Name: Mihir Shukla, Automation Anywhere
Uploaded: 2025-02-26T18:00:00.000Z

More from theCUBE + NYSE Wired: Mixture of Experts Series

Mihir Shukla

CEO & Cofounder

Automation Anywhere

Gemma Allen

Host, theCUBE + NYSE Wired

theCUBE

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Mihir Shukla, Automation Anywhere

Mihir Shukla

CEO & Cofounder Automation Anywhere

Gemma Allen

Host, theCUBE + NYSE Wired theCUBE

HOST

>> Palo Alto Studio Connection, Silicon Valley and Wall Street.

>> I'm John Furrier, co-host of The Cube here with David Lanvey, my co-host.

>> Welcome to The Cube studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired's Mixture of Experts. And my next guest has spent 23 years running the company that many enterprise credit with timing robotic process automation into its own category. They are now trying to do the same thing again at the GenTech AI. Automation Anywhere is running north of a billion IT service requests to its autonomous service desk. CEO of Automation Anywhere, Mahesh Shukla, great to have you.

>> Good to be with you here, Gemma.

>> So maybe just to start, we talked about kind of RPA there a little bit in the intro. It's a term we all knew well for a long time. It's had a little bit of a downer moment though, right? It's being replaced with agentic process automation. Help us understand, like help level set with me and the audience. What is actually happening?

>> Okay. How about, how about I set us up on today? Automation Anywhere's vision is to solve one of the hardest problems in the industry, which is your AI spend is here and the level of autonomy that AI is achieving is here, and that gap is widening. And we know this through other large technological revolutions from electricity to internet, that technology by itself doesn't create the impact. It's the level of autonomy. If you achieve with it, it will change the entire operating model of the society and the companies. So that's what we have to achieve with AI, and that's what Automation Anywhere is trying to do. And our answer to you, as you rightly said, is that Agentique automation is how we are doing it, relying on our heritage of automation with machine learning and other more RPA and other things. But now with Agentique, we are taking a few steps Now with agents and the combination of other technologies, we are able to reason what to do, decide what to do, and sometimes takes a human input and then execute an end-to-end process with governance and security. We, we today, Autonomous and Automation Anywhere stands for autonomous work. That's what we stand for and we do it in 2 or 3 different ways. We offer a platform, Agentique automation platform. That's number one platform in Agentique automation today. We offer out-of-the-box Agentique solutions across functions like IT support, HR, and many others, but verticals in healthcare, manufacturing, and banking and financial services. And we do it across thousands of customers, about 30 to 40% of the Global 2000.

>> So let's talk about your first point there, which is that spend on AI and tokenomics and everything, it's here, right? It's constantly headline news. And actual realized value within Agentic AI, within enterprise, is somewhat of a debatable topic, right? Yeah, there, there is certainly a wedge to overcome when we think about the parameters of Agentic for enterprise. We're used to a world where we could tell, you know, run a script and tell a system, Give me this data, do this. The word of Vagentik though is far more than that, right? It's about actual execution. So when you think about, you know, that gap, what are the parameters of what we are actually talking about here? Are we talking about systems operating, you know, collectively and completely autonomously? Agents making decisions that humans made 2 years ago? Help me understand like where you see that gap being the deepest in terms of what the market says versus what enterprise realize?

>> How about we do it with a few customer examples?

>> Perfect.

>> Please do. So take an example, and I'll give a few— maybe I'll give 3 versions of it. So there are customers who are identifying one process, two process, and trying to get going. Then there are customers who are saying, I want to do a large process end-to-end, order to cash. 'Imagine my entire business around that process.' And there are a third class of customers who are saying, 'I'm reimagining the whole department and how do I operate.' So you get different varieties of how people are thinking about it. Take an example of NHS UK, their healthcare system.

>> I know it well.

