theCUBE + NYSE Wired: C-Suite Business Transformation | Rob Bearden, Sema4.ai

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Rob Bearden

CEO

Sema4.ai

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play_circle_outline Sema4.ai: Agentic Automation for Procure-to-Pay, Order-to-Cash and Invoice Reconciliation — Boost Accuracy, Throughput, Reduce Headcount

play_circle_outline From Hadoop Lessons to Autonomous AI: Evolving Analytics from Insight to Prescriptive Action

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Rob Bearden, Sema4.ai

Rob Bearden

CEO Sema4.ai

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John Furrier

>> Palo Alto studio connecting Silicon Valley and Wall Street. I'm John Furrier, co-hosting theCUBE here with Gabe Olave, my co-host. Hello, I'm John Furrier, your host of theCUBE here in theCUBE's NYC studio. Of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street. This is our NYSE Wired program and community. This is a program around Business Transformation Edge. The competitive edge in the future with AI will be around the business transformation, not just the technical transformation. Next guest to weigh in on this is Rob Bearden, industry veteran, CEO and co-founder of Sema4.ai, theCUBE alumni going back, I think, 2013. I can't remember the year. Rob, great to see you. That's our 17th year with theCUBE. Now we're at the NYSE and Palo Alto. We get the event coverage. Good to see you.

John Furrier

>> Well, great to see you, too. Thanks for having me here and great to be here.

John Furrier

>> So you're here for the Jefferies Conference. Everyone is here for the CFO Summit. Which is basically the future of the CFO suite and all that good stuff. But you've been by default kind of a historian. You've lived through all the open source generation. You're now the chief of Sema4. You do a lot of work with agents. The role of business transformation is the top topic of the day, not IT.

John Furrier

>> Mm-hmm

John Furrier

>> . IT is everywhere. AI will be infused. That's intelligence.

John Furrier

>> That's right

John Furrier

>> . That's going to impact the work, how people think. How they do their job. Of course, the software runs it. The infrastructure that runs it will be AI infrastructure with Agentic coding. What is the business model transformation, Ed, in your opinion? How about competitive edge? What do you think is top of mind for the C-suite?

Rob Bearden

>> Yeah, I think it's about outcomes and how we get to better, faster, more predictable, accurate outcomes with higher velocity and better leverage and how we do that and how we do a transformation to get to that objective is going to be done with AI in a much more accelerated way than any other technology shift we've seen. And the difference between this is the technology will be there to do it, but it's going to be all measured on outcomes, velocity, and accuracy.

John Furrier

>> we were talking before we came on camera for this segment and we were kind of riffing on, oh, it's not like the cloud era, the cloud native generation. AI native looks a lot like the same music being played during the cloud native days. The one difference I would say is that with AI, because it's kind of infused horizontally across companies, revenue is involved.

Rob Bearden

>> Yeah

John Furrier

>> . In cloud, I heard revenue only in context of the SaaS apps.

John Furrier

>> Yeah

John Furrier

>> . Not in terms of the customer. Yeah, there was probably some revenue there, but it wasn't like front and center in the AI conversation, revenue. Now, that's a CFO conversation.

Rob Bearden

>> That's going to be—a hard one

John Furrier

>> . But revenue is a very key indicator.

Rob Bearden

>> Yeah.

John Furrier

>> And if you get it right, it's a good thing. That's why we're seeing the world shift back from probabilistic to deterministic.

John Furrier

>> Yeah.

John Furrier

>> What's your take on this revenue component? Why is it such an important factor when evaluating vendors, workflows, how to inject intelligence into a company?

Rob Bearden

>> Well, it's all about creating a new and differentiated high-velocity model. And that model is— yes, of course, it's going to have table stakes. It's going to be more efficient. But more importantly, it's going to drive the next generation of operating models, which are going to drive much more scalable revenue and leverage business models that, again, drive not just better efficiencies in the deployment of the tech, but change the operating model that generates revenue and generates revenue in a very scalable way. And that's the difference in this that's going to create breakout nonlinear growth, not just the redeployment of cloud and compute or excuse me, storage and compute to cloud. This is absolutely transformational to business model.

