In this episode, Mihir Shukla, CEO and Chairman of Automation Anywhere, shares what it takes to build an autonomous enterprise—where AI doesn’t just assist work, but actually does it. With over 300 million AI agents already running on their platform, Mihir argues the future of work isn’t ahead of us—it’s unfolding now. We explore how Automation Anywhere is reimagining its own operations, automating everything from tax to customer service, and how other companies can do the same. Mihir shares a playbook for identifying high-impact automation opportunities and overcoming the cultural inertia that holds teams back. The conversation also zooms out: from the global productivity crisis and shrinking workforce to the ethical responsibility of upskilling at scale. Mihir shares powerful stories from communities that went from minimum wage to six-figure AI careers in just months—and reminds us what’s at stake if we don’t move fast. If you’re rethinking how work gets done, this episode is a blueprint for what’s possible.
In this episode, Mihir Shukla, CEO and Chairman of Automation Anywhere, shares what it takes to build an autonomous enterprise—where AI doesn’t just assist work, but actually does it. With over 300 million AI agents already running on their platform, Mihir argues the future of work isn’t ahead of us—it’s unfolding now.
We explore how Automation Anywhere is reimagining its own operations, automating everything from tax to customer service, and how other companies can do the same. Mihir shares a playbook for identifying high-impact automation opportunities and overcoming the cultural inertia that holds teams back.
The conversation also zooms out: from the global productivity crisis and shrinking workforce to the ethical responsibility of upskilling at scale. Mihir shares powerful stories from communities that went from minimum wage to six-figure AI careers in just months—and reminds us what’s at stake if we don’t move fast.
If you’re rethinking how work gets done, this episode is a blueprint for what’s possible.
Key Takeaways:
LinkedIn: Mihir Shukla | LinkedIn
Automation Anywhere: The Leading Agentic Process Automation System | Automation Anywhere
00:00 Introduction to Mihir Shukla & Automation Anywhere
01:20 Vision for Autonomous Enterprises
03:03 Reimagining Work Processes
04:17 Principles of Automation
06:12 Challenges and Solutions in AI Adoption
09:15 The Importance of AI in Modern Workplaces
21:31 Studying and Rethinking Work
26:55 The Challenge of Adapting to AI-Powered Workflows
27:55 The Impact of AI on Task Management
31:24 AI in Customer Service and HR Operations
33:03 The Strategic Value of AI in Finance
37:30 The Social Impact of AI and Upskilling Initiatives
40:40 Final Thoughts and Takeaways
📜 Read the transcript for this episode: Transcript of How Mihir Shukla Is Reimagining Work With 300 Million AI Agents (and Counting) |
[00:00:00] Mihir Shukla: I'm Mihir, Shukla, CEO, and CEO, and Chairman of automation Anywhere. Automation Anywhere is a software company. we help customers AI agents and automation on our platform, We have over 5,000 customers in 90 countries. some of the largest companies on the planet to many mid-size CU users. we have 300 million AI agents running on our and this has been doubling at a pace. So at one point you're looking at billion AI agents on our platform, anybody thinks that future is yet to come. It is here and it is accelerating fast. But what we are about is , trying to reimagine how work happens. How much of work can happen autonomously so that we can on work that matters how much work AI can assist you so that you can be more creative, and what of work really needs to be manual in year 2025?
[00:00:58] Jeremy Utley: We're really, really thrilled. Um, maybe just by way of introduction, Mihir, can you talk for a second about. You are, I know you're undertaking a pretty broad transformation effort at Automation Anywhere. Tell us what are the kind of key, elements of your vision that you are seeking as you are pushing forward into this
[00:01:20] Mihir Shukla: So we, have the vision, uh, with that, we thought with AI it possible to run a large part of enterprises an autonomous And so the question is, what percentage of your enterprise run autonomously? What percentage will run assisted by ai and what percentage will be manual? And we've ourselves a license to rethink what that looks like. So we are doing this for 5,000 plus of our customers and ourselves, to to, reimagine. How work could happen very Um, it turns out in places like customer service, uh, you could have 40% of customer ticket handled autonomously, no human in the loop within matter of seconds. , in places like, uh, tax and certain finance operation, over 90% can be autonomous. So it varies by functions.
[00:02:21] Jeremy Utley: Yeah, I think I saw the CEO of Goldman say something like, 95% of a prospectus can be written with ai, right?
[00:02:27] Mihir Shukla: That's correct.
[00:02:30] Jeremy Utley: have so many questions. Jeremy, do you wanna start?
, to me, Mihir, I know we, we talked about as well earlier. Um, start with. Your organization itself, because it's one thing to go tell other people to do something. It's another thing to say, here's what we've done. Yeah. Can you talk about the organization itself? Because I would say you are a leading indicator of what's possible for others. How did you start by assessing your own organization? What, were some some of the early. Areas of focus, early wins that led you to believe there's actually something here for our customers as well
[00:03:02] Mihir Shukla: Happy to. Um, so we, , try to reimagine work across every single aspect of our work. So, uh, let's take an example of our customer service. We provide, , enterprise software some of the largest and mid-sized companies on the planet. and so when we DD deployed our software on customer service. We found out that 40% of customer requests didn't need a human in the loop. There are certain kinds of workload where let's say if you're trying to figure out what is the status of my ticket, you don't always want talk to somebody if you can get a status right So about 40% of tickets, could be completely handled. Uh. And it lift the level of quality of service we could provide. Because now yeah, , people could focus a lot more on the other, uh, tickets that needed more time. Um, take example of tax operations. tax operation fund at the highest level is applying set of rules to a set of numbers. , there is always a aspect of strategic. Tax, , view, , that you can apply, but that's about 10%. The 90% could, could be fully automated.
