60% Fewer People, 90% Faster: The Vacancy Strategy (Diego Mogollon, VentureCrowd)

Welcome to our flight AI Australia. I'm Ramon Noudry. This show cuts through the noise and delivers only on what matters. Growth, margins and time. Each week I translate AI into practical outcomes, leaders can act with you. Today I am in conversation with Diego Moggo-John, Chief Marketing Officer and Chief Technology Officer of Venture Ground. A regulated Vintech platform. Over the last two years, Diego's tech team has shrunk by 60%, but development and production down 90%. So doing more with less. And the kicker is his marketing team went from 6 to 1 and they're handling more volume than ever before. Today he's going to share exactly how he did it, including the framework he also vacancy strategy. Let's get into it. So Diego, welcome. Thanks for coming under the show. Thank you. Great to hear. Trying to think how long ago we met, maybe he was late last year, up at Salesforce. How's things going with you? Yeah, it's been a while right. I think back then you and I talked about how fast things were moving and I did not anticipate for everything to accelerate even more than it did. You know, so it's been exciting past few months since I last saw you. We've been building along. Yeah, no, no. Good, Matt. Good. So, Diego, I wanted to get you on the show because what you told me recently that really shook me and thought we need to get you on here is, you told me over the last two years your tech team have shrunk by 60%. But you're moving 90% quicker. Tell me how you did it. I guess to clarify, it's not that I purposely got rid of my tech team. Actually, they were very talented and we've always been very lucky to have a really strong tech team. But what happened is because they were so talented, they were being poached. Right. And so every time that happened, I challenged myself to rather than going back to, well, this is the classic team structure that I've always had in the classic org chart that we've always run with. The world has changed a lot. So every time that a role was vacated or empty, I challenged myself to rethink how might I change our processes and the way we work so that we wouldn't have to necessarily go back to market right away. And the more I did that, the more I realized that actually is an opportunity to rethink your business, to rethink your processes, your body preposition. And that led to ultimately working with a very, very lean but highly efficient team as we are now. That's the dream. That's the dream. So the vacancy strategy, we need to hold onto that because that's going to be a segment within this episode. But before I start, if I'm understanding your business, you're highly regulated and it's not a compliance that comes along with what you do. We are, which is why, you know, it's always exciting to build in a highly regulated place, believe it or not. I confirm a superannuation background, which is very regulated. And I joined Adventure Crowd when it was early days and it was only a team of four or five. And we have now raised over $440 million via that digital platform. But what I'm really proud of is the infrastructure that's built underneath. And so that's a very robust infrastructure that's heavily focused on compliance, on scalability. And we've always been thinking many steps ahead. So we've always been thinking, how might these scale up for different products, for different user types across different jurisdictions? So what we build is an operating system in a way that handles funds management, investor relations, operations, marketing, sales, capital raising, all under one platform. And what the investor sees at the top or the resources at the top is just a very seamless experience end to end that gets them to either at least their deal on our platform or invest in it or investors. Right. Right. Right. So which one of the three did you do, Diego, to generate these results? Was it higher, the big four consultant? Was it higher in you, AI team? Or was it spending millions of dollars on the most expensive AI stack? It was none of the above. Good. What was it? Was really building strong fundamentals, believe it or not. About four or five years ago, we could see where the technology was going. Right. So we knew the importance of investing in your data. We knew the importance of mapping out your processes and we knew the importance of off-skilling your people. Right. So the three classic tenants of good technology, people, process and data. Right. And so we spend a lot of time on that. And so that really accelerated the process. I myself am a mix of strategic creative and technical background. So I was able to quickly focus on the technical aspect of off-skilling our team. And what what has happened since then is that we've now been able to implement agent decay solutions really fast. It was very easy for me to then off-skilling our team to really focus on how how could we use that very strong foundation to apply agent decay. Right. So three years ago, we were building our first agents before agent it was a buzzword. Two years ago, we were building multiple agents throughout the year. Last year it was literally monthly. We were launching new cases. A few months ago, we went down to weekly. We had the agent of the week. And now we are talking about the agent of the day. Right. So it's like, what's special today. Right. And that's our team and begins their stand-ups. And so what we built was that culture of if you got sprung fundamentals, then