John Furrier

>> And of course, the infrastructure CapEx we saw Just this week, NVIDIA announced the $500 billion deal. You had BlackRock, KKR at the table. You had the CEO of Goldman Sachs, Brookfield. you had all the major capital providers. This kind of puts an answer to the bubble question because, unlike other bubbles, there was massive demand. And again, some people look at the demand in terms of supply and supply is constrained. So obviously that's going to change prices in the short term, but what really people are trying to figure out is what's the demand for intelligence? So as people get through scarcity and manage that problem, let's just say more supply does come in.

John Furrier

>> Mm-hmm

John Furrier

>> . The question then is where we'll be in the discovery phase of the enterprise? Because once the enterprise completes their discovery journey of where this all fits, which could be a year, could be a half a year, but they're already in with coding.

John Furrier

>> Mm-hmm

John Furrier

>> . Agents are already in the enterprise. You're starting to see some use cases, but it's not yet fully proliferated.

John Furrier

>> Right.

John Furrier

>> What's your take on this bubble mentality, the supply dynamics, and then the discovery of the value with intelligence and the demand for intelligence?

Rob Bearden

>> Well, if we look at the adoption curve and where we are on that, clearly AI has proven its ability to drive both revenue and efficiency and leverage from the model itself. And that's played through on the personal assistant level ChatGPT, that's then moved aggressively into the coding space and the efficiency, the leverage, the accuracy that drives there. But really what the enterprise wants is to do transformation of the operating and business models to get back to the revenue. And so how do we pick use cases that are high volume, high velocity, generate a big change in my operating model that will drive both top line and bottom line? And that goes back to that discovery phase. They have seen the use cases that are repeatable with velocity that will drive both acceleration of revenue and better efficiency on the operating model if delivered through AI. Now it's how do we start? Where do we start? How do we apply this? the next question becomes what are the best leverageable models? And this goes back to the intelligence layer. It started 10 years ago with big data. Bringing a lot of data under management. Having visibility into my entire 360, how my product interacts with my customer, with my supply chain, and bringing velocity there in the use cases that came from there. And that led to machine learning use cases. That lent itself to predictive analytics. the natural next evolution is going to be agentic automation for strategic workflows across multi-step processes. That begins largely because of the role the CFO's taken on to be the operational owner for the outcomes and the leverage in the new models and creating the new models, not just reporting the analytics of the transactional world. So the best CFOs in the world are moving to becoming operators and applying AI into their models to achieve those new— because they have a unique perspective.

John Furrier

>> They're updating their operating model because they know the operations. But now for the first time they could self-serve. They have autonomous platforms to drive the outcomes that they're instrumenting.

Rob Bearden

>> Yeah, I want to get— they know what that curve looks like.

John Furrier

>> Yeah. And the unit economics behind it. And they'll cherry-pick the use cases. That's going to be the transition.

Rob Bearden

>> So big wins fast and then rinse and repeat, rinse and repeat.

John Furrier

>> I was talking to someone at McKinsey at Google Cloud, and I was asking everyone the same question. in IT back in the day, doing the old retro view, you get a pilot, you put it out in the hinterlands of the enterprise, test it, a lot of politics, hands are in it. What's the approach now? And he said something interesting. He says with AI, because of what you just said, they're going after the hardest problems that they know produce revenue. Because for the first time he said they're seeing the capability to attain it. Yeah, gettable.

John Furrier

>> Right.

John Furrier

>> And that is a huge nuance because I said, what do you mean? He's like, well, before the biggest problems required human capital, massive IT stack to be deployed.

Rob Bearden

>> Yeah

John Furrier

>> , the CapEx, the duration, the risk management, big process change with humans.

John Furrier

>> Yes

John Furrier

>> , that's a huge— and by the way, I'm like, that's the best answer I've heard on that because that's what the best people are doing. They're saying, okay, If you're Picasso, here's some paintbrushes, here's some stuff, go paint, right?

