The Operator's Guide to Building a Go-to-Market Engine

0:00 Today we have Justin St. Louis Wood, a true operators operator. He's built and scaled BizOps, RevOps at some of the fastest growing Saas companies out there and is currently leading new initiatives and operational efficiency at Novisto. After meeting Justin, what really set him apart to me is his clarity of thinking he doesn't just chase metrics or tools, he builds systems that drive focus, accountability, and real revenue outcomes. He's one of the few voices and ops who consistently blend strategic thinking with deep technical execution. Justin, so excited to have

0:39 you. Thank you for doing this. Thank you for being here. Thank you for having me. Absolutely. Absolutely. I know we have a lot to dive in and I think just kicking things off. Too many operators, they really get stuck in the weeds. They're chasing dashboards, they're tinkering around with tools, or a lot of people right now, they're just riding the latest AI hype wave that's out there, but I think some of the best operators I know have a really unique ability to take a step back, look at first principles, look at fundamentals, make sure the foundation is set first before

1:15 stacking on top of that. I think when we were prepping for this, you shared a really, really good model to take a company from zero to foundational to then sprinting and layering in all of the advanced and modern techniques you want. I think it'd be really helpful if I kick it to you and you walk us through your thought process on that and just where those fundamentals begin. Yeah, so I guess the first step is when I joined the company, roughly after series B, there's a lot of momentum that was happening in terms of volume for sales teams, but everyone was

1:53 doing their own book of business, their own prospects. There's not systems in place to measure and keep the accounting executive accountable to those standards that we want as a company to be predictable in our revenue, to be highly efficient in what it takes to be a successful accounting executive at the company. So my thinking was first learn the tools that we already have, learn the existing systems, and then from that you'll have the ability to understand the gaps in the current business. So a lot of the work came in roughly when I joined in the first few

2:26 months with the CRO to establish a new system, kind of revenue intelligence, revenue operations intelligence to really systemize the way that account executives were measured. And it starts at the forefront of measuring the activity of account executives into different categories of activity that we would want to measure. There's different types of activity in terms of the quality of activity and not just the quantity of activities. There's targets in terms of qualified pipeline and building your book of business as an account executive that you need to be able to systematically assess. So there's all these kind of predictive insights that we're

3:07 building through these foundations to assess really the success and what it takes to be successful as an account executive in that seat. And you'll know that early on when you join the company based on those standards and based on how you progress versus those standards if you're going to be successful, but it's building those principles first. No, that makes a ton of sense. And I like starting with the reps, starting with the people in the field, starting with people who probably have the most information of what's working, what's not working. Am I getting good leads?

3:36 Am I in a territory that is successful? Am I equipped to do the things that I need to do to get the job done? About how long would you say, so when you get into a new company, how long do you take to do this initial discovery? And what's the process that you follow to get that information? Is it interviews? Is it ride alongs? How do you gather that intel to let you know what to do next? I can't say I'm the best at being deliberate in asking people on what they're doing. I'm just trying to see the point in which I can push to, in one case, kind of push things to break

4:12 to see what's the limiting factor of the existing systems. I learned through doing not necessarily through asking different people questions. I am just trying to get a feel for how things are being done, the limitations and the breaking points for the existing platforms. And I'm just trying to get to work in creating things from scratch. There's a lot that you can do from learning the existing systems without relying on kind of interviews or chats with people. It's really my core instinct to just try and build things as quickly as possible. After that, I think it's easier to

4:48 collaborate and assessing the gaps that maybe your system that you've built might have, but it's building things first and then assess, collaborate with people down the line. But that's my first instinct. It's build first, then ask questions. Got it. I like it. Ask for forgiveness, not permission. Exactly. No. And I think there's something to be said about it's really tough to build new things in a vacuum. And I think what tends to be helpful is if you build a V0, V1 first, give people an opportunity to poke holes in something that you built, then that gets the conversation going in a better way.

