This episode explores business transformation and decision intelligence in enterprise artificial intelligence, abbreviated AI. Fred Laluyaux of Aera Technology, co-founder and chief executive officer, explains how decision intelligence digitizes the memory of organizational decisions and enables agentic automation. On theCUBE Research, hosts John Furrier and Gabe Olave explore enterprise AI adoption and governance shifts from accuracy to authority, present the Decision Ledger concept and examine Aera's approach to operationalizing AI across supply chain, finance and revenue operations.
Key takeaways include that frontier large language models, abbreviated LLM, are commoditizing and competitive advantage now resides in encoded business logic, context and execution, according to Laluyaux. They emphasize building a decision infrastructure comprising a Decision Ledger, a policy layer and a real-time execution loop—Understand Recommend Act Learn—to govern agents, enable learning from decisions and scale operations. The discussion highlights momentum in 2026 as requests for proposals shift from why to how quickly to implement and outlines practical approaches to applying decision intelligence to accelerate business transformation.
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
theCUBE + NYSE Wired: C-Suite Business Transformation. If you don’t think you received an email check your
spam folder.
Sign in to theCUBE + NYSE Wired: C-Suite Business Transformation.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open the link to automatically sign into the site.
Register for theCUBE + NYSE Wired: C-Suite Business Transformation
Please fill out the information below. You will receive an email with a verification link confirming your registration. Click the link to automatically sign into the site.
You are already logged into TheCUBE Network as
You’re almost there!
We just sent you a verification email. Please click the verification button in the email. Once your email address is verified, you will have full access to all event content for theCUBE + NYSE Wired: C-Suite Business Transformation.
I want my badge and interests to be visible to all attendees.
Checking this box will display your presense on the attendees list, view your profile and allow other attendees to contact you via 1-1 chat. Read the Privacy Policy. At any time, you can choose to disable this preference.
Select your Interests!
add
Upload your photo
Uploading..
OR
Connect via Twitter
Connect via Linkedin
EDIT PASSWORD
Share
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
theCUBE + NYSE Wired: C-Suite Business Transformation. If you don’t think you received an email check your
spam folder.
Sign in to theCUBE + NYSE Wired: C-Suite Business Transformation.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open the link to automatically sign into the site.
Sign in to gain access to theCUBE + NYSE Wired: C-Suite Business Transformation
Please sign in with LinkedIn to continue to theCUBE + NYSE Wired: C-Suite Business Transformation. Signing in with LinkedIn ensures a professional environment.
Are you sure you want to remove access rights for this user?
Details
Manage Access
email address
Community Invitation
Fred Laluyaux, Aera Technology
This episode explores business transformation and decision intelligence in enterprise artificial intelligence, abbreviated AI. Fred Laluyaux of Aera Technology, co-founder and chief executive officer, explains how decision intelligence digitizes the memory of organizational decisions and enables agentic automation. On theCUBE Research, hosts John Furrier and Gabe Olave explore enterprise AI adoption and governance shifts from accuracy to authority, present the Decision Ledger concept and examine Aera's approach to operationalizing AI across supply chain, finance and revenue operations.
Key takeaways include that frontier large language models, abbreviated LLM, are commoditizing and competitive advantage now resides in encoded business logic, context and execution, according to Laluyaux. They emphasize building a decision infrastructure comprising a Decision Ledger, a policy layer and a real-time execution loop—Understand Recommend Act Learn—to govern agents, enable learning from decisions and scale operations. The discussion highlights momentum in 2026 as requests for proposals shift from why to how quickly to implement and outlines practical approaches to applying decision intelligence to accelerate business transformation.