John Furrier

>> Exactly

John Furrier

>> . Go solve that problem. And by the way, the timescale takes the risk management out because if they fail, yeah, send someone else in. metaphorically speaking, but this is where people are thinking. So that brings me to my next question. in your years of experience running companies, being an entrepreneur, you've seen disruptive enablement, open source, JBoss, many other examples. I was talking to Jensen Huang on the NVIDIA team and I said, I love the disruptive enablement of AI. And he goes, whoa, whoa, whoa, whoa, we don't use the word disruptive. It's accelerated. Now they have accelerated computing, so a little bit of a marketing narrative, but it was interesting nuance. Their view is we're accelerating, not disrupting something. So share your thoughts on that because you've been involved in disruptive— open source was certainly disruptive, killed the mainframe, Kill the Mini, kill the Unix, bring in Linux. That was very specific mercenary bomb thrower-like behavior, right? Take care of the incumbent. We don't want that here. Everyone loves technology, so it's really not about disruption. It's just accelerating faster. What's your thoughts on that?

Rob Bearden

>> Well, it's about getting to an outcome and getting to an outcome with great unit economics and great scale. With high velocity behind it and through it. And so we can call it accelerating disruptive innovation. Ultimately, it's about getting to a high velocity outcome at scale. And that is the era we're in right now. And that's what Agentic Automation is all about enabling. And the ability to do that is now. And the CFO is the one who's making and taking the lead role in applying the tech to make that outcome happen at high velocity.

John Furrier

>> We've known each other about 15 years or so, give or take, just with theCUBE. If you look at this generation, we've been observing a fusion of generations because we're at a systems revolution. Okay, if you look at all the top tech, it's a system game. Operating system is a term that's been kicked around more in one year than 10 years on theCUBE, but operating system for this, or the control plane for that. So you start to see systems, they got the edge. This is not just young. The old model was the young replaces the old, right?

John Furrier

>> Yeah

John Furrier

>> , but no, I'm seeing both generations coming together. Do you see the same thing?

Rob Bearden

>> Yeah, so I think that's the most powerful thing behind AI is you do not have to disintermediate fully something. So this isn't about what you replace, it's how you leverage what's in place to accelerate that outcome. Now, will it eventually dissolve its way down? Maybe, maybe not. But you're able to use the existing assets and reuse the existing systems of record the data structures of record, and to create that agentic outcome at its own new layer with high velocity and outcome.

John Furrier

>> Talk about Sema4.ai, the company you're running now. Give us an update on the company. What are you working on? Talk about some momentum.

John Furrier

>> Yeah.

John Furrier

>> What's the core market? What are you going after?

Rob Bearden

>> So we started the company about 2 years ago. We're a horizontal platform that gives the enterprise the ability to build, run and manage agentic capability. We uniquely bring documents and data, structured, unstructured data together. Ingressing the LLM and focus on the upper end of the Fortune 500 mission-critical use cases. That's landed us principally in the back office, the CFO, doing things like procure-to-pay, order-to-cash, the automation cycles that come with that. Our customer base is in the Fortune 300. We have about 25 paying customers. About 250 production agents running real mission-critical outcomes at scale. Very proud of what our team's accomplished in the short time we've done it. We've had phenomenal support from our investors and our customer base. We've had a core set of early adopters that have taken us a long way and we've been able to deliver a lot of value. So it's been a lot of fun.

John Furrier

>> What's the success formula for the early stage of Agentic?

Rob Bearden

>> What would you say the playbook looks like in terms of where we are in

John Furrier

>> the—No, no. Within your customers, your customers and our customers, what are some use cases where people are knocking down wins today?

Rob Bearden

>> Well, we've got— let me give you one: invoice reconciliation. That was the beginning point of an end-to-end cycle that they want to create transformatively across. And it's their long pole in the tent to start and prove AI is effective. we've got $1 billion a year that's going through our environment. We've been able to get to an accuracy rate on that transaction side where humans were at about 62 to 64% accuracy in their work. We within 6

John Furrier

>> months—You upped it from 60-something

Rob Bearden

>> to—To 94% accuracy.

John Furrier

>> That's a major, major advantage.

Rob Bearden

>> Right. And we've been able to allow them to increase their throughput by 150% and reduce the human capacity in those areas of the business by half.

John Furrier

>> So quantifiable metrics. Do you see that?

Rob Bearden

>> It's all about the ROI is under a year.