5:28 And you're at least giving someone a starting point rather than trying to ideate from scratch. Yep. I strongly believe in that. I mean, you get the vision of other people who have been in the company, so they'll know how and they'll know the context of the business really well. But you as a builder simply need to create like a working prototype. You're not going to have the complete scope, but you build things first. And then with collaboration, you get like the stuff that you are missing. But if you start from zero and you're constantly asking for things without you yourself learning how you operate, you're kind of operating from behind, I feel.

6:05 Absolutely. Absolutely. Something you mentioned during our prep, you have the three pillars of the modern revenue system. What are the three pillars? What keeps that revenue system grounded and marching towards growth? And how can those be implemented for any startup? Yeah. So kind of back to the first question of like building the foundations first. So the system itself is structured in the way that you're basing the information off of our existing CRM, which is HubSpot in this case. The three pillars that we would assess in this case are number one, volume, number two, accounts, and number three is the kind of accuracy and validation.

6:48 So number one, it's the account activity itself. So every AE, when they're joining the company, they will have their name and they will be assessed on the activity of the meetings themselves. So there's different categories of meetings. There's different kind of gates to each meeting. And you have to essentially have a good depth of different meeting types. So for you to escalate a meeting or sorry, a deal from A to Z, you'll need to have different types of meeting, whether it's discovery meetings first, and then technical assessments, which are kind of more technical

7:21 deep dives, eventually an economic buyer meeting, et cetera. So you have to cover a real depth of activity. So that's a real point of measurement for account executives. And then the second point of that in terms of activity is prospection. So every AE needs to have a certain book of business that they're creating by themselves, not relying on the BD team, the ADR. So in this case, there's a certain way that we assess the quality of the deals themselves. So how much are being pushed to a certain stage in the deal funnel and how many deals is that? How much in terms

7:57 of monetary value is that to the pipeline that you're creating within the current quarter, within the last quarter, within looking forward into the next quarter. So that's the first pass in terms of volume and activity piece. The second piece is understanding at an account level. Your book of missus in terms of not just how many accounts you're talking to, but what's the quality of these accounts themselves. So it's understanding the tier of the accounts from tier one, tier two, tier three. So you're speaking to the right people, you're connecting with the right accounts. The accounts themselves have a sharp enough pain point. It's understanding

8:35 the quality accounts are solid enough for you to pursue them in the first place. And the last part is kind of the detail, which is in this case, it's the meaning activity and kind of validation of the opportunities themselves, or your simple, like keeping up to your book of business so that it's clean and structured. So in this way, it's understanding one who the people are that you're speaking with in this case, the meetings that you have, are you having meetings with people who are senior enough to drive decisions in terms of validation. It's our, is your book of business,

9:10 you know, categorizing the right way where every meeting is tagged for us to follow. So most of it's automated, but we still rely on an element of judgment for, you know, assessing which type of meeting it is. So it's, we have kind of backstops in the back that operate to flag if certain things aren't met for the account executive to, you know, change things to be able to be optimal for the system to work its full capacity. It's alerts being sent to the VP of sales, to the CRO, if things are falling behind, if things are not operating at a certain regular cadence.

9:43 So that's like the final piece, which is the backstop and ensuring that everything that you have is following the right cadence, the right pursuit, where it can go from A to Z and being a successful deal, deal outcome and essentially winning the deal. Yeah. I think something, it often comes up when we're engaged with companies. So at LeanScale, we work with series, ABC companies are scaling, they're growing, they're investing quite a bit and go to market. And what we have found is one of the biggest misses is account planning and figuring out that tiering, like you mentioned, tier one, two, three, and figuring out no fly zones. Like, hey,

10:25 we do not serve these companies. So let's not waste a single marketing scent on them and get hyper focused on the lowest hanging fruit, highest propensity to buy. And let's equip and arm our sales team with those targets and get them focused on those. And I think it's easier said than done. It's actually a pretty difficult process because it's not only gathering the data, getting it into HubSpot, assigning it out, how do we set up the scoring model? That part can be relatively straightforward, but actually knowing who's a good fit and why for your product and how are you positioned in the

