Gabriel Carrejo: Hello, hello, hello! Good morning, good afternoon, and good evening. Thanks for joining us, you guys. We have a really special event today, an oversubscribed event, so I’m glad all you guys got the lucky ticket into today’s session. We appreciate you guys joining.
As I mentioned, today we’ve got a power-packed experience. Full of best practices, tried and true, real-world success, learnings from the front lines. And not only are you going to be able to see some of the reporting from the front lines, but you’re going to get hands-on.
You’re going to see and feel the difference between true enterprise agentic process automation and the personal productivity and sort of siloed, you know, agent solutions that are out there, all of which, you know, have their place, but if you’re really looking for that enterprise transformation, we’re going to show you how to do it. And you’re going to be able to do it yourselves with hands on the keyboard.
Today’s not only inspirational, educational, but practical. We also want to have some fun. So before I keep just blabbering on, let me bring two of my favorite minds in Agentic Automation to the stage. Mr. Todd Foley, the CDO and CISO at Lydonia Technologies. Todd, welcome! How are you, sir? And also, my good friend and colleague, Akshay Mehtani from Automation Anywhere, who’s our Director of AI Solutions Engineering. Guys, thanks for joining, really excited about the day today. I know we’re going to get really deep, but let’s start at the top really quick, and just do a quick refresher, and sort of maybe let’s call it a level set. I’ve got a basic question. This maybe isn’t a poll question, but maybe I’ll get the swag pack anyways. Let’s talk a little bit about, set the stage here. What’s the difference between generative AI, an AI agent, and then agentic automation?
Akshay Mehtani: I can, I can answer. Then, Todd, I want your take as well to that. So if we, if we think about it, right, generative AI is an umbrella term. Everything that we see from large language models, to fine-tuned models, to, you know, learning and things like that. That falls into the whole generative AI world as we have it today. And this really became famous, you know, three years back, is it, or two and a half years back, when ChatGPT came, when OpenAI launched ChatGPT to the world, and everybody had a new agent to talk to, or a new conversation to talk to. That’s really when it came to the forefront.
AI, in general, is a much larger umbrella, which has been around for almost two decades, if not longer than two decades, and generative AI comes within this whole AI world that we have. So, you know, we’ve been messing around with machine learning models, and whenever you said, hey, I’m doing, I’m extracting data off a document, what was actually working in the background was AI.
And then today, when we talk about agentic AI and agentic automation, well, we’ll come to agentic automation in a minute. But when we talk about agentic AI now, what that is really is, how can we take things that generative AI can do, that AI could do back in the day and add personalities, add humans. So now we have agents, essentially, that can help you understand what next to order in your inventory, or together can help you process a prior authorization request, or that can help you process a loan request. That, you can essentially generate agents specialize on those special, on those specific tasks, and that’s essentially what you would go for when we say agentic AI or agentic automation.
Todd, do you want to kind of give your worldview on that? You’re definitely in a different worldview than where, you know, Gabe and I are sitting from.
Todd Foley: Yeah, I would say it doesn’t matter. And then in that the solutions we deliver to clients today incorporate generative AI, they incorporate a lot of the older AI capabilities around machine learning, computer vision, you name it, including some of the key things that have made Automation Anywhere so successful in the past. But when we deploy solutions today, we’re using agentic AI, agentic automation, that’s built not only with those pieces, but with all of the foundational elements that have made the AA platform so powerful in the past.
You know, when we’re performing a particular process, are we using a GenAI call? Sure. Are we using an AI agent? Yes. Are we still, you know, leveraging different types of machine learning? We might be. Natural language processing or image recognition capabilities, yes. Does anyone need to know? No. So I’m not sure it matters, and I think what we’re going to demonstrate today is how easily accessible all of these capabilities are, even with something as simple as prompting.
So my take is, it’s good to know from a computer science standpoint, and from a historical standpoint. But it’s almost irrelevant day-to-day now.
