How Enterprises Lose Control as AI Scales

Speaker A: Foreign.

Speaker B: You're tuned in to IOT and AI Leaders your go to show for insights, predictions and big Ideas on how IoT is reshaping the world of AI.

Speaker A: Hi, my name is Nico and in this Week's episode of IoT and AI leaders, uh, we're talking to Pro Arc. They are a very interesting company. Offices in the U.S. uh, based out of Atlanta, um, ah, but they've also got offices in India. In fact they've even got a presence here in the uk. And we're talking about AI not just the normal way, but how it is being adopted so quickly and it is so cheap and it's so available and the marketing hype around AI is so big that actually we're creating chaos. Um, and this is a company that, um, you'll hear on the recording. Uh, you could argue that their, uh, motto is slow down and define your processes before you get started. Don't do what you're about to do. There will be chaos. And we talk about, uh, what that means in terms of business processes and workflow definition and security. We talk about what that means in terms of IoT devices and how that's going to potentially make the number of data sources 50 times bigger. And we talk about it in terms of the responsibilities at the board level to understand this because of the chaos, reputational damage, security issues that could come up by just encouraging people to adopt AI because that's seen as being the right thing to do because everyone's talking about it. It's a very interesting podcast with two very knowledgeable people, um, the CEO Santosh and Jim, who's the director of Cloud Strategy and AI enablement. And these guys are working with clients all the time and across different industries. And they even, they talk about case studies as well that they found and the lessons that they've learned. So with that, I'm going to hand you over to the podcast recording with Santosh and Jim and I hope you enjoy it. Here we go. So, Santosh and Jim, welcome to the IoT and AI leaders podcast. Great to have you.

Speaker C: Thank you so much.

Speaker D: Thank you so much. It's great to be here.

Speaker A: Great. Uh, this is, uh, we've only done a few where we've had a double act at the other end. So this is more like the chat show format, um, late night TV or something like that. So I'm sure this will be very, uh, interactive. As I said in my intro, Satosh is the CEO of ProArc and Jim is the director of cloud strategy and AI enablement. And that's what we're going to be talking about is the AI enablement and how your company, what your company does and how it's changed and how it will change as IoT gets sort of blended into AI and then a prediction section which we always finish on. Predictions for the future and what will work and what might surprise us and we need to watch out for. So with that, uh, let's uh, get going. Maybe I can just start off with you, Santosh and you Jim, just to give a little bit of background on the company when it started and um, uh, how you would describe what you do today to the market.

Speaker D: Yeah, I'll kick it off. Well, we are celebrating our 20th anniversary this year. So it's been a great journey and we're actually excited about our future. Historically, we started off as a product engineering, uh, company. Later on built a strong partnership with Microsoft to build cloud, data and security practices. Today, the best way I would describe ourselves is we help our customers operationalize AI. I know that's a broad word, but that's really our core value proposition. We're at the intersection of cloud, data, security and AI and what we bring to the table is a full stack. And these conversations are collapsing fast, especially in highly regulated, complex environments in energy, healthcare and manufacturing.

Speaker A: Before I ask Jim what he does, maybe I can just jump in on that last point for the listeners. Why are they collapsing particularly fast in regulated industries? Why is that?

Speaker D: So historically, conversations, you know, the security or sometimes even data ah, where either with the IT folks or with the security teams and maybe there is a data team involved in there. Right now when we look at majority of our conversations, they are with the business folks. Business functions are beginning to realize that they can now take advantage of AI. And as a result of this conversation, what's happening is of course to operationalize AI, I mean to build a chatbot is one thing, to deploy a copilot is one thing. But to uh, actually operationalize AI involves putting embedding AI into your workflows and redefining them. And now all of a sudden all the conversations are collapsing where you have all the key stakeholders coming together and saying, how do we unblock? And the blockers typically are in the operating M discipline, which boils down to their data security, quality, governance and in general the security posture. We're seeing a lot of these conversations collapse very fast, whereas before they were siloed focused on one function.

Speaker A: And of course those issues to do with security and governance are very prominent in regulated industries. So thanks for that. And Then Jim, your role at the company and what you focus on.