play_circle_outlineDecision Intelligence Platforms: Decision Ledger, Policy Management, and Continuous Governance
replyShare Clip
play_circle_outlineAera Digital Brain: Understand‑Recommend‑Act‑Learn for Supply Chain, Finance, Revenue and Operations with ERP/Data Lake Integration, Normalized Models on Kubernetes
replyShare Clip
play_circle_outlineProprietary enterprise data unlocking specialized intelligence and competitive advantage
replyShare Clip
play_circle_outlineDeterministic logic combined with frontier models for scalable, real-time decisions
>> Palo Alto Studio connecting Silicon Valley and Wall Street. I'm John Furrier, co-host of theCUBE here with Gabe Olave, my co-host. Well, I'm John Furrier, host of theCUBE here in theCUBE's NYSE studio. Of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street with all the technology, AI infrastructure, AI agents, and the transformation that's going on. This is our Transformation Edge series. We talk to the leaders who are making it happen Fred Laluyaux is here, co-founder and CEO of Aera Technology, veteran in the business, seen many waves on the enterprise side, on the software side. But now that the AI wave is here, Fred, great to have you on our Transformation Edge program.
John Furrier
>> Thanks for having me, John. Great to be with you.
John Furrier
>> We could probably squeeze you into our Mixture of Experts series because you're an expert. You've been there, done that. I have to ask you first, this enterprise AI wave is looking very strong. We saw a little bit of a wait and see a couple years ago. And then coding came in. Yeah, we saw the search stuff happen on the enterprise side. We saw good AI copy, chatbots. But now with coding, that has opened up the door for agentic. And you're seeing a lot of people looking at their data, looking at what's going on. So what's your take on the enterprise AI? How would you categorize, the current market relative to one, appetite, and two, reality?
John Furrier
>> Well, you talk about coding, you talk about ChatGPT, right? It's 4 years old. If you look at the last couple of years, just to set a marker, right, the conversation has shifted from what is our strategy, what do we do with it, to today, real shift. How do we operate our business with AI at its core? Which decisions, which processes are we running with AI? What do we learn? And I think everything else follows from that change, right? So at a high level, agents now, they've got hands. They can— you mentioned coding— they can reason. 2, 3 years ago, they were advising, they were reasoning. Now they can transact, they can place orders, they can move inventory, they can release payments. Right. The governance has shifted from accuracy to authority. What is an agent permitted to do? Up to what value? And how do we reverse it? Right. Models have commoditized, right? Frontier models are now rentable by anyone at a declining cost. So no one gets really an advantage from the model itself. The differentiation has moved up the stack to, to context, to encoded business logic and to ability to execute and learn. I think scale is exposing— you talk about that wave, the excitement around the topic. It's beyond excitement, actually. It's exposing some real bottlenecks, right? So enterprise spent years modernizing their data estate, but they're still running in siloed organizations where people are still making and executing decisions. Now it's really moving toward a model where decisions are made digitally. So there's a lot of shifts that are happening. I think maybe a last one would be a shift from IT to business to executives, right? That you're now thinking about this not as an IT project, but as to the future of work, as your future of your organization design, as to the future of how your organization operates.
John Furrier
>> You know, that's, this series was motivated by initially CFOs that came to us and said, hey, we're architecting our business operating model. We have to look at the CapEx. We have to look at how we operate our business, not just an IT project, but how we're gonna drive that revenue. And what's interesting is it's evolved to the human resource officers. Now the CIO and CISO, of course, are involved. But the C-suite are actively involved in this transformation. This is unlike previous IT transformations where, hey, here's a dashboard, look what happened in your business, to real-time, real money, real operating impact. So there's an operating model impact, which will then impact the commercialization, which is essentially how they serve customers. And with agents making decisions, you've got work. So you got CFO, CHRO, you got CIO and CISO kind of all playing together. I've never seen it that way before. I've seen C-suite activity, but it's more like, okay, fund the project. How do we roll it out to the team? How do we serve customers? But never this transformative.
