Speaker 1Why are people getting paid a million dollars as forward deployed engineers? It sounds crazy, right? But if you think about it, it isn't. Because if you're deploying agents and you're able to drive 5, 10, 25 million dollars of value for companies, would they be willing to give you a slice of that pie? It turns out yes. So the question becomes, how could you become a forward deploy engineer? How could you deploy agents to make companies run more efficiently, drive revenue, lift margins? Well, today I brought on Voss from Veric Agents for an inside look at how this works. He's sharing examples from client engagements with the details changed that you just don't get to see publicly. This is for the first time ever on the internet. And that's really cool. I think a lot of people have talked about forward deployed engineering on the internet, but they haven't gone into concrete examples of how to do it. So let's take a look. how you can actually do this. By the end of this episode, you're going to understand how to find the work worth automating. You're going to be able to decide where agents belong. You're going to understand open source versus closed source. You're going to understand where Muse, Grokbot and Dots fit in and all of this and how you can start putting this into practice. This is a masterclass for how to become a forward deployed engineer. I did one other episode with Voss not too long ago, but we kept it high level. And by popular demand, we're going deeper. So send this to a friend like and comment and I'll see you at the end of the episode. One of the most common questions I get nowadays is Greg, how are you using voice AI in your everyday life? Well, today I'm going to break it down in 60 seconds. I'm going to give you my voice AI toolkit right now. And this section is sponsored by Google. So Google's actually been crushing it with voice AI. I've been using Gemini 3.8 live recently. So what's really cool about this is that you can use voice AI in your everyday life. And I've been using voice AI for a long time. So what's really cool about 3.8 live is you can just go and have a phone call basically with it and say something like, hey, you know, how am I doing with my launch campaign? And it's connected to all my tools. And it's basically running my business in the background. So I think about it as if my it's almost like my chief of staff. It's smart. It's intelligent. It's connected to my tools. And it allows me to live my life while having voice AI help me run my business. The second is Gemini 3.8. Transcribe. Now, what's really cool about this is I can go and leave voice notes and it'll go and parse those voice notes. It removes the arms, the likes. And, you know, how I use it is, you know, me, I've got a lot of ideas. I got a lot of startup ideas. And it just allows me to go on walks and just basically give those those ideas and get back text that is clear. And that also just, you know, allows me to remember because, frankly, I forget things. The third is Gemini 3.8 Flash text to speech. Now, what's really cool about this is you can go and say, hey, build me a voice that has a Brooklyn accent and it'll go and do it. And you might be thinking, well, how can you actually use that? Well, you know, there's so many products that need a voice. You know, for me recently, I've been building a mobile app and I just included an onboarding voice into the mobile app. And the last thing is speech to speech. You know, not everyone speaks English. So if you're doing business in Mandarin in China, you get this real time translation via Gemini 3.5 live translate and it just opens up new markets. So these are just four tools that I've been obsessed with lately. Shout out again to Google for sponsoring this part of the episode. And I'll include links for where you can play with these tools and models in the description. Have fun with it. Have a creative day and I'll see you at the next 60 Second Masterclass. Voss, by the end of this episode, what are people going to learn?
Speaker 2People are going to hopefully learn the end-to-end workflows that we're seeing as part of AI transformation and better understand how they can go ahead and do this themselves.
Speaker 1Yeah, because there's lots of talk. And I'm sort of guilty for this too. Just keeping it high level. Sometimes I'm just like, here's an interesting topic. Here's an interesting business model. Just apply AI. But the big question is, well, how do you actually apply AI? What does this forward deployed engineer piece look like? So we're going to actually get into the nitty gritty of that all, right? So that's the commitment you're going to make to the person listening to this. By the end of this episode, they'll understand what it actually means to apply AI. What does it actually mean to forward deploy into something? And where are the business opportunities and monetization opportunities? And how big is this thing? Is that the commitment you are going to make today? 100%. You have my word.
Speaker 2Let's do it. All right. Cool. So I put out an article a few weeks ago, and it was titled, Don't Apply AI. Obviously, a play on applied AI, et cetera. And again, the point of that is to go into AI isn't something that can be applied like a coat of paint. It's something that really involves process re-engineering. And that's the goal of today's kind of presentation. And thanks for having me on. So as I'm sure everyone already knows, and I know, Greg, you talked about this a week or two ago, this is already happening, right? Roll-ups is a huge part of the private equity playbook these days, where you'll buy a firm that runs very much on people, outdated processes, maybe outdated software as well. For example, accounting firms, IT shops, law practices, et cetera. And these AI, holding companies or AI transformation companies are buying up these portfolio companies and putting AI engineers, forward-deployed engineers inside of it. So what they do is they'll find the processes, they'll map out the systems of record, the exceptions, what happens where, what cycle time is recurring as a result of handoff between two different pods of people. And they'll rebuild that process with agents from the ground up. Thrive, for example, comes to mind. They have 35 engineers across 70 firms as of me creating this and doing some research. I'm sure that might be even higher now. And what they're seeing is that it's actually quite successful. The numbers move. Tax returns 30% faster, 98% accuracy. Agents are actually able to take work off of people's plates. And it is feasible. It's possible. The only caveat being is that it's a lot more involved than people had maybe initially surmised. So one example is gross margin at one call center firm. It was 60% and above, which is actually quite high. And as a result of that, when they buy the firm, let's say they buy it for a billion dollars and they increase the margin and they double it, for example, in the best case, that actually translates into the valuation of the entire company. So all of a sudden, you can buy it for a billion, implement AI across 3, 6, 12, 24 months, whatever that is. And then you can sell it for 2 billion or 4 billion or 8 billion. And that's the goal of these companies.