Speaker C: Yeah, absolutely. I've been with proarch um, over nine years now, uh, mostly in consulting capacities. My current role really is around helping clients modernize their environments, moving to more cloud first, uh, solutions, uh, and then most recently is around AI enablement and adoption. And typically when I get a client they're at the point where they don't even know the right questions to ask necessarily. They know that they're supposed to be doing something. They may be getting pressure from m, their executive team or their board. Um, you know I saw a podcast the other day or read an article, it seems that we should be doing something in this space. So I really help them set the foundation and um, the building blocks to uh, ensure that they are successful. So that includes things like establishing usage policies and a center of excellence, champion programs, getting uh, them familiar with frameworks to establish use case development, but also having a clear and honest discussion around data risk and coming in and actually helping them review their current environment to expose where some of those risks might lie as they begin to think about piloting some of these tools initially so that when they want to go to scale they're doing it safely and securely.

Speaker A: You know it's interesting you guys say uh, that um, I always try and relate it to recent events and uh, your point you made about they don't even know what questions to ask I think is a really good one. I was at a dinner uh, last night in Central London roundtable and people were talking and you know when you are involved in AI and you are at the sharp end or near the sharp end, then sometimes it's easy to forget that an awful lot of people actually are uh, don't uh, know what questions to ask as you said. And one guy around the table said, you know, we're in the Donald Rumsfeld era of AI. There's no, no, no, there are no known knowns. Some people know what they don't know, but most people don't know what they don't know. And I think that's, and as you say a much more succinct way of describing that is they don't even know what questions to ask which leads into. I guess the question for you is that has your company sort of given that and the pace of change is. And look where you came from 20 years into today. Does that mean that are you a solutions uh, company, you talked about Microsoft, you're a consulting company. Are you becoming a strategy front end company and then consulting and then, yeah, what are you.

Speaker C: It's a little bit of all the above. Um, you know, we do still, we still have managed service practices both, uh, in the security and support space. The company that I belong to that uh, got acquired in 2020, was very big into the managed services and consulting side. Um, but I would say as we think about the next three to five years, we really want to kind of reposition ourselves as a company that um, is building systems and our own intellectual property around AI solutions, uh, and all of the other things that go along with that as far as services to support organizations in data governance and security. And those are all new realms to be explored. And so I believe, uh, ahead of the curve, which is good. That doesn't mean that there aren't others who are also thinking the same mindset as we are. Um, but we're looking to kind of transform the way our business is seen to be more A.I. focused. Um, and also realizing where is the revenue streams as well. Right. Where are the next, you know, the, the revenue streams that we want to chase?

Speaker A: So both for you, both for you and I guess for your clients. Because the ROI AI, there's a lot of talk about, well, you don't have to hire as many people or potentially even reduce people, but you can get more work done. But, but at the end of the day, if, from what I understand, you're looking to, yes, you give them advice, but you're looking to sort of get into work strategically position yourself as getting into their workflows. So yeah, absolutely, help them transform. And that's very different to people just using ChatGPT right at their desk. Right. That must be a conversation you regularly having with people.

Speaker C: Yeah. And really the focus becomes more about outcomes.

Speaker B: Right.

Speaker C: And looking at, you know, why are we bringing broad in and what are we going to help them with and how are we going to demonstrate that we've actually improved or, uh, help them in some, some material way, hopefully in some sort of quantitative way. Um, although, you know, there's definitely qualitative ways to, to assist. But uh, that's really come become the expectation, I believe in this particular business, uh, environment now, uh, especially when, you know, if you're using AI to develop solutions, it's more of, well, you're not just developing the solution for me, you're developing outcomes that I want to actually, you can, you can actually guarantee that I'm going to be able to recognize. Uh, and that's very different. And then also trying to, you know, price your, your products and solutions around Being able to deliver those outcomes.

Speaker D: I think if I can add to what Jim said, Nick. So for us, our goal has been to help our customers operationalize AI. And to do that effectively we have to consult, we have to strategize, we need to build. We're not there to just tell them, hey, here is what you need to do. We're there to actually execute. So we need to bring our own solutions and ip. And at the end of the day everybody asks the question, oh, what did this do? For me to be able to answer the CFOs or CEOs and the boards, we have to show the ROI and that's auditability. Delivering or measurable auditable outcomes is becoming super important for us. So essentially the worlds are also converging the consulting strategy and implementation, again thanks to AI, uh, and that's where we are at the intersection of all of that right now.

