Masterclass: How FDEs make $1M/yr deploying AI agents

Why are people getting paid a million dollars as for deployed engineers? It

sounds crazy, right? But if you think about it, it isn't. Cuz if you're deploying agents and you're able to drive 5, 10, $25 million 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

yes. So the question becomes, how could you become a foreploy engineer? How

could you deploy agents to make companies run more efficiently, drive revenue, lift margins? Well, today I brought on Boss 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 for deployed engineering on the internet, but they haven't gone into concrete examples for 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

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

source. You're going to understand where Muse, Grockbot, and Dots been in in all of this and how you can start putting this into practice. This is a master

class for how to become a Ford deployed engineer. I did one other episode with

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,

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

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

now. And this section is sponsored by Google. So Google's actually been

Google. So Google's actually been crushing it with voice AI. Um, I've been using Gemini 3.8 Live recently. 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

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 uh voice AI help me run my business. The second is Gemini 3.5 Transcribe. Now, what's really cool

3.5 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 ums, the likes,

notes. It removes the ums, 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. Um, and that also just, you

is clear. Um, and that also just, you know, allows me to remember cuz frankly I forget things. Um, 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." Um, and it'll go and

Brooklyn accent." Um, and it'll go and do it. And you might be thinking, well,

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,

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 speechtoech. You know, not everyone

speechtoech. You know, not everyone speaks English. So, if you're doing

speaks English. So, if you're doing business in Mandarin in China, you get this realtime translation via Gemini 3.5 Live Translate and it just opens up new

markets. So, these are just four tools

markets. So, these are just four tools that I've been uh obsessed with lately.

Uh, 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

description. Have fun with it. Have a

creative day, and I'll see you at the next 60cond master class.

Voss, by the end of this episode, what are people going to learn?

People are going to hopefully learn the endto-end workflows that we're seeing as part of AI transformation and better understand how they can go ahead and do this themselves.

Yeah, because there's lots of talk and I'm sort of guilty for this too of just keeping it high level. Um, sometimes I'm just like, here's an interesting topic.

Here's an interesting business model.

Just apply AI. Um, but the big question is, well, how do you actually apply AI?

What is 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

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, uh, 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. Let's do it.

All right.

Cool. So, you know, I put out an article a few weeks ago um and it was titled don't apply AI. Obviously, a play on, you know, applied AI, etc. And again, the point of that is to to go into, you

know, AI isn't something that can be applied like a coat of paint. It's

something that really involves process re-engineering.

Um, and that's the goal of of today's uh kind of presentation. And thanks for having me on. Um, so as I'm sure everyone already knows, and you know, I know you, Greg, you talked about this a

week or two ago. Um, this is already happening, right? Rolloffs is a huge

happening, right? Rolloffs 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. Um, for example, accounting firms,

well. Um, for example, accounting firms, IT shops, law practices, etc. And these AI holding companies or AI transformation companies are buying up

these, you know, 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 occur is recurring as a result of, you know, handoff between two different pods of people. And they'll

rebuild that process with agents from the ground up. Um, Thrive, for example, comes to mind. They have 35 engineers across 70 firms as of, you know, me creating this and, uh, doing some

research. I'm sure that might be even

research. I'm sure that might be even higher now. Um, and what they're seeing

higher now. Um, and what they're seeing is that it's actually quite successful.

Uh, the numbers move. You know, tax returns 30% faster at 98% accuracy.

Agents are actually able to take work off of people's plates and it is feasible. It's possible. The only caveat

feasible. It's possible. The only caveat being is that it's a lot more involved than people had maybe initially surmised. Um, so one example is gross

surmised. Um, so one example is gross margin at one call center firm was 60% and above, which is actually quite high.

uh and as a result of that you know when they buy the firm let's say they buy it for a billion dollars and they increase the margin uh and they double it for example in the best case um 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 you know 3 6 12 24

months whatever that is and then you can sell it for 2 billion or 4 billion or 8 billion um and that's really the goal of these of these companies.

Cool. Let's keep going.

So here you can just see a few 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.