Speaker 1Cool. Let's keep going.
Speaker 2So here you can just see a few different examples of that. Thrive Holdings with Josh Kushner, General Catalyst, AI-enabled roll-ups. And then the people who are doing those steps of two and three are those four deployed engineers.
Speaker 1Yeah. And I think we'll get into that by the end. But I think that's the big question a lot of people have is, I think people hear this and they're like, okay, cool. But you're basically saying, like, buy a business, add AI, and, you know, question mark, question mark, profit. You know what I mean? So I think it's like, okay, but how do you actually do this? We will talk about that.
Speaker 2100%. I'm not going to keep it super high level. We're going to get into it. And the analogy that I like to give everyone as you're thinking about what it means to be a forward deployed engineer is your life is already complicated, right? You have five different inboxes, four file stores, Google Drive, Notion, iCloud Drive, your desktop. You even have five messaging apps. You have iMessage, WhatsApp, Slack, Google Calendar, Outlook Calendar, all this stuff. And it's actually very hard to understand exactly how you like to use your systems, right? So which app is the easy part? You can make a simple tool call, for example, to an API of, you know, whatever system of record of your choosing. But if a client emails you, you know, it's Gmail, it's work. But if a family messages you, then it's iMessage or it's WhatsApp. And understanding this is quite key. Even at a personal level. So imagine at a company, and this is to your point, right? This question mark, question mark, question mark. People are so annoyed at this, like, you know, AI is going to fix everything. Just use AI. Because they know that it's actually quite complicated. Imagine a company, right? This company has acquired eight other companies in the past. So now they're existing in five different regions. They're in Sao Paulo. They're in Bangalore. They're in Australia. They're in Sydney. And they have 23 different systems of record. Each region is doing things differently. Chicago is on SAP, Salesforce, and Workday. Toronto is on NetSuite, HubSpot, and ADP, and so on and so forth. So really, it is quite complicated. And this is exactly why, again, AI cannot be applied. If you're applying AI over this entire company, which literally spans the entire globe, you're going to end up with just making shit faster. I hope I can curse on this podcast and bleep that out for the end. Absolutely. Absolutely. And that's the fundamental issue. So now, again, I don't want to be too beating a dead horse on the problem. The problem is very clear. It's very complicated. So how do you actually go about doing this? This is our view, and I'm sure there's many others. different ways of doing this, but our view is process mapping, then re-engineering, then building, deploying, and rolling out. And there's a few different ways that we do that. So if you were to go into a company on day zero, here's what I would suggest that you do. The first is interviews on the human side. So let's start off with a single department, right? You'll have in finance, you'll talk to the head of AP, AR, reconciliations, billing, banking, FP&A, etc. And you'll work your way down from there. And the reason why these interviews are super helpful is because a lot of information lives in their heads. Very rarely do you have a very clean document source that you can just point your agent at, it'll learn it and go from there. Very often, it's not written down, it's not documented. And we hear this all the time. When we talk to companies, they tell us saying, oh, yeah, this person's been at the firm for 20 years. And they're like, oh, yeah, this person's been at the firm for 20 years. And they just handle it. It's so common. It doesn't matter the size of the company. It could be a massive Fortune 50. It could be a small S&B. They all have these critical key people where information lives in their head.
Speaker 1Yeah, I mean, people, if you think that you're going to walk into a company and they're going to have like an obsidian second brain hooked up to AI agents, you are just mistaken, my friend. Like, 100%. There is no second brain happening here. Yeah, there's nothing you
Speaker 2can just plug into call an API. And all of a sudden you have your knowledge store. It's kind of on you to create that. And it's per department and it's cross departments. And it's very, very involved.
Speaker 1Okay, so step one is basically, in a sense, it's creating like a human API. It's getting all the, you know, data systems, SOPs, you know, all that stuff into modern digital systems.