Speaker A: Has that changed? Santosh, when I've spoken to some companies that, as you were saying that I was thinking of that do similar things. As you say, there's a lot of companies doing are in this space just because of the opportunity being raised by AI. But one of the questions is what's the trigger where you get called in, changed? In other words, is it that people say, I've got a blank bit of paper, I need some advice before I get started. Or if you're going to climb Everest, you'd probably be a good idea to go with a guide because you know you'll screw up. If AI looks really simple, it's free, and uh, a lot of people seem to be using it, probably a chatbot, not an agent, then you may say, oh, I don't need advice, but the board, then the board asks the question which is what are we doing with an AI strategy? And um, then suddenly people say, well I'm not sure. And they ask a few more questions about security, about compliance, about governance and about risks. And then it can be, well, we need to bring in some people to help. So to what extent is it individuals or board level, you know, what's the introductions or uh, opportunities that you see and what's the trigger and is it changing? Is it a subject that's being embraced at the board level?

Speaker D: Now I'll start and Jim, you can add to it. So absolutely. So the conversations today as uh, companies move away from or move from toilets, whether they are successful or unsuccessful, to scalability aimed to um, embed AI into their current workflows, the CEOs and boards are beginning to ask the real Question. As companies are beginning to allocate budgets, especially for AI driven transformation, these questions are becoming not just relevant, but critical. I think there is a lot of excitement and um, there is a lot of activity. But when it comes to operationalizing AI and doing it the right way, right now the boards and CEOs and CFOs struggle because even they, as Jim said earlier, they don't know what questions to ask. Um, in fact we strategize with them for them to help them come up with a framework on how they should approach a strategy, what's important for them, what they should look for. Um, AI should not change the accountability. AI of course will magnify the chaos, but accountability becomes even more important. Um, and one of the key things that we do or teams do is we build that truth layer, we build that operating discipline. That's where most failures of AI occur is because of weak operating model that goes into governance, that goes into security, that goes into transparency and so on. But that's what our teams help. And of course ultimately always try to draw that. You know, always have your North Star. Why are we doing this? Are we going to get to that outcome? And if so, how can we prove that we have gotten to that outcome, uh, which is operational efficiency or cost savings or new revenue streams, whatever they are, that CFOs are very interested in and are we costing everything the right way? So yes, these are. Even at the top level, especially at the top level there is more confusion, lack of clarity on how do we approach AI, how do we create a strategy that takes into account all the factors, how do we allocate the budget, how do we really measure the roi?

Speaker A: And Jim, you want to add to that?

Speaker C: Yeah, I would say, um, that's right on point. And um, you know, part of what we see a lot of is there hasn't been a tremendous amount of governance within organizations, especially the small to medium sized businesses. And even at the enterprise level I would argue there hasn't been this type of collaboration, um, at a business level, um, from a governance perspective either, because I think AI is now exposing that there needs to be multiple teams involved and working together. And one uh, of the biggest things, uh, which gets exposed very early on are undefined or poorly defined process, uh, and ownership and accountability. And that can really uh, have a serious impact on the ability to realize some of the potential of AI. But the upside is uh, that if you can recognize those shortcomings and address them, you're going to come out much better on the other side. Of this, um, you're going to have more well defined, better working teams, uh, with automation and AI layered onto that.

Speaker A: Again, there's always, uh, echoes of the past in it. Um, we tend to go around the same loops, uh, every few years, but we're using different technology and different terms. And, and certainly, uh, I was thinking, as you were saying that when we were, when we went from um, MRP Manufacturing requirements planning to erp, people were saying the same thing. You know, it is, if you don't have the processes defined, you're just going to get basically crap quicker. Um, uh, and a lot of people had to press the reset and say, first of all, we need to map out the processes. And then of course, and I've talked about this before on the pod, companies like SAP in particular, Oracle as well, and others start saying, started saying, you know, it's all about the object layer, the workflow, uh, the consistency, the governance, the fact that whoever uses this system, it'll be auditable, it'll be traceable and um, you know, you're not going to have arguments around the, around the management table where people are saying, my system says this and my system says that. And it seems that with AI, the potential for that chaos with everyone experimenting and the fact that they don't need IT approval to use, to use AI tools, I mean you don't, I mean it's, it's free and even if, even if you want, uh, the version with more memory or recall or, or advanced research facilities, it's maybe 20 bucks a month. I mean it's not, it's not an IT decision. It's sort of like when cloud first came and a lot of people were breaking governance by storing data outside the organization. Is that analogy correct? Um, and therefore is the implication of that, that the potential for getting inconsistent processes and automating point solutions rather than looking at workflows and horizontal processes, is the potential for that chaos getting bigger in your view?