John Furrier

>> Yeah, yeah, that's a no-brainer. I think people are getting it right. They do their homework on the governance and get those harnesses and the scale right. I want to ask you about your experience with Hortonworks and Cloudera. Hortonworks came out of Yahoo. You were CEO of that. The team eventually merged in with Cloudera, which you ran that company. A lot of lessons were learned. They had a data— they were the first to have a data platform.

John Furrier

>> Sure

John Furrier

>> . And a small little company called Databricks had Spark, which kind of like copied Hadoop. Look at them now, data lakes. So you've seen the early wave of the Hadoop, which is essentially the big data. What lessons are you applying from that into this cycle now? What's the biggest change? What have you learned and how do you see that playing out? Because it was a precursor to what we're doing now, almost the same kinds of things, but a lot's changed. More AI infrastructure, the clusters are better, rack scale performance, got edge coming. So you have a distributed computing environment that if it was in place then, there might have been a different outcome for those two companies that pioneered big data.

Rob Bearden

>> Well, in the early days of big data, what we were able to do is bring data they could never bring under management at an economics they never could afford to do it at. And what that gave them the ability to do was to open up visualization to parts of their business they never had the ability to, and they understood what best practice KPIs were about. And that then gave them the ability to move to machine learning and predictive analytics, and it just opened up the proactive. What it did is it changed their operating models from being very reactive with analytics to now being able to use data to be very proactive in their models of how they changed, how they did order management, how they did supplier management, how they built product, how they report to their customers, how they went to market to sell, how they changed price elasticity because of their visibility of data into the customer and the velocity. AI is going to do that on a 10, 20, 30x basis because the velocity that we can create— and this goes back to now we can once again shift the models from not just having the visibility and predictive analytics, but taking prescriptive action in real time and going out proactively to generate customer reaction, customer offers.

John Furrier

>> So analytics was phase 1, see what's happening. Now phase 2 with GenAI was action.

Rob Bearden

>> Execute. Execute.

John Furrier

>> Take action.

Rob Bearden

>> And third would be fully autonomous reasoning, or we're going to get to fully autonomous reasoning very, very quickly because the models are advancing so quickly because of the unit economics on the compute and storage.

John Furrier

>> Rob, what happens next? You have a lot of contacts and ears to the ground in the financial community. You know the entrepreneurial community very well. And as CEO at Sema4.ai. What's the vibe in the market? How would you classify the sentiment right now in the market?

Rob Bearden

>> Yeah, I think the sentiment is as optimistic and aggressive as it's ever been because of the upside opportunity to completely transform our operating models and the unit economics that underpin them. But it's how we innovate, how the enterprise engages with their customers, their supply chain and their products at a velocity they never could at unit economics that they would have never gotten to. And so it's less about how more efficient we get. Yes, we will. Yes, we'll change the game there. But the new operating and business models that we open up, you think about the radically different change that the internet made and the kind of products the kind of services, the companies that it launched because of the internet operating and distribution models. We're going to see that 10x because of AI and AI, not just foundation models, but the kind of applications that AI will generate and the consequential business models that result from that. And so, we're going to see the efficiency in every line of business. But what we're going to find is you're going to see brand new businesses start and go to $1 billion in 2, 3 years.

John Furrier

>> And we were just speculating, I think the IPO market's also going to be faster too, because with faster escape velocity— and by the way, all the prep work on IPOs are going to go faster.

John Furrier

>> Yeah

John Furrier

>> , because remember how many times it comes— you take public, it's like, that's a grind. AI could come in there, say, I'm sure the bank's got some So we're expecting to see, we're already seeing some signals of IPOs for only 2-year-old companies.

John Furrier

>> Yeah.

Rob Bearden

>> IPOs.Sure. And the public market wants to do it. And I think the question becomes how quickly in velocity do you go from an A round to the public market?

John Furrier

>> Yeah, it's fascinating. Rob, great to have you on theCUBE. A lot of the conversations we've had over the many years are playing out now. look how far we've come together in this industry. It is an exciting time. I think it's the best time. I agree with

John Furrier

>> you.The best is yet to come. And this is the fun part.