[00:04:13] Jeremy Utley: So when you, when you use those two examples, customer service and tax, for example. Yeah. when when I'm thinking in terms of principles, others can leverage, and I don't think it's necessarily Look at your customer service, look at your tax. Right. It's more. What were the principles that led you to those areas? , were you thinking where is our operation? Heavily rule-based. For example, I, I'm making this up right, just based on what I'm listening to you, but what were the selection criteria that led you to customer service or tax for your own business?
[00:04:42] Mihir Shukla: So I think the way to think is that, uh, there, there are two different criteria we applied. Of course, the rule-based where is very easy to automate. Uh, , even though rules like text rules can be 185,000 pages long. uh, with the power of LLMs. Now it is possible to understand all of those rules and apply it to set of numbers. But the other way to think is that where there are a large number of knowledge and. Wherever there are large number of knowledge workers, what we have done, uh, in, our industrial way of scaling is we have defined standard operating procedures so that we can, produce consistent quality. means like we have, let's say we have 500 customer service agents, we have a way to operate it in a more standard way. Um. which means there is a significant room for automations So, um, uh, if you take a look at our customers, let's say if you are a healthcare customer, claims management, um, millions and billions of claims that come, in their way could be significantly automated if you're a bank. Mortgage applications, KYC, uh, can be significantly automated. A huge part of procurement processes, uh, significant part of HR operations. Uh,
[00:06:07] Henrik Werdelin: Maybe I can ask you on the. The more the social engineering side of things. I think a lot of CEOs and boards and senior members are sitting and having obviously these conversations, and then you go down to, let's say your finance team and you're saying, I. I've heard that 90% of your operation can be automated with Gen High, and then. Your CFO might say, yeah, but like ours is very complicated and like, like it, it seemed to me that increasingly the technology is increasingly there and available. And what seems to be kind of lacking is the social engineering tools, all the processes to kind of help us adapt organizations that was designed for a time of internet to now, , a time of, gen ai. How do you think about that?
[00:06:57] Mihir Shukla: I, I think first, uh, it makes sense to talk about why should we do this. Uh, because AI is available doesn't mean we have to use it. Uh, it's a tool, a technology. So, uh, maybe if you start with a why, and if once it becomes clear to you that this needs to be done, then often human beings find a way I. So there are two reasons. Uh, I always start with the why because what and how. people can often figure out if they're clear about I think there are two reasons why we have to use ai. Um, I. One is that if you take a look at stats, , for many, many, years, every decade, our productivity was So it used to take eight people to produce a million dollar, then it six people, and then in 2008 it took 5.1% to produce a million Since 2008, that hasn't 2020 4 25, it still takes 5.1% to produce a million dollar, which means in the last 16 years we have spent. Uh, collectively on the planet, over $40 trillion on it spend, and we haven't improved, uh, productivity at all. Uh, that that's a problem. If you keep doing it, , , your business will not
[00:08:17] Jeremy Utley: So something has to aging, aging, aging workforces. Right. Especially with population decline. Right. It's the second piece. You're finding way to that number. Yeah. I mean, I, I didn't mean to steal your thunder there, but my mind immediately goes to, but quickly it becomes an existential necessity, right .
[00:08:31] Mihir Shukla: Exactly. There is a second piece with a 3% declining workforce, right? So, uh, for last 2000 years, the, the economy has been a twin plane engine. One, one productivity increase, and other is the more people working in the workforce.
On one hand, we haven't improved the productivity as much and second hand. It's a declining First time in human history, we are living in a society where you have globally 3% declining workforce. And it is, it's going to remain that way for foreseeable So how do you, operate in economy? How do you operate in a world that are less people working every year? This is, this is entirely new for everybody.
Um. And what happens when we can't figure out? So, the fundamentals of this that we have to double the productivity that we have produced in last many years Now think about it. The internet, computers, every software ever return, it so many things, so many innovations, the iPhone, everything combined produce the productivity where we are. Does anybody have an idea how to double it?
[00:09:39] Jeremy Utley: Wait, but you said it hasn't changed 2008, which if my memory serves, I was in business at that time and I didn't have an iPhone.
[00:09:45] Ekstra: Yeah.
[00:09:45] Jeremy Utley: Which is to say the introduction of the iPhone social media and all of, call it web two. Didn't, this is a question to you. It did not move the productivity needle, is that correct? It did not
[00:09:56] Mihir Shukla: move the productivity in, uh, for, for businesses. You, you know, we might have, our personal life got better, but in businesses it , still takes 5.1% to produce a million dollar. That hasn't changed. Wow.