Speaker C: I think the potential is absolutely getting bigger. And this is why we're very clear when we work with our customers, uh, that although these things can be done this way, right, people are able to go out and use some of these free tools. Uh, and then now we're talking about the security portions of our business. You have to give visibility into what folks are doing and you have to clearly define through a good policy what is and what is not permitted, establish the guardrails and guidelines and also layer into your security stack, uh, tools that can place controls or you've got to claw back Some of this, um, some of the chaos and um, apply some of the tooling that you have in order to understand what is being done. Even I would say, and you do have to go through and set up a process through your governance counselor committee, how do something get approved. Right. And at the same time you don't want to create a lot of red tape and bureaucracy and you do want to still encourage innovation. So if you have that visibility and you can be assured that you understand how these tools are being used, you can confidently innovate at a pretty rapid cycle. But without that, you really need to step back and say, okay, these are the things we're missing and we have to not necessarily put the lid back on it, but rein it in and make sure that we can, we can absolutely control where our data is going, you know, where our data flows. And um, but again it's, it's all the combination of all those things that you have to have in place in order to go forward with confidence.

Speaker D: Yeah, I'd say that, you know, most companies today are building unstable foundations and lot of assumptions. Um, as we've said before, you can't really bolt an AI onto the current processes and systems. They are layering AI on top of fragmented data, legacy processes, disconnected systems, and really with unclear governance. So they have to shift from. The question is, it's not about how do I deploy AI. The first question should be how do I prepare my environment to support AI? Think that shift has to happen. It's, we have to go from the tool first to foundation first approach. That's, that's, that's what's going to matter.

Speaker A: I can imagine that you guys, uh, this is my assumption on your business. I have a services background. I run a large part of Cisco Services business globally. But, but we qualified if when we had to deliver tough messages around the need to slow down, to speed up. You need to do this first. And people have said no, no, no, I, I, I just want to implement this. Which I can imagine is conversations that you get into. We had to have the discipline from a sales point of view to say well you can do that but, but not with us. We'll, we'll walk away because it, it'll fail and we don't want to be associated with that. And our advice is get this bit sorted out first. Uh, which is a consulting led, we call it services LED is what we called it. And you have to do that and you have to get senior management buy in and then we'll do the implementation. But a lot of people just wanted to get going with the implementation. And I imagine that's pretty, pretty similar in. In. I can see you smiling. That's something that, uh, I suspect you come across fairly regularly.

Speaker C: Yeah.

Speaker D: So everybody looks at AI and of course there's a lot of excitement, possibility, but the reality is grounded. Reality is complex. And everybody also thinks intelligence is the hard part. It's really not. The hard part is the trust. Hard part is asking really good questions like, can I trust the data? Can I trust who is accessing it? Can I trust the output? Can I explain how a decision was made? We've had time and again our own clients, uh, you know, they think, hey, we could do this on our own. I think our team can do this few months and, uh, they call us and say, look, we need help. And the help is really not on technology side. Help is really on. On creating the truth layer. We need your help to create that trust layer.

Speaker A: I wanted to pick up on that. Um, I made a note to come back to that. You said that Santosh earlier a few minutes ago, the trust layer, the truth layer, um, uh, you've described it in various forms. Is that a sort of process governance policy that a company needs to implement or is it also enabled by technology? Maybe you can just expand on that a little bit because that's clearly extremely important as part of this.

Speaker D: Yeah. In fact, this is the work that Jim does a lot of. To put it simply, if you cannot. It's combination of both, by the way. Combination of actually not both. All the things that you said. Um, the trust or truth layer is where you have explainability, observability and transparency and auditability. That becomes super important. The second one is, hey, how can I reduce the risk and how can I really protect myself? That's where the technology comes into play again. There are really good tools now these days that even, you know, that we use all the time to be able to reactively and proactively help with your compliance and security. Because AI brings in whole new level of security considerations that did not even exist before. And that landscape is only evolving.