Gabriel Carrejo: I think it’s a great point, Todd, too. In that, me as the business user, you know, all I care about is the outcome, right? Like, I don’t care which prompt, or sorry, which model I’m using, or whatever it is, I care about the outcome. I know what my process is. In many cases, most of what my process is. And for me personally, like I, you know, I’ve said that. And Todd, you’ve been on my livestreams and whatnot, but the beauty of working at Automation Anywhere is we get to, you know, drink our own champagne, and so, as a simpleton business user, not a technologist, you know, I get to see these things evolve and how, where we’ve gone, but I think you also brought up an interesting point, which is, it’s not just about not, sort of. I’m generalizing, but not caring what sort of tools are being used, or elements of AI, but also this idea of an agentic process automation. Right. Having that historical, you know, expertise in process automation.
So I was talking to Mihir, our CEO, recently, and I said, you know, we went from RPA to APA. And, you know, in another year or two, there’ll be something else that replaces the A and PA, but the process automation piece is the core of who we are and where we excel. Right? And so, whatever that technology is, whether it’s robotic and deterministic or cognitive via agent, you know, AI and agentic, soon there’ll be AGI and all these kinds of things, right? That process automation part. It’s also core to the success. And I think that’s a big deal, and Todd, I don’t want to take over the whole stage here, we can have a separate session on this, but from a CDO, especially a CISO perspective, I know that’s front and center with, you know, many customers and centers of excellence. What is our security protocol and stance? How are we, you know. Sharing data with knowledge bases and putting, you know, governing that, and what an LLM can call and not call and train on, etc, etc. So, lots of interesting things there. I’m sure you’re going to touch on some of those.
Todd Foley: Yeah, and I think it’s critical, right? Anytime you have newer technology innovations, especially in the climate that others CISOs face today, where seems every day there’s another data breach, or another concern from a security standpoint. It’s important to know that these capabilities aren’t bolt-on, but they’re part of the platform, that the guardrails, that the trust, that the security protocols are in place in a way that you’d expect from an enterprise platform like Automation Anywhere.
And I think a lot of people, certainly when the ChatGPT announcement came out, a lot of people spun up committees and studies and tried to look at, you know, how do they prevent data loss, data compromise, how do they deal with what’s potentially a new threat in terms of a vector where things could be compromised? Fortunately, you know, the fact that it’s now part of an enterprise platform and has those guardrails mean, that you can use the tools to develop solutions around Agentic AI, not without that concern, but with the understanding that the controls and the security and the surety of integrity are in place.
Gabriel Carrejo: Yeah, that is there. You don’t have to worry about creating it from scratch or something like that. It’s therefore you have to dial in, right?
Well, exciting stuff, guys. I think, Todd, you’re going to be up first. I don’t see why we should wait any longer, right? Let’s just dive right into it.
Todd Foley: Love it. Bella, if I could ask you to share? I wanted to kind of kick things off and share a little bit about our experience with our clients. Lydonia’s been around now for just over 6 years. And we’ve, through trial and error, come up with different ways to engage broadly within organizations, in particular around enabling business users and developers to be productive more broadly than just the core teams that are responsible for AI or responsible for automation.
And we’ve built a model around that that I want to share. I’m not trying to keep secrets close to the vest. That we’ve branded Agentic AI Accelerator. Right? It’s a program that we’ve been successful at now with just under 50 of our clients. And it’s really designed to accelerate the adoption, but more importantly, the practical realization of value from Agentic AI and AI automation. And it’s meant to fast-track innovation by broadly upskilling business developers and in assisting them with the development of AI automation solutions. Ultimately, what we see this initiative doing is drive a real renaissance of innovation across organizations and really support that Center of AI or center of excellence approach from a governance model in a lightweight way, so it doesn’t become something that burdens those teams down, but rather becomes an accelerator for what they want to do as well.