John Furrier
>> Yeah, look, it's kind of fascinating that you mentioned I'm a veteran. I've been in the space for close to 30 years. It's the first time that the conversation that we're having has moved to the C-level. We had last week, the executive committee of a large company coming and pitching to boards. Because we're not talking about a next wave of an IT solution that does the same thing as before, but faster or better or with better economics. We're talking about digitizing the work. And if you think about it, work is what makes a company great, it's the ability to make intelligent decisions in real time. That part, if you think about it, that's the most valuable asset that a company has. Its ability to make intelligent decisions, and yet it's the least governed one. You think about it, companies are still making decisions using people that are then getting access to a lot of data and information. But we're now able to govern that asset, and that asset is the memory of the decisions that a company makes thousands of times a month to actually run its operations, to improve its performance. With that asset, I can now learn, I can get better insight, I can get more accuracy, I can have policy management that's centralized. So this is a shift not just in technology, but is technology enabling a fundamental shift in the way the company actually operates?
John Furrier
>> Yeah, that's a great insight. I would just share that, the discussion always goes to, hey, let's automate this, let's automate that. So that's great, cool, you can automate a lot of things. However, when you say, okay, I'm automating, But the real question is, are you injecting intelligence into the organization. So then the next question is, what does that mean? So I wanna ask you, what does it mean to inject intelligence into an organization? Because if you frame it that way, it looks different. It looks like a different discussion. Share your thoughts and perspective on that one.
John Furrier
>> I always start my conversation with two questions. Do you agree that more intelligent decisions will help unlock value for your business? The answer is usually yes.
John Furrier
>> Yeah.
John Furrier
>> Second question, do you agree that there's no intelligence without memory? And the answer is usually yes. And that's the first aha moment. In your current stack, in your future roadmap, where is the memory of all the decisions that you make every day? where does it sit? And it's in their head. So injecting intelligence is really digitizing intelligence through decision intelligence, which is the ability to automate, augment, and digitize decisions. But the outcome at a high level is a decision infrastructure that contains the memory of all the decisions that you've made, that in itself allows you to learn and improve, allows you to make decisions across the value chain. The decision becomes an object, a new object in your IT infrastructure, but an object that really delivers value by being leveraged thousands of times a day or a month And looking at structured decisions, situational decisions, it doesn't matter. It all gets captured. It all gets accessible through what we call a Decision Ledger. And the next question I always ask, if you allow me for one second, is the policies. John, you wanna make a policy change in your company. It's easy for travel and expense. Try to change a policy which drives all the ways you make decisions. How do you do it? And the answer is, well, we get people together, we send a memo. Now, in a world where decisions are made by people and agents, how do you govern your policies? How do you continuously improve your policies? So ingesting intelligence is building that memory. It's having a policy layer that allows you to govern the human-based, in the loop, on the loop, out of the loop, and the agent-based decisions.
John Furrier
>> Fred, that's a great segue from kind of what's happening. We all agree it's a business model transformation enterprise-wide. Executives are in the loop. They see the shadow AI going on. They feel it. They see the value. You're kind of getting into what does it mean? I want to get into what you guys do. You mentioned memory. I think of memory, I think of when I had a PC and I had not a lot of memory, I would have to swap it out. Now we hear memory, HBM, you got the clusters, these AI factories. We have a whole series dedicated to kind of the AI factories, but I just want to get value. So what do you guys do? How does this stack evolve from a software standpoint? Because now you're taking analytics data practices that have been over 15 years in the big data world into real-time systems that are now, I will say, I'll use the word refactor only because they're now supercomputer systems. They're fast, they have a lot of horsepower, and they can do a lot of things. There's more memory on the hardware, but on the data, we're hearing things like state. We're hearing things like, cross-domain namespaces comes up now on theCUBE. So I've never had that before? So you start to see the addressing system, all these nerdy topics. How do companies figure this out? What's your approach? What's different about what you guys are doing? How do you help the customers figure out how to make it work?