And I think like we'll get into that by the end, but like I think that's the big question a lot of people have is like I think people hear this and they're like,

"Okay, cool." But like you're basically

"Okay, cool." But like you're basically saying like buy a business, add AI and you know question mark question mark profit. You know what I mean? That mean

profit. You know what I mean? That mean

so I think like the big qu it's like okay but how do you actually do this?

We will talk about that 100%. I'm not going to keep it super

100%. I'm not going to keep it super high level. We're going to get into it.

high level. We're going to get into it.

Um 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

complicated right? You have five different inboxes, four file stores, Google Drive, Notion, iCloud Drive, the desktop. You even have five messaging

desktop. You even have five messaging apps. You have iMessage, WhatsApp,

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 a API uh of, you know, whatever system of record of your choosing. But if a client emails you, you know, it's a Gmail, it's work. But if a family messages you and

work. But if a family messages you and it's iMessage or it's WhatsApp, um, and understanding this is quite complicated even at a personal level. So imagine at a company and this is to your point,

right? This question mark question mark

right? This question mark question mark question mark. People are so annoyed at

question mark. People are so annoyed at at this like, you know, AI is going to fix everything. Just just use AI because

fix everything. Just just use AI because they know that it's actually quite complicated. Um, imagine a company,

complicated. Um, imagine a company, right? This company has acquired eight

right? This company has acquired eight other companies in the past. So now

they're existing in five different regions. They're in Sa Paulo, they're in

regions. They're in Sa Paulo, they're in Bangalore, they're in Australia, they're in Sydney and they have 23 different systems of record and each region is doing things differently.

Chicago is on SAP, Salesforce and Workday. Toronto is on Netswuite,

Workday. Toronto is on Netswuite, 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

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 faster. I hope I can curse on this podcast and bleep that out with you.

Absolutely.

Um, and that's the fundamental issue. So

now, again, I don't want to, you know, be too uh beating a dead horse on the problem. The problem is very clear. It's

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

this? This is our view. And I'm sure there's many different ways of doing this, but our view is process mapping, then re-engineering, then building, deploying, and rolling it out. Um, and

there's a few different ways that we do that. So, if you were to go into a

that. So, if you were to go into a company on day zero, here's what I would suggest that you do.

Mhm.

The first is interviews on the human side. So, let's start off with a single

side. So, let's start off with a single department, right? You'll have in

department, right? You'll have in finance, you'll talk to the head of AP, AR reconciliations billing banking FPNA, 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

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. uh they tell us saying, "Oh yeah, this person's been at the firm for 20 years." And they just handle it. It's so common. It doesn't

handle it. It's so common. It doesn't

matter the size of the company. Could be

a massive Fortune50. It could be a small SMB. They all have these critical key

SMB. They all have these critical key people where information lives in their head.

Yeah. 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. You

know what I mean? Like

100%.

There is no second brain happening here.

Yeah. There's nothing you can 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 department and it's very very involved.

Okay. So step one is basically in a sense it's creating like a human API. It's getting all the uh you know uh

API. It's getting all the uh you know uh data systems SOPs

you know all that stuff into modern digital systems. Absolutely. You always have a good way

Absolutely. You always have a good way of articulating I like that it's a human API. Um that's what it is and you have

API. Um 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

theater? Which step is legitimate? What

happens when exceptions take place? how

often do exceptions take place? Um, all

of these things are not written down.

From there, we move on to then mining the systems of record and mining everything else. Right? So, really

everything else. Right? So, really

there's a lot of data that exists in their Salesforce, their Netswuite, their dynamics, their workday, etc. And that doesn't mean it's written down, right?

It's not in a document which has like once 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 course of you know 3 to 4 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, etc. 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

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

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,

um, 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 varies crystal clear.

So here's a very concrete example. You

wanted to go away from high level. This

is what we're trying to do. Um this is a public software company. This is

actually an engagement that we that we completed. Um so it was a $5 billion in

completed. Um so it was a $5 billion in revenue company. They have over 150

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

they're public. So this actually makes them quite complicated. you have to deal with regulation uh certain laws apply etc. So the process document which they actually had and they had completed this I think

it was engagement with deote that mapped this out for them um was you build a quote then you submit it then it goes to deal desk then you approve it then you send it you negotiate then 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 um but what the reality was and this was as a result of process mining agents that we

had deployed over their CRM which I believe was Salesforce. Um it's actually a 20step process with seven different loops. So from step one to step, you

loops. So from step one to step, you know, four or five, then you loop back to one if there's an issue. Um and

that's 61% of requests actually follow that loop. Then later down the chain,

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 uh 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.