[00:10:08] Jeremy Utley: That's a, that's a staggering realization actually. That's right. Because I would say , there's this kind of, I get choked up when I think about it. No. There's this kind of almost delusion of productivity, right? The fact that we've got our iPhone, we're going to bed with these devices by our beds, you know, we wake up, we look at them first thing in the morning. I'm sure the implicit rationale, even though none of us would say it this way, is. We're being far more productive, we're producing far more value. But what you just said, since 2008, the amount of kind of dollars per capita productivity have not improved despite all of the advances to our quote unquote productivity suite. Is that right?
[00:10:47] Mihir Shukla: That's correct, And it is because the software that we use is, is, uh, you know, the time you implement some of the enterprise software, . It takes few years to implement them fully, and the world has changed. So by the time you start using it, all the benefits that you had thought would come and, you don't realize all of them. And then very soon world changes even more. And now you're behind in , how your software is configured versus how world is configured.
[00:11:16] Jeremy Utley: You know, what I think of, and I mean, there's kind of a direct impact, and there's also, it's analogous, but a lot of the research, if you look at, you know, what, Cal Newport, for example, great researcher, wrote a book about slow productivity, but one of the things that he highlights is the cost of distractions.
And for every, you know, ping or notification, we lose something like 15 minutes of focus, right? For every time your phone vibrates. And say, I'm right. Last night I was, you know, up late writing a blog post 'cause I was distracted during the day. I wasn't able to do it basically for every, you know, ping that comes in.
While I'm doing that work, I lose 15 minutes of productivity. , and certainly there's kind of a direct implication there productivity, but I also wonder to your point about organizational renewal and integrations, if you could think about kind of. I. Software updates as effectively distractions or effectively notifications to the organization. And every time the organization has to update or adopt a new technology, it's the effective productivity impact of a distraction on an individual worker. , what do you think about that?
[00:12:17] Mihir Shukla: Yeah. Uh, I, I, think we still work in a World War II task or task oriented era, and we think replying email or replying messages is work. Uh, we studying how work gets done, and it takes 32 emails, 82 messages, six meetings, three later. You cannot get your work done. We are the weavers of our generation because each of those a thread in how we are weaving work. Right? And , now when you, when you sit in of an AI system, and if you get your prompt right, you get the whole cloth out, right? Uh, instead of a step by step. So if that analogy makes sense, you are at a very different way work gets done Look, look, I was, I was going with though is, is. Just to finish the thought on why is. if we fail to solve these problems that we talked about, about declining population and lack of productivity. What happens is there is less to go around and when there is less to go around, the society cannot support its infrastructure. It cannot support bridges, cannot support healthcare, cannot support social security.
The democracies weaken. There are more riots on The There are more wars on planet. all of this sound familiar? , but I there is more at stake here than just, uh, use ai. Yeah. Look, this is a technology. If the, if there is a new energy source or a new metal or something new that would help solve the problem of this, we should use it. Uh, it's about solving a problem. I think AI is coming at an opportune moment to solve a larger societal. Challenges. It's a, , tool like we have harnessed, uh, horses and winds and nuclear energy. It's a, it's power to be harnessed
[00:14:13] Henrik Werdelin: But just 'cause been through I'm kind keen try to. Kind of like unpack little bit more practicality 'cause I, obviously it a lot kind of starting the why the can range everything like, need of improve our EBIT to, Yeah. we able to hire people in team. And you're getting more and more work on your plate to, , we to kind of create productivity for the world. 'cause otherwise all these negative consequences have it. still I find going then into your finance team. They're still kind of like, I think this kind of invisible wall between the technology that is available today Yeah. then even might what seem to be kinda like an, a real intent for people to do it.
Have you found a way of kind of breaking down this invisible wall, uh, maybe in your organization or for some of the customers that you work with
[00:15:06] Mihir Shukla: That, that, that's a great question, Henrik, because, uh, often the first, thing you hear from people is that, no, but my work is very unique. Or what we do is so Uh, what we found is that if you get people started in this direction , if you have 10 people in a group. You will find that people will always looking for a different, better way to work, uh, in general. That has been true across customers. And they will, get you started by doing
[00:15:36] Jeremy Utley: Did you say two out of 10? Is that the ratio? Two out of every 10 people.
[00:15:39] Mihir Shukla: Two out of 10 people could be three out of 10. Uh, and then other four or five people see that, see the possibility. Then they get excited about it. They be the first one to start, but they see it. And they can imagine the possibility having seen it.
Uh, then it it catches like a wildfire. Uh. You'll always have one or two in the room who, only use the, new smartphone because old phones are not available. So are always people who will resist change, but in general, you could get 80, 85% of the organization moving if you get them started. Um, I. think the keys to, instead of change happening to them, the, the key is the teams leading the change. That's the key to bringing change.