John Furrier
>> Forget tech for a second. It's not about the tech. It's about companies are struggling because the business rhythm is increasing continuously. The complexity is increasing continuously. The ability to cope with this volume of decisions is not there. What we do is we provide a layer of technology. It's called a decision intelligence platform. That's what we created when we launched 9 years ago. And what it does, it digitizes the thousands of decisions that you make every month to run your business from supply chain operations, finance, revenue management. We pull the data from the transactional system, internal and external. You build an ontology that we've built for the last 9 years, but then you deploy logic. The logic can be deterministic, can be agentic, can be hybrid. You do the math, you project, you predict, you allocate, you optimize, and then you engage with the people if you need them to provide an input on how they want decisions to be made. But then the system will execute the decisions back into your core systems. So pull the data, deploy the logic, do the math, engage and execute. And that execution is what allows you to actually build that perpetual memory of the decisions that are made. And from that model, the system learns from the decisions that have been made. It doesn't lie. The memory is built with objects of decisions. That's a new dataset that's available for you to leverage, to continuously improve, and more importantly, make decisions across the value chain, which you cannot do today. After so many years of investment in data, infra, all the things that we've talked about for many years, you still realize that the way decisions are being made is in your head. Now we're pulling that back into a system that you can manage.
John Furrier
>> Yeah, and I think that's a great point because the user experience with the AI has changed. So people are expecting prompts or expecting agents to work on their behalf. And then the question becomes, okay, it's not about does AI work? people can get it. It's where to apply the intelligence to get the right decision at the right time, at the right cost. That's a data opportunity. That's a data challenge. Take us through how this works because, you're seeing a lot of on-premises activity where, the form factors of some of these systems, they're rack scale, some are super big racks and they deserve their own little big centralized data centers or a NeoCloud. But you start to see enterprise say, hey, I already got this preexisting stuff. I don't need to pave over, I can just abstract away the complexity, apply software to connect the data. So it becomes more of, okay, what do I do next? So take us through how your system works. Do I just load it in? Is it an on-prem? Is it a cloud solution? Is it single tenant, multi-tenant? Take us through this deployment component.
John Furrier
>> Cloud solution hyperscaler Kubernetes-based allows the largest companies in the world like Exxon, like Unilever to actually generate thousands of recommendations a month and execute them. So the system is fully integrated with the data models that you have. And that's— thank God companies have standardized on ERPs and data lakes. So the core data is usually there available. We've got to do a process of refining it because if you want to make decisions digitally, 100% of the information that you need for that decision to be made has to be available in a normalized data model. And as I said before, then you deploy the logic. So we have tools that allow those enterprises to not only load their own logic, but also derive logic from their core data models, which we do. And then you run it at scale. So the system is that digital brain that's deploying that intelligence to execute, to run the logic of decisions and then execute it. So that loop is what we do, right? We talk about Aera Understand, Recommend, Act, and Learn. And that's the loop that the system delivers. And it's fully integrated with not only just your IT landscape, but it's all about IT people and processes. And that's pretty much what we do.
John Furrier
>> I love that brain example because every company should have an intelligent brain and that's a moat. They have a lot of data that's locked down from an enterprise perspective. Now we're starting to see an unlocking of that data. It's proprietary data, it's workflows. Yeah, they can be standardized. Maybe I'll use a Big Frontier model on the general side, but specialized intelligence is a hot wave. And we're seeing that in a lot of the AI conversations specifically around, hey, I don't need a big model. I got the logic, I got the data. It's in a system of record. Now I just got to kind of integrate in the kinds of domain data I need.
John Furrier
>> Yeah.
John Furrier
>> To roll out. What's your take on that?
John Furrier
>> You got the data. The data is available. When we started 6, 7 years ago, conversation was like, oh, we don't have the data. We've sorted that data problem, thank God. We've got the intelligence. Those frontier models are intelligent enough to help run everything that we do. Now it's a question of how do I connect that frontier model and trust it to operate with the right guardrails, with the right policies on the right data at the right time. So the layer of decision intelligence is the one that orchestrates the link between the frontier model, so to speak, and the data and the system and the processes. Now, one thing that's important is that the reasoning, it doesn't always require a model. You can actually use deterministic logic to run decision intelligence at scale. Not every process requires tokens and dynamic reasoning. So the ability to connect the frontier models to the deterministic logic, to the data in real time— this is a technology that needs to run like an ERP. In real time at scale, because if it doesn't work, the trucks won't leave your warehouse or whatever problem you want to tackle. But it has to— you bring the full power of analytics and reasoning down to the transactional level. And that layer that sits between your LLMs and your data and your data infrastructure is what the decision intelligence model
John Furrier
>> does.It's a great market, Fred. You're in a good spot. Obviously, the infrastructure is booming. The capabilities and the speed and performance are getting there. It's going to enable and accelerate reasoning and all kinds of new decisions. Thanks for coming on this Business Transformation Edge series. Let's round this out, play us out with just some momentum for you guys. What are you seeing? Are people getting it? Give us some momentum points for your company and how things are going.