It'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.

And 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

uh 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 you usually the

work done. There's the dig step one is

work done. There's the dig step one is like the digital tools like the sales forces, the net suites, all those products. Then there's the agents that

products. Then there's the agents that actually do the work and then there's the human beings. There's often times like a human being step to it, right?

Like not everything could be fulfilled by agents. So what you're doing here is

by agents. So what you're doing here is you're kind of like exposing the whole system.

um 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 eval or um which you can talk about but basically make sure that you know it's doing its job at be uh you

know as good as as possible.

Yeah, 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. Um it's it's actually never been shown before to

anybody in the department so cleanly to the point where you know the CRO and CFOs are telling us like I 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,

issue, right? If you go and say, "Hey, we want to use AI." Well, on what? What

are we doing? What what is the broken process? What's the problem we're trying

process? What's the problem we're trying to solve? And that's why you need this

to solve? And that's why you need this step. And and then to your point of, you

step. And 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 AAI world. Um, some steps are plain code

world. Um, 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?

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 then 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 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 one thing that I really want to call out, right? A lot of people think of AI

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. Uh,

and there's no new surface that you need to go into to interact with this. 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 one 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 you know, maybe in SMB where they don't have a CRM, you can put them on one. That's great. But otherwise, try to

one. That's great. But otherwise, try to be inside of systems 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. Um, 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

staff, right? they are already used to this. You're working inside their their

this. You're working inside their their records and you can just hit the ground running with a much faster um rate of you know utilization, much higher efficiency etc. Yeah. I mean in general like you're

Yeah. I mean in general like you're trying to sell anything to anyone like you want the path of least resistance, right? So,

right? So, um 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 maybe some of them maybe have but some of them probably haven't around

this like whole agentic world that we're living in. So,

living in. So, uh, yeah, my point my take here is like or I'm just agreeing with you like obviously it makes sense like if especially if someone's listening to

this and like wants to be a forward deployed engineer or wants to like start a forward deploy engineering business like path of least resistance.

Yeah, 100%. They haven't heard of Jev or Muse or any of his stuff, right? So,

just, you know, keep it simple.

Yeah.

Um, here's another example, right? And

we mentioned there's a lot of PE firms that are looking to uh identify their their stack. For for anyone who's not

their stack. For for anyone who's not very familiar, a private equity firm will have ownership um in dozens of companies, right? And their mandate is

companies, right? And their mandate is all right, let's roll out AI across all of them. And it's impossible to do so,

of them. And it's impossible to do so, right? If you have 26 different

right? If you have 26 different companies, that's 26 different engagements. Um and then each one of

engagements. Um and then each one of those companies 26 has 10 different departments. Each department has 10

departments. Each department has 10 different workflows. and all of a sudden

different workflows. and all of a sudden you'll need to deploy 50,000 Ford deployed engineers across 3 years if you want to even make a dent. Um so the way

that we recommend going about this is grouping together portfolio companies.

Um often times it's on systems of record. So for example, if we have a

record. So for example, if we have a port a PE firm with 26 different companies and we're tackling finance for all of them, we'll group together the

you know five that are on Netswuite as their ERP and four that are in Dynamics.

So if you're an FTE, your job is to make sure you really understand what capabilities already exist in each software. So what does Netswuite offer?

software. So what does Netswuite offer?

What does it not? What does Dynamics offer? What does it not? And then where

offer? What does it not? And then where can you fill in the gaps and how do you make that talk to whatever else they're on which is ramp or bre or tipi or

blackline or concur or expensify. On the

sales operation side it means some are on Salesforce, some are on HubSpot. Um

how do you integrate that with everything else across their stack and their spreadsheets etc. You get the idea. Um, and what happens is if you go

idea. Um, 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

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. Um, it also simplifies the

yourself. Um, it also simplifies the politics. You know, you're working with

politics. You know, you're working with CFOs uh with the same buyer over and over again.

It's interesting that the CFO is the sponsor. Like it's, you know, you would

sponsor. Like it's, you know, you would think that it might be the CTO or or someone just from technology.

