Jacob Ortega: Hey there folks. My name’s Jacob Ortega. I am the senior Solutions Engineer at Lydonia Technologies. This is actually really a dream come true. It’s a long time coming. I’ve been working in this space for the better part of seven years, both, you know, working with customers, working with businesses, and really getting us to a place where both the businesses and the companies like Lydonia are actualizing the value that these technologies are promising.
So, I’d like to walk you guys through very briefly a quick use case that we found during one of our programs that we help our customers with, and then kind of walk through the program itself, show you guys how we actually help these businesses both identify and then quantify, implement, and then ultimately recognize the value that they’re being promised.
Just real quick, some safe harbor. I’m not going to read through this. I think I’ll take up all my 15 minutes.
So, the agentic AI accelerator, here’s an example of how we really, you know, identify ourselves in this market, right? We’re a premier business implementation and transformation partner. We help businesses reimagine their operations, you know, by both designing, deploying, advanced solutions. But really, the way I will describe this is we are singular in focus on business outcomes. That is the entire goal, right? Technology comes second to ultimately providing and establishing an outcome, right?
And so here’s one of the examples of how we’ve actually been able to accomplish that. So working with one of, actually a global filtration products, a manufacturer, they were really struggling with legacy predictive maintenance workflows. And so the way that our engineering team kind of looked at this process is we said, hey, you guys are doing really good work already. There’s already a lot of value established technology, mature machine learning practice, but as we looked at this, we said, hey, we have the opportunity to bring agents and agentic capabilities to this process that can ultimately augment some of the work that’s already taking place or the business is actually struggling with.
And some of those areas really revolved around, you know, machine downtime as well as an overall slowness to the response and the ability to actually remediate some of the issues that they were seeing. And so what we did is we looked at this and we said, hey, the UiPath platform is capable, you know, of augmenting this process right through agents. What we can do is we can build on top of the good work that’s already happening, taking data from IOT devices, structuring it in a way that’s, you know, capable for a machine learning model to act on. Once that machine learning model actually determines that, hey, you know, a machine on the factory floor is at risk for failure, we can then prompt an agent, right? We can give that agent context capabilities like the actual historical machine performance data, technician reports that this business has been filling out for the better part of 10 years. They’re sitting on some file server somewhere, right? Not being used to actually influence the way that the process and the technicians are actually handling these issues.
We can look at technician manuals, SOP documents, and really ground this agent in reality to the way that it is capable, you know, of helping in a process like this, right? Really kind of train it the same way we would a person, right? So that’s exactly what we did, right? Did some integration service with Jira to go handle tickets and go produce actual, you know, issues for those technicians to go and act on. And then we created a recursive training method so that once that machine is then serviced and a decision is made, we can actually have that information fed back to the agent. So it ultimately becomes smarter over time, right?
And what we saw is really this concept of a controlled agency become a reality for this business, right? What we want is we don’t want just agents, right? We don’t want technology capabilities. We want agents that we can trust, right? And if we can trust those agents, we can do more of it, right?
And so part of that really comes down to how do we quantify the impact and what this business saw, right? It’s reduced unplanned downtime, it’s lower operational and service costs, they’re making faster data-driven decisions, and they’re ultimately scaling knowledge transfer. When technicians age out, a big issue inside of manufacturing organizations, sometimes we see, and especially with this customer, right? Individuals working and maintaining these machines for sometimes 10, 20, even 30 years. I met a gentleman 30 years. It’s remarkable.
And so how do we actually perform that knowledge transfer in a scalable way? We can use agent automation, we can use knowledge bases, rag techniques, vector databases to capture this data that exists for pretty much every business, not just manufacturing, and actually provide intelligent, scalable agentic solutions.
So this is one use case, right? But I think it’s better to look at this, you know, in more of a high level, right? Understand the overall program that leads us to help businesses actually figure out how they can go and scale and identify these opportunities and then bring forth solutions, right?
What we’ll walk through is kind of these three pillars. Um, but when we think about the What we’ll walk through is kind of these three pillars. But when we think about the agenda accelerator, the first place to get started is identification. We need to be able to do value engineering. We need to be able to justify to that business why we’re going to do what we’re going to do. Everyone has use cases, but let’s be honest, some use cases are better than others.
And so if we can identify through value engineering, we can identify high value AI backlog, right? We can help work with the individual lines of business to help articulate the best story possible. We can then quantify through metrics and not do it just in a simple ROI justification. Some simple KPIs. I’m talking real time process modeling. We really love UiPath process mining capabilities for this reason alone.
But what it’s allowing us to do is to provide a snapshot, a real time snapshot into the way that these automated solutions are actually impacting the business operations. And so what that’s allowing us to do is identify and justify more, you know, get more buy-in, more executive sponsorship, more visibility to the entire organization that, hey, this stuff is not, you know, just theory anymore maybe a couple years ago. But at this point, we actually have an opportunity to do the things that we are promised to do.