Speaker 2Absolutely. You always have a good way of articulating. I like that. It's a human API. That's what it is. And you have to understand, you know, why do they do things the way they do them today? Who really decides them? Which step is theater? Which step is legitimate? What happens when exceptions take place? How often do exceptions take place? All of these things are not written down. From there, we move on to the next step, then mining the systems of record and mining everything else, right? So really, there's a lot of data that exists in their Salesforce, their NetSuite, their Dynamics, their Workday, etc. And that doesn't mean that it's written down, right? It's not in the document, which has like one step one, step two, etc. But you can sort of create that SOP by living on top of their systems of record. So for example, if I have real time access to a Salesforce, for example, over the three to four weeks, I have a pretty good understanding of what sort of data enters Salesforce, how often is it getting corrected, etc. And this runs constantly. And you can use AI to analyze what the hidden meaning is behind each one of those actions, right? So if a certain record comes in, and it's from a certain company, it's of a certain size, we see these actions taking place. And now we can create sort of a graph of okay, once something happens, once something enters of a certain category, A goes to X, B goes to Y, C goes to Z, et cetera. But obviously, if you just do that without the interviews, you're missing half the picture. The final aspect is what lives outside of them. So then you actually go into the existing documentation, sometimes outdated, sometimes it's pretty good. This is in SharePoint, this is in Drive, this is in Notion, this is in Slack, this is in Teams, this is in Gmail, this is in spreadsheets. And again, through these three steps, you have literally the entire company's picture. And it varies the split amongst companies, right? For a very large company, they've had 10 years of Salesforce historical data for you to go off of. For an SMB, they probably don't. They probably have mostly in people's heads, in interviews. And no processes, no system of record, no software. So it's up to you to determine what angle you need to take per company, and it
Speaker 1varies. Crystal clear.
Speaker 2So here's a very concrete example. You wanted to go away from high level, this is what we're trying to do. This is a public software company. This is actually an engagement that we completed. So it was a $5 billion in revenue company, they have over 150 products, tons of solution consultants, and we were brought in by the CRO. And they're public. So this actually makes them quite complicated, you have to deal with regulation, certain laws apply, etc. So the process document, which they actually had, and they had completed this, I think it was engagement with Deloitte that mapped this out for them, was you build a quote, then you submit it, then it goes to deal desk, and you approve it, then you send it, you negotiate, you sign it. Very cut and dry. And this is always what happens if you just look at one angle, even just one person, right, they might tell you the wrong story. So you have to interview other people as well. But what the reality was, this was as a result of process mining agents that we had deployed over their CRM, which I believe was Salesforce. It's actually a 20 step process with seven different loops. So from step one to step, you know, four or five, then you loop back to one if there's an issue. And that's 61% of requests actually follow that loop. Then later down the chain, legal sends it back 12% of the time. Later down the road, 30% of the time, a new quote has to repeat, getting approval, etc, etc. So none of this was really documented. And it was up to us and actually my team of four deployed engineers to go in and create this process mapping.
Speaker 1It's interesting, like the way I'm thinking about it is every business is sort of like a factory. And a factory, if you think about, imagine you're looking down at a factory floor. You know, there's an assembly line, there's different parts of the assembly line, there's probably maybe some offices where people are doing some accounting or, you know, and they all kind of work together to create a product. And what's really cool about what you're doing, and just for deploy engineering as a service in general, is you're basically saying like, how do I distill every business down to these systems or set of systems per department? And then you're, you're basically, what's really cool is like now, well, we have everything we need to actually get the, you know, usually the work done. There's the step one is like the digital tools, like the sales forces, the net suites, all those products. Then there's the agents that actually do the work. And then there's the human beings. There's oftentimes like a human being step to it, right? Like, not everything could be fulfilled by a human being. So what you're doing here is you're kind of like exposing the full system. You're acknowledging that a lot of people think that they have a full system, but it's actually usually just the tip of the iceberg. And you're basically saying, how can I open up this system, optimize it? And then I would imagine, like, have evals or, you know, which you can talk about, but basically make sure that, you know, it's doing its job at the, you know, as good as, as possible.
Speaker 2Yeah, 100%. And it's funny that you mentioned that, like the mapping of the processes, let's say across a sales department, as we have here. It's, it's actually never been shown before to anybody in the department. So cleanly, to the point where, you know, the CRM and CFOs are telling us, like, I feel like you understand our department better than we do. And it's true, because no one has done that yet. And that's the real issue, right? If you go and say, hey, we want to use AI, well, on what? What are we doing? What is the broken process? What's the problem we're trying to solve? And that's why you need this step. And then to your point of, you know, not everything can be agentic, some things should be deterministic, etc. These are the 20 steps. And this is what we bucket them into, right? There's four buckets. One is delete the step. This shouldn't exist in a post AI world. Some steps are plain code where you have, you know, rules, there's no judgment required. For example, it's a simple API call. If this happens, then this happens. Five are agentic, right? You have building a quote that requires some level of judgment with who's the customer, what's it worth to us? How much do we need to spend? What resources do we need to allocate, etc. And finally, to your point, humans in the loop, human decision makers who are going to be handling the most risky tasks, things that you really cannot afford to, you know, get wrong. And it's approval, it's negotiation, it's signing, it's submitting payment, etc. Yep. So this is also how we do it. And this is a one thing that I really want to call out, right? A lot of people think of AI agents as a new surface. And we take the opposite approach. And this is what I've seen really resonate with a lot of executive leaders. And it's what I strongly recommend to anybody who wants to be an FTE is pitch yourself as building these agents inside their systems of record. So one thing that we do is, for example, we'll have agents that mine your Salesforce and take action in your Salesforce. And there's no new surface that you need to go into to interact with us. We're not asking you to replace your Salesforce, your CRM. That's impossible. Real quotes from our customers. They spent several years and several million dollars, I think $10 million when time on migrating from one ERP to the next. Same thing for, you know, one CRM to the next, etc. if your pitch for AI is, hey, we're going to move you from Salesforce to this AI-native CRM, you've lost them. And maybe in SMB where they don't have a CRM, you can put them on one, that's great. But otherwise, try to be inside a system of record. Even here, when you have human in the loop, it's a message in Slack. And that's what we've really seen resonate with our clients as well. So I strongly recommend this being the case. And this is also how you don't have to retrain staff, right? They are already used to this. You're working inside their records and you can just hit the ground running with a much faster rate of utilization, much higher efficiency, et cetera.