>> And then one of the largest trusts there came to us and said, 'We want to reimagine the entire workflow.' A workforce of 18,000 healthcare people in their system. And what would it look like? Could we get 70% of automation of everything that was today happening manually or through other old technologies? So we came in and we looked at their scheduling, how emergency room visits happen, how do we plan compliance along every aspect of healthcare and looked at the entire problem and did an agentic workforce management solution for them. Now, that's an example that ultimately saves a lot of cost because you are preventing a temporary staffing that, that was required. If you plan early, you don't require that and ultimately improves patient care. That's one example. Another example, let's take an example of Petrobras, Brazilian oil and gas company. We took their autonomous tax, which is in their finance world. So we took a complex Brazilian tax code, I'm told it's one of the most complex in the world, and we computed taxes for a subset of their business. In 3 weeks, we were able to save them $120 million.

>> Wow.

>> In both of these examples, the way these solutions work is a collection of agents that are autonomously figuring it out, certain things like tax code, figuring it out what the thousands of pages of tax laws mean and trying to guide what is the right tax law to apply, set of, set of tokenless technology, so more deterministic technologies like API, RPA, machine learning comes in. Multiple of these technologies come together to create that autonomous process end-to-end. And for those of us who have been doing it for a while, this much is clear that answer isn't just one technology all the time. Because if you have a deterministic answer to something, making it probabilistic is probably a bad idea. So collection of these technologies create a seamless autonomous experience and change a complete operating model.

>> So if we take the NHS as an example and talk about this world of agentic, we know that historically in an enterprise or an institution like the NHS, there were challenges with line of business migrations to the cloud, right? Like that, you know, that happened across many government organizations the world over.

>> Yeah.

>> Credential access, you know, depending on your domain accessibility for a clinician versus a managed member of the executive. Systems didn't really connect or talk to each other. There wasn't the same level of interoperability. And a lot of that was because like the governance wasn't in place historically, the structure, perhaps even just the, the actual workflow itself had never been fully mapped or fully discovered, right?

>> Yeah.

>> When we think about Agentic, it seems as though a lot of problems were solved. And almost like a flick of a switch. How were some of those legacy challenges like structured data, clean data, accessibility, systems connecting and talking to each other? Are they solved at the point of the agent? Like where, where does this, I guess, magical formula—

>> that's a great question. So I think, I guess some problems are solved with Agentic and some is what is the hardest part that we solve. So the part that we— you rightly said that healthcare, banking, they're not on the cloud yet, as we know fully. So much of this needs flexibility to operate wherever customers are and integrate with systems that they use. So one of the unique offerings we have is to be able to orchestrate across thousands of applications. In an NHS scenario, there are hundreds of applications that we have to integrate and orchestrate work across it. So that's a hard problem to solve that we solve. The second piece is, is data clean? Some of it has been solved with Agentech because you could, you could, for example, we have a, we have a, we have unstructured data solution that is powered by generative AI that could take a large amount of unstructured data and make sense out of it relatively quickly. Now, that piece of solution, because it has gone through millions of this, it keeps getting better every time. So today it has seen more than any human being has ever seen. So it has— it has removed the need to rationalize all of the data. You can throw all the unstructured data at it. And more often than not, you will come up with the right answer, and you save all the time to clean up the data. So that has gotten easier after 3 years of processing it, so it has become better. Governance and security is not solved, as we have seen in various news articles. So that's something we bring to the table. I'll share one of the announcements in context of this. One of the announcements we made earlier this year, we announced the capability of a next level of autonomy that enterprises could get with security and governance. And we partnered with, in this scenario, with NVIDIA, OpenAI, Cisco, and a few other players. And we brought multiple capabilities together to create a full governance model, but a level of autonomy that is unseen before. And with that, we showcased— I'll take an example. Let's take an example of an outage in an IT system. Sometimes some of the issues take a month to figure out what caused it. Now, with an agent governed and secure, can go through your system and within an hour figure out what the problem was and suggest you a solution. And if you let it, it will solve it as well. One month that took you to figure that out can now happen in a month. Like that in a fraud, in a claims, in a supply chain, there are specific areas where the secure and governed agents can achieve unimaginable outcomes that we never thought possible.

>> And is that like, in that scenario, are we talking about a collaboration with like a Nemo Claw? You know, like, I think, like, help me understand.