What we then help the business do is enable actually either through live instruction, and we love flying out to customer sites and going and meeting with them, understanding their team stories and figuring out how we can actually help enable them on the technology solutions that are out there. How does this actually impact their operations? Bring the domain experience, you know, and really help the organization build a community, right?
And I think what this does, right, is it becomes infectious for the organization itself. They start to see the possibilities. They see the opportunity and they see, hey, there really is a pathway to get to this. And the way that we’re going to do it is together, it’s a collaborative effort. And so we can do the enablement, right? We can help the organizations actually learn in real time and then ultimately start to take action.
And that action comes typically from, you know, this concept of management, right? Upfront management. And I’m not talking about putting like a leader in place and managing the individual work can do that. But really what this involves is the collaborative effort of all of the enablement, the identification activities, really being able to justify to this business, this is why we need to do this now.
And so it really starts with executive support. Our CEO Kevin Scannell says to me all the time, he says, every COE and AI program will fail without a north star goal and executive sponsorship. And I’ve seen that play out over our 140 some odd customers time and time again. If we don’t have executive buy-in, we don’t have an articulable justifiable story to bring to them. We’re not going to get anywhere, right? And these things will ultimately die on the vine before the fruit can be picked.
We need a center of excellence. We need a centralized and sometimes federated, but we need a team of individuals that are actually going to own this, right? And it’s the same way with RPA, the same way we saw from the foundations of RPA and intelligent automation. We’re going to need the same thing with agentic AI solutions. We actually can’t do it without it, right?
And the best part is, is that this technology is just a natural evolution of what we have already seen in the past with UiPath and the technology space in general. What this is allowing us to do is ultimately build on top of all of the initial investments that were made, you know, sometimes by businesses back in even 2015, back when UiPath got started.
A lot of the customers that I work with, you know, come from a variety of different situations, right? Their COEs, you know, are either brand new or they’ve been very well established, sometimes hundreds, you know, better. Part of, you know, I think customer we have has over 600 different automations. And so what we’re seeing is this collaborative effort between these teams to say, look at the technology that’s out there that’s now available and say, hey, regardless of where we are, right? And I think we see this as well, regardless of where we are in this space, we have the opportunity to apply this technology in an effective way.
But it really comes from all these points that we just kind of walk through, right? If we can’t do it in a concise and articulable way, even the most advanced COE will fail to get buy-in, right? The use cases and the pilots, they’re great, but ultimately we need to get to a point of production, right? And it comes from all of these different areas.
So, you know, how do we help with the accelerator program, right? It comes from, you know, again, three kind of areas, right? It’s program setup. It’s the overall execution of that program and the completion. And I think as I’ve mentioned, there’s really some key areas here, right? Both really tying down scope, right? And success criteria.
The KPIs are important. We’ve been hearing KPIs for probably a better part of the time I’ve been alive. But it is the reality. If we can’t justify why we’re doing what we’re doing, we are going to fail. And the program helps that way through methodology that we’ve curated since we got started in this space, right?
From an execution point, it really is identification, it’s discovery, it’s that enablement, it’s the management, right? And then ultimately the articulation to the executives to show them how possible this really is. And then from a completion standpoint, right? What we believe is that in addition to the value engineering and the real time kind of buy-in and justification, there also comes this point of a feedback loop from the business itself.
We need to understand how this has actually impacted the day-to-day operations. And so if we can kind of wrap all of these different areas together, we’re going to see real value, we’re going to see operational operationalization, and we’re going to see the things that we were promised, right? And I think what we’re seeing is that customers are recognizing that.
I believe about at least 12 of our customers have gone through this program. We’ve identified hundreds of use cases at some of the largest banks, financial service, insurance and manufacturing organizations on the country. And what we’re seeing is that these are the key steps that they need to get out of this phase of pilot and POC and really step into the next phase, which is leveraging and scaling this technology.
It’s what we were promised. It’s what we at Lydonia are committed to actually providing.
So the benefits of this, right, outside of actually doing it, is truly the speed, the innovation and the scale of the program, right? In about eight weeks, we typically see somewhere in between 80 to about a hundred different agentic ideas. And a lot of these can come from a different combination of places, both existing automations, but as well as brand new things that say a couple years ago, it just wasn’t possible for us to do based off the way the technology stack had existed.
And so what we’re seeing is really the revolution of business operations. It’s the impact of the evolution that we were promised. And so we want to do more of it. We’re very excited and we’re happy to work with the customers that are doing it. Please, if you have any questions, I’m happy to answer them now. But I appreciate you guys attending and listening, and I hope you enjoy the rest of Fusion.