11:04 market. That is such an important aspect of go to market that people really overlook. They think, Hey, I can just go out, spray and pray. Let's go get in front of as many people as possible. But then their messaging is bland. It lands flat. Their sales teams are wasting their time on accounts that aren't good fits. And then you don't even know to the next point, when you're talking about, okay, what's the actual sales choreography post that? Let's say you get in front of one of these accounts. Now what do you do to get that really, really dialed in? You have to know the accounts that you're targeting in the first place

11:41 and know what you should be measuring along the way to have the best chance of getting that deal closed. So I really liked the way you laid that out, sequencing it that way, because if you don't get that part right, then you don't have the choreography, choreography, right? Then it's really, really tough to know what's off in the whole process. I agree. So once you have an idea of the strategy, the go to market plan for the team, how do you instrument everything and as specific as you can be? What tools are you using? What metrics are you measuring? Are you measuring

12:18 those metrics and dashboards or spreadsheets or BI? What's the best way now that you have a plan to pull together all the data and then decide where the bottlenecks are and what you need to attack next? Yeah. So I think my thinking first in terms of laying the groundwork and what are you trying to build before you find the platform to build it is simply how much can I compile into a single dashboard, a single system? Because I find that the issue is you often have maybe very good dashboards, but they're specific to marketing, which uses a certain platform. It's

12:58 specific to sales, which uses a certain platform. So you have different systems in different places. So my thinking is first simplify it to the most core metrics. It's stuff that's most important to the success of the team, of the function itself. So if it's marketing, it's let's say the six or seven most foundational metrics for marketing to be highly successful within the current year. Same goes for sales, same goes for all the other teams in cadence. But you're simplifying to what's most, most tactful to assess and important for the overall success of the company. And then after that,

13:36 it's, well, can I compile all this information into a singular source of truth? Which in my case, what I'm doing instead of building systems to fulfill a certain capacity for a certain team, like just doing something for marketing, just doing something for sales, I'm bringing everything in one system. And instead of kind of doing it as a dashboard, it's what I'll call more of a data skeleton. So it's much larger than a dashboard itself, which will have maybe 10 to 12 metrics and be a lot more visual. Instead, it's building tiles with the numbers, percentages themselves instead. And you have like 50, 60 metrics on one system.

14:19 Let's say it's a spreadsheet in this case, you're having everything in one place. And you'll start from level one, which is like your corporate metrics, things that are, you know, very important for the board, your investors, your kind of North stars. And then after that, it's your level two, which is the functional areas. So it's starting from the full kind of inception of when a deal starts. So it's marketing and BD when you're trying to initiate correct leads. After that, it's assessing for the KPIs with sales after its customer success, product engineering,

14:55 finance, and then HR. So you're having a full scope front to back. And you're essentially for each of these functional areas, you'll have six, seven, maybe sometimes up to play 10 KPIs for each of the teams. But each of them is kind of interconnected and lockstep with one another. If marketing doesn't bring in the lead, sales doesn't have much of a pipeline, much of a qualified pipeline to try and pursue and close deals. And then customer success doesn't have much to expand to improve their, you know, gross revenue retention, net revenue retention, their expansion pipeline, all this stuff is kind of in lockstep to one another.

15:35 So you have to build a very comprehensive skeleton. If you're doing it in spreadsheets, it has to be automated because it needs to be workable all the time. So you have to build systems behind it to get data in, you know, very easily, you could use it with API connectors, you could use it with existing connectors like coefficient or other similar platforms. So if you're using spreadsheets as your default, which a lot of people do, and we use it for certain purposes too, because it's essentially user friendly, people really know and understand spreadsheets. But you have to augment to a level that it operates like a business intelligence

16:11 tool, not a static spreadsheet. If you do it that way, and you build kind of a really comprehensive system, you can have the same benefits of a BI tool, but it's built in spreadsheets. And that's essentially what we've done for this year, in terms of aligning to the goal of being one much more operationally efficient and being highly, highly predictable and data driven. So we're doing it that way. And that for us has been working well. Yeah, and I think, you know, spreadsheets get a lot of shade thrown their way. But I think they tend to be effective, especially depends if