Bella? There’s some key ingredients here. The first is something that I think is important to call out as needed for the same way it’s needed for almost everything else, which is, you’ve got to be focused on value attainment. You’ve got to be able to measure what you’re doing, what the value of doing it is, and what the returns are from it. We’re very KPI-focused in that regard, and we have a way that we engage in intake triage and in the program itself to ensure that everything we do returns value, and more importantly, measured value that is validated by the business.
We also really want to drive cultural change. I think AI is doing that in a lot of organizations, not always in a managed way. Opportunity to couple Agentic AI initiatives with an innovation culture, and doing it in a way that rather than being disruptive to an organization, accelerates its ability to evolve. That’s a big part of our program and how we approach it. And I think it’s important. I think if you’re not taking into account people, if you’re not taking into account organizational structure and what people do, then you’re not going to be successful.
That enablement and upskilling that we do can be applied not only to people who have some skills in technology, people who might already be prompt engineers, at least in their own world, but also for people who really aren’t technology-friendly, even technophobes. And that the entry point for these things these days is as simple as being able to type on a keyboard, or even speaking to a microphone, right? And that low barrier to entry enables people to more readily adapt and gain value from the tool sets and the things that we do with them.
I think also it’s really important to recognize that some of the traditional approaches that you might use for development teams, for training, things like boot camps or teaching people to program using a Hello World approach, those don’t work really well broadly across a workforce, especially not with non-technical users. And being able to engage people and recognize that it’s not their day job, but it is something that has value to them and has tremendous value to the organization is a hallmark of how we approach it and how we think it has to be approached to be successful.
And then finally, we take a community support and community-building approach to these programs. Our concern is that enabling people around agentic AI becomes a support burden, right? And we want to avoid that at all costs. We want to avoid teams that are already fairly short-staffed with a backlog of high-priority things. We don’t want them distracted or pulled into things that are less critical for the business from a case-by-case standpoint. Instead, we want to enable the community of Agentic AI developers, users, consumers to be able to be self-supported. We’ve got a unique model for how we do that and how we get business support for it that we found to be successful, so that you can have hundreds of users producing value, using tools, doing things that benefit them without it becoming a need for hundreds of people to support them.
Bella? I think as we look at some of the key things there, the value engineering piece of it, I’ll call it, that real framework for finding what the key opportunities are, being able to drive and then measure ROI is critical for the long-term success of the program. We want to avoid doing something that’s a flashy sort of check-the-box in the AI space, but rather it adds real value to the organization and continues to drive increasing value over time. That means right from the get-go being able to have some metrics around value, some ways to measure, a business validation process that’s recognized. So that when you say, hey, we did this, and this was the return, it’s not just that people say, hey, that’s great, it’s that they say, hey, that’s great, and we know that’s true. And I think that piece alone is a place where we’ve seen a lot of programs that we’ve been involved in rejuvenating, where we’ve seen them originally stumble.
The sessions themselves, how you teach people how to use these tools, how they engage it, how you apply it to what their day-to-day is, are a key part of that. And our education sessions are meant to be very lightweight. So instead of that boot camp approach, it’s very much a collaborative office hours kind of engagement. It’s something that recognizes that you can’t pull people out of their day job for days and weeks, but rather you have to find a way to incorporate it into their schedule. So typically, it’s an hour or two a week, even at the beginning of the program, where it’s most intensive, and then a commitment of about an hour a week on an ongoing basis.
The training too, you know, we’ve certainly gotten leverage out of things like e-learning and different types of remote engagement in the past, but there’s no substitute for having someone who’s available, at least virtually, live, able to respond to questions, able to interact with people in a way that’s productive. And that’s a key part of the success that we’ve seen.