Speaker A: Um, sorry, excuse me for jumping in previous podcasts just for context for listeners, they may have listened to the previous episode, um, but it was, it was all on security. And, and, and we're going to come on to security, go deeper on in this podcast. But you're absolutely right, it's not just security of your data. It's the fact that when we talk about IoT devices are coming in where you're not in control. Of the security policy. And on the previous podcast, we went into the fact that when people buy software, they're now buying software which contains agents. And how do you actually trust, uh, measure, uh, get auditability, governance, accountability, all those things. When you're buying agents off the shelf, you think about that and you ask people that question and they will. I don't know. I mean, people are making individual decisions, um, software with agents inside, not knowing what the security policy is and the governance policy is of the agent. I mean, it's a huge growing issue.

Speaker D: Yes, it is. That's one of the first questions we ask is they have all of these ERP systems in addition to AI systems. And every ERP these days says, hey, I have a set of agents. Which is great again. But the minute you enable AI in any shape or fashion, you have to understand that you're introducing risk and that risk amplifies when a machine fails. In the previous context or the human error, it was a manageable risk for the most part. It could be catastrophic, but it was manageable. But in the future, that will not be the case. And we're already evidencing that with the recent examples. Jin, I'm sure you can add more color to that.

Speaker C: Yeah, no, absolutely. The more we hand over to agents and AI to do work, the more safety measures we have to have in place to ensure that what it's doing is what we want it to do and the outcomes are what we expect it to have. And also, um, it's not all that dissimilar from managing individuals and identities, making sure that those agents can only perform the work that they're intended to perform. Uh, and that it's at the end of the day, especially when it's decisions that can have consequential impact, that there's a human at the end of that decision process to be able to evaluate whether or not this is something we're

Speaker A: going to continue judgment, uh, which is the, the hope as to why all the jobs won't go away. Because you do need a human at the end of the process.

Speaker C: Right.

Speaker A: To make the call. Yeah.

Speaker C: And we have quite a few, um, clients in the medical space. And one of the things that, you know, is becoming very apparent as far as responsible and ethical AI for them is there should never be any decisions made by AI that will determine the outcome of services. For instance, for a patient or member. Right. We should never be letting those decisions be in the hands of artificial intelligence. They can certainly give input and they can help with the decision making process. But ultimately those types of decisions are the ones that still need to be human directed. And there's, you know, plenty of other examples across other industries. So that's something that needs to be factored in when we start to think, well, yeah, we can do that, but should we?

Speaker B: Right.

Speaker C: Just because, um, it can do these things on our behalf.

Speaker A: Yeah, right.

Speaker C: We, until, until we've, you know, and maybe in 10 years, that'll sound quaint because the technology's evolved, you know, tremendously. But at this point in time, you know, we've all seen, uh, AI do tremendous things. We've also seen it fail miserably and failed spectacularly. And so, you know, you don't want to be on that, that other end of that story.

Speaker A: So especially if it's a regulated industry, as you said, uh, maybe just a little double click on that. Jim, you certainly, uh. We've started to see a lot of people saying, oh, well, you know, coding is dead. Claude, uh, Claude, code is, you know, you can do 10 times quicker, 10 times cheaper, 10 times whatever. It doesn't sleep, it doesn't get paid, it doesn't take vacation, you know.

Speaker C: Right.

Speaker A: So, um. But there's no. Unless you have. And you can, you know, unless you have governance, uh, which I think, uh, I think I'm using the right term, scaffolding.

Speaker B: But.

Speaker A: But unless you have governance around it, then you actually create something really dangerous much, much quicker.

Speaker C: Oh, yeah.

Speaker A: And if you get an audit, which your decisions have to be auditable, particularly, uh, if they have consequences, like in medical health care, it got a very severe consequence. But most industries are held to account for like data protection, personal data protection. Even if it's just that what you're doing with storing and using people's data, it seems like now that people have got tools in their hands that they've not been trained to use and they're really cheap and they can impress their boss by saying, look what I built. Uh, let's implement it. Uh, that must be a big. Well, both an area of opportunity for you as a company, but a big issue once you get in there. You must be finding stuff all over the place, frankly.