16:44 you're like a product led growth company, and your volume of interaction is just so high that you need a data warehouse stuff, then that may be the case. It's like, okay, it's time to upgrade and go that route. But I mean, a lot of companies I worked at a fortune 1000 company, I was leading rev ops at a massive company, the entire company is run on spreadsheets, entire forecast, they had thousands of reps was all rolled up in a spreadsheet. And, you know, so you can do it. I'm not saying you should run everything that way. But it's not it's not impossible. If you don't mind, I'd like to

17:15 back up real quick. So you mentioned three levels of metrics. You have North Star KPIs stuff that your investors are looking at, then the functional levels, then like hyper specific activity level, maybe based on people. What are some examples? Some people are not as familiar or they think they're measuring the right things, but maybe they're not. What are some examples of North Star KPIs? Like, hey, you better be measuring these specific things. The North Star would be very kind of investor and board adjacent. So it'll be things like number one, your revenue. So your

17:47 ARR will be most importantly, usually your number one target to aim for, you have a certain kind of target era, you would want to hit for the next year. That usually is pretty high up on the list. Things like net revenue retention for CS is deeply important. Net promoter score for product engineering in terms of the quality of the code. There's ways of assessing that through bug rates, deployment efficiency, things like that. For HR, it's all about talent retention. Because at the end of day, the talent that you have, the people that you have is the company that you build. So

18:25 as a North Star, you have to essentially have a way of measuring the talent that you have and not losing that talents elsewhere. So those are kind of four or five ones that are very important to our company. And for most company in the venture space, I'll say within maybe B2C of having four or five targets that align the entire company itself in that direction to essentially push for what it's trying to achieve and solve the goals for the most case, both the company and the investors tied to that company. I won't make you go through all the other ones, because I know

19:00 as you go a level deeper, it balloons out into more KPIs to be measuring. But are there a few KPIs that you feel like within marketing, sales and CS, when you get down to that function level, level two, things that you're measuring that you think a lot of people are probably missing, or maybe some of your favorite, like, hey, this is an indicator that actually tells me how healthy either of these functions are doing. So the metrics that I think people don't look deep enough into is the quality of the opportunities themselves. So from marketing into sales, you have a big push on volume. Marketing obviously, obviously has a very high KPI

19:40 for the amount of volume in terms of lead generation, the size of the accounts themselves. So if your target for marketing is creating 100 million in pipeline, that's very good for one, because you have the ability to funnel down over time. But if you're closing 10% of that 100 million, that's a fairly lofty target itself, because a lot of this pipeline will not be really deals that close, will not be pipeline that you actively engage with, will just be added accounts to your CRM, which would most of the time kind of be read as bloat. So I think a real benchmark or like

20:19 quality assessment is if you have a high volume target for marketing, sales has to be kind of the inverse in a sense of you have to create in a way that they're assessed not on volume at all, really, but on the quality of the opportunities themselves. So a real gate would be moving the accounts that you take from marketing from the accounts that generate from BD past a certain gate until the deal process. So it's moving from the discovery phase into what we would call prove value. So the value of the account has been proven enough to be a qualified opportunity.

20:53 When it reaches that gate, we'll have metrics on qualifying pipeline creation. Things like the number of meetings that it takes for you to be able to get to a true value stage, like the metrics are pushed in a way so that you're tracked on how much is starting from that step forward. Because for us, it's a very big quality gate on assessing only opportunities that are one, you have the ability to close reasonably. And also within the quarter, you have the ability to close that not just within a year, but within a certain timeframe. But that's a real gate for us that we assess. And

21:27 that's something of a high metric that we would push for sales, in terms of the data skeleton, in terms of dashboards that we measure. It's something that's really foundational to quality itself, not just the quantity. Yeah, I think that makes total sense. And that's something that we're typically looking at as well as a huge leading indicator. Are you going to hit your number? Are you going to hit your growth targets? Is ARR going to get there? It's how many qualified opportunities or deals are coming in past that stage. And I do like, if you're in a typical sales led growth motion, I do like having the definition being sales had their disco,