>> So the Nemo Claw Nemo Claw is a— Nemo Claw and Nemo Tron models are useful everywhere, but especially in international markets where people are going for more open source models. That's a key partner there. OpenAI in that scenario was our frontier partner, although we work with all other models. But in this particular example, we closely partnered with them to, to show enterprises what is possible. With some of the latest models, as you know now. Cisco brings Defense Cloud. So, right. Okta brings identity. So multiple partners brought multiple pieces to it and we brought our Agentik automation execution and governance framework along with it to deliver an end-to-end solution to the customer.

>> I want to go back to something you said around trust and security, because I think it's a very important part of the enterprise conversation. Conversation. Yeah, in one marketing spiel it's used as an advantage, and in one marketing spiel it's used as a threat, right? So security is kind of like playing this interesting role right now. Some folks, you know, would argue that certain systems of record didn't connect for good reason, right? Again, because you needed to have silos in place in certain institutions to ensure that there was no dilution of data, right, and no breach, I guess. Yeah, others argue that that is an excuse Right. That has been an excuse that's been used for a long time to keep knowledge is power in the minds of an executive function.

>> Yeah.

>> Okay. What do you see from the perspective of this world of like highly interoperable systems of record, you know, data being taken from various parts of any one organization, centralized or collected and then used, especially from the perspective of an agentic workflow in an autonomous way. To actually execute on a decision. Like, what are your thoughts on how real that is? And do you see that as a risk? And I know it's kind of a little bit of a hoodwinking question or an opportunity.

>> That's a great question. So how does, how does our customer think about this? I'll tell you what they don't want to do. So let's, let's just rule out a few things. I don't think they want to put all of that data, bring it in from every system and give it to an LLM vendor. They don't want it there. That's out of question because of data retention. Because they want— I mean, some of these businesses have acquired knowledge. The data is there, not training on your data and training on it. So and they don't want to do a vendor lock-in with one particular vendor, as you would imagine. So at a given— they want optionality. So they don't want to do that. They don't want to operate in an old way where they're all siloed and all agents are siloed and it's more of the same just with agents. Neither of them are an option, right? So what is the right option is to design a security and governance-based new architecture. We have done this with people, right? There are people who could go across with the right authorization to make things work. You can— if you do a security-first mindset, you could do that with an agent with the right amount of security in place so that you could make fast decisions across system in a governed way. And in a way, that data remains with the company that owns it. Only their data is there, their learnings are there. But you could bring in the knowledge, that generic knowledge of how work happens to them. So what we do is, that's an example of, we have a technology called Process Reasoning Engine. It is designed based on 450 million agents and automations that we run. Nothing identifiable, but it has an intrinsic knowledge of how work happens. Think about just like ChatGPT or Claude has an interesting knowledge of the language, right? It knows what a language is. We have an intrinsic knowledge of what work is and we bring it to the people. So it, it outperforms any other models or anything by 3x because it's just better at performing work. They are okay to receive that, but keep data themselves, learning themselves. I think that's the model we see working with Global 2000 companies at scale. And when customers use it that way, they can achieve these remarkable outcomes that I mentioned. Today, there are only maybe 5 or 10% of companies are chasing it that way. But I think more and more of those stories come out. People say, that's what I want, that's what is possible. And we'll get there as well.

>> A certain amount of the control plane is still within your own reach, I guess. Right.

>> That's right.

>> As well as the data plane. Now, those two things are no longer the same fully. But Mihir, I want to ask you, so Automation Anywhere, I know you've been on theCUBE before. John Ferrier mentioned to me you were on, I think, quite a while ago. And when I looked at that, you know, I had a quick glance at the interview and I thought, wow, So much has happened. Yeah, right. Since, you know, you first became acquainted with us on the team here. Talk me through where this company is at, at this market moment, though, because it feels like it is an opportunistic time and a challenging time. Talk me through it. Like, where are you guys headed? I mean, you know, this world of agentic, it seems as though it's a huge top-of-mind conversation for many CISOs, CIOs.

