theCUBE + NYSE Wired: Mixture of Experts Series | Mihir Shukla, Automation Anywhere

Clips
More from theCUBE + NYSE Wired: Mixture of Experts Series

Mihir Shukla

Co-Founder, CEO and Chairman

Automation Anywhere

Gemma Allen

Host, theCUBE + NYSE Wired

theCUBE

play_circle_outline Agentic Automation: Closing the AI Autonomy Gap and Replacing RPA Across IT, HR, Healthcare, Finance

play_circle_outline Petrobras $120M Tax Automation in Three Weeks Using Hybrid Agents, API, RPA, ML and Generative AI

play_circle_outline Security, governance, and partnerships with NVIDIA, OpenAI, Cisco, Okta.

play_circle_outline Process Reasoning Engine trained on 450M automations for work-specific knowledge.

play_circle_outline Agentic Automation Platform and Autonomous Enterprise: 70% Generative-AI Bookings, 30–34% Growth, 1B Tickets 80% Autonomous

Info
Transcript

Mihir Shukla, Automation Anywhere

Mihir Shukla

Co-Founder, CEO and Chairman Automation Anywhere

Gemma Allen

Host, theCUBE + NYSE Wired theCUBE

HOST

In this interview from theCUBE + NYSE Wired: Mixture of Experts, Mihir Shukla, chief executive officer of Automation Anywhere, joins theCUBE + NYSE Wired's Gemma Allen to discuss how agentic automation is closing the widening gap between AI spend and enterprise autonomy. Shukla explains why technology alone never drives transformation — pointing to electricity and the internet as precedent — and how true impact depends on the level of autonomy achieved. He details Automation Anywhere's three-pronged approach: an agentic automation platform, out-of-the-box age... Read more

explore Keep Exploring

What is Automation Anywhere’s vision for AI-driven autonomous work, and how is the company implementing agentic automation (platform, solutions, industries, and customer reach)? add

How can autonomous agents and a mix of deterministic technologies be used to automate complex financial tasks (for example, automating corporate tax computations for a company like Petrobras), and what results can this approach achieve? add

What problems does your agentic process automation solve—especially around integrating with legacy systems in sectors like healthcare and banking, handling unstructured data, and ensuring governance and security—and how do partners (e.g., NemoClaw, OpenAI, Cisco) contribute to those solutions? add

What is the Process Reasoning Engine, how does it work, and why does it outperform other models for large enterprises while allowing customers to retain their own data? add

What are the company's main growth drivers, and how has the adoption of agentic/generative AI—supported by your single control plane and large work-data set—driven recent bookings, ARR, and product strategy? add

bolt Powered by CUBE AI

Mihir Shukla, Automation Anywhere

search

(INTRO)

Gemma Allen

>> Welcome back to theCUBE 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 building a company that many enterprises credit with turning robotic process automation into its own category. They are now trying to do the same thing again with Agentic AI. Automation Anywhere is running north of a billion IT service requests through its autonomous service desk. CEO of Automation Anywhere, Mihir Shukla, great to have you.

Mihir Shukla

>> Good to be with you here, Gemma.

Gemma Allen

>> 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, help level set with me and the audience, what is actually happening.

Mihir Shukla

>> Okay. How about I set us up on today Automation Anywhere's vision is to solve one of the hardest problems in the AI industry, which is your AI spend is here and the level of autonomy that AI is achieving is here and the gap is widening. And we know this through other large technological revolution from electricity to Internet, that technology by itself doesn't create the impact. It's the level of autonomy. that you achieve with it, that 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 agentic automation is how we are doing it. Relying on our heritage of automation with machine learning and other RPA and other things. but now with agentic we are taking a few steps up now with agents and the combination of other technologies we are able to reason what to do decide what to do and sometimes take a human input and then execute an end -to -end process with governance and security we today are autonomous and Automation Anywhere stands for autonomous work. That's what we stand for. And we do it in two, three different ways. We offer a platform, agentic automation platform, that's number one platform in agentic automation today. We offer out -of -the -box agentic solutions across functions like IT support, HR, and many others, and 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.

Gemma Allen

>> 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 actually realized value within agentic AI, within enterprise, is somewhat of a debatable topic, right? There is certainly a wedge to overcome. When we think about the parameters of agentic for enterprise, enterprise we're used to a world where we could run a script and tell a system give me this data do this. The world of agentic though is far more than that right? It's about actual execution. So when you think about that gap what are the parameters of what we are actually talking about here? Are we talking about systems operating collectively and completely autonomously agents making decisions that humans made two years ago? Help me understand where you see that gap being the deepest in terms of what the market says versus what enterprises realize? Could we do? It with a few customer examples? perfect.