Speaker 1Yeah, I mean, in general, you're trying to sell anything to anyone. You want the path of least resistance, right? So I think that if you're going to ask them to completely move softwares and then kind of like, introduce all these new concepts to them, because they probably haven't heard of a lot of these. I mean, some of them maybe have, but some of them probably haven't around this whole agentic world that we're living in. So yeah, my take here is, or I'm just agreeing with you, obviously it makes sense. Especially if someone's listening to this and wants to be a forward-deployed engineer or wants to start a forward-deploying engineering business, path of least resistance.
Speaker 2Yeah, 100%. They haven't heard of Jev or Muse or any of this stuff, right? So just keep it simple. Yeah. Here's another example, right? And we mentioned there's a lot of PE firms that are looking to agentify their stack. For anyone who's not very familiar, a private equity firm will have ownership in dozens of companies, right? And their mandate is, all right, let's roll out AI across all of them. It's impossible to do so, right? If you have 26 different companies, that's 26 different engagements. And then each one of those companies, 26, has 10 different departments. Each department has 10 different workflows. And all of a sudden, you'll need to deploy 50,000 forward-deployed engineers across three years if you want to even make a dent. So the way that we recommend going about this is grouping together portfolio companies. Oftentimes, it's on systems of record. For example, if we have a PE firm with 26 different companies, and we're tackling finance for all of them, we'll group together the five that are on NetSuite as their ERP and four that are in Dynamics. So if you're an FDE, your job is to make sure you really understand what capabilities already exist in each software. So what does NetSuite offer? What does it not? What does Dynamics offer? What does it not? And then where can you fill in the gaps? How do you make that talk to whatever else they're on, which is Ramp or Brex or Tipolite or Blackline or Concur or Expensify? On the sales operation side, it means some are on Salesforce, some are on HubSpot. How do you integrate that with everything else across their stack and their spreadsheets, et cetera? You get the idea. And what happens is if you go about it this way is you only have to tackle similarities, right? Instead of getting pulled in different directions, you just streamline with, one, you know, entry point. And that's a system of record. That's what we strongly recommend, especially if you're an FDE. If you're being asked to do 10 different companies, you have to simplify it for yourself. It also simplifies the politics. You know, you're working with CFOs with the same buyer over and over again.
Speaker 1It's interesting that the CFO is the sponsor. Like it's, you know, you would think that it might be the CTO or someone just from technology.
Speaker 2Yeah. We very often get brought in with CIOs. And then that's just from like the, we want to identify everything perspective. But also it's like CEOs, CFOs, CROs who are like, well, this is my department and I want something there. Now, caveat thing, CFOs are self-proclaimed notoriously skeptical. So good luck selling into CFOs. But that's just how it goes.
Speaker 1Hmm. Yeah. I mean, to me, I don't want to offend any CFOs listening to this, but to me, like CFOs just care about optimizing costs. So when they hear, you know, agentification or agents, they're just thinking, how do I lower my costs, increase margin? Are you finding that's like the best way to get in? Like, hey, like we're going to like deploy all this stuff and like, you're going to save a lot of money. Is that how you're thinking about it? Or am I missing it?
Speaker 2Something? No, I would say that's largely correct. We obviously deliver value on three buckets, right? One is cost savings, but the second is revenue uplift and third is risk mitigation. And what we've seen actually resonate with CFOs is yes, cost cutting, absolutely. First and foremost. But then also like, how long does it take you to close your books? And we've heard like about 22 days. Four weeks, six weeks. Okay. That's it. That's over a month. It's called month end close. What are we talking about here? So if we can bring that down to four days or eight days with higher accuracy at the same cost that you're running it today, even they've really found that to be useful. And then obviously further, if we can cost cut, you know, down the line, that's even better, but it's a bit of both. It's not just that we want to keep our existing slop, but cheaper. They actually do want to move towards faster, more accurate, et cetera.