Yeah, we we very often get brought in with CIOS um and then that's just from like the we want to identify everything perspective. Um, but also it's like

perspective. Um, but also it's like CEOs, CFOs, CRO who are like, well, this is my department and I want something there. Now, caveat being CFOs are

there. Now, caveat being CFOs are self-proclaimedly notoriously skeptical, so good luck selling into CFOs, but um,

that's just how it goes.

Um, yeah. I mean,

to me, 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? Um,

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 bunch of money.

Is that how you how you're thinking about it or am I missing it something?

No, I would say that's that's largely correct. Um, we obviously deliver value

correct. Um, we obviously deliver value on three buckets, right? One is cost savings, but the second is revenue uplift and third is risk mitigation. Um

and what we've seen actually resonate with CFOs is yes cost cutting absolutely uh 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 uh 4 weeks 6 weeks well okay that's that's over a month that's it's called month end close what are we talking

about here um so if we can bring that down to 4 days or 8 days with higher accuracy at the same cost you're running

it today even they've really found that to be useful and then obviously further if we can cost cut um 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 etc it'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

nicer. They might just care about like the revenue uplift and and and uh lowering cost. 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.

Yeah. 100%. 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 there is

better output. Um, so it it there is cool. So here's again deep dive into a

cool. So here's again deep dive into a concrete example. Um five portfolio

concrete example. Um five portfolio companies all on Netswuite but even then they have very different ways of running things. Uh you know 12 steps, 9 steps,

things. Uh you know 12 steps, 9 steps, 15 steps, 18 steps, 13 steps and then imagine you know they have regional differences, regional variances 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?

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 Netswuite, this is the concrete way of doing things. Here you

go. You turn this into six steps, agents in certain locations, etc." 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 efficiency. But

you'll actually come quite close to this and you'll get that by being very deeply involved mapping it out working with the stakeholders then re-engineering it with um a lot of foresight and a lot of uh

thought thoughtfulness. So again this is

thought 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 etc um and this we

uncovered over the course of I believe 2 3 weeks with interviews process mining etc 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,

it gets crazy. But your job as an FTE is to one, map this out. Don't skip this step. Don't skip educating the client on

step. Don't skip educating the client on on what their, you know, mess 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 etc. Um these green boxes are you know steps after I think human decisions um and this is still like an exception where like you

know determines the code etc. So, this is your job as an FTE.

By the way, if you're selling this to a CFO, the way to do this is like you have the agents there and then you put the estimated cost per month of the agents because it's going to be like shockingly

low right?

Relative to human beings 100%. And you have the same thing with

100%. And you have the same thing with like the accuracy and all the KPIs that you can throw at them. They love that.

Any sea suite, right? Um, 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 6

months from now you can say I did this.

Love it.

And you you keep you keep stealing my thunder, Greg. That's exactly what we're

thunder, Greg. That's exactly what we're showing here. Um so you know you show

showing here. Um so you know you show them you know from 17 process steps to seven cycle time from 24 days to six exceptional loops from 6 to 1. 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 gamecher for them

client. And that was a gamecher for them because all of a sudden, literally a majority of their invoices were going through exception routes. Like that's

terrible. That means you have really bad processes. And we fixed that process.

processes. And we fixed that process.

That's a process re-engineering flow, by the way. That's not even um all about

the way. That's not even um all about agents. And then finally, we drove the

agents. And then finally, we drove the cost of handling a single invoice down from $31 to $6. It's an 80% reduction.

Um, so to your point, right, you show them that in this slide, right? And to the people who are like,

right? 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

more margin. Yes, they might take some of that margin, but they also might give back some of that margin to customers.

Absolutely. And also use that margin to hire because right the truth be told, right, the the clients that we work with are very often

like Fortune 1000, Fortune 500. Um

they're they want to win, they want to grow. Uh and we've very often seen that

grow. Uh and we've very often seen that it's reallocation of resources. It's not

about doing mass layoffs, right? 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, right?