[00:16:28] Jeremy Utley: Yeah. That, and that's, that's actually dovetails perfectly with I was curious to go, which is what are the organizational structural implications? What do businesses need to do reorganize? And you kind of hinted at it that teams may need to lead the change, but what does that look like practically
[00:16:44] Mihir Shukla: Yeah, so the, the videos many of our customers do is they start by challenging the team and say, look, uh, want 10% of work be fully autonomous in a department, or 20% fully autonomous. And they, uh, often people start with some funding available because otherwise you get stuck in funding and you, you seeing the larger and as I said, there'll always be team members who are about new ways of working Once you get to a certain outcome in six months or a year, then the CFO goes back to the team and say, look, since you got to the 20% autonomous, can I get 10% return back in my financial? And you, you gotta keep the 10%, but I need 10%, on my ebitda. Um, and then 10% is reinvested back in Slowly you begin to negotiate with Once they have seen the possibility, they would resist less. Uh, sometimes you start with it, uh, you, you know, you could face a resistance, , because it, looked like a way to cut people or, um. So be better to engage people on out of the possible before you begin to restructure teams and the possibilities ahead.
[00:18:05] Jeremy Utley: So what I'm hearing you say is there needs to be an ambitious goal, something like 10% autonomous. Yeah. You know, uh, in any given function, then you've gotta some funding, I suppose, for exploration, for training, for experimentation, et cetera. And then there's, you know, an outcome that then becomes a negotiation With the CFO, is it sufficient for a typical team to have the goal and have the funding? Are they off to the races, or do you find is there, and, and maybe this is a loaded question, I find. That giving folks permission is one thing, equipping them and enabling them with not only psychological safety, but also actual pragmatic skills. You know, if you told me Jeremy, , go make a song. I mean, other than with ai, which now I can, the problem is I don't know any instruments. Yeah. So like you can say, and you can say, Hey, I'm gonna give you $10 million unless I invest in my ability to do that. I, and you can gimme a year. I'm not coming back with a song. Right. So how do you think about other elements besides call it a goal funding? Or is your observation, Ben, that that's sufficient to tip the people, as you said, the two outta 10 who are eager to make a change. Is that enough for them?
[00:19:14] Mihir Shukla: Uh, I, I, I you, you, uh, you pointed out a key element, which is to train them on the, some of the tools and capabilities what is possible so Automation Anywhere, we have trained a million people on, uh, how to use this capabilities all over the world in about 90 countries. Um, the just of one week, there is a basic training available for one week. That one week training opens up your eyes to the, to the what is possible. Uh, human beings are amazing. They're amazing everywhere. Once you them that, uh, you your will start, start churning and everybody up with ways to do things , in their respective teams and departments. . We have today 300 million processes running on our platform. Can you imagine this is all , uh, innovative thinking by cover customers and partners, who knew 300 million things could be automated. people are very resourceful to your point, Jeremy, once you, uh, train them and give them some funding. And And tell them why this is so important.
[00:20:27] Henrik Werdelin: much do think if productivity hasn't changed, uh, since 2008, what's your kinda like gut feel on how long you'll take AI kind of make a dent into that stat and how impactful it'll be?
[00:20:43] Mihir Shukla: So I, I think, , as said today, it takes 5.1% to produce a million dollar and another 10 years. You get to three, which where we should be in a normal course of action. I think our state is stagnated, but if we we continue
in know last 70 years of our journey towards getting work done better, that's what you're looking at. Um, does the technology have the capability? Absolutely. Uh, change is hard for people, so we'll have to rally around us, reorganize ourselves around it, and, you know, use this amazing capabilities to get
[00:21:21] Jeremy Utley: Well, Well, well you talk, yeah. I mean, it sounds like you've been studying, as you said earlier. I was just looking back through my notes. You said we've been studying how work is done. And you, you kind of characterized it a second ago. I'd love for you to tell us first, what have you found as you studied how work is done? And then second, how are you rethinking how work should be done?
[00:21:40] Mihir Shukla: Yeah, so we observed that, uh, a couple points, uh, one is that, when we of this enterprise applications whether it is your ERP applications or or CRM applications or HR applications or many others. We found that a user primarily spends 15% of their time in that application and they use about eight to 18 different applications to get work done. So, , the nature of work is very different than what might believe. So you are spending far more time outside of your core application, across application to, to kind of go from outlook to Excel to eRP, to something else, to something else, , to orchestrate a work. That's the nature of the work that has become, uh. And, , as a result, it takes a lot longer, , to get work done. Um, the second piece I mentioned is that, we are kind of weaving the work. , we work with the task. This is the World War since World War II mindset. You know, task is a work. Uh, so sending an email sending messages and going to meetings . But one of the things that we, we did this experiment with people and say, what if instead doing all of this, you have to finish this, uh, work, produce, end outcome in three steps. And when people were with it, eventually they figured out how to not do all of those things and find a way to get it done in three steps. Right? Take a shortcut. You, you know, you don't have to send 32 emails. , to get to the outcomes. Always. Sometimes , there is a, a place for it. , so we task with the work, right? And, uh, the reason why this is very important is when AI will get to it in one and couple of prompt changes and you get the outcome. We better begin to think about it differently. Um.
The way of thinking about our work has to fundamentally , I was just gonna mention, that I saw this happening in a, in few workplaces including ours, where people who got better at prompt engineering with AI system begin to ask a different question in the meeting. Because , they begin to expect the same out human being and say, I'm, I'm not asking you, did you send the email? Because that's not what I ask in AI system. I'm not asking a task. I'm saying, when I get this outcome that I get? Right? Can I get that? Can I get that? Um.