John Furrier

>> Yeah, this is fun. Yeah, as grizzled veterans. I'm John Furrier, this is theCUBE. We are here at our NYSE studio. Of course, the NYSE Wired program that we have with capital markets and the technology intersections really hitting this relevance, money-making transformation message at the C-suite and beyond. This is our Transformation Edge series. The next competitive edge will be who can leverage the technology, inject intelligence into their organizations, We're doing our part to track up and down the stack. Thanks for watching.

Rob Bearden, Sema4.ai

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John Furrier

>> Palo Alto studio connecting Silicon Valley and Wall Street. I'm John Furrier, co-hosting theCUBE here with Gabe Olave, my co-host. Hello, I'm John Furrier, your host of theCUBE here in theCUBE's NYC studio. Of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street. This is our NYSE Wired program and community. This is a program around Business Transformation Edge. The competitive edge in the future with AI will be around the business transformation, not just the technical transformation. Next guest to weigh in on this is Rob Bearden, industry veteran, CEO and co-founder of Sema4.ai, theCUBE alumni going back, I think, 2013. I can't remember the year. Rob, great to see you. That's our 17th year with theCUBE. Now we're at the NYSE and Palo Alto. We get the event coverage. Good to see you.

John Furrier

>> Well, great to see you, too. Thanks for having me here and great to be here.

John Furrier

>> So you're here for the Jefferies Conference. Everyone is here for the CFO Summit. Which is basically the future of the CFO suite and all that good stuff. But you've been by default kind of a historian. You've lived through all the open source generation. You're now the chief of Sema4. You do a lot of work with agents. The role of business transformation is the top topic of the day, not IT.

John Furrier

>> Mm-hmm

John Furrier

>> . IT is everywhere. AI will be infused. That's intelligence.

John Furrier

>> That's right

John Furrier

>> . That's going to impact the work, how people think. How they do their job. Of course, the software runs it. The infrastructure that runs it will be AI infrastructure with Agentic coding. What is the business model transformation, Ed, in your opinion? How about competitive edge? What do you think is top of mind for the C-suite?

Rob Bearden

>> Yeah, I think it's about outcomes and how we get to better, faster, more predictable, accurate outcomes with higher velocity and better leverage and how we do that and how we do a transformation to get to that objective is going to be done with AI in a much more accelerated way than any other technology shift we've seen. And the difference between this is the technology will be there to do it, but it's going to be all measured on outcomes, velocity, and accuracy.

John Furrier

>> we were talking before we came on camera for this segment and we were kind of riffing on, oh, it's not like the cloud era, the cloud native generation. AI native looks a lot like the same music being played during the cloud native days. The one difference I would say is that with AI, because it's kind of infused horizontally across companies, revenue is involved.

Rob Bearden

>> Yeah

John Furrier

>> . In cloud, I heard revenue only in context of the SaaS apps.

John Furrier

>> Yeah

John Furrier

>> . Not in terms of the customer. Yeah, there was probably some revenue there, but it wasn't like front and center in the AI conversation, revenue. Now, that's a CFO conversation.

Rob Bearden

>> That's going to be—a hard one

John Furrier

>> . But revenue is a very key indicator.

Rob Bearden

>> Yeah.

John Furrier

>> And if you get it right, it's a good thing. That's why we're seeing the world shift back from probabilistic to deterministic.

John Furrier

>> Yeah.

John Furrier

>> What's your take on this revenue component? Why is it such an important factor when evaluating vendors, workflows, how to inject intelligence into a company?

Rob Bearden

>> Well, it's all about creating a new and differentiated high-velocity model. And that model is— yes, of course, it's going to have table stakes. It's going to be more efficient. But more importantly, it's going to drive the next generation of operating models, which are going to drive much more scalable revenue and leverage business models that, again, drive not just better efficiencies in the deployment of the tech, but change the operating model that generates revenue and generates revenue in a very scalable way. And that's the difference in this that's going to create breakout nonlinear growth, not just the redeployment of cloud and compute or excuse me, storage and compute to cloud. This is absolutely transformational to business model.

John Furrier

>> And of course, the infrastructure CapEx we saw Just this week, NVIDIA announced the $500 billion deal. You had BlackRock, KKR at the table. You had the CEO of Goldman Sachs, Brookfield. you had all the major capital providers. This kind of puts an answer to the bubble question because, unlike other bubbles, there was massive demand. And again, some people look at the demand in terms of supply and supply is constrained. So obviously that's going to change prices in the short term, but what really people are trying to figure out is what's the demand for intelligence? So as people get through scarcity and manage that problem, let's just say more supply does come in.