Speaker 1It's, I mean, maybe it's just like you speak to who you're selling to. So if you're speaking to like a CIO or CTO, it's like the efficiency, maybe it's the efficiency, it's, it's, you know, the output, it's, it's productivity, it's stay up to date, you know, it's all of that. But like, then it gets like handed over to the CFO. It's like, they might not care. As much about the efficiency in terms of like, or the output that it's like way cleaner and nicer. They might just care about like the revenue uplift and, and, and, uh, lowering costs. So I mean, obvious to say, but like that's, you know, for people listening, it's like sell to who you're, you're speaking to.
Speaker 2Yeah. A hundred percent sell the outcome, right? If we're talking to a CHRO or a chief people officer, it's, you can hire better people faster. And train them quicker on day one. It's not about the cost. They don't want to save money here. They want to get way better output. Um, so it, it varies. Cool. So here's again, deep dive into a concrete example. Um, five portfolio companies all on NetSuite, but even then they have very different ways of running things. Uh, you know, 12 steps, nine steps, 15 steps, 18 steps, 13 steps. And then imagine, you know, they have regional differences, regional variance, at each portfolio company level. So again, this is why mapping it out is so important. And this is useful, not just for each portfolio company, right? Each CFO has the same, you know, uh, investment that we talked about earlier, where they see this and you understand their department better than they do. But the PE firm does as well, where they can see things get mapped out and streamlined. And what we've seen is a lot of PE firms ask us for, okay, what's the playbook, right? Tomorrow, when they acquire a new company, what process should they follow? Um, how should they go about it? What software should they adopt? Um, and this mapping of saying, Hey, look, if you're on NetSuite, this is the concrete way of doing things. Here you go. You turn this into six steps agents in certain locations, et cetera. Now, this is obviously a dramatization. You will very rarely get to as clean as like, Hey, everyone's on six steps and we did it guys. Perfect. If you're not on six steps, you're not going to get the same efficiency, but you'll actually come quite close to this. And you'll get that by being very deeply involved, mapping it out and working with the stakeholders and then re-engineering it with, um, a lot of foresight and a lot of, uh, thought and thoughtfulness. So again, this is actually, and this is anonymized, obviously we can't share, you know, details about our clients, actual workflows, but this is a real process mapping for, I think it was accounts. It was accounts payable. Yeah. Um, and it was 17 different steps with exceptions being handled, right? So these are all like exceptions, and these are all the steps, et cetera. Um, and this we uncovered over the course of, I believe, two, three weeks with interviews, process mining, et cetera. Um, and this is actually a less complicated, you know, workflow for them. And in general, we've seen workflows that are 40 steps or 200 steps. Um, when it gets crazy, but your job as an FTE is to one map this out. Don't skip the step. Don't skip educating the client on, on what they're, you know, saying is they want to see this. It breaks their heart, but they need it. Um, and then you turn it into, here's the agentic future. It one visually looks much cleaner, so they can take a deep sigh of relief. And two, it actually runs much smoother where you have agents that are handling what they need to handle and you have humans in the loop where needed, et cetera. Um, these green boxes are, you know, steps after I think human decisions. Um, and this is still like an exception where, like deterministic code, et cetera. So this is your job as an FD.
Speaker 1By the way, if you're selling this to a CFO, the way to do this is you have the agents there and then you put the estimated cost per month of the agents. Because it's going to be shockingly low, right? Relative to human beings.
Speaker 2100%. And you have the same thing with the accuracy. And all the KPIs that you can throw at them, they will run that. Any C-suite, right? And part of your job is based on those KPIs. Here's how bad it runs now and here's how it's going to run in the future. And then you hold yourself to that standard. So six months from now, you can say, I did this. Love it. And you keep stealing my thunder, Greg. That's exactly what we're showing here. So you show them from 17 process steps to seven, cycle time from 24 days to six, exceptional loops from six to one. This is a really good one. The next two, the straight through rate of an invoice. We drove that from 18% to 87% for this client. And that was a game changer for them. Because all of a sudden, literally a majority of their invoices were going through exception routes. That's terrible. That means you have really bad processes. And we fixed that process. That's a process re-engineering flow, by the way. That's not even all about agents. And then finally, we drove the cost of handling a single invoice down from $31 to $6. It's an 80% reduction. So to your point, right? You show them that in this slide.
Speaker 1Right. And to the people who are like, Voss is just replacing human beings with agents. The other piece of this is if you're able to optimize a company such that their cost per invoice is going from $31 to $6, now all of a sudden that company has more margin. Yes, they might take some of that margin, but they also might give back some of that margin to customers.