>> So it has been a huge tailwind for us. So we, we, we had a— we had an advantage because we had a single control plane for all of our customers, even though whether they are on-premise or cloud, we had a single version of code everywhere. And that gave us an advantage to train some of these work-related models on how to produce accuracy, and that gave us a leg up. In the market. And even today, the fact that we have 450 million data points about the work, that's just unmatched. It's not something anybody could acquire easily. So we use that. And now today we have 3 growth, growth drivers. I mentioned our AgentX solutions out of the box. Let me step back. So today, 70% of our bookings is led by generative AI-based products, agentic products, 70%. So that's quite— if you, if you draw a parallel of a foundation model being on this side and traditional automation company on this side, we are somewhere in the middle. You can decide where we are. That's 70% today, right? That's a significant change from when we spoke last. 50, 40% of ARR, which is a cumulative of everything we have done, is agent tech now. So it's a fast acceleration to agent tech for us in 3 areas. Out-of-the-box solutions, for example, autonomous service desk. You mentioned earlier we process 1 billion tickets, resolve about 80% of them autonomously. That's a game changer for many organizations. You can reduce your ITSM, whatever system you use, spend by about 40%. That's unbelievable for many organizations. Like, you spend $100 million or $50 million, getting 40% back is free money. So very transformative outcomes there. Autonomous finance, autonomous revenue cycle management, and many other. All Agentek. All. So most of them or all of them are outcome-driven deals. So a cost of a ticket, let's say, is $2, or a cost of an invoice is $1, cost of customer onboarding is $100. So customers don't have to worry about multiple technologies and how it comes together. We and our partners bring it together and deliver the outcome. We had exponential growth in that space. We publicly announced signing one of the largest outcome-driven deals in our history in Q1 earlier this year. Q2 accelerated that even better. So very fast growing. The second piece of tech is the Agentik automation platform that we bring. This is the This is the large enterprise customers who wants to build an agent platform and agent solutions using what we bring to the table. The power of everything that I mentioned earlier is available to them. They want to customize it to their needs. So I think combination of these two is growing at about 30 to 34% for us and accelerating. And then one of the key other offering we offer is we call Autonomous Enterprise. Where we go to the customer and say, let's just reimagine the entire function. Let's just reimagine what's possible. That offering is growing at 62% for us on a smaller number, but we're very excited because some of the examples that you see tells you what is possible. So when we look back, almost 70, 80% of everything I talked to you I'm talking to you about wasn't there 3 years ago. That's how much more—

>> that's crazy, right?

>> Crazy.

>> Well, I'm here. I have to ask you, you're here at the New York Stock Exchange. Obviously, we love nothing more than a bell ringing.

>> Yes.

>> Are we going to see you and your wonderful colleagues at some point up on that podium?

>> We are. We are large enough. We have been profitable for 3 years, growing fast, waiting for the right moments for markets to open up. I'm told it is not this year for sure. It's only open to a few large companies, as we know. But we look for an available opportunity. Well, whenever that is.

>> Well, we certainly hope we get an invite to the party when that happens. We hear. Thank you so much for joining us on NYSE Wired.

>> My pleasure. Nice to be with you.

>> I'm Gemma Allen here at the Cube Studio at the New York Stock Exchange. This is NYSE Wired, a mixture of experts Thanks for watching.

Mihir Shukla, Automation Anywhere

>> Palo Alto Studio Connection, Silicon Valley and Wall Street.

>> I'm John Furrier, co-host of The Cube here with David Lanvey, my co-host.

>> Good to be with you here, Gemma.

>> How about we do it with a few customer examples?

>> Perfect.

>> I know it well.

>> Wow.

>> Yeah.

>> And is that like, in that scenario, are we talking about a collaboration with like a Nemo Claw? You know, like, I think, like, help me understand.

>> Yeah.

>> A certain amount of the control plane is still within your own reach, I guess. Right.

>> That's right.

>> that's crazy, right?

>> Crazy.

>> Well, I'm here. I have to ask you, you're here at the New York Stock Exchange. Obviously, we love nothing more than a bell ringing.

>> Yes.

>> Are we going to see you and your wonderful colleagues at some point up on that podium?

>> Well, we certainly hope we get an invite to the party when that happens. We hear. Thank you so much for joining us on NYSE Wired.

>> My pleasure. Nice to be with you.

>> I'm Gemma Allen here at the Cube Studio at the New York Stock Exchange. This is NYSE Wired, a mixture of experts Thanks for watching.