I mentioned the community. I think there’s a commitment we have as part of this program to establish an ongoing community, not just to build it for a temporary period of time. And it doesn’t happen without the buy-in organizationally, without management and executive support. Being able to show them that value, that value engineering piece, is a key part in obtaining that and ensuring that it’s sustainable. And then finally, you know, there has to be somebody within the organization ultimately who owns the program, who owns its success, who’s able to measure its impact, who’s able to coordinate the platform use, and that’s, for most of our clients, a Center of Excellence model that we help people build if it’s not fully formed. And we help people become more efficient at that program, at that Center of Excellence, if they already have it in place. We do tailor the program to our clients. Everybody’s at a different spot in their journey. But those things are kind of universal in terms of the success criteria and what we’ve seen work across our clients.
Bella? I think there’s three phases to these programs too. You set things up and you set yourself up for success. Before we ever start enabling people directly, we’ve got to make sure that the security concerns, the governance, the guardrails are well understood, signed off on. That there are appropriate stakeholder ownership, that there’s that RACI in place, that the communication plan and the environment itself where people are going to be working is ready to go. And that’s a key foundational part of success. This isn’t something you want to do on the fly. Having those pieces in place just makes everything move effortlessly, right? Enables people to be successful right from the get-go.
Likewise, when we start this, we enable people not with that Hello World, let’s teach them how to do this from a textbook approach, but rather everybody who participates in the accelerator has an individual thing that they want to do, something they want to accomplish and bring into production. That ensures that not only are they motivated, but that the whole program ends up with a significant amount of production AI agent deployments in a way that can be measured right away. So it’s not, let’s do this and hope for good things down the road. It’s, let’s do this, here’s what it’s going to return, and we’re going to measure it right away. It’s also a huge motivator for people to be able to self-solve for challenges that they’ve been living with for a long period of time, or with great ideas that they have, that they can then actualize themselves.
I think, you know, when we look at how we move that program forward too, it’s not just that we measure it, that we validate it. We also turn it into internal marketing collateral for our clients so that they can do not only the executive readout but use that as a way to bring other people into the program, to continue to grow and scale and drive success.
And then, you know, when we wrap up the accelerator and hand it off to the Center of Excellence at our clients, we’re doing it with comprehensive telemetry on what we’ve done. Not only have we walked through a process, shared that process, handed that off, but we’re also collecting the data. That’s not just feedback but also where we’ve seen success, how we’ve handled the platform transition, how we deliver a roadmap for ongoing learning and enablement.
And let’s go to the wrap-up, Bella. The program itself occurs over 8 weeks. It’s not an 8-week time commitment by any stretch. Like I said, it’s an hour or two a week. It involves that setup, that program foundational piece, where we do the initial agent deployment if it’s not already in place. We execute the enablement and the training. We make sure that we’re actually delivering value, that we have use cases identified and completed for everybody who’s participating in it. We have that longer-term roadmap that’s a key part of it, and the security framework, documentation, best practices, all of those things that need to be in place for ongoing surety of execution. And the knowledge transfer, not only on what we’ve done but how we’ve done it, right? Because our goal and our focus at Lydonia is enabling our clients to be successful, and for us not to be needed going forward for that type of execution.
Bella? I think what we’ve seen be the real benefit here is that this is fast. This is a real way to help people with their process of how do I use AI, how do I apply it, where’s their value. And it’s a way to demonstrate success all the way up to a board level, what you’re doing, what you’ve done, and how you’re measuring things. And it’s a driver for innovation. If you open things up to people where they can self-solve, where they can take ideas and make them real, and do it quickly, you’re changing the pace of innovation within your organization, and you’re able to derive those benefits in a really immediate way.
And finally, a key part of this is how to scale it. It’s not just to do it, but to do it in a sustainable way that’s going to allow you to grow and continue to turn the wheel in terms of driving incremental value and ever greater value over time, without it becoming a burden for the core team.
That’s what we’ve seen as best practices. That’s the framework I think that we really want to set for today. This ability to use this not just to accomplish specific things, but to use it in a programmatic way to drive innovation and evolution within an organization. And with that said, I feel like I’ve covered that approach. Certainly answer any questions that people have around it, or how to effectively take what we covered today and apply it within your organization.
But I want to hand it off to Akshay to dive into the meat of this a lot more as well.