Speaker C: Yeah. So an example of development work, I just wrote an article on LinkedIn, I think it got released today, possibly about how AI is really good at. Well, uh, it's kind of a rookie at writing code. It'll, uh, get the job done. It'll do exactly what you wanted it to. But don't look underneath the hood and figure out how it got from point A to Point B, because there may be all kinds of security flaws or architectural design issues that don't allow it to scale. Um, but at the same time we can use. AI is really good at finding bugs. So it's kind of, it's very ironic that it, that it, um, in its mission to accomplish the work, it takes a lot of shortcuts. But then when you turn that lens around and tell it to go examine its own work, it can find all the mistakes it's make, it's making. So, uh, that's where some of the concern lies. Yes, we were able to build something that accomplished the task, but was it done well? Was it done securely? And, and how do we make sure that we build things into that, uh, to follow our frameworks, to make sure that it's doing in a way that is safe and effective.

Speaker B: Enjoying this episode of IoT and AI leaders hit the follow button to get notified when new episodes come out. Now let's get back into the show foreign.

Speaker A: Let's pivot if we can, and uh, get into IoT. And the theme of this podcast series, as regular listeners will know, certainly since the beginning of this year, has been how IoT and AI are coming together. The basic premise being, in case anyone's new, that, um, there's 50 times more data that's going to be generated at the edge by things that than is currently has been used to train the models in terms of the data, uh, the sound, the video. And I always said it's, yeah, the industry says train the models. The people who own the content say stolen data to feed the models. But either way, uh, that's flattening out in terms of, uh, we're not creating content as fast as the models can absorb it. But now you've got this 50 times, at least more data being created by things at the edge. And so the opportunity is clearly huge. I mean, the idea of almost creating like a digital twin, I mean you guys do a lot of work with Microsoft, you know, a digital twin of, of something that's really complex, like a jet fighter or a manufacturing process or product or whatever. And, and to say I'm going to collect data from all these things and I'm going to use AI as a layer, uh, notwithstanding the problems that we just talked about. But AI is a sort of overall layer to produce this enterprise dashboard to show what's exactly going on. And so I can turn all my processes from reactive to proactive to preemptive, which is it didn't break because I made an adjustment before it Broke. It's very attractive for companies, but those of us who are in that part of the business know it's extremely difficult to, to do that. So the question is, are you starting to see people ask about that, uh, maybe make similar mistakes of, of getting started and screwing things up? Is that becoming a, a growing part of the work that you do?

Speaker C: Dante, you want to take that one? Yeah.

Speaker D: I'll give you an example. Um, yes, it is possible to preemptive, prescriptive are, uh, proactive and become prescriptive and even become reflective. But I think at the moment there is still a lot of ROI to be gained just to be able to do proper predictive analysis.

Speaker A: Yes.

Speaker D: I'll give you where we're having really great success. We have, uh, one of our customer segmentation or segment is power. Power plants. We help a number of power plants, whether they are renewable, fossil, hydro gas, natural gas in the US here. So what we're beginning to do there with some of our customers is build what we're calling as an equipment AI hub. Essentially we take a type of equipment. We of course go through the process of, uh, you know, everything we talked about from creating the truth layer, and after that we get to a point where we're looking at, uh, high risk, high value use cases. Um, and let's say we identify certain equipment that falls into that category. We're now able to do predictive analysis much more accurately, um, and also connect to the IT side of the systems. So we're able to say, hey, look, this particular equipment based on all the data that we have gathered so far and looking at their optimal performance, their manufacturing curves, the drift potential, what should have happened here. But this is what we're noticing. And therefore these are the possible reasons and these are the possible recommended actions based on all the access that we have with the trained data, and then also take it to one extent and say, hey, by the way, one of the recommended actions could be you're better off replacing this particular equipment as opposed to scheduling maintenance window. And these are the reasons why. These are the costs. But in order to do all of that, we're having to connect systems across the pond, meaning it and ot to be able to get all the data and also the trained data. Of course, as we continue to say, human in the loop here is very important. But detecting these anomalies early on could save like six figures easily or more easily.