Speaker 2Absolutely. And also use that margin to hire. Because the truth be told, the clients that we work with are very often like Fortune 1000, Fortune 500. They want to win. They want to grow. We've very often seen that it's reallocation of resources. It's not about doing mass layoffs. They would rather have their best people in finance not spend their time doing manual invoice routing and approvals and parsing of an invoice. That's ridiculous. They would rather have those people on higher leverage tasks, planning out FP&A, certain aspects of that, or migrating them across cross-functional or building their own FTE teams. So I hope that no one is under the impression where these are going to replace everyone's job. Yes, there may be migration of job, but I do think that the companies who want to win are reallocating. They're not doing 50% layoffs. It's crazy. Cool. Another example, 60% accounting. This is more of the SMB side. If this is your first FTE project, you should probably start here. This was not actually one of our clients, but someone else in the industry that I had chatted with. They had $12 million in revenue, 400 clients, four systems. And what made it hard is every client sends its books in a different way. I actually heard that some invoices were sent as a picture of someone scribbling in a notebook. And that was when I knew, like, okay, you can't just apply AI. You got to really get in there and do it deeply. I won't beat a dead horse here because, you know, one, this link will be in the description. And two, it's more of the same. But reality is, they said they had six steps. The reality is they had 14. They have a lot of loops. It's on you to go in and figure this out. Previously, we talked about it from a sales perspective. Now we're talking about it in a finance perspective. So collections, then you re-ask 70% of the time that happens. Then a partner sending it back, you see step four here, that happens 35% of the time. To be very clear, if you're an FTE, your job is, yes, to map this out, but also to educate them on what is the cost of this happening. So if 35% of the time you have to do this loop, what does that cost? Not just in terms of money, but in terms of time. What is the cycle time of this one person on the team to the next person on the team? That's where a lot of the time goes. In the article, and I put out Michael Hammer, who did this study of digital transformation, said that you might have 20 steps. And if you speed up each step, you might not make the process any faster because it's the cycle time between steps that makes all the difference. That's where the 20 days of time comes out to be. And that's seen time and time again. And that was 30 years ago that he said this. So we're seeing the same thing today. Again, same four buckets, sorry. Deletion, plain code, three agents, two human decisions. It's on you to figure this out. If you want to know how to go about this, it's very simple. Plain code is to be used when it's a simple if X, then Y. There's no judgment. There's no variance. And if there is variance, it's a switch case, right? If X, then Y. If Z, then A, whatever. I ran the letters. You can get the idea. On the agent side, it's where you have enough historical data and judgment is required that you can be pretty concrete about, hey, for example, we have an invoice, a line item shows monitors. That's very likely going to be office supplies. But there are exceptions. If it's from a certain vendor, then we know that it's actually not office supplies. It's, you know, some other thing. I don't know. You get the idea. And then finally, human decisions, right? If we have to send out payment, there should probably be a human on that. We don't want an agent to go end to end because then you have phishing scams, right? You have an invoice that comes in. Agent says, this looks legit. We're going to go ahead and pay it. Human in the loop, is always super helpful for reviewing and for delivering approval, et cetera.
Speaker 1How should people think about frontier models versus open source models, Chinese models versus American models? We've just talked about agents as agents, but if you're actually going to deploy these agents, how should people think about these ecosystems? Super good question.
Speaker 2So on one hand, most companies are on Copilot, Microsoft Copilot. Now, behind them is Cloud Code. Behind that is Codex. And a lot of what you want should start, especially for an SMB, in skill files, wherever you are. Now, if you're actually building agents, which requires engineering expertise, I'll be honest, and the big labs don't want you to know this, but most of what you're looking to achieve does not need to be leveraging a frontier mop. There's very few cases where we've seen the need to deploy Fable or Astra. Now, that doesn't mean that you're not using their models. We're using Opus 4.8 or Sonnet, more likely, or GPT with lower thinking model. I don't even know what their naming convention is anymore. And also open source. Now, the other part is, if you're talking to enterprise, and this is some sauce for the viewers, they have an aversion to Chinese models. Even though their floating point number is not actually how that works, they don't want to work with models that are out of China. They can't. Or, you know, there's a strict aversion to it. But we have leveraged open source models like, you know, Muse. And sometimes Chinese models where we're allowed to. Grok has been a great model that we've used as well. So don't feel the need to solidly, yourself into just ChatGPT, Claude, Frontier models. You can use their non-Frontier models. You can use Grok, you can use Muse, you can use GLM, Kimmy, Quen. All these different things are toolkits. And you should benchmark every single workflow against every single model to determine what model is the right use, is the right model for your use case.
Speaker 1We've seen personal agent platforms start to get big. So we have GrokBot now, we have Muse. Muse actually has, Muse for small business, they just announced that. Instinct, which is more on the consumer side. You got to think the other big players are going to come into that space too. How are you thinking about using the GrokBots of the world to, you know, deploy into these enterprises? Are you thinking about it?