Planning out FPNA certain aspects of that um or migrating them across cross functional or building their own FTE teams. Um, so I hope that no one is

under the impression where these are, you know, going to replace everyone's job. Yes, there will be migration of

job. Yes, there will be migration of job. Um, but I I do think that the

job. Um, but I I do think that the companies who want to win are reallocating. They're not they're not,

reallocating. They're not they're not, you know, doing 50% layoffs. That's

crazy.

Cool.

Another example, 60 person accounting firm. This is more of the SMB side,

firm. This is more of the SMB side, right? And this is for, you know, if

right? And this is for, you know, if this is your first FDU project, you should probably start here. Um this was uh not actually one of our clients but someone else in the industry that I had

chatted with. Um they had $12 million in

chatted with. Um they had $12 million in revenue, 400 clients, four systems. Uh and what made it hard is you know every client sends it books in a different way.

I 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

same. But reality is, they said they had six steps. The reality is they had 14.

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 in a sales perspective. Now, we're talking about it

perspective. Now, we're talking about it in a finance perspective. So,

collections, then you reask 70% of the time that happens. Then a partner sending it back later. You see step four here. that happens 35% of the time. To

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 if if 35% of the time

happening. So if if if 35% of the time you have to do this loop, what does that cost? Not just in terms of money, but in

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. Um in the article that

I put out uh Michael Hammer who did this study of you know 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 of time comes out to be and that's seen time and time again. That

was 30 years ago that he said this. So

we're seeing the same thing today again same five the same four buckets sorry uh 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 random letters you get the idea um 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 uh 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 fishing 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, etc.

How should people think about frontier models versus open- source models, Chinese models versus America models?

like in in like we've just talked about agents as agents but if you're actually going to deploy these agents how how should people think about these

ecosystems?

Super good question. So

on one hand most companies are on co-pilot, Microsoft copilot. Now behind

them is cloud code. Behind that is codecs. Um and a lot of what you want

codecs. Um 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 map. There's

very few cases where we've seen the need to deploy Fable or Astra. Um,

now that doesn't mean that we're not using their models. We're using, you know, Opus, you know, 4.8 aid or sonnet more likely or you know GPT you know with lower thinking model I don't even

know what their naming convention is anymore um and also open source now the other part is if you're talking to enterprise uh and this is some sauce for for the

viewers they have an aversion to Chinese models even though they're floating point numbers not actually how that works uh they don't want to work with models that are out of China they can't or you know there's a there's a strict

aversion to it but we have leveraged open source models like you know Muse um and sometimes Chinese models where we're allowed to uh grock has been a great model that we've used as well. Um so

don't feel the need to silo yourself into just chatgbt claude frontier models. You can use their non-frontier

models. You can use their non-frontier models. You can use gro. You can use

models. You can use gro. You can use muse. You can use GLM, Kimmy, Quinn. All

muse. You can use GLM, Kimmy, Quinn. All

these different things are are toolkits and 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.

We've seen personal agent platforms start to get big. So we have Grockbot now. We have Muse. Muse actually has

now. We have Muse. Muse actually has Muse for small business. They just

announced that. Um, Instinct, which is more on the consumer side. Um, you got to think the other big players are going

to come into that space too. Um, how are you thinking about using the Grock bots of the world to to, you know, deploy into these

enterprises? Are you thinking about it?

enterprises? Are you thinking about it?

Yeah. and then open AI yesterday with dots, right? Um

dots, right? Um I think 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? Um

I think that is an extension. I think my my philosophy and the philosophy that we follow here at Veric is that there's two streams of agents for for any business or whatever. There's the sidekick agent,

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 Grobbot and Muse are extensions

of that where you have to chat with it and they 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

need to. They know what they have to do already. And that's why this deep dive

already. And that's why this deep dive process mapping process engineering is so valuable. Now I can see them

so valuable. Now I can see them connecting at some point where you can use a muse or a Grockbot to set up these background agents that just take work for you all the time, but that

governance isn't there yet. Uh there's

still a massive gap in how much involved you have to be and how involved you have to be from a software engineering perspective. Um, so so far the use cases

perspective. Um, so 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. Mhm.

Cool.

So again selling the idea of an FTE you need to be three people in one. uh one

you need to understand how the work actually gets done. Uh ideally that means you go off on your own and you really study these systems of record.