John Furrier
>> Market has completely shifted. We've been at it for 9 years prior to building this company. I built another analytics company. I've seen the moments where actually the curve bends and the model starts breaking in a good way. It's happening now. Gartner released the first Magic Quadrant for decision intelligence platforms earlier this year. And what I see, John, which is fascinating, we just had a big RFP with one of the largest CPG companies in the world, is the convergence of all the forces in the software industry. We were competing both with the OpenAIs of the world all the way down to the bottom of the stack. Everybody in the software industry is converging toward that concept of decisions. Which we've been talking about forever, but now it's becoming real. The shift from people making decisions using systems to systems making and executing decisions is— it's started now. 2026 is the moment where we see RFPs are flowing and the market getting— we're shifting from the why do I need this to how do I do it and how fast can I do it?
John Furrier
>> Yeah, I think we have a strong ramp up into 2027, starting to see the momentum. The clarity, the confidence starts to come in. Yeah, there's a little bit of doom and gloom, it's very politicized, but we're starting to see the real results coming in and the market's hot. Thanks for coming on.
John Furrier
>> Thank you so much for having me, John.
John Furrier
>> Appreciate it. I'm John Furrier, host of theCUBE. This is our Business Transformation Edge series. As you look at the transformation, this is not like any other we've seen in technology in the enterprise, specifically where the data is very valuable, not yet integrated into the big models, proprietary to the company, and you're starting to see a whole new architecture and new systems emerge and it's getting everyone's attention, certainly the C-suite, the CFO, the CHRO, the CIO, and the CEO all paying attention, have to work together. That's the focus of this series. Thanks for watching.
>> Palo Alto Studio connecting Silicon Valley and Wall Street. I'm John Furrier, co-host of theCUBE here with Gabe Olave, my co-host. Well, I'm John Furrier, host of theCUBE here in theCUBE's NYSE studio. Of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street with all the technology, AI infrastructure, AI agents, and the transformation that's going on. This is our Transformation Edge series. We talk to the leaders who are making it happen Fred Laluyaux is here, co-founder and CEO of Aera Technology, veteran in the business, seen many waves on the enterprise side, on the software side. But now that the AI wave is here, Fred, great to have you on our Transformation Edge program.
John Furrier
>> Thanks for having me, John. Great to be with you.
John Furrier
>> We could probably squeeze you into our Mixture of Experts series because you're an expert. You've been there, done that. I have to ask you first, this enterprise AI wave is looking very strong. We saw a little bit of a wait and see a couple years ago. And then coding came in. Yeah, we saw the search stuff happen on the enterprise side. We saw good AI copy, chatbots. But now with coding, that has opened up the door for agentic. And you're seeing a lot of people looking at their data, looking at what's going on. So what's your take on the enterprise AI? How would you categorize, the current market relative to one, appetite, and two, reality?
John Furrier
>> Well, you talk about coding, you talk about ChatGPT, right? It's 4 years old. If you look at the last couple of years, just to set a marker, right, the conversation has shifted from what is our strategy, what do we do with it, to today, real shift. How do we operate our business with AI at its core? Which decisions, which processes are we running with AI? What do we learn? And I think everything else follows from that change, right? So at a high level, agents now, they've got hands. They can— you mentioned coding— they can reason. 2, 3 years ago, they were advising, they were reasoning. Now they can transact, they can place orders, they can move inventory, they can release payments. Right. The governance has shifted from accuracy to authority. What is an agent permitted to do? Up to what value? And how do we reverse it? Right. Models have commoditized, right? Frontier models are now rentable by anyone at a declining cost. So no one gets really an advantage from the model itself. The differentiation has moved up the stack to, to context, to encoded business logic and to ability to execute and learn. I think scale is exposing— you talk about that wave, the excitement around the topic. It's beyond excitement, actually. It's exposing some real bottlenecks, right? So enterprise spent years modernizing their data estate, but they're still running in siloed organizations where people are still making and executing decisions. Now it's really moving toward a model where decisions are made digitally. So there's a lot of shifts that are happening. I think maybe a last one would be a shift from IT to business to executives, right? That you're now thinking about this not as an IT project, but as to the future of work, as your future of your organization design, as to the future of how your organization operates.