[00:24:14] Henrik Werdelin: I actually very interesting. On that point, I'm increasingly in a crazy way looking the organizations I'm involved in. In the same way where everything is an input output, everything is a prompt and a response. And prompt might be to a human, Yeah. or it might be to an it doesn't really matter. But if you start to actually look at your organization structure API calls,
[00:24:39] Mihir Shukla: Yeah.
[00:24:40] Henrik Werdelin: um, it kind of actually changed it a a little bit, how you about it. Um, I did have question though. Uh, one is, you know, you. Through the organization are a good student of how do you repetitive kind of workflows and then basically replace I. basically replace it with a bot. When you personally are looking at your own work, I imagine that in spite of the processes you have there, you started do that. Could you mention a a few of how you kind of looked at your own personal and repetitive workflows, and then where do you use Gen AI to, to reduce that workload?
[00:25:15] Mihir Shukla: It, it has been eyeopening, Henrik to on that question, so take an example. Uh, my team has made a specific LLM for the CEO for all the interactions I have and all people I meet. And so when I typically go to an event, a large CEO event, uh, , my meeting and interactions would, generate work for my team. They would keep them busy for the next 10 days at least. Um, now they feed all of that, and before I get home, all of this is ready for the next step. , and it is driving me harder. You know, I, I used to,
[00:25:52] Henrik Werdelin: What is that? Is that for example, like, uh, following up a potential client, you know, coming back some different materials that you promised somebody? Is that the kind of, that's right.
[00:26:01] Mihir Shukla: Yeah. Let's say we, spoken to a CEO of Fortune 500 company and. I'd give my team a brief of, you know, , how this company wants to transform being an autonomous enterprise. Our system will automatically generate a proposal based on their last public statement. Everything available, The way their teams are organized, where the spends are and what it could be. and you all of that is autogenerated. Uh. Um, of course there some that human being can add to, but it's ready to go. Um,
[00:26:35] Henrik Werdelin: And how do you think about, um, you know, one of the things we talked about in another episode was that it's kind of a new skill for some people go from the idea of being a , task orientated human to be more of somebody who productizes the, the solutions into a bot flow. so that's what a product developer. Traditionally did. Now humans have to do that task. Do you have any of tricks on how to start to make that mind change that you yourself, maybe go, Hey, I'm about do these tasks over and over again. Can we please make an LM for that?
[00:27:11] Mihir Shukla: I, I I think that, that not an easy switch, but that happens over time. So I this task. Let's this example of task of, uh, after met a CEO of a Fortune 500, somebody was doing series of tasks and took 10 days Uh, that all of it is available within first hour, um, , it accelerated, it exponential pace, the level of interactions and follow ups and conversations. The nature of work fundamentally changed. It, I don't think even I was ready for it, where after I met 10 CEOs the next day I'm ready to have a detailed conversation with 10 CEOs. That's, that's completely new. But it is exciting. Uh, but , that was the end result of it. Why, why should I wait for 10 days? So, um, I, don't think I've fully adjusted to that, , but I am learning to, right. That's just it, it's a new, it's a new, new pace. Everything has a new pace to how fast you move.
[00:28:13] Jeremy Utley: The industrial, I think part of the whole industrial and task orientation, and the reason that we lob an email back is because now we have plausibility to not do anything until we hear back from the person. That's right, right. Yeah. And I've noticed that, you know. Stuff that I to work with people on, now I work with AI on, and the challenge is I don't get a break because it just gets back immediately. You know, I, mean, like a silly example is I was taking a class, like pre custom GPTs. I was taking a class on how to build bot. And I used to think, you know, uh, I post question to the class discord, and then the TA answers within 12 hours. So I post question and I go to bed and I'd wake up, see they got back to me, and if not, like, ah, I, can, I can't keep going on my bot. Right. And the TA kept saying, have you asked Chad g Bt Have you asked Chad g pt? I thought, dude, I paid thousands of dollars. This answer my question. I realized the TA is actually teaching me the meta skill of you don't have to wait for me to respond. And I realized that, I mean, that's when I started pulling all Myers, because Chad, GBT is a TA that responds instantaneously. And all of a I realized, you know what I, and, and here's the point that I was making. I actually enjoyed, quote unquote needing to wait for another human because it gave me an excuse to stop working. And with ai, weirdly, we don't have that excuse of, I need to wait on this other human
[00:29:34] Henrik Werdelin: It's so fascinating. 'cause I think, I mean, that's what you. the point you just made we're now totally excited But I mean, I have the same thing. I, I have coach and he's introducing all these like mental frameworks of kind of how I act stuff like that. And now he has an agent that every Monday will ask me basically, have you worked on this stuff that we talked about at the session? And then I will reply. Like, you know, and often like thoughtful and a little bit emotional, like, you know. Yeah. You know, I had this conversation the other day that difficult and within like eight seconds they'll be like, there's a very detailed kind of reply and another question, sometimes you're like, I'm too emotionally drain the email I sent you like a minute ago to kind of go that. And so to your point, it is fascinating, all these things that we now have to learn how tackle.