You study salesforce net dynamics as we talked about earlier. What do they offer? What do they not? And then

offer? What do they not? And then

finally and then and then part of that is you know how should accounts pay table function and you learn that either on your own in combination with going into a company. Uh the second is

shipping production code. You have to be able to do engineering work. Now doesn't

mean you need to be a undergrad in computer science and you know software engineer for 10 years especially with the advances in AI engineering. Uh 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, um 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 eval, you know, optimizing your harness.

And then finally, you know, how to handle agents taking incorrect actions, right? Roll backs on agents

right? Roll backs on agents hallucinating, etc. And if you can do all three of those, you are the best FTE. You're a very cabley. And this

best FTE. You're a very cabley. And this

is actually very very rare. Usually you

just have one or two of these. Um or

you're even kind of mediocre at all three. You have to be exceptional at all

three. You have to be exceptional at all three. Plus the communication of it all.

three. Plus the communication of it all.

Right? Being able to speak to senior leadership uh and 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. Um and that being said, you'll also have ample opportunity everywhere else, right? Everyone's

looking for top FTEES. Totally

100%. Yeah. I mean, she I like that you shot your shot there, you know, respect.

Um I think uh this person is like your NBA player,

right? It it's it's it is top 0

right? It it's it's it is top 0 1%. But if you can figure this out and

1%. But if you can figure this out and the cool thing is you can figure this out in like you you know like you said like you don't need to be a have a CS

degree. You just have to

degree. You just have to dedicate yourself to learning the craft.

You you need experience deploying the craft and you also need a lot of

uh reps around just all the different ecosystems open source versus closed source like a lot of the different um I mean even like the the Microsoft ecosystem and the Salesforce

ecosystem you have to understand all these words bring it together communicate it in a way that you know sells to execs so it is hard, but like

that's why these people get paid what they get paid. You know what I mean? And

that's why the value is so huge, right?

Like the problems that you're solving with deploying FTEES like I mean, as we've seen in this episode, like is

multi-million dollars of savings and efficiency per year easily. So, like

someone once gave me advice, well-known well-known person, well-known founder, uh several multi-billion dollar exits

when I was, you know, young. And he he he he always said like, you know, if you're if you're finding a job, the best job to find is the one closest to the money.

um the one that could show that you can optimize that you um you know revenue profit um because the people that do that are the ones that are naturally going to get

paid the most because there it generating as much value for that as we talked about earlier like that factory system. Um, so it's like, yeah, you you

system. Um, so it's like, yeah, you you know, you you gave $10 million of value to this company. Do I could I pay you 10 10% of that? A million dollars? Like

maybe, you know, like that might be a trade. And that's why I think FDEEs are

trade. And that's why I think FDEEs are so in demand one, and two, getting paid so well.

Yeah. In a heartbeat, you would pay them 10% of what they can deliver for you.

And there's even PE firms who are hiring FTEES and giving them a percent ownership in the carry where you know they bought it for a billion and they

hope to sell it for 5 billion and you'll get you know.5% of whatever that delta is based on the work that you're able to do for them.

Um like to your point it's it's the NBA players and like you have to be good you have to be great. You have to be the best of the best.

Yeah.

So action items, if you want to be, you know, deeper in the FTE space, if you want to kind of get started, maybe dip your toes in, if you're starting from

nothing, this is what I would do on a personal level.

List every single app that holds your stuff, right? I I talked about mine

stuff, right? I I talked about mine earlier today, earlier today earlier in the presentation, which was, you know, five different inboxes, three different texting communication channels, etc. Do

this for yourself.

um write which one wins, which one disagrees, you know, 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 cancelled a subscription. This is a big one. I'm

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

about. I got to do that with Adobe, by the way. Adobe, if you're listening,

the way. Adobe, if you're listening, charge me 40 bucks a month for two years. Um on Wednesday you write one

years. Um on Wednesday you write one process down step by step. You know 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

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,

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 uh, you know, personal assistant, instinct, muse, crockbot, dots, whatever it is, and build the agents and give them a timeline. Do it for free if you have to,

timeline. Do it for free if you have to, if you're getting started. Trust me, the experience is worth more 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.

Um, and then you you go from there and take what you learned from this from this presentation. How important is it

this presentation. How 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 uh you know they have sensitive data and so they you know they want to you know they want open source models on

you know at on premise basically versus versus cloud agents.