John Furrier

>> Mm-hmm

John Furrier

>> . The question then is where we'll be in the discovery phase of the enterprise? Because once the enterprise completes their discovery journey of where this all fits, which could be a year, could be a half a year, but they're already in with coding.

John Furrier

>> Mm-hmm

John Furrier

>> . Agents are already in the enterprise. You're starting to see some use cases, but it's not yet fully proliferated.

John Furrier

>> Right.

John Furrier

>> What's your take on this bubble mentality, the supply dynamics, and then the discovery of the value with intelligence and the demand for intelligence?

Rob Bearden

>> Well, if we look at the adoption curve and where we are on that, clearly AI has proven its ability to drive both revenue and efficiency and leverage from the model itself. And that's played through on the personal assistant level ChatGPT, that's then moved aggressively into the coding space and the efficiency, the leverage, the accuracy that drives there. But really what the enterprise wants is to do transformation of the operating and business models to get back to the revenue. And so how do we pick use cases that are high volume, high velocity, generate a big change in my operating model that will drive both top line and bottom line? And that goes back to that discovery phase. They have seen the use cases that are repeatable with velocity that will drive both acceleration of revenue and better efficiency on the operating model if delivered through AI. Now it's how do we start? Where do we start? How do we apply this? the next question becomes what are the best leverageable models? And this goes back to the intelligence layer. It started 10 years ago with big data. Bringing a lot of data under management. Having visibility into my entire 360, how my product interacts with my customer, with my supply chain, and bringing velocity there in the use cases that came from there. And that led to machine learning use cases. That lent itself to predictive analytics. the natural next evolution is going to be agentic automation for strategic workflows across multi-step processes. That begins largely because of the role the CFO's taken on to be the operational owner for the outcomes and the leverage in the new models and creating the new models, not just reporting the analytics of the transactional world. So the best CFOs in the world are moving to becoming operators and applying AI into their models to achieve those new— because they have a unique perspective.

John Furrier

>> They're updating their operating model because they know the operations. But now for the first time they could self-serve. They have autonomous platforms to drive the outcomes that they're instrumenting.

Rob Bearden

>> Yeah, I want to get— they know what that curve looks like.

John Furrier

>> Yeah. And the unit economics behind it. And they'll cherry-pick the use cases. That's going to be the transition.

Rob Bearden

>> So big wins fast and then rinse and repeat, rinse and repeat.

John Furrier

>> I was talking to someone at McKinsey at Google Cloud, and I was asking everyone the same question. in IT back in the day, doing the old retro view, you get a pilot, you put it out in the hinterlands of the enterprise, test it, a lot of politics, hands are in it. What's the approach now? And he said something interesting. He says with AI, because of what you just said, they're going after the hardest problems that they know produce revenue. Because for the first time he said they're seeing the capability to attain it. Yeah, gettable.

John Furrier

>> Right.

John Furrier

>> And that is a huge nuance because I said, what do you mean? He's like, well, before the biggest problems required human capital, massive IT stack to be deployed.

Rob Bearden

>> Yeah

John Furrier

>> , the CapEx, the duration, the risk management, big process change with humans.

John Furrier

>> Yes

John Furrier

>> , that's a huge— and by the way, I'm like, that's the best answer I've heard on that because that's what the best people are doing. They're saying, okay, If you're Picasso, here's some paintbrushes, here's some stuff, go paint, right?

John Furrier

>> Exactly

John Furrier

>> . Go solve that problem. And by the way, the timescale takes the risk management out because if they fail, yeah, send someone else in. metaphorically speaking, but this is where people are thinking. So that brings me to my next question. in your years of experience running companies, being an entrepreneur, you've seen disruptive enablement, open source, JBoss, many other examples. I was talking to Jensen Huang on the NVIDIA team and I said, I love the disruptive enablement of AI. And he goes, whoa, whoa, whoa, whoa, we don't use the word disruptive. It's accelerated. Now they have accelerated computing, so a little bit of a marketing narrative, but it was interesting nuance. Their view is we're accelerating, not disrupting something. So share your thoughts on that because you've been involved in disruptive— open source was certainly disruptive, killed the mainframe, Kill the Mini, kill the Unix, bring in Linux. That was very specific mercenary bomb thrower-like behavior, right? Take care of the incumbent. We don't want that here. Everyone loves technology, so it's really not about disruption. It's just accelerating faster. What's your thoughts on that?