Speaker A: Yeah, millions. Um, we had a, a guy. Well, uh, it was a guy in, um, in the oil platform business. He was talking about, I Don't know how many oil platforms there are, but give you an example. He said that something like 8 million things get measured on an oil platform at sea, which I found completely mind blowing. But the cost and they can't, they can't gather data from 8 million things. They're on a very long journey. But if you think about it, when something fails, if they have to turn the well off, uh, in the most extreme example, and they need a part, they didn't do what you just said, they didn't predict that it was behaving and therefore there's a pretty good chance that within X hours or days it's going to fail. And it's critical in their case. They, they prop. There's not enough space to have spares for a second rig on the rig. So it's a helicopter. It's a helicopter. Which just means it's like two, three days, um, two, three days with, with no oil flow. I mean six, seven, could be eight million. Eight million. Yeah. And so that is, I think you're right, I think that is absolutely the low hanging fruit. And there's certain industries where like energy, like you say, uh, oil, there's, I mean arguably most industries have their own version of why that is the biggest low hanging fruit to go after first.

Speaker D: They do, they do. Um, and our goal there is we're beginning to start with certain equipment. Again we're choosing the high value use cases. Uh, and, but our goal here is to cover all of the equipment that goes into production of uh, electricity and then create an ontology on top of it. At that point it will be easy for us to build operating agents or data agents. The good news is that we partner with Microsoft of course, but good news is that technology is now available to do this. But again at the moment our journey is we're beginning to go equipment after equipment, uh, and beginning to really understand the data, understand the workflows, understand the nuances to say how do we really predict accurately.

Speaker A: Phrase popped into my mind. Uh, as you said that is the ontology. I mean that's a very um, ambitious ambition. Uh, it's almost like mapping the physical genome, not the human genome but the know if you, yeah, millions and millions and they all use different interfaces and the data is all can be interpreted a different way. I mean it's, people think connectivity, just getting the data is, is simple but we uh, know from being an IoT company that just uh, connectivity is one of the most complex things because all the devices are made differently. There's like in the cell, in the cell phone area, there's sort of six or seven cell phones that make up something like you know, 97, 98 of all the cell phones and you know, how they behave and it's all documented and whatever but every product, every piece of equipment, every sensor is kind of different. And the, and the company didn't make it, they bought it. So it's all using different standards. And ah, so to map that, that Jim, I suspect that as an ambition to map the ontology, that's going to keep you busy, isn't it?

Speaker C: Yeah, for sure. But I do believe that you're going to get the maximum roi. Those are the types of um, activities that these organizations will have to undergo. And I think it extends beyond just the industry types of organizations. Manufacturing, um, power generation, utilities. And we go back and have that conversation about companies that are in professional services, medical, legal, whatever. The opportunities are too big not to really kind of clearly define how work gets done in this place. Right. And ah, for so many, for so many organizations, the organization actually survives and somehow even uh, thrives to a certain extent without a full recognition and a full understanding of the function of the business and how things are.

Speaker A: Absolutely. Most people, no idea, uh, they know a bit of it but as things speed up I always like to reference it back to previous podcast guests. We had a guy, I'm from Turkey actually, who was uh, trying to do it for a hospital. You talk about healthcare. Well, he was doing for a series of clinics and hospitals and healthcare groups. And uh, basically he said they don't know what their processes are. If you say to them what is the process for coming in for a hip replacement? Nobody knows. Nobody. The end to end process.

Speaker C: Well they know their piece.

Speaker A: Yeah, yeah, they know a little piece but they don't know uh, uh, what happens anyway. And most of the delays are to do with inefficiency between parts of the silo. So he basically was put a little sensor, a Bluetooth sensor on every piece of equipment. He put a bracelet on uh, every patient, uh, when they came into the hospital they got a little bracelet so they could track them. And he put a tracker on every physician and mhm, uh, whatever, uh, and so then you then had this map of people, uh, uh, staff and physical assets and just collecting that data and putting it into this high level would actually give you your first pass at what your end to end processes are. And you could see that, that your biggest issue is the fact that he said the example was once people have been diagnosed and the Doctor says, well, this is the medication you need. You then go back, sit back down again, and you can wait an hour until your medication is available, just because there's a very inefficient process from that department to the dispensary, uh, or whatever. So just having a visible dashboard can make a massive change. And that's exactly what you're doing.

Speaker C: Yeah, yeah, 100%. Because again, if you're not sending the upstream and downstream effects, what you're trying to solve for you may, um, create some unintended consequences. Uh, we even see this in our own business, right? Someone makes change to a system they didn't know another group depended on a specific field within a data set, and now they changed it for their benefit and now breaks it for somebody else.

Speaker D: Right?

Speaker C: Yep.