John Furrier
>> You know, that's, this series was motivated by initially CFOs that came to us and said, hey, we're architecting our business operating model. We have to look at the CapEx. We have to look at how we operate our business, not just an IT project, but how we're gonna drive that revenue. And what's interesting is it's evolved to the human resource officers. Now the CIO and CISO, of course, are involved. But the C-suite are actively involved in this transformation. This is unlike previous IT transformations where, hey, here's a dashboard, look what happened in your business, to real-time, real money, real operating impact. So there's an operating model impact, which will then impact the commercialization, which is essentially how they serve customers. And with agents making decisions, you've got work. So you got CFO, CHRO, you got CIO and CISO kind of all playing together. I've never seen it that way before. I've seen C-suite activity, but it's more like, okay, fund the project. How do we roll it out to the team? How do we serve customers? But never this transformative.
John Furrier
>> Yeah, look, it's kind of fascinating that you mentioned I'm a veteran. I've been in the space for close to 30 years. It's the first time that the conversation that we're having has moved to the C-level. We had last week, the executive committee of a large company coming and pitching to boards. Because we're not talking about a next wave of an IT solution that does the same thing as before, but faster or better or with better economics. We're talking about digitizing the work. And if you think about it, work is what makes a company great, it's the ability to make intelligent decisions in real time. That part, if you think about it, that's the most valuable asset that a company has. Its ability to make intelligent decisions, and yet it's the least governed one. You think about it, companies are still making decisions using people that are then getting access to a lot of data and information. But we're now able to govern that asset, and that asset is the memory of the decisions that a company makes thousands of times a month to actually run its operations, to improve its performance. With that asset, I can now learn, I can get better insight, I can get more accuracy, I can have policy management that's centralized. So this is a shift not just in technology, but is technology enabling a fundamental shift in the way the company actually operates?
John Furrier
>> Yeah, that's a great insight. I would just share that, the discussion always goes to, hey, let's automate this, let's automate that. So that's great, cool, you can automate a lot of things. However, when you say, okay, I'm automating, But the real question is, are you injecting intelligence into the organization. So then the next question is, what does that mean? So I wanna ask you, what does it mean to inject intelligence into an organization? Because if you frame it that way, it looks different. It looks like a different discussion. Share your thoughts and perspective on that one.
John Furrier
>> I always start my conversation with two questions. Do you agree that more intelligent decisions will help unlock value for your business? The answer is usually yes.
John Furrier
>> Yeah.
John Furrier
>> Second question, do you agree that there's no intelligence without memory? And the answer is usually yes. And that's the first aha moment. In your current stack, in your future roadmap, where is the memory of all the decisions that you make every day? where does it sit? And it's in their head. So injecting intelligence is really digitizing intelligence through decision intelligence, which is the ability to automate, augment, and digitize decisions. But the outcome at a high level is a decision infrastructure that contains the memory of all the decisions that you've made, that in itself allows you to learn and improve, allows you to make decisions across the value chain. The decision becomes an object, a new object in your IT infrastructure, but an object that really delivers value by being leveraged thousands of times a day or a month And looking at structured decisions, situational decisions, it doesn't matter. It all gets captured. It all gets accessible through what we call a Decision Ledger. And the next question I always ask, if you allow me for one second, is the policies. John, you wanna make a policy change in your company. It's easy for travel and expense. Try to change a policy which drives all the ways you make decisions. How do you do it? And the answer is, well, we get people together, we send a memo. Now, in a world where decisions are made by people and agents, how do you govern your policies? How do you continuously improve your policies? So ingesting intelligence is building that memory. It's having a policy layer that allows you to govern the human-based, in the loop, on the loop, out of the loop, and the agent-based decisions.