Rob Bearden

>> Well, it's about getting to an outcome and getting to an outcome with great unit economics and great scale. With high velocity behind it and through it. And so we can call it accelerating disruptive innovation. Ultimately, it's about getting to a high velocity outcome at scale. And that is the era we're in right now. And that's what Agentic Automation is all about enabling. And the ability to do that is now. And the CFO is the one who's making and taking the lead role in applying the tech to make that outcome happen at high velocity.

John Furrier

>> We've known each other about 15 years or so, give or take, just with theCUBE. If you look at this generation, we've been observing a fusion of generations because we're at a systems revolution. Okay, if you look at all the top tech, it's a system game. Operating system is a term that's been kicked around more in one year than 10 years on theCUBE, but operating system for this, or the control plane for that. So you start to see systems, they got the edge. This is not just young. The old model was the young replaces the old, right?

John Furrier

>> Yeah

John Furrier

>> , but no, I'm seeing both generations coming together. Do you see the same thing?

Rob Bearden

>> Yeah, so I think that's the most powerful thing behind AI is you do not have to disintermediate fully something. So this isn't about what you replace, it's how you leverage what's in place to accelerate that outcome. Now, will it eventually dissolve its way down? Maybe, maybe not. But you're able to use the existing assets and reuse the existing systems of record the data structures of record, and to create that agentic outcome at its own new layer with high velocity and outcome.

John Furrier

>> Talk about Sema4.ai, the company you're running now. Give us an update on the company. What are you working on? Talk about some momentum.

John Furrier

>> Yeah.

John Furrier

>> What's the core market? What are you going after?

Rob Bearden

>> So we started the company about 2 years ago. We're a horizontal platform that gives the enterprise the ability to build, run and manage agentic capability. We uniquely bring documents and data, structured, unstructured data together. Ingressing the LLM and focus on the upper end of the Fortune 500 mission-critical use cases. That's landed us principally in the back office, the CFO, doing things like procure-to-pay, order-to-cash, the automation cycles that come with that. Our customer base is in the Fortune 300. We have about 25 paying customers. About 250 production agents running real mission-critical outcomes at scale. Very proud of what our team's accomplished in the short time we've done it. We've had phenomenal support from our investors and our customer base. We've had a core set of early adopters that have taken us a long way and we've been able to deliver a lot of value. So it's been a lot of fun.

John Furrier

>> What's the success formula for the early stage of Agentic?

Rob Bearden

>> What would you say the playbook looks like in terms of where we are in

John Furrier

>> the—No, no. Within your customers, your customers and our customers, what are some use cases where people are knocking down wins today?

Rob Bearden

>> Well, we've got— let me give you one: invoice reconciliation. That was the beginning point of an end-to-end cycle that they want to create transformatively across. And it's their long pole in the tent to start and prove AI is effective. we've got $1 billion a year that's going through our environment. We've been able to get to an accuracy rate on that transaction side where humans were at about 62 to 64% accuracy in their work. We within 6

John Furrier

>> months—You upped it from 60-something

Rob Bearden

>> to—To 94% accuracy.

John Furrier

>> That's a major, major advantage.

Rob Bearden

>> Right. And we've been able to allow them to increase their throughput by 150% and reduce the human capacity in those areas of the business by half.

John Furrier

>> So quantifiable metrics. Do you see that?

Rob Bearden

>> It's all about the ROI is under a year.

John Furrier

>> Yeah, yeah, that's a no-brainer. I think people are getting it right. They do their homework on the governance and get those harnesses and the scale right. I want to ask you about your experience with Hortonworks and Cloudera. Hortonworks came out of Yahoo. You were CEO of that. The team eventually merged in with Cloudera, which you ran that company. A lot of lessons were learned. They had a data— they were the first to have a data platform.