You
know, I'm like to a Fortune 500 CEO, it's like emotionally draining. 'cause you have to be completely on game. And suddenly, like the day your calendar booked with six of you then we have to figure out how to tackle that.
[00:30:30] Mihir Shukla: That's right. But I think if you, if you play out the future of this, if this working out and we figure out how to do this, it'll few years. But you are looking at four, and a half day work week, right? you are able to do so much more. and maybe you just need a little more time about how to do this right, versus just too busy doing your task. That's a good thing. Not it'll happen immediately because everything has to adjust to the new dynamics. But we have seen happen before, right? When the automation came in, the manufacturing and assembly line, we went from six days to five day Um, most of us can't imagine going back to six days. Um, I think this is the au automation and the knowledge worker economy. And so , you could imagine at least in that part of the life you could. Uh, you don't have to work as hard as we we do. In a task oriented
[00:31:24] Henrik Werdelin: What are some of the areas that you've either seen or that you've pondered about that you hope will not be automated you think can't be automated,
[00:31:35] Mihir Shukla: Uh, there are so many areas, so take an example. In a customer service, uh, let's say you, , you provide an emergency travel insurance. picking an example that most of us can relate to, and in emergency travel insurance, there are two types of reasons why you're calling. One is you are in trouble, in case you want to talk to a human being. Absolutely right. Uh, I, I'm calling it because it's an emergency I need to find a way, uh, or you're calling the status to know your claim or something else. Or something else. AI agents can do that, right? They can tell you where, where, everything So for every type of a workload, think about this workload is for an AI agent, and this workload is for a human being Um. Now when you think like that, uh, almost across every aspect of work that change Uh, take an example of HR operations. HR operations. So much from payrolls to 401k, so much of it could be automated. Um, we now assisted carrier development plans for every single employee that are, uh, using ai. We can but then you want a manager to have a conversation. That's different. Now you have an entire plan made. But to have a human conversation on, how to develop people's career, that's a human conversation. Um, in finance I mentioned that in certain areas about 90% automation is possible. This example, that first time when I saw this, , of tax operation being automated I'll use this example to make a point. The tax laws change about 43,000 times a year. Uh, and I always wondered how do people do this? You know, volumes and volumes of tax And we, we started automating this about a year and a half ago for one of our large customer and, um. two weeks, we found that there were a hundred million dollars worth of errors, that our our autonomous and platform figured out that if the text was calculated correctly, you save a 100 million Since we have done this for hundreds of customers, and has been true in every case that is a large amount of error in , how businesses calculate tax. This was never a human job. It, it's a human job to follow 183,000 page tax laws. That changes 43 times a year. Um. We the best we could, but, you know, uh, uh, so when you automate 90% of that work, the strategic value finance is far more for, for me, as a CEO to my business than calculating taxes. Uh, my business. I can tell you, and for many of our customers if is not calculating taxes and he is sitting on a table and thinking about. How strategically finance a tool and what you could do with it. He is a far better partner to me than
[00:34:47] Henrik Werdelin: I think that's a good point. I I have a last question you. I saw a clip, uh, on YouTube of talking, I think in Davos about , the consequences of AI and sometimes kind of like the. Consequences that we're not aware of. Um, and you kinda like, . Began to talk a little bit about how when invented social media, we obviously didn't know kind of what would happen with it. you talk a little bit on that? 'cause I think it's an important conversation, for somebody who sells AI systems. It's a conversation that we often don't have, 'cause we're, many us are kind of like excited about the and we, we tend to talk about the excited part, not, not as much, some of things, what are some of the consequences that we should be aware of that we might not be.
[00:35:29] Mihir Shukla: I think of them. But, uh, one that you're referring to is the example. What we learned from social media that. Who would've thought a like button would change how? you know, social groups are organized it could pose a risk to the elections and, , democracies and, um, uh, but now we know now we know that this tools can scale, , , to do good as much as it could be misused , by, a rogue agent. So. , now that we understand it, uh, we can't take it for granted the second time. So all of this tools and systems needs to be governed. Uh, they have to be used in a responsible way. way. , it is possible to use this capabilities to remove bias in , how we operate, but it could also be used to, to the existing So like any tool, it is up to you how you use it, right? So. I think we, we, are better having learned from power of social media and how that scaled , to better leverage think one more thing we have to be aware of is we have to take everybody with us. This are big changes changes are hard for people. And how do we take, uh, different parts of society with us in this journey? , I mentioned that. 3%. you Jeremy, you also highlighted 3% decline workforce And, And, we are gonna need 11 million people on AI skills. It's not like there are 11 million people sitting on some island that we don't about, right? I mean, these are the people we know about. So the best thing to do is to re-skill everybody and you know, where are the people gonna come from, right? So this, are all the people we have on planet. So reskilling is an important initiative. Um. And taking everybody with us in this journey.
[00:37:30] Jeremy Utley: Mihir, you are referring to upskilling. I know that's a somewhat of a passion of yours. We could probably dedicate an entire episode to that. But could you just speak briefly about the kind of social impact initiatives and what you've been doing with upskilling there?