Yeah, truthfully we've seen zero of that. Um I know there are companies who

that. Um 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. Um,

like banks, financial services, healthcare, pharma. Um, and I don't

healthcare, pharma. Um, and I don't think that they're there yet. Maybe

eventually they might be, but not right now.

Did you see OpenAI launched some security features yesterday at dev day or the other?

No, I missed it. What was that?

They launched pull it up so I don't butcher it. So they launched private

butcher it. So they launched private intelligence. Open AI private

intelligence. Open AI private intelligence helps businesses use frontier AI with greater confidence that their data is protected. So there's a zero data retention with private private

safety processing which enables automatic automated safety reviews.

Basically you don't have to give open AI uh p personnel access to the underlying content. I think a lot of people were

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 um businesses are like I want to use some of these

models but do I will really want to give the keys to OpenAI? Like maybe not. So

they end up launching a feature like that.

Oh, very cool.

Yeah. I mean we we route through like Azure Foundry, you know, AWS Bedrock, Google Vertex, and they have, you know, again, they've agreed to not train ZDR,

etc. Um, but I can see how this becoming more and more of an issue for a lot of companies. It makes sense why they

companies. It makes sense why they launched it.

Cool. And yeah, I like this step-by-step uh process. This is valuable for

uh process. This is valuable for really not just for a lot of people.

Number one, if you want to be an FTE, this is valuable. Number two,

uh if you want your company to be more AI native, it's valuable except Friday stuff, like you're not reaching out to people, but Monday to Thursday, you're just like understanding the system and optimizing

the system.

So, it's it's valuable for a lot of different people.

Yeah. And one of the questions we get asked is like, how do we make our own FDES internally? And I'd have them

FDES internally? And I'd have them follow the same playbook, right? Um so

again this is if I if I leave the viewers with nothing else this is 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 is they try to slap AI on um on on the business and it's it's very handwavy and you know you maybe it's rolling out a license with claw code to

everyone maybe it's you know building an agent that doesn't really understand the workflow. it doesn't work. Um when we go

workflow. it doesn't work. Um 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 baseline 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

build. And then we build it once and we deploy it everywhere. And we measure it constantly. So we'll go to these CFOs

constantly. So we'll go to these CFOs over the course of four weeks do an audit um where we you know we understand their systems we build the

PC's and all in four weeks the next four weeks we build the agents and then 3 months after that 6 months after that we say hey this is what it used to be this is what it is now and we 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 um so hopefully that was helpful for for all people watching and I'm wishing everyone

best of luck.

We'll include the link to this in the show notes in the description so people can access it. Uh sometimes people ask me actually they don't even ask me. They

go in the comment section. They're like

you're involved in this company. No, I'm

not involved in this company. You know,

like Vos 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

stuff. And I think that if you understand this stuff, you have an unfair advantage. And that's why I bring

unfair advantage. And that's why I bring him on here. I'm I'm on here because he he's world class when it comes to this stuff. He's not afraid of sharing the

stuff. He's not afraid of sharing the sauce, and that's why he's here.

I appreciate that. Well, now I feel bad now. I do owe you a beard. Uh but uh

now. I do owe you a beard. Uh but uh yeah, I mean, look, if I get one thing out of this, it's it's I need to hire people. So, if people can apply, that's

people. So, if people can apply, that's one thing that, you know, maybe I can I can send grand kickback for. But yeah,

we, you know, just really appreciate you having me on.

No kickback needed at all. Um,

I uh I like what you're doing. And it's

funny you're saying like don't apply AI, but apply to my company. So that's it's hilarious. Uh, and uh, yeah, I wish more

hilarious. Uh, and uh, 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 FTE or you want to apply these this methodology, like go and do it, get your hands dirty. Uh, if people want me to go

hands dirty. Uh, if people want me to go deeper uh, on these topics, please let me know in the comment section. I read every single comment. I respond to most. Voss,

single comment. I respond to most. Voss,

you're a legend for coming on, sharing sharing the sauce. I appreciate you. Um,

please come back again. I'll include

links where you can follow uh 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.

Come come come back on the pod." Um, and uh, I'll see you next time, my friend.

Cheers. Thanks so much for having me.