you can enable and empower everyone to move fast. And so, you know, for me, for me, building AI solutions is like working out, you know, working at the gym, right. The more you do it, the more you train the muscle and you build strength to do more faster. But you're going to get started and you got to show up. That sounds great, right. And all of us, all organizations, I speak to want what you're describing. But the key thing that we see Diego is there's going to be, you know, some leaders and some executives that are strong with AI and essentially, you know, drive that within the organization. There's going to be some that largely just the bottom of the organization driving it and the executives know nothing. But it's really rare that you see an AI first culture and AI first organization and it's democratized across the organization. That's where I see the biggest impact. So how did you approach AI adoption within your organization? I guess something that I like that I always like to recommend is following the customer's zero strategy, which is when when you are thinking how might you implement a genetic within your organization? Don't get stuck by the fears of, you know, what will happen if you implement these with external customers. What will happen if this goes wrong in the public or in your live environment? Start by building internal use cases short, concise, hyper targeted use cases that will augment your team members. And you build you build that muscle, right? And there is lower risk, right? Of course, and you've got to follow fundamentals of guard rails and rainbows, which I'll touch and later. But the concept is you have to get started. And in order to get started, if you follow the customer's zero strategy, then you're thinking, how do I implement AI within my organization to augment my team so that they can do more work more efficiently and reduce the toil? And everyone starts getting used to that mindset of you're not necessarily constrained by you've got to develop the perfect use case that your customers will love because you're developing it for your internal team first. Then you cross functional teams. And once you do that over and over again, you're more than ready to release that across external users. Right? So that's how we started actually, especially because we are a highly regulated company. Yeah, you've got two financial service licenses. And so I was always very mindful of that. And so for me was if AI solves your problems first, then it's going to solve your customers problems better. Right? And so that led us to really really getting comfortable with it. And it wasn't them me talk down talking about AI adoption. It was when when we build more and more internally, I use this new naturally developed these AI champions across the business that are just asking for more and want to. And want to do more and understand what can be done rather than just talking about the theory of AI. And then you've got this AI champions that will then push for their old news cases that I would have even missed. Right? Yeah, wow. Okay. Now what I found really interesting about what you said before you go to market with your product and the revenue stream test internally with use cases. So how did you approach that? You know, so what's obviously tech has been embedded into business, but generally of AI turned up in your doorstep. When did you go from that to 100% growth in the numbers that we expect to know today? Like how long did that take and how did you achieve it? So I guess when I talk about the customer's zero strategy, I think it's important to frame it in the context of I recommend that if you are not comfortable with knowing where to start, right? And we are not comfortable with how might you start with with Gen AI? If you are AI native and you're ready for it, you could you could do both simultaneously. Right. One of the blockers that we always had is that for every issue that wanted to raise on our platform, we. we always hit a ceiling, right? There's only so much that our humans could do to distribute that deal to our investors to get the issue ready to raise on our platform, to onboard them, to train them, to do the funds management, the due diligence and all of that, right? So there were a lot of steps and it was very cross-functional. So we spent the time thinking how might we unblock that in the front end via a highly automated, highly digital experience powered by AI. And then along the way, we're also thinking how do we augment our eternity members to be able to handle more issues without necessarily linearly increasing the headcount as more issues came in. And because I work both have some CMO and a CTO, right, shift marketing and technology officer, I could see how I could quickly implement our technology into our marketing team because our marketing team was one of the one of the teams that was feeling the most burden every time that we added issues and product to our platform. And so we build a crew of AI agents end to end that handle the process of distributing campaigns as issues came into the platform. And so what that led is that again as more vacancies happened across the team, and I was able to run with a one-person marketing team regardless of how many more deals we were adding to the platform. And so we in the past three, four years ago, I used to say we cannot handle more than four, five deals at a time. But after that, we were able to onboard as many founders as they wanted to raise through the