22:03 moved it to the next stage, because it means it's positive, and then pegging like marketing metrics to that stage. Because then they're going to get a little more creative of, can we dial in intent? Can we make sure that we're not just bringing in good firmographic companies, but ones that also have a high propensity to buy at this stage and at this time, and it will help focus their efforts. So I do agree. I think navigating the quality, making sure that you're getting good through the gate, not just high volume, is going to help keep your go to market operation

22:37 super efficient. So that makes a ton of sense on setting up the data infrastructure. So you can start to assess what's working, what's not working. I imagine this is like, you're a pilot flying the company, and this is your cockpit with all of the different dials and everything you need. And I love the way you laid it out. Tier one being those North Star, hey, this is what the board needs to see executive team. Tier two, leadership across the go to market departments. Tier three, this could be down to like individual reporting. I think bringing all that into one place too, is really

23:10 helpful. So you can see those tiers all in one screen, all in one area, and then you can start to cross check certain things like, hey, if this isn't working well, maybe this is an area that I should go check into. So you're not sifting through a million dashboards and reports. So I think it's any company like getting to that stage, super important, you have to do it. Now once you have the process figured out, you have the pillars of getting deals through the pipeline, sales choreography, what to measure, why to measure it. Then you have the data and you have all of the

23:46 ways to measure what is working, what's not working, you can identify bottlenecks and attack them. Now let's get a little advanced. How do you go from here, which I would call fundamentals, first principles of ops, get all that in place. How do you go from there to supercharging your team with AI? And this is an area, I am literally in the middle of it. We have hundreds of companies that we're working with where everyone's trying to implement AI. We're seeing really creative ways of doing this. It's a really difficult thing to do, but I think you have some really interesting

24:21 approaches. So how do you then just throw fuel on the fire and get your team hyper productive with AI? Yeah, so my approach would be setting the foundations clearly, not just kind of embedding AI into existing workflows because it might help to a certain capacity or efficiency, maybe 10-15% more. The best way that I think I'm approaching it is, one, to the existing systems that we have, can we build something from scratch entirely to completely solve the need and potentially eradicate the need for a certain vendor, a certain platform that we're paying every month. So if you think about your go-to-market stack, you have all these tools. You have

25:07 tools for enrichment, tools for prospection. You have so many tools in one area of the business. And instead, if you think about AI, maybe you have the ability instead to build foundations custom proprietary to the highly specific problems of the business. And you can do that in a few different ways. The ways that I'm doing it myself is building systems or what I'll call hubs in Replet. So I'm using Replet. I'm initiating kind of product requirements documentation, creating the full scope of the product of the tool itself I'm trying to build. And I'll build it through Replet. It's

25:46 going to take me about a week, maybe two weeks at the most, and really refine it, get it tweaked exactly to the problem I'm trying to solve. And then over time, you'll see that these tools are essentially eradicating the need for spending $300 on a go-to-market tool, another $200 on another tool. So over time, it's $200, $300, $400, maybe $1,000 a month. And you're compiling this cost over time that you're able to create this platform for $150, maybe less than $200 on Replet. And you're eliminating the need to have this really convoluted, really kind of terribly

26:22 orchestrated tech stack that you have currently, which I think is the issue for a lot of companies right now into something that's deeply proprietary, deeply internal to the company. And you only do that by building custom tools to the problems you're trying to solve. And that's what we're doing with the hub space. Yeah. And I think your perspective, one thing I think is really interesting. A lot of the zeitgeist of how to use AI right now is how do I automate something I'm doing right now? And how do I get really efficient at something where I think instead of thinking

26:57 about it that way, it's how can I do things that I was never able to do before? And how do I do things that are completely brand new that give me an edge on the marketing and sales side that just weren't even available before AI was here? So I think those are two different gears that your mind is in, because you're just looking at everything you're doing today. Let me just make it a little bit more efficient, which in reality for hyperscale, hypergrowth startups, that doesn't matter too much. If you can get a little bit more efficient, you're not going to realize these massive

27:34 multimillion dollar gains by helping your salesperson get like one to 5% more efficient. You're going to realize it by helping them find creative ways to close new deals and build more pipeline than they've ever been able to build before and help them manage much more and also compete better against whatever the reps or the marketing teams that your competitors are doing. So if you can just give this like unique advantage rather than just make them a little bit more efficient, I think you'll realize much more gains. Is there anything specific that you feel, hey, this is something I wasn't able to do before AI? I was able to build it. And then now

28:20 my team is realizing the benefits of it. Really solid system that we built that we were, we would not be able to do before is with the ability of replit and code, so code and replit, I essentially was able to create what we call a contract or MSA extraction tool. So what it's doing is you're building the logic in replit to have over time, you build a really tight extraction system or exactly kind of extraction protocol and you do it by first building the instructions in Python. And then over top, you had a layer of AI on top of it using cloud with a direct API integration.