Speaker 2Yeah, and then OpenAI yesterday with DOTS, right? There's an unlock for that, but it's hard to see governance for those, you know, agents, personal agents in the enterprise use case, right? I think that is an extension. My philosophy and the philosophy that you follow here at Veric is that there's two streams of agents for any business or whatever. There's the sidekick agent, which is your co-pilot. You chat with it, you get stuff done. And Instinct and DOTS and GrokBot and Muse are extensions of that, where you have to chat with it and get stuff done. The other angle is background agents that truly do work in the background. They don't bother you. They just do the same thing and they ping you when they need to. They know what they have to do already. And that's why this deep dive process mapping process of engineering is so valuable. Now, I can see them connecting at some point where you can use a Muse or a GrokBot to set up these background agents that just take work for you all the time, but that governance isn't there yet. There's still a massive gap in how involved you have to be and how involved you have to be from a software engineering perspective. So far, the use cases, to answer your question, are limited. And we would rather take work off of their plate rather than make them move faster, because that's the difference in ROI. This gives them 10-20% faster output. This gives them 70-80% faster output with higher accuracy, etc. Cool. So again, selling the idea of an FTE, you need to be three people in one. One, you need to understand how the work actually gets done. Ideally, that means you go off on your own and you really study these systems of record, you study Salesforce, NetSuite Dynamics, as we talked about earlier. What do they offer? What do they not? And then finally, and then part of that is, you know, how should account stable function? And you learn that either on your own in combination with going into a company. The second is shipping production code. You have to be able to do engineering work. Now, it doesn't mean you need to be an undergrad in computer science and software engineer for 10 years, especially with the advances in AI engineering, but you do need to be able to ship production code. Agents that call into these different systems of record and do so with auditability, governance, security in place. And then third is the AI layer, right? Knowing what model to use, knowing what you can trust a model with versus what you can't, knowing how to test each model through evals and, you know, optimizing your harness. And then finally, you know, how to handle agents taking incorrect actions, right? Rollbacks on agents hallucinating, etc. If you can do all three of those, you are the best FD. You're a very capable FD. And this is actually very, very rare. Usually you just have one or two of these. Or you're even kind of mediocre at all three. You have to be exceptional at all three, plus the communication of it all, right? Being able to speak to senior leadership and convince them this is the right way to go. And if you are this person, we really need to talk. We want to hire you. And that being said, you also have ample opportunity everywhere else, right? Everyone's looking for top FDEs.
Speaker 1Totally. 100%. Yeah. I mean, I like that you shot your shot there, you know? Respect. I think this person is like your NBA player, right? It is top.01%. But if you can figure this out, and the cool thing is you can figure this out in, like you said, you don't need to have a CS degree. You just have to dedicate yourself to learning the craft. You need experience deploying the craft. And you also need a lot of reps around just all the different ecosystems. Open source versus closed source. Like a lot of the different, I mean, even like the Microsoft ecosystem and the Salesforce ecosystem. You have to understand all these words, bring it together, communicate it in a way that sells to execs. So it is hard, but that's why these people get paid what they get paid. And that's why the value is so huge, right? The problems that you're solving with deploying FDEs, as we've seen in this episode, is multi-million dollars of savings and efficiency per year, easily. So like, someone once gave me advice, well-known people, person, well-known founder, several multi-billion dollar exits when I was young. And he always said, if you're finding a job, the best job to find is the one closest to the money. The one that could show that you can optimize revenue, profit. Because the people that do that are the ones that are naturally going to get paid the most. And they're generating as much value for that as we talked about earlier, like that factory system. So it's like, yeah, you gave $10 million of value to this company, can I pay you 10% of that, a million dollars? Maybe. That might be a trade. And that's why I think FDEs are so in demand, one, and two, getting paid so well.
Speaker 2Yeah. In a heartbeat, you would pay them 10% of what they can deliver for you. And there's even PE firms who are hiring FDEs and giving them a percent ownership in the carry, where they bought it for a billion and they hope to sell it for $5 billion and you'll get 0.5% of whatever that delta is based on the work that you're able to do for them. To your point, it's the NBA players. And you have to be good. You have to be great. You have to be the best of the best. Yeah. So, if you want to be deeper in the FDE space, if you want to kind of get started, maybe dip your toes, and if you're starting from nothing, this is what I would do. On a personal level, list every single athlete that holds your stuff. I talked about mine earlier today, earlier in the presentation, which was five different inboxes, three different texting, communication channels, etc. Do this for yourself. Write which one wins, which one disagrees, which one doesn't. How to route this. Kind of do a whole process mapping of your own life. And the next step, take 20 things you did last week, right? You paid a bill. You canceled a subscription. This is a big one. I'm seeing a lot of use cases on Instinct and Muse where they go in and cancel their, you know, stuff they forgot about. I got to do that with Adobe, by the way. Adobe for listeners. Charged me 40 bucks a month for two years. On Wednesday, you write one process down step by step. You know, how you pay, how you pay a bill end to end, or how you submit an invoice if you're a freelancer for work, you know, map this out and try to get as detailed as possible. So, you know, there's five different, you know, ways of doing it. There's 10 different exceptions, etc. And Thursday, sort every single step. Like we talked about the four buckets, what gets deleted, what's deterministic, what's agentic, what do you still need to be there for? And then Friday, figure out who you want to reach out to. And reach out to a bunch of SMBs that you can either get connected with or you can do cold outbound to to do this for them. And offer this in a single process. Start with one workflow. Make it super simple. And do everything for them. Do the skill files. Do the, you know, personal assistant, instinct, muse, grokbot, dots, whatever it is. And build the agents. And give them a timeline. Do it for free if you have to. If you're getting started. Trust me, the experience is worth more. And your next step is to do it for free. And your next one, you can charge that five figures, that six figures. But if you haven't done this ever, get some experience in. And then you go from there. And take what you learned from this, from this presentation.