platform, right? And today I still have a one team member and marketing team. Wow, okay, so this is actually funny enough, which is I just found out actually, I'm throwing up the same process. I read about this two days ago and I'm throwing up, it went to market for the past year and a half with a one person marketing team. So so it is difficult at a company like us and it is doable at a billion dollar company like Anthropic. It's not a matter of how much budget you've got is how do you want to rethink the process? That's that's right. So everyone's a builder, hey, so the role traditional role JD's are collapsing. So just on the marketing point, how big did you say the team was before it's collapsed three to one? I would say four or five years ago, I mean, we were as as many as five or six and we and we and we hit and we hit a ceiling with issuers every single time because it compounded the amount of work that we had to do to onboard an issuer and the more we spent on on how can we make this process more more efficient with internal and external and AI capabilities, the more we could do without necessarily losing control of it. And so that's the part. And on the topic of everyone's a builder, which you know I'm very passionate about, that happened at a marketing team level as well. Someone that that was only focused on specializing in one area of marketing could leverage or crew of agents to then do multiple multiple tasks. And the way that we build those that crew of agents is that, you know, with compliance in mind, right? So highly specialized agents that are really good at one thing. So we've got a copywriting agent, you've got a compliance agent, you've got an email marketing specialist agent, you've got an integration agent that sends that to the right distribution platform, you've got the boss of the agent is managing the process, right? And then we always kept a human in the loop. And that's another tip that I always recommend for anyone that has it and about implementing AI is, well get get started and then keep a human in the loop at the end. If the worst the worst thing that could happen is that towards the end of humans says this didn't work. But then what do you do? Then you do continuous improvement onto the words. And if you do that enough, you will be able to get it to work, right? But so that's that's like an easy gut rail that you can always set up in any use case that will ensure that you can finally the experience at the end. Yeah, that is probably one of the most simple effective guard rails that you can put in place, right? And to the point of everyone's a builder, I see it is everyone's a manager, whether you manage people or agents here in the loop essentially equates to all of that. So we spoke about marketing, right? I'm not going to have time to go through what the marketing strategy looked like. But what are some other examples of how traditional roles flattened and people, you know, AI was unlocking new skills for people that they traditionally didn't have because what I'm seeing with AI, it's not just expanding your current skill set where I may be able to code in Python and now I can do JavaScript or whatever it is or, you know, I drew one thing and I'm able to do something else slightly better. New skills and roles have been completely created that they never had before. For me, I am I could never code, right? Now I've coded multiple products that have been gone, bought and gone through the production that are working well. So how does that equate to what you're seeing within your business? How are you seeing the role-slotting? Oh, yeah. And we are we are very strong advocates of that we are rather than getting scared of of that change. We embrace it deeply, right? So for me, what AI has done is it has collapsed the distance between I understand what needs to be built and I can build it, right? And so that gap has now shortened and that for me changes everything about how you build innovation and I am a programmer myself. I have been coding before in a language, and it was much harder, but then right? But I'm also a marketer, right? And so I chose I chose not to be afraid of automating myself and I chose not to not to be afraid that if I implement cloud code in the organization, then that means that I might lose my job, right? It's more if I implement solutions that are groundbreaking like this, how will that augment our team? And so what that has happened is that in a team that usually we have a very clearly defined role, you are a designer, your product manager, your front end developer, your back end developer, your focus on Salesforce admin by upstealing my team members that the gap has been flattening and flattening. And so now I have designers that are pushing code to production that's compliant, that's functional. I've got product product managers that are making technical and architectural decisions and recommendations. I've got developers that are providing design and UX feedback that they wouldn't have done otherwise. JuniRTN members that are proposing strategic decisions, I see leaders in the company that are automating a lot of what JuniRTN members used to do. So I think it's more than a replacement or similar is if you think it from the perspective of how can you augment people, all of the sudden you realize that democratizing access to knowledge on logs a lot of potential with your team. And what that has done for us is we have cut down the development process for 90% or more. And every week we're getting