28:59 And with the two kind of working well together, the system itself will be able to read through a document, you upload a PDF and it'll go through each of the areas. So you have a maybe 10, 20 page document and it's going to find through extraction techniques, the information that you want. It's going to collect, you know, if it's a three-year contract, you're getting year one, year two revenue, sorry, year one, year two, year three revenue, the contacts, the billing contacts. If there's exit clauses for legal, if there's different advisory that we're doing for this

29:30 customer, all of these different things that you'll find in the order from part of the document, also in other sections for, you know, legal parts of the document, you're able to extract all this information and then you'll have all this stuff being pushed out to different platforms. So if you have all this revenue, you can push it back into HubSpot to have a really accurate portrayal of your revenue. Because an issue that you have when you first have deals in a very early stage is account executives will approximate the value of the deal. But when they eventually close it,

30:05 usually they'll have some form of discount and negotiation. So it's not the real amount. So if you're using the MSA as your source of truth, you have to put this information from a static document back into your CRM pushing out to your financial platforms like Xero. So you're able to get what was highly, highly useful information from, I'll say kind of archaic or static document into the platform where you can operate and push things out again. So that tool was built entirely using AI, using Python for the ability to get all the stuff that we need and essentially get this

30:42 information into downstream processes. We wouldn't be able to do that if it wasn't for AI being at a capable enough level where it is now to be able to do stuff with, you know, agentic capabilities, where the models themselves are solid enough. So you could create this logic and be really, really precise on how much you can get out of it and the tools that you built with it. And I imagine the amount of manual work it would take to do pre AI would have just been a complete barrier of even doing this in the first place. Yeah, I mean, it's, it's finance team, it's sales

31:16 teams with account executives, it's customer success team getting information. Everyone's compiling in the same document. So you have four or five teams at any given point. And also Slack, you have, you know, stuff in Slack that lives in deal rooms. So you have all these kind of poorly integrated systems or manual workflows, a bunch of different teams, a bunch of hours being spent on it. And you're reducing it by just you build the tool from scratch itself. And then you connect that tool that you built this hub, this custom proprietary tool that you built into

31:48 pushing information to all the channels that you want. So it's eliminating the need for multiple teams, you know, it's not having, you know, to do this stuff anymore and have it done automatically by system. Now, there's so many and that's such like, it's one example of I'm sure a million examples of how you can leverage this to do things you just weren't able to do before or the barrier of work was just too high to where you needed like to hire people to do it. And maybe it wasn't worth the investment or something like that. You know, we've internally here at Lean

32:23 Scale, we've also taken certain approaches where like we have a custom GPT where we've loaded all of our best practices, we've loaded all of the types of projects that we've done and how we've done them and what's worked and what hasn't worked. So we're like feeding a lot of data to this so that our consultants can go in and then we have like, they can go ask questions and say, hey, I'm looking to do something like this for a customer, like what's the best way to do this? And then it pulls it out already like, hey, yes, we've done this, we've used this combination of

32:54 tools, we've, here's the metrics you should be measuring if they're looking for something like that, and just gives like a complete blueprint that's based on real world, you know, engagements that we're engaged with too. So, you know, we're, we have our own internal stuff as well as the stuff we're implementing externally too. And that's such a great example of just how you can get super efficient with your team and get more accurate data too. So the next piece of this, if you're building the hubs, so in this case, the kind of custom built applications or pieces of

33:27 software that you're building to solve a certain need for the business. The other point is you have to make that platform, you know, really useful for an agent to take it over. So when you