Gabriel Carrejo: Also, really quick, I wanted to just follow up on two things. One: for those interested in engaging and leveraging the program, what’s the best mode to get in touch? Do they simply email you, or is there a form? How are we getting folks activated?
Todd Foley: Yeah, absolutely. There certainly will be follow-up after this for all the participants, but it’s as simple as sending an email to [email protected]. Can’t be an AI company without a .AI domain. And that [email protected] is the surest way to get in touch with us and get a response quickly.
Gabriel Carrejo: That’s awesome. I love this whole approach. As I mentioned, we have the luxury of drinking our own champagne here with a similar approach in working with you guys, our Pathfinder community as well.
You touched on something also I think is really important. I spoke to a number of our customers, innovation leaders basically in this space, through the course of the various Imagines globally. And every one of them said the need for evangelism, and effectively for marketing, selling the capabilities and the program, etc., is paramount.
But everyone that I asked that led an AI function or an automation function, I’m like, so wait, do you have a sales and marketing team in your organization? And of course, they said no. But I want to underscore to those that are watching just how crucial that is, and that with partners like Lydonia and us in concert, we can help you with those things too, with sort of these out-of-the-box assets and marketing in a box, if you will. Just that communications piece is really, really key.
The last thing I want to touch on is something we’ve been talking about since I started here, which is this idea of sit dev, or citizen development. And it’s always been a scary term, I think, to some extent, with IT in particular. But one of the things that strikes me, again as a non-technologist, is that if you just think about ChatGPT a couple years ago or whatever, and you started prompting and doing some things, in essence, that’s a bit of citizen development. Me playing with ChatGPT to create a prompt that works for me.
And so, when I think about the business users out there who might be afraid of like, oh my gosh, this is too techno, you know, it’s actually not, right? Where the technology is now, you call it vibe coding, call it whatever you want, or just prompting, but that’s really the essence of how I think a layperson like me needs to think about it. It’s not this onerous, scary, technological thing. It’s just, oh, it’s kind of like the prompt, it’s just a little more capable and powerful.
Todd Foley: There’s already been, to my mind, the consumerization of AI. It’s not like it’s just the data science team that’s using it anymore, and it’s not just IT who’s going to be the consumers of AI. It’s now part of every commercial software application. It’s part of everyone’s day-to-day, whether they realize it or not. And it’s really incumbent upon organizations to try and drive value from it as best they can. And the right way to do that isn’t by blocking it. The right way to do it is by leveraging it, making sure that the right guardrails are around it, that the right environment for sustainability is there. That’s what that Agentic AI Accelerator program is all about, and I think it’s where everyone needs to be.
There was a point in time when no one in their right mind would think of using anything but internal email servers, right? And I don’t think there are that many left. I think we’ve turned the corner as far as that goes with AI as well. And I think there’s a really simple way to ensure that the concerns are addressed, that value is maximized, and that people are able to do things well, quickly, and responsibly. And that’s what we’re going to talk about today, because Automation Anywhere has built that for its clients.
Gabriel Carrejo: Well, it’s a perfect segue for our friend Akshay here. Sorry, Akshay, I jumped in there. I just had to get Todd, because I was so inspired after taking my notes and stuff, but he just did a perfect tee-up for you to talk about the actual enablement via the technology.
Akshay Mehtani: So let’s go into that. Yeah, let’s go into that. For that, we do want to go into the product a little bit, so we dive deeper into the product. But before that, most of you must be asking this one question: where does Automation Anywhere sit? What can I do with Automation Anywhere, or what should I do with Automation Anywhere? And what should I not be doing with Automation Anywhere?
So let’s take a view into that as well. Let’s go to the next slide. Perfect.