[00:37:44] Mihir Shukla: Yeah. Um, , we believe this technology should make our lives better. It should societies better. Uh, technologies are the sources , that you harness to, make world a better place So we took technologies to, , various parts of the world. Uh, in Africa, we trained 700 women. In, , parts of the Mississippi Delta in United States where there is extreme poverty, uh, we took these technologies there and india and Nepal, in various parts of the world. And what we learned was it was, uh, it was amazing to see because people didn't have to unlearn anything. Uh, and technologies that operate on a human language is a more natural way for everybody to And so we were able to take a person in Mississippi who was flipping burgers for $15 an hour or less, to $120,000 job in three months. , and what it told us is that. Talent is evenly distributed. Opportunity is not. Uh, this person in Mississippi flipping Berg was capable of doing many things that would expect from somebody in Silicon Valley, but they just didn't get exposed to it. So, um, this is an opportunity for us and since then we have seen this out again and again in all parts of people amazing everywhere out. 700 women who got educated in first two months, 450, got a job, , in ai. Uh.
[00:39:24] Jeremy Utley: One I would love for our audience to be able to do is, is there a pathway? For recommending if you know of a community in need of upskilling and they wanna connect to your initiative, is there, is there an easy way to do that?
[00:39:37] Mihir Shukla: Yeah, so on our, uh, on our Automation anywhere.com, , you can go to the other, section about Automation Anywhere University, where we have made all the education free. on Ai. Uh, and within a week you can get a basic education on ai. , the advanced course is couple of weeks. Uh, what we learned is that, , you don't need two or four degree to get to this point. Within three months, you enough about. How to leverage what you know and how to apply AI to it. And that job could often pay you 120,000 or more uh, for for whatever that you're doing, whether you're in HR or finance or operations doing it with AI will pay you far more. Um, uh, incredible. And , as I said, it's not even hard. Uh. , now the technologies have made it, uh, they're , more human friendly. It is , easy to learn.
[00:40:36] Jeremy Utley: Mihir, this has been an amazing conversation. Thank you so much for joining us today.
What an amazing conversation.
[00:40:42] Henrik Werdelin: It's such a, it's such an interesting, I mean, it also must be such a fascinating world to just sit in the center of all this bot creation.
[00:40:50] Jeremy Utley: Mm-hmm. Mm-hmm. Yeah, I agree. I think we could have part two and part three and part four with Mihir for sure. I think I have a prediction. You know, it'd be fun to do at some point. We don't have to do it today, just 'cause I don't wanna put you on the spot. It'd be fun for us to predict what resonated with the other person at some point. And can I, okay, so I'll go first. I'm not gonna push you on the spot. I'm gonna go first
[00:41:08] Henrik Werdelin: you know that? I always do. If I just wait until you say something smarter and then I slightly reframe it it and say the same thing. Brown
[00:41:13] Jeremy Utley: Henrik Lin. I know what resonated with you. Are you ready? Here's, hang on. I'm scrolling through my notes here. What resonated with you is talent is evenly distributed, but opportunity is not. Boom. Tell me.
[00:41:26] Henrik Werdelin: That was one.
[00:41:27] Jeremy Utley: Okay. So if you're listening at home, you know, put, put one on the, chalkboard for Jeremy Henrik. What resonated with me, and by the way, it's a little bit unfair because. we're biased to say yes, right? Like to say no is like, is there any part that particularly didn't resonate with you? You You have to be a jerk to say no.
[00:41:41] Henrik Werdelin: I'm pretty sure that you had noted down the stat that basically that basically didn't change since 2008.
[00:41:47] Jeremy Utley: Oh dude. I mean, I'm eating. That's like, that's like catnip. Are you kidding me? The fact, , I mean, 2008 of all years. Okay. That's nuts because that's the year the iPhone was introduced I know and the fact that productivity hasn't changed since the introduction of what is probably the singular. Uh, you know, visualization of, human productivity and the knowledge, you know, in the Web 2.0 economy, that blew my mind. I think there's actually pieces of brain on the bookshelf behind me. I was shocked. So, you're right. One to one. It's one to one.
[00:42:19] Henrik Werdelin: Okay.
[00:42:19] Jeremy Utley: I don't have any other predictions for you. I gotta say this one thing because just it's fresh and I'm looking at it right now. Um, , when mihir was mentioning the whole tax errors that, you know, in two weeks, they found a hundred million dollars in errors. And he said this phrase, he said, A human simply cannot keep up. And that, I don't know if you remember, but it reminded me of our conversation with Anvisha Pai, the founder of Dover, and the big opportunity there. You remember what it was? No. It was the job that couldn't hire fast enough for.
[00:42:51] Henrik Werdelin: Hmm.
[00:42:52] Jeremy Utley: It was the thing that they keep up with, right? Bucketizing, the email responses from prospective kind of roles for, , new positions. And it just made me real, it put a new frame on perhaps where to look for opportunity. What's the inhumane job? What is the thing that literally is impossible for a human to do? I mean, and I think what Mihir said was there are 43,000 changes to the tax law every year. We've never expected a human to be able to do that. That should be in the, cross hairs of every organization because now you can get AI to do the inhumane thing. Yeah, I love that as kind of a paradigm for hunting, so to speak, for opportunities.