John Furrier

>> Sure

John Furrier

>> . And a small little company called Databricks had Spark, which kind of like copied Hadoop. Look at them now, data lakes. So you've seen the early wave of the Hadoop, which is essentially the big data. What lessons are you applying from that into this cycle now? What's the biggest change? What have you learned and how do you see that playing out? Because it was a precursor to what we're doing now, almost the same kinds of things, but a lot's changed. More AI infrastructure, the clusters are better, rack scale performance, got edge coming. So you have a distributed computing environment that if it was in place then, there might have been a different outcome for those two companies that pioneered big data.

Rob Bearden

>> Well, in the early days of big data, what we were able to do is bring data they could never bring under management at an economics they never could afford to do it at. And what that gave them the ability to do was to open up visualization to parts of their business they never had the ability to, and they understood what best practice KPIs were about. And that then gave them the ability to move to machine learning and predictive analytics, and it just opened up the proactive. What it did is it changed their operating models from being very reactive with analytics to now being able to use data to be very proactive in their models of how they changed, how they did order management, how they did supplier management, how they built product, how they report to their customers, how they went to market to sell, how they changed price elasticity because of their visibility of data into the customer and the velocity. AI is going to do that on a 10, 20, 30x basis because the velocity that we can create— and this goes back to now we can once again shift the models from not just having the visibility and predictive analytics, but taking prescriptive action in real time and going out proactively to generate customer reaction, customer offers.

John Furrier

>> So analytics was phase 1, see what's happening. Now phase 2 with GenAI was action.

Rob Bearden

>> Execute. Execute.

John Furrier

>> Take action.

Rob Bearden

>> And third would be fully autonomous reasoning, or we're going to get to fully autonomous reasoning very, very quickly because the models are advancing so quickly because of the unit economics on the compute and storage.

John Furrier

>> Rob, what happens next? You have a lot of contacts and ears to the ground in the financial community. You know the entrepreneurial community very well. And as CEO at Sema4.ai. What's the vibe in the market? How would you classify the sentiment right now in the market?

Rob Bearden

>> Yeah, I think the sentiment is as optimistic and aggressive as it's ever been because of the upside opportunity to completely transform our operating models and the unit economics that underpin them. But it's how we innovate, how the enterprise engages with their customers, their supply chain and their products at a velocity they never could at unit economics that they would have never gotten to. And so it's less about how more efficient we get. Yes, we will. Yes, we'll change the game there. But the new operating and business models that we open up, you think about the radically different change that the internet made and the kind of products the kind of services, the companies that it launched because of the internet operating and distribution models. We're going to see that 10x because of AI and AI, not just foundation models, but the kind of applications that AI will generate and the consequential business models that result from that. And so, we're going to see the efficiency in every line of business. But what we're going to find is you're going to see brand new businesses start and go to $1 billion in 2, 3 years.

John Furrier

>> And we were just speculating, I think the IPO market's also going to be faster too, because with faster escape velocity— and by the way, all the prep work on IPOs are going to go faster.

John Furrier

>> Yeah

John Furrier

>> , because remember how many times it comes— you take public, it's like, that's a grind. AI could come in there, say, I'm sure the bank's got some So we're expecting to see, we're already seeing some signals of IPOs for only 2-year-old companies.

John Furrier

>> Yeah.

Rob Bearden

>> IPOs.Sure. And the public market wants to do it. And I think the question becomes how quickly in velocity do you go from an A round to the public market?

John Furrier

>> Yeah, it's fascinating. Rob, great to have you on theCUBE. A lot of the conversations we've had over the many years are playing out now. look how far we've come together in this industry. It is an exciting time. I think it's the best time. I agree with

John Furrier

>> you.The best is yet to come. And this is the fun part.

John Furrier

>> Yeah, this is fun. Yeah, as grizzled veterans. I'm John Furrier, this is theCUBE. We are here at our NYSE studio. Of course, the NYSE Wired program that we have with capital markets and the technology intersections really hitting this relevance, money-making transformation message at the C-suite and beyond. This is our Transformation Edge series. The next competitive edge will be who can leverage the technology, inject intelligence into their organizations, We're doing our part to track up and down the stack. Thanks for watching.