So this is probably, according to our CEO Mihir, he mentioned this to us in one of our town halls, this is the most photographed slide at World Economic Forum earlier this year. And this is essentially kind of where we sit. If you think about AI agents, everybody is creating an AI agent. And we divide them into three different areas. There’s the personal AI agents. Help me write an email, summarize this report for me, write a blog for me, those kinds of things. That is what we call personal productivity tasks. That is what a lot of companies are doing, and it’s working great for them, but the impact that you see from an employee productivity standpoint pertains to 15% or so.
Next, we come to these app-specific AI agents. And these app-specific agents sit within applications like Salesforce, or ServiceNow, or SAP. And as long as your entire process, whatever you’re looking at, sits within that one application, it works great. But at Automation Anywhere, we’ve been automating processes for two decades now, and we know for a fact that no single process sits in a single application. It goes across applications and across teams.
Which is why your productivity impact on that is also lower than what you would expect. That’s where we come in. We focus on these high-value processes, essentially looking at business processes that go across applications and across teams. These are high-complexity, mission-critical processes.
Think about, if you’re in BFSI, loan processing or client onboarding. If you’re in healthcare, healthcare claims or prior authorization. We’re able to automate all of that, and these processes don’t live in a single application. They go across multiple applications, and that’s essentially what we’re here to automate. Because if you’re able to provide your efficiencies in that, those efficiencies could go up to 75, 80, 85%. That is essentially what we want to focus on.
So, that’s where Automation Anywhere focuses. And how do we exactly do this? How do we get you these processes? How do we bring you up to that 80% productivity impact? It is through our platform. So let’s go to the next slide and take a look at what this platform looks like.
So, the platform we have is the Eject Tech Process Automation System. And with this platform, you have two layers to this platform. At the base of this platform, you have your automation system. This is what helps you integrate with systems, move data around multiple systems. We’ve been enhancing all the different features, the integrations on the automation system. If you want to interact with systems that have APIs, we could easily do that. And that’s the system that’s been around for a while now.
On top of this, what we’ve essentially built out over the last 18 to 24 months, or a little longer than that, is this agentic system. And if I really divide this agentic system into 3 parts. It’s, number one, with document automation. How can we help you extract data of documents? And these documents are not simple documents. We can obviously extract data of those simple documents, but think about contracts, lease agreements, unstructured data. We want you to be able to extract information off those documents, and that is essentially what document automation helps you do out here.
We then help you build these automations faster. We have Copilot for automators, where you can easily just come in and say, hey, build me an automation that picks up my data from SAP, performs these different tasks, goes to Coupa, does a three-way match, and comes back for me. It will build out that skeleton of an automation for you. It’ll obviously not be something you would put into production, but we have had our development teams come back and say that this has been able to take their development time out by about 60-70%. And that is essentially what you want to look at.
The third thing is self-healing agents. Have you ever been in a situation where you automate something on a particular UI, and you come in one morning, and voila, the UI has changed, and your automations are breaking all over the board? That is essentially what we’re addressing in here. How can we reduce your support costs? How can we reduce the amount of time you take supporting different automations and different agents? That’s what we do with self-healing agents.
We then help you build out custom agents around your processes. Remember the loan processing agent and the inventory management agent? All of that is built out using custom process agents, an AI Agent Studio, and Enterprise Knowledge. And we’ll actually be working on AI Agent Studio during our hands-on exercise right after this. And we’ll do a quick 5-minute walkthrough of what enterprise knowledge also looks like. We will not dive into building something on there, but I’ll definitely be showing that to you as well, to give you an idea of what we’re doing in there.
And all of this is put together with the agentic orchestration. So, agentic orchestration across this entire system helps bring together the automation system, the agentic system, and bring in humans to facilitate that human-agent collaboration.
The last thing I want to dig deeper into is the process reasoning engine. The process reasoning engine is essentially what powers the agentic system. Everything I mentioned about the agentic system is powered by the process reasoning engine. So, let’s dive deeper into that. Let’s go on to the next slide on the process reasons.