Speaker 1How important is it, is it to know how to deploy hardware with agents at these enterprises? Is that something a lot of people are asking for? Hardware in terms of like GPUs? Yeah, like, you know, they have sensitive data. And so they, you know, they want to, you know, they want open source models on, you know, on premise, basically. Gotcha. Versus cloud agents.
Speaker 2Yeah. Truthfully, we've seen zero of that. I know there are companies who do that, where they kind of like either rent or sell GPU clusters to very large, maybe heavily regulated companies. We've worked with some of the most heavily regulated companies on the planet, like banks, financial services, health care, pharma. And I don't think that they're there yet. Maybe eventually they might be, but not right now.
Speaker 1Did you see OpenAI launched some security features yesterday at Dev Day? No, I missed it.
Speaker 2What was that?
Speaker 1They launched, pull it up, so I don't butcher it. So they launched private intelligence. OpenAI private intelligence helps businesses use frontier AI, with greater confidence that their data is protected. So there's a zero data retention with private safety processing, which enables automated safety reviews. Basically, you don't have to give OpenAI personnel access to the underlying content. I think a lot of people were kind of like, I want to use some of these models, you know, but, I mean, when I say people, I mean businesses. Businesses are like, I want to use some of these models, but do I really want to give the, the keys to OpenAI? Like, maybe not. So they end up launching a feature like that.
Speaker 2Oh, very cool. Yeah, I mean, we route through like, Azure Foundry, you know, AWS FedRock, Vertex, and they have, you know, again, they've agreed to not train, ZDR, et cetera. But I can see how this is becoming more and more of an issue for a lot of companies. It makes sense why they launched it.
Speaker 1Cool. And yeah, I like this step-by-step process. This is value, for. really not just for a lot of people. Number one, if you want to be an FTE, this is valuable. Number two, if you want your company to be more AI native, it's valuable, except Friday stuff. You're not reaching out to people, but Monday to Thursday, you're just understanding the system and optimizing the system. So it's valuable for a lot of different people.
Speaker 2Yeah, and one of the questions we get asked is, how do we make our own FTEs internally? And I'd have them follow the same playbook, right? So again, this is, if I leave the viewers with nothing else, this is what it should be. Don't apply AI, right? Too many times, and this is the reason why most AI pilots fail, is they try to slap AI on the business, and it's very hand-wavy, and maybe it's rolling out a license with cloud code to everyone. Maybe it's building an agent that doesn't really understand the system. It doesn't understand the workflow. It doesn't work. When we go into these large companies, this is the exact process that we follow. And this is why we successfully have transformed departments with AI. We do these steps. We find the real process. We measure the time based on all of the KPIs. We pick processes with owners. We sort every single step. We baseline before we build, and then we build it once and we deploy it everywhere, and we measure it constantly. So we'll go to these CFOs over the course of four weeks, do an audit where we understand their systems. We build the POCs in all in four weeks. In the next four weeks, we build the agents. And then three months after that, six months after that, we say, "Hey, this is what it used to be. This is what it is now. Let me prove it to them." So it's not just, "Oh, we built AI. We deployed it. We're done." That's the job of an FD, doing everything end to end. So hopefully that was helpful. I hope it was helpful for all of you watching,
Speaker 1and I'm wishing everyone the best of luck. - We'll include the link to this in the show notes, in the description, so people can access it. Sometimes people ask me, actually they don't even ask me. They go in the comments section and they're like, "You're involved in this company." I'm not involved in this company, you know? Like, Voss hasn't bought me a beer. He hasn't sent me money. He hasn't given me a coffee, nothing. I think that he's just really smart when it comes to this stuff. And I think that if you understand this stuff, you have an unfair advantage, and that's why I bring him on here. I'm on here because he's world-class when it comes to this stuff. He's not afraid of sharing the sauce, and that's why he's here. -
Speaker 2I appreciate that. Well, now I feel bad, and I do owe you a beer. But yeah, I mean, look, if I get one thing out of this, it's I need to hire people. So if people can apply, that's one thing that, you know, maybe I can send Greg kickback for. But yeah, I just really appreciate you having me on. -
Speaker 1No kickback. - No kickback needed at all. I like what you're doing, and it's funny. You were saying, like, "Don't apply AI, but apply to my company," so that's hilarious. And yeah, I wish more people, I hope, if you've made it this far, that you go, no matter if you want to be an FDE or you want to apply this methodology, like, go and do it. Get your hands dirty. If people want me to go deeper, on these topics, please let me know in the comments section. I read every single comment. I respond to most. Voss, you're a legend for coming on, sharing the sauce. I appreciate you. Please come back again. I'll include links where you can follow Voss on the internet and his Apply AI article that I saw, and I reached out to him, and I was like, "Hey, you got to come back on the pod." And I'll see you next time, my friend. -
Speaker 2Cheers, thanks so much for having me. We'll be right back.
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