John Furrier
>> Fred, that's a great segue from kind of what's happening. We all agree it's a business model transformation enterprise-wide. Executives are in the loop. They see the shadow AI going on. They feel it. They see the value. You're kind of getting into what does it mean? I want to get into what you guys do. You mentioned memory. I think of memory, I think of when I had a PC and I had not a lot of memory, I would have to swap it out. Now we hear memory, HBM, you got the clusters, these AI factories. We have a whole series dedicated to kind of the AI factories, but I just want to get value. So what do you guys do? How does this stack evolve from a software standpoint? Because now you're taking analytics data practices that have been over 15 years in the big data world into real-time systems that are now, I will say, I'll use the word refactor only because they're now supercomputer systems. They're fast, they have a lot of horsepower, and they can do a lot of things. There's more memory on the hardware, but on the data, we're hearing things like state. We're hearing things like, cross-domain namespaces comes up now on theCUBE. So I've never had that before? So you start to see the addressing system, all these nerdy topics. How do companies figure this out? What's your approach? What's different about what you guys are doing? How do you help the customers figure out how to make it work?
John Furrier
>> Forget tech for a second. It's not about the tech. It's about companies are struggling because the business rhythm is increasing continuously. The complexity is increasing continuously. The ability to cope with this volume of decisions is not there. What we do is we provide a layer of technology. It's called a decision intelligence platform. That's what we created when we launched 9 years ago. And what it does, it digitizes the thousands of decisions that you make every month to run your business from supply chain operations, finance, revenue management. We pull the data from the transactional system, internal and external. You build an ontology that we've built for the last 9 years, but then you deploy logic. The logic can be deterministic, can be agentic, can be hybrid. You do the math, you project, you predict, you allocate, you optimize, and then you engage with the people if you need them to provide an input on how they want decisions to be made. But then the system will execute the decisions back into your core systems. So pull the data, deploy the logic, do the math, engage and execute. And that execution is what allows you to actually build that perpetual memory of the decisions that are made. And from that model, the system learns from the decisions that have been made. It doesn't lie. The memory is built with objects of decisions. That's a new dataset that's available for you to leverage, to continuously improve, and more importantly, make decisions across the value chain, which you cannot do today. After so many years of investment in data, infra, all the things that we've talked about for many years, you still realize that the way decisions are being made is in your head. Now we're pulling that back into a system that you can manage.
John Furrier
>> Yeah, and I think that's a great point because the user experience with the AI has changed. So people are expecting prompts or expecting agents to work on their behalf. And then the question becomes, okay, it's not about does AI work? people can get it. It's where to apply the intelligence to get the right decision at the right time, at the right cost. That's a data opportunity. That's a data challenge. Take us through how this works because, you're seeing a lot of on-premises activity where, the form factors of some of these systems, they're rack scale, some are super big racks and they deserve their own little big centralized data centers or a NeoCloud. But you start to see enterprise say, hey, I already got this preexisting stuff. I don't need to pave over, I can just abstract away the complexity, apply software to connect the data. So it becomes more of, okay, what do I do next? So take us through how your system works. Do I just load it in? Is it an on-prem? Is it a cloud solution? Is it single tenant, multi-tenant? Take us through this deployment component.
John Furrier
>> Cloud solution hyperscaler Kubernetes-based allows the largest companies in the world like Exxon, like Unilever to actually generate thousands of recommendations a month and execute them. So the system is fully integrated with the data models that you have. And that's— thank God companies have standardized on ERPs and data lakes. So the core data is usually there available. We've got to do a process of refining it because if you want to make decisions digitally, 100% of the information that you need for that decision to be made has to be available in a normalized data model. And as I said before, then you deploy the logic. So we have tools that allow those enterprises to not only load their own logic, but also derive logic from their core data models, which we do. And then you run it at scale. So the system is that digital brain that's deploying that intelligence to execute, to run the logic of decisions and then execute it. So that loop is what we do, right? We talk about Aera Understand, Recommend, Act, and Learn. And that's the loop that the system delivers. And it's fully integrated with not only just your IT landscape, but it's all about IT people and processes. And that's pretty much what we do.