This is essentially what the process reasoning engine is all about. At the core of this, we have our knowledge. We have process models, we have UI models, we have document models. We take all of that and put that together in a single automation model for you. On top of that, we add your process context. This is where your standard operating procedures, your documents, your knowledge, your learnings, all of that comes in, and we secure that only for you. Nobody else can see it. That is definitely secured only for your context in your scenarios.
We add our memory and learning on top of that. So now, if I give this entire system a goal, it can develop a plan. We’ll work with you to execute the plan, and also reflect on that plan. And that, all while having human-agent collaboration come together. Because these tools that we have, these memories and learnings, these automation models, they’re not just RPAs. They could be agents that you have sitting in different systems that you want to bring in using the A2A communication protocol, or you could have other tools sitting in systems that you want to bring in using the MCP communication protocol. We can support all of that on our agentic system and essentially help you build out goal-based agents that’ll have access to a variety of tools and a variety of agents to help you achieve your goal and execute that as well.
That is what we want to get to you with the process reasoning engine. And the process reasoning engine is what powers custom process agents, document automation, Copilot for automators, self-healing agents. All of that is powered by the process reasoning engine, which has different goals to perform different tasks. That is what the process reasoning engine will do for you.
And lastly, this is not something new to us. Let’s move on to the next slide. This is something we’ve been doing for a while now. Yes, perfect, thank you. This is what we’ve been doing for a while now. We have customers in all different industries that have deployed us, be it a key bank in the banking space, or Petrobras in the oil and gas space, or Boston Children’s in the healthcare space. We have customers who’ve been using AI agents and the Automation Anywhere platform over the last 18 to 24 months and have been seeing those efficiency gains, seeing those improvements in their processes over the last 18 to 24 months. These are just some examples across different industries. We have actually deployed more than 1,500 use cases across these different industries and have been able to unlock transformative business outcomes at scale for all these different customers.
Gabriel Carrejo: Akshay, fantastic job. Super interesting to watch. I think I love the simple prompt and then getting that. Get it wrong and get it. I think it’s such a crucial thing, because so often we expect zero-shot prompting to just work. And if you really think about it, I was having this conversation recently. If you really think about it, if you have someone you’re training, an intern, a new hire, whatever it is, and you just give them a very simple directive with no context, it’s kind of a zero-shot prompt. They might get it, they might not. And I think that’s one of the things I often tell people from a non-technical perspective, is just think about it how you would deal with a new hire, in some cases an intern, somebody maybe with a little less context, a little less experience in the specific thing, but that can quickly level up. So thanks for sharing that example. I thought that was crucial. It’s really easy to just show the things that just work right away, but inevitably, when you’re doing it yourself, you start to discover these things. So I appreciate you doing that.
Todd, any final thoughts as we let the folks go here? This is an amazing engagement and event here.
Todd Foley: Yeah, I just want to thank everyone for participating, and Akshay, as always, just a fantastic job.
Gabriel Carrejo: I’ll remind everyone, for the accelerator program with Lydonia, as Todd mentioned earlier, [email protected]. Reach out. We’ll of course be following up with everyone, with all of our attendees, but reach out, get involved, and take advantage of this offering. I think it’s so crucial. As I said, I’ve experienced firsthand the benefits of this, to getting from ideation to deployment to now we’re starting to scale within my little organization, even.
So whether it’s a small shop like me within the confines of marketing, or something more enterprise-wide, these approaches work. So I encourage you to take advantage, and of course, Akshay’s a mensch and is there to help with this hands-on feedback. Feel free to contact him, and I recommend to everybody to connect with these two gentlemen on LinkedIn. Keep the dialogue going. I think it’s so crucial right now. It’s always crucial to have open lines of communication, but right now we’re blessed to have two experts like these at the ready, with the power of social media and other channels. You can always reach out and get the insights you need. Really great to have you all, and I think we were oversubscribed, and we’ll probably be doing this again. So gents, put your seatbelts on and get ready for more. Until the next time, y’all, thank you so much.
Akshay Mehtani: Thanks, everybody.