better at it. So a project that would use to take months is now taking us two weeks, sometimes even a week, agents that used to take us weeks are now taking us a day. Right. And that's because we haven't raised that. You've got a thing we think, well, why should your work chart look like? What should your team structure look like? What should the new checkpoints look like? In the past, I needed to be across everything because I am the team leader and I need to validate a lot of decisions. But if you spend a lot of time identifying what are the decisions for which I can provide peer guard rails that I can then build an agent that can then validate those guard rails, then that means that I can speed up my decision making dramatically. And I could then approve 10, 15, 20 code changes in a day from six different team members. If I put in the time to think about the system design, the context, how it matters to my business. And I think that's what I would like to encourage leaders and executives to think about nowadays is if you do that homework, if you think from a systems design perspective, and you think, well, who are we? Where do we want to play? How do we want to win the fundamentals? Right. We want to, in our case, we want to be the best in private capital markets. And we want to enable that across across the world. Right. So how do we do that with technology? Number one, so we have a clarity on our strategic decision making across the business. And that is clear for everyone. And we think that then how do we connect the system in a way that any agent has access to the right business complex? So any agent that is plugged to the light to the layer, every agent will know who is venture crop? What do we stand for? Where are we playing? How do we want to win? We sometimes feel like a prompt to another line will be enough. But I think it's more think about the pipeline and the connection with your context and who you are as a business and then enable ducks or bulls, people and agents and you get a much better output. So you're point about democratizing AI. It isn't something that you just give to three or four or five special people, the ELT or your frontline admin because everyone needs to have it. It's like saying who needs the internet in 2026. It's critical and I think the bigger risk is if you don't give it to the organization, employees are still going to do it. And then you run the risk if it been put into God knows where. So it needs to be there and it's be a fundamental part of every business. So definitely agree. Now going back, you mentioned multiple different AI wins within the business. Compliance is important. In your sector being highly regulated and compliance driven, what do you normally look at for? What are the telltale signs? Do you have a framework that you normally follow? Anything you would recommend to the listeners? Well, I mean, first of all, you touched on a really good point that's highly connected to that, which is the rise of the shadow IT. Yes. Yes. And you cannot hide from that. If you as an organization, if you're burning your hand, your head in the sand thinking that AI is not going to advance and that you can leave it for your IT department to handle with a few licenses, what's really happening is a big compliance issue building up without you seeing it because you're not offskilling. Yes. You're not offskilling your team members. They are going into their own solutions and building their own things. They are dumping in data that might be sensitive to LLM's that's not following any governance. That's not following any framework. And so the more you you run away from that, the more that compliance issue rises. So that's I think that's the problem number one. If you don't have an AI culture and an AI strategy at a company level and it's not supported or championed from top to bottom and bottom soft, then that problem will happen. So I think that's from the mental number one. For the mental number two for me is acknowledging that everyone will use AI, whether you allow them to or not, then spend the time again on systems design. What can you build as a safe sandbox for them to experiment? And a lot of that knowledge strategically is hiding within the people that do the job, right? Because the more you do the job, the more you realize how can you add more value if you understand AI? And so the way that we've been doing it is we've got an AI champions cohort that's a cross-functional team that's from different teams across the business. They are building agents. They are there safely experimenting with frameworks like Salesforce agent force. That's that's built around their Salesforce environment as well. And so I've put in the time thinking what are those solutions that I can provide company-wide for which I set the garals at the top like a world garden of some sort, right? And where they can then safely experiment with the right garals, the right permissions. And that unlocks a lot of capabilities. That unlocks a lot of innovation. In parallel, depending on how AI really you are in the journey, think about your agents exactly the same way. They are a highly smart new colleague that just joined, yeah, they just joined your team. They are keen to help you. They don't know better. You need to guide, then you need to give them the garals, the processes, and then they know how to do it. And then human in the loop, right? But then you're also able to see how are they performing along the way. You don't just hire people