Mihir Shukla

>> So take an example and i'll give a few. Maybe there will be three versions of it. So there are customers who want to agentifying one process two processes 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 reimagine my entire business around that process. And there's a third class of customers 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 health care system.

Gemma Allen

>> I know well.

Mihir Shukla

>> And then one of the largest trusts there came to us and said, we want to reimagine the entire 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 the 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 temporary staffing 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 a 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 three 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 out certain things, like tax code, figuring out what the thousands of pages of tax laws mean and trying to guide what is the right tax law to apply. Set of token -less technologies, 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 the 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 a collection of these technologies create a seamless autonomous experience and change a complete operating model.

Gemma Allen

>> 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? That happened across many government organizations the world over. credentialed access, depending on your domain accessibility for a clinician versus a 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 the governance wasn't in place historically, the structure, perhaps even just the actual workflow itself had never been fully mapped or fully discovered, right? When we think about agentic, it seems as though a lot of problems were solved in 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? Where does this magical formula?

Mihir Shukla

>> That's a great question. So I think some problems are solved with agent tech and some is what is the hardest part that we solve. So the part that you rightly said that healthcare, banking, they're not on the cloud yet. As we know fully. So much of this needs the 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 data cleaning. some of it has been solved with agent tech because you could for example we have an 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 these it keeps getting better every time so today it has seen more than any human being has ever seen. So 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 three 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. We partnered within 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, it 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 an hour. 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.

Gemma Allen

>> And is that in that scenario, are we talking about a collaboration with NeMo Guardrails? Help me understand the difference.

Mihir Shukla

>> So the NeMo Guardrails and Nemotron models are useful everywhere, but especially in international markets where people are going for more open source model. 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 show enterprises what is possible with some of the latest models. Cisco brings DefenseClaw, right? Okta brings Identity. So multiple partners brought multiple pieces to it. and we brought our agentic automation execution and governance framework along with it to deliver an end -to -end solution to the customer.

Gemma Allen

>> 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. 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 playing this interesting role right now. Some folks would argue that certain systems of record were disconnected 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. Others argue that that is an excuse, right? That has been an excuse that's been used for a long time to keep knowledge as power in the hands of an executive function, okay? What do you see from the perspective of this world of highly interoperable systems of record, or 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? 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.

Mihir Shukla

>> That's a great question. So how does our customer think about this? I'll tell you what they don't want to do. So 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.

Gemma Allen

>> Fair.

Mihir Shukla

>> Out of the question.

Gemma Allen

>> Because of data retention?

Mihir Shukla

>> some of these businesses have acquired knowledge. The data is their knowledge.

Gemma Allen

>> Training on your data?

Mihir Shukla

>> And training on your data. And they don't want to do a vendor lock -in with one particular vendor, as you would imagine. So again, 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 systems 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, a 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 intrinsic 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 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 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 more and more.

Gemma Allen

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

Mihir Shukla

>> That's right. That's right.

Gemma Allen

>> 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 Furrier mentioned to me you were on, I think, quite a while ago. And when I looked at that, I had a quick glance into it, and I thought, wow, so much has happened, right, since, you first became acquainted with us and 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, where are you guys headed? this world of agentic, it seems as though it's a huge top of mind conversation for many CISOs, CIOs.

Mihir Shukla

>> So it has been a huge tailwind for us. So we had an advantage because we had a single control plane for all of our customers, whether they're 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 three growth drivers. I mentioned our agentic 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 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 at 70 % today right? That's a significant change from when we spoke last. 50 40 percent 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 three areas. Out -of -the -box solutions for example Autonomous Service Desk you mentioned earlier. We process one 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. 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 agent tech. All, so most of them, or all of them, are outcome driven deals. So, cost of a ticket, let's say, is $2, or cost of an invoice is $1. Cost of customer onboarding is $100. So, customers don't have to worry about the multiple technologies and how it comes together. We and our partners bring it together and deliver the outcome. We had an 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 is the Agentic Automation Platform that we bring. This is for the large enterprise customers who want to build an agentic platform and agentic 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 percent for us and accelerating. And then one of the key other offerings we offer is what 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 percent for us on a smaller number but we're very excited because some of the examples that you see tell you what is possible. When we look back, almost 70, 80 % of everything I talked to you about wasn't there three years ago. So that's how much... Crazy, right? Crazy.

Gemma Allen

>> Well, 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?

Mihir Shukla

>> We are large enough. We have been profitable for three years, growing fast, waiting for the right moment 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, whenever that is.

Gemma Allen

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

Mihir Shukla

>> My pleasure. Nice to be with you.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired, Mixture of Experts. Thanks for watching.