[00:43:34] Henrik Werdelin: I like that too. I mean, I win, as you know. I always get. Kinda buckled down and kinda like the very humanistic kind of of side of things. And I, was kinda like, just fascinated by what do you do? when suddenly you are not the bottleneck anymore? I. I, think in part I'm feeling that a little with some of my projects 'cause I can now output so fast with gen AI that uh, it used to be like people are just waiting for me. And so do you then use that to become more productive? You know, do you, do you you use that to tense some time of thinking? How do you then start to , incorporate that into your workflow that now that you actually have 10 to dos and a thousand emails to reply? Do you you then like make thinking time can you become better at that? And so the whole kind of idea of what happens when you are no longer just running after all the signals that are telling you to come back with something immediately, I. What happens to, business and to us as individuals. I thought that was just a fascinating thought.
[00:44:38] Jeremy Utley: You know, you, went existential with it. I went deeply practical. 'cause I, because I also resonated with that same idea of we don't, I think even in the conversation I was resonating, right? We, we don't have to wait for a human anymore. To me, and I don't know if , you and I have talked about this, but this I was talking to my dad about. It sounds like a weird thing, but you'll, I think you'll gr it immediately. Tying means nothing to an ai.
[00:44:58] Henrik Werdelin: Hmm.
[00:44:58] Jeremy Utley: And when you realize that. Uh, I mean, for example, like an hour long video. If you ask Jim and I, what are the points of this video, it will tell you instantaneously. Whereas if I ask you, you'd hang on, let me watch the, and maybe watch it at two x, it still takes you 30 minutes, right? But all of a sudden we have these models where time doesn't really, and you know, uh, Mihir's example was. Usually I had a 10 day break between when I got back from an event and when my team was ready. Well, again, if the team has been thoughtful about codifying the workflows and the things that they're doing in those 10 days, the reason that he comes back and he has no break is because, again, it doesn't take an AI to do 10 days worth of work. Now, it may take a human who's experience with that work a hundred days to codify it. , I'm not diminishing the, the. Rigor required to actually do that. But my point is, I think another perhaps selection criteria, if you will, similar to what's the inhumane thing is, what would you do if time were no limitation?
[00:45:58] Henrik Werdelin: Hmm.
[00:45:58] Jeremy Utley: Because the truth is, with an ai, time is not a limitation. And if you think about, you know, I was talking with my dad about an issue , and he, he's basically, you know, I, I don't wanna give away any kinda secrets 'cause he has a very important job, but in his world, he's often given only an hour to do something that could easily take a week to do. And if he had a week to do it, he would do it a lot better. And I go, dad, in that hour, you can give an AI a week to do something. You know, I, I mean that's, and that's just kind of like a mind blowing thought to realize. What would you do if you had unlimited time to do something? Like what would you commission? I think
so. I think that is
[00:46:34] Henrik Werdelin: probably like, you know, I think used to be when people were asking what's a good way to think about what to do, AI people would say something like, you know what we do if you had a hundred MBAs, or what we do if you have a hundred interns. To your point, maybe the two questions that we're kind of like teasing out now is, one is what you do if you had unlimited time? And two, what is thing that you basically would never be able to do no matter how much time you had? Yeah. And I think those are kinda like maybe could trigger kind of questions to figure out what I, things you'd like to do.
[00:47:02] Jeremy Utley: And I think they seem so, call it hyperbolic that I think people may be prone to dismiss them. But you for myself I really want to, you know, sit with them and say, no, really what would I do if time where no issue. You know? Yeah. It's, it's actually worth sitting with it. And I think for organizations that are, you know, Mihir talked about, you know, setting ambitious goals, setting budget for organizations that are really thinking like that. I think they owe it to themselves to just start, you know, fresh sheet of paper. Legitimately. If, if time were not an issue, what would we do? Yeah. And for each organization, their capabilities and advantages and customers, right, all that, it will be different, but I think that it will probably yield some ahas and epiphanies that right now people don't even realize are possible.
[00:47:50] Henrik Werdelin: My brain goes the Tourette's way of saying, if I had unlimited time, I would let Jeremy oddly finish sentences. When you get excited about something,
[00:47:59] Jeremy Utley: wait so that you shorten the time. I.
[00:48:02] Henrik Werdelin: I'm just make it a little bit
[00:48:03] Jeremy Utley: Wow, dude. Ouch. Okay. Hey audience, give us your feedback there. But seriously, if you liked this episode, if you enjoyed this conversation, please hit like, please hit subscribe. Please share with a friend. Hash that button. What'd you say? Smash that button. Smash that button, smash that. Like, and, uh, and leave us comments. you know, especially on YouTube. Love that people are now leaving comments on YouTube. That's super cool. Does Spotify have comments? I don't even know. They do. Yeah, but I feel like we don't, I don't, at least I don't get notifications. I've been getting notifications, YouTube comments, and that's super rewarding and fun. So drop us a comm. I don't know. Is that a thing? Drop comm and, uh, we'd love to hear from you. Until next time. Bye.