make those decisions. And there's some iteration that's required because we'll walk into organizations, and the bulk of our initial engagement is often getting to understand which data is actually where, especially with legacy systems. But once we get the pipelines hooked up, now you've got right near real-time visibility. And as you make changes, you can see what the impact of those changes are. It can be a fundamental shift in terms of empowerment to make decisions. 

Lindsay McGuire: If a lot of organizations thought that way, we'd probably solve a lot of these internal employee struggles that we see over and over again. And so I have two final questions for you. This first one's a little loaded, but if you could tell an organization to invest in one initiative around automation, what would it be?

Prabhjot Singh: You need to automate your process improvement capabilities. I'm not saying that in a selfish way, that you should go and invest in Pyze. It's important to understand your process before you make changes to it because you might wind up making the wrong decision. Think about it this way: I go to the doctor, and the doctor talks to me and says that, okay, I need to operate on your left kidney. And then, yeah, takes me into the operating room, and they take out the left kidney, and it might be that my issue is with the right kidney. So it's important to be able to do an MRI of the patient and identify exactly what the issue is. So Pyze, in the same way, provides an MRI of an end-to-end process so that you know not just where to operate, but how to operate. Is it intelligent automation? Is it better business process orchestration? Is it robotic process automation? What is the right automation solution? Is it better training and I don't need automation at all to address this issue? Once you have that understanding, and if you can automate that process of being able to have constant visibility into the business performance of that process, now you really have the power to continuously improve it.

Lindsay McGuire: It's almost the idea of the holistic health of your business or organization. And also the reminder to not make assumptions. I think that catches a lot of people up and causes a lot of mistakes of we assume we know the rule of the problem. We think this, or we believe this, but we don't actually have the data to point out and say exactly, yes, this is the bottleneck. This is the issue. This is the breakdown between tools or people or departments. So I think those are excellent points. And going into our very last question: So, as you know, this show is called practically genius. And the reason behind it is we believe that genius ideas are found all throughout organizations, by all kinds of people. And those people who implement those practically genius ideas deserve to be celebrated. So what do you think creates a practically genius person? What are some attributes that go into these people who are able to bring these ideas to the surface in their organizations? 

Prabhjot Singh: Oh, these questions are getting harder as we go. It really has nothing to do with business. It's really kind of getting in touch with yourself and understanding what are your strengths. You can do, in the corporate contex, a strengths finder exercise, but more plainly speaking, it's really about identifying what are your motivations. And now we're getting a philosophical level. What drives you? What doesn't drive you? You know, trying to assess the impact that you're having on whatever it is that you're doing. And trying to think about how can I do that better? That's really the journey of identifying problems. And then devising solutions to solve those problems because, as they say, need is the mother of invention. So if you can understand that need and why that need exists and how you relate to that need, you can then come up with a solution to solve that. 

Lindsay McGuire: Thank you so much for joining us for this great conversation with Prabhjot. If you want to keep the conversation going, join me and my co-host Ryan for next week's episode of Practically Speaking, where we'll be diving in with some data-driven insights around how the most optimized organizations are using automation to reduce busy work and turnover. If you want to find your next practically genius idea, head over to for inspiration, insights, and more.

Hosted By
Lindsay McGuire
Senior Content Marketing Manager
Co-Hosted By
Ryan Greives
VP, Brand & Communications

Practically Genius is a show built for innovators championing digitization within their organization.

Hosts Lindsay McGuire and Ryan Greives host conversations with real-world innovators sharing stories of digital transformation while also providing helpful advice and insights to listeners.

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