Speaker A: And AI enabled. Listen, we're getting towards the end of our time, so I want to finish, if I can, for both of you, with a question. Uh, we always do this on the pod. It's sort of called the, uh, look forward section. So I'm gonna, uh, I don't mind. Maybe one of you wants to take the lead or you both chime in. But essentially the question is, let's imagine it's two years from now. Uh, everything that we talked about is happening, but it's also accelerating. And who knows, you know, they say AI is getting better at 4x per year, not 40% like Moore's Law. So, you know, we could have 16. Potentially some areas could be 16 times more effective than they are today. So what are the. I think we've seen the opportunities like mapping out the ontology, connecting everything, identifying the processes, optimizing the processes and the workflows. What are the things that people should be really concerned about that we. What are your concerns that are big industry issues that we haven't yet solved in this space? Maybe you could each have a crack at that.

Speaker C: Sure. Yeah. I think the, um, uh, if we go back to the initial, this conversation that kind of kicked us all off, there has to be a reckoning in most organizations about how they're handling their data and the quality of that data and what they want out of the data as far as understanding the impact it has to their business, um, so that they can apply this technology appropriately. And that includes looking at their security stack. I would almost guarantee the vast majority of organizations out there today do not have tooling in place that is ready to deal with managing and controlling, putting controls in place for AI systems. And so that's not something we can hide and ignore. From anymore. Right. It needs to be top of mind. Let's assume that they don't even go down a path of implementing AI, which would be almost ridiculous. But, um, there's still benefit from going through that exercise. That exercise. And, and I think the organizations that can get there quicker are going to be able to take advantage faster and see a competitive advantage over their peers. Those that continue to struggle or continue to drag their feet because it's just not fun or not exciting or it's the dirty work or we're going to have to hire additional resources to make that happen. Those are the ones that are going to start to be left behind.

Speaker A: And I guess Santosh, that would mean at the board level it's not just to get their left behind. Something could go really wrong and you've got reputational damage and brand damage and it's really, it's potentially really hard to recover from those things if they don't do what Jim. Jim says up front. If they don't pause and focus on this up front. Right.

Speaker D: In the board level, I would say three risks, Nick. One is obviously the security and compliance risk. AI is continuing to amplify that risk and it will explode. Um, as my really concern what keeps me up at night. The second one is at the board level, the business model risk. Because AI is disrupting so many business models, commercialization models, uh, that everyone will have to reimagine the value that they're adding to their own customers and come up with the new way of managing the customers, including commercials. I think there will be, I mean we've seen that with SaaS companies now, uh, I mean almost consulting companies. Many of these models, legacy models historically were very strong, are completely being disrupted right now. The third equation is human capital. I think that's probably one of the big disruptions that boards will have to think about is how will the team look like in a company two years from now and how will an AI native? Almost everybody is now there's a demand and there's a requirement for every individual, whether they're technical or not, to kind of become a workflow engineer.

Speaker A: But if I was to take one takeaway from it, apart from the Donald Rumsfeld, uh, AI quote, I think it's, you've got to slow down before you get started, um, and think uh, about it and take advice, you know, to do this on your own without, without working with a partner, uh, such as Pro Arc, uh, as we say over here, a brave choice, which, which is an English way of saying that's a really dumb thing to do. Yeah. Working with a partner is. It seems to me to be essential because suddenly we've democratized access to AI and everyone is innovating. And as you said, and just on that finishing point, now we're telling people, we're even measuring people and doing their performance reviews by how many tokens they're using. Right. So we're actually encouraging them to do the opposite of what we're saying here. We're saying everybody has got to start innovating and do vibe coding and use Claude. And it's like, whoa, whoa, whoa, whoa, whoa. Stop, stop. You know, um, you're walking around a dry forest, throwing matches everywhere. I mean, this. This is not going to have a good outcome. Right. Um, so, uh, it is going to be interesting as always, as it always has been in tech. So, Jim and Santosh, thank you so much. I think Pro is doing some very important work, and I'm sure you're going to be very busy going forward. Thank you again for being my guest on the IoT and AI leaders podcast. Thank you very much.

Speaker C: Thank you.

Speaker D: Thank you for having us.

Speaker A: Thank you. Thank you.

Speaker B: You've been listening to IOT and AI Leaders. We hope today's insights help you drive smarter, faster business innovation. With IoT and AI at the center.