John Furrier
>> I love that brain example because every company should have an intelligent brain and that's a moat. They have a lot of data that's locked down from an enterprise perspective. Now we're starting to see an unlocking of that data. It's proprietary data, it's workflows. Yeah, they can be standardized. Maybe I'll use a Big Frontier model on the general side, but specialized intelligence is a hot wave. And we're seeing that in a lot of the AI conversations specifically around, hey, I don't need a big model. I got the logic, I got the data. It's in a system of record. Now I just got to kind of integrate in the kinds of domain data I need.
John Furrier
>> Yeah.
John Furrier
>> To roll out. What's your take on that?
John Furrier
>> You got the data. The data is available. When we started 6, 7 years ago, conversation was like, oh, we don't have the data. We've sorted that data problem, thank God. We've got the intelligence. Those frontier models are intelligent enough to help run everything that we do. Now it's a question of how do I connect that frontier model and trust it to operate with the right guardrails, with the right policies on the right data at the right time. So the layer of decision intelligence is the one that orchestrates the link between the frontier model, so to speak, and the data and the system and the processes. Now, one thing that's important is that the reasoning, it doesn't always require a model. You can actually use deterministic logic to run decision intelligence at scale. Not every process requires tokens and dynamic reasoning. So the ability to connect the frontier models to the deterministic logic, to the data in real time— this is a technology that needs to run like an ERP. In real time at scale, because if it doesn't work, the trucks won't leave your warehouse or whatever problem you want to tackle. But it has to— you bring the full power of analytics and reasoning down to the transactional level. And that layer that sits between your LLMs and your data and your data infrastructure is what the decision intelligence model
John Furrier
>> does.It's a great market, Fred. You're in a good spot. Obviously, the infrastructure is booming. The capabilities and the speed and performance are getting there. It's going to enable and accelerate reasoning and all kinds of new decisions. Thanks for coming on this Business Transformation Edge series. Let's round this out, play us out with just some momentum for you guys. What are you seeing? Are people getting it? Give us some momentum points for your company and how things are going.
John Furrier
>> Market has completely shifted. We've been at it for 9 years prior to building this company. I built another analytics company. I've seen the moments where actually the curve bends and the model starts breaking in a good way. It's happening now. Gartner released the first Magic Quadrant for decision intelligence platforms earlier this year. And what I see, John, which is fascinating, we just had a big RFP with one of the largest CPG companies in the world, is the convergence of all the forces in the software industry. We were competing both with the OpenAIs of the world all the way down to the bottom of the stack. Everybody in the software industry is converging toward that concept of decisions. Which we've been talking about forever, but now it's becoming real. The shift from people making decisions using systems to systems making and executing decisions is— it's started now. 2026 is the moment where we see RFPs are flowing and the market getting— we're shifting from the why do I need this to how do I do it and how fast can I do it?
John Furrier
>> Yeah, I think we have a strong ramp up into 2027, starting to see the momentum. The clarity, the confidence starts to come in. Yeah, there's a little bit of doom and gloom, it's very politicized, but we're starting to see the real results coming in and the market's hot. Thanks for coming on.
John Furrier
>> Thank you so much for having me, John.
John Furrier
>> Appreciate it. I'm John Furrier, host of theCUBE. This is our Business Transformation Edge series. As you look at the transformation, this is not like any other we've seen in technology in the enterprise, specifically where the data is very valuable, not yet integrated into the big models, proprietary to the company, and you're starting to see a whole new architecture and new systems emerge and it's getting everyone's attention, certainly the C-suite, the CFO, the CHRO, the CIO, and the CEO all paying attention, have to work together. That's the focus of this series. Thanks for watching.
John Furrier