and then hope they perform and then don't give them KPIs and don't manage for supervised work that they do. And there is there is unique performance metrics that you can follow for agents that you might not normally follow for humans, right? So there is resolution rates. Are the agents closing the loop or are they just creating more downstream work for your employees? Escalation rates, how many times does an external user engaging with your agent asks to ask can it to a human? The more that happens, the more you are able to pinpoint, okay, well, there's something wrong with this agent because every time users are asking to escalate to a human. That means that the agent is not doing its job, right? And so you've got resolution rates, you've got escalation rates, you've got latency. How long is the agent trying to, you know, waiting to get back with an answer, right? And if it's taking too long, then it means there is a engineering, that engineering problem, right? They might be overthinking, they might be trying too hard to find the right context. So you've got to then go in and identify why the agent's struggling so much to find the right context. And then again, if you then spend the right amount of time in system design, you will understand that this agent is trying to access all of these contexts. When in reality, it only needs to know this bit. And that's something the companies get wrong. They might think that giving more data to the agent is better. It's not always a case. It's not cool. It's context poisoning, it's what's that goal, right? This an agent should know what the agent is to know in the same way that you don't hire an interior engineer until then everything about your company. You focus on what they should know about the role, about their job description. So you can identify that via latency. And then you know, other more technical aspects like tokens per agent and credits per agent that you can dive deeper once you get more advanced in the use cases. But the bottom line is agents like humans need KPIs agents like humans need performance metrics. And and the more visibility you have into that, the more you're able to optimize them. Ultimately, you should treat it like a continuous improvement. The agent, and I think this is really critical. The agent is not the end goal. The agent is the beginning of a journey of continuous improvement towards your business. So you've got to be really clear with AI and just like any other business problem, a fuzzy challenge or business problem you're trying to solve doesn't work. And as you said before, AI agents, they are enthusiastic, often too enthusiastic and become a bit of a cheerleader. So they're in intern and you don't put an intern in the CEO's office straight away, right? They need to earn their stripes. What is your favorite agent and what are they KPIs? Technology is my biggest passion. So the agent that we have implemented with team, our tech team, our personal favorite. So one of the biggest challenges is we are a complex platform, right? And we've got different user types, different product types. We've been building it for over seven years or eight years. And so there is a big mix of legacy code, new code code that has been developed by different developers over different points in time, different processes. And so every time that a bug comes in via our support desk, that will take a lot of my time because I have to, I have to look through the communication. I mean, I had to clarify. I have to look through all of our documentation. I had to look through the code. I had to connect to the business process. I had to connect it with recent changes that have happened in the code base. And so I built an agent for myself that will do that, right? I built an agent that could access all of our code base, that could access all of our documentation, that could look through conversations in Slack, which is our main communication channel. And that could then connect the dots. And that could down my time in debugging by more than 90% right. And so my metric for that agent was that is, how can it help me reduce the time in identifying with the problem is. And then we've got agents that are good at solving the problem. And we've got agents that are good at triaging the problem. So we also build agents that triage the request as it comes through. We've got cart rails of what is high priority, what is medium priority, what is low priority, who does what in the team? Who should you direct that bug request into? Don't always send requests to me because I'm the team leader. Some things can be solved without me, right? And so for me, the metric that I measure for these kinds of agents is efficiency. Now, if it's an external user that it's different metrics, but it's as you said, NPS, it's making sure that the delegation rate or the escalation rate is low. And it's making sure that it's actually increasing conversions. It's reducing the time spent in calls with the user. And so it's ultimately, as you said, agents are really eager to please. And you've got to give them the context of how much do you want them to please you versus how much do you want them to challenge you or your customer, right? And and and large language models are by design trained mathematically to try to always give you an answer, even if they don't know it. And that's where hallucinations really come from without getting too deep into the math. That's really the main source of hallucinations is that they're trained for a reward. And with and they don't have the right context, they will try to get that reward no matter how, right? And so they will come up with something. I appreciate that. Diego, one thing I wanted to touch on, I'm conscious of your time. I'm enjoying the conversation. It'd be remiss if you're not to talk about because I think that is excellent. The vacancy strategy. Talk me through it. What does it look like? And as the disclaimer out there, we're not saying don't hire more people. It's just just think different challenge yourself. So Diego, we spoke about it off air, but can you unpack it? - Yeah, I think you've got to change your products and you've got to, when you're thinking about AI implementation, you've got to realize that if the world has changed and you're trying to adapt to this world, adding new technology to the top of the old world is not gonna work, right? And everyone has had a traditional org chart, everyone has had a traditional way of working. And if you then try to implement this new, very fast-paced way of doing things in an old way of working, there's going to be a clash, there's going to be lack of adoption, there's going to be inefficiencies. So what I do is I challenge myself every time that someone leaves, I don't just go back to my old org chart and say, "Okay, well, I've always needed this role." And so as soon as this person leaves, I will go to market with this role. That is the best opportunity to challenge yourself as a leader and rethink, well, let's map out everything that they did. Let's see our capabilities right now. Based on your AI positions, see, what are the agent use cases that you can launch right now? That will be an opportunity for you to test that. How can you use this as an off-skilling opportunity for team members that might be willing to do some of that with the help of agents, right? And, first of all, if the more you do that, the more you realize that there were some team members that will take this as an opportunity to grow, there are team members that are keen to learn to do more, to be more part of the process of building, right? And that's what I call now the full-step builder, right? Is that more and more people are being able to participate in areas of the work and of building that they were not able to do before. And I use the word building not developing on purpose because I think ultimately everyone in the company is building toward something, right? This building, a product, this building, a service, and I think more and more everyone is becoming an end-to-end builder. And the more you enable that to happen, the more you realize there is people there that wanted to grow that felt limited because they didn't have access to knowledge. And someone leaving is an opportunity to explore that. And if you do it efficiently, that doesn't mean that you're in an out-of-the-world workload. That's not at all. Because if you do it in a scalable way, they're gaining knowledge, they're gaining a bigger spectrum and accountability and ownership of the process of building. And they can do it efficiently without necessarily meaning more hours of working a day, right? And so that's what I've been doing consistently, which is why we've had that 60% reduction of our team within the tech team. And it's something that I would encourage everyone to think about at a company level, right? Is don't get stuck in your old org chart. You will now identify that in this new world, some roles have evolved and some roles can be expanded and some roles can be augmented and some roles, there will be new roles. We briefly touched on agent, human in the look. Now we're moving towards human at the helm, which is a very different conversation, right? It's human in the loop is there is a human at the end of the process, right? Human at the helm is a human supervising multiple agents. And that's becoming quickly evolved, right? And that human has more visibility across the pipeline of what needs to be done, but has got more help with agents doing the toil that we were not capable to automate before, right? - Yeah, there's a lot of interesting stuff that we can unpack. What would you say, you know, the one or two things that you would recommend everyone doing in their organization, if they're not already at a personal and at an org level, that's going to drive the biggest impact based on what you've seen, what's your top tips? - You yourself need to dig ownership, right? You need to embrace a change and you need to experiment with the idea yourself so you understand it more, right? And so what we do within our team and within the company is we give ourselves very time frame challenges. The next 24 hours, let's build a basic agent. What do we need? Let's go. It doesn't need to be perfect, doesn't need to go in production, doesn't need to deal with a customer, doesn't need to deal with sensitive data. One quick win. What is the fastest quick win that you can get started with that will get your toes deep into the water, right? And that's again, the G-manology, that means that you are starting to frame that muscle on what do you need. I think speed is something that you need to bear in mind, yourself, your team members, your competitors, the industry, everyone's moving faster than ever before. That means rethinking your business model and your processes in a way that you can keep up with that speed. Speed is everything this year. I think everyone's able to build faster, everyone's able to innovate faster. So it's not necessarily what better features you offer. It's how fast you can innovate that you can improve those features more than you compete through wheel at the same time. But so speed is critical, which starts with getting started. And then as you do it, all of this in parallel, take care of the data, take care of the process because that will compound with everything else that you're doing here. So it's kind of like thinking fast and slow at the same time, right? And then put some time in thinking system, system-wife, system design. What are the gut rails and governance that I can build across the whole framework to the more comfort dissipate in that process? And that's all I would summon up in yourself. - Yeah, I know, I love that. - And on a personal level, challenge yourself as an executive, if I'm always doing this task, I don't need your tasks for the day. What is it that's taking the most of my time? And then starts experimenting with an LLM, ask the LLM, give it that task, put your day into end, and then give it to an LLM and ask an LLM, how can either an LLM or an agent help me with optimizing my day? Does when you're so realizing, oh, okay, I could do this or I could do that? And I think that's one part that people means is that you can use agents to help you build agents. You can help LLMs to help you interact better with LLMs. You can literally ask a large language model, what is the best way for me to prompt you in order for me to get this business outcome? And the large language model will guide you through how to prompt it better so that you get better at using the large language model. So you can then implement it better across your day. And the more you do that with your own calendar, the more you're able to proficient you talk with others about how they might do it better, how the company could do it better. So it's not ownership of embracing the change at a personal level with your time, with your day, with your calendar, that it will be felt because the company, if you do it more, right? And so AI literacy for executives, I think it's pretty important because then that's right's culture. But also ensuring that you're bringing others in the journey and everyone's being upskilled as well. - Yeah, yeah, thanks. Yeah, you're going to the proof is in the putting with your numbers in the organization. And if I was just quickly at two points, most of you are probably using Microsoft in the organization or Google, one of the two. Even if you're not using these systems regularly, personally, it's going to have data on you and your workflows. So I run a prompt recently with the workshop just a bit of a digital mirror. Ask it what it already knows about you. Ask it where your bottlenecks already are. And self-awest is going to be a bit of a slap in the face. So most of you, it's a great place to start. Just start having a chat. And when we think about an organizational level, look at your key teams that diverge off the ELT and look at which one of your roles or functions generally get bottlenecks because they're requiring a system or brother process to be implemented. Is it your sales team constantly waiting for marketing collateral to be done or is it your marketing team waiting on finance for a dashboard or whatever it is? It's going to Diego's principle of everyone is a builder, the roles are collapsing. Start experimenting. Encourage your sales team. Next time your sales team is waiting for a call from finance, encourage them to go into AI and look at how they can actually generate it. Because if you're talking about something like a dashboard, a dashboard can be built in 30 seconds. And sometimes even better than the one you're really getting. So just experiment. Everyone's a builder. Echo that throughout the organization. Thanks for coming on the show today, Diego. Where does the audience get in touch with you? - The evening is at this place. I'm very happy there. There are sports that I would like to end with for people to get inspired by that I really, you know, that I hold dearly. One is from Jamie Dimmon, who is the CEO at JPMorgan Chase. He said last year, "If you're not confused, you don't know what's going on." Why do I say that now? Because I want everyone to feel comfortable with being confused. I consider myself literate in AI. And I'm confused every day. So that should give you some comfort that if you're feeling confused, that's fine. That means that, you know, you are aware of what's happening and that means that you are practically taking steps towards trying to do something about it and trying to learn. Not being confused, that's scary because then that means that you're not aware of how fast things are changing and that's something that has been true in nature since the beginning of time and that's true in business and that's more important now than ever. The number one skill that you've got to have is an executive as a leader and as a company is being adaptable to chimps. Alright well thanks for coming on the show. If you're having people with sufficient thanks for watching. Thanks. See you next time guys. This is Bezoosle. Make sure you subscribe. We publish regularly one podcast, one newsletter each week. All you need the same form and execution ready. If you want to understand where your AI is, head over to apply AI Australia from the RU. Now remember you can't control the speed of change but you can control how quick you would get. Thank you my name is from Monvigriga. This is apply AI. Thanks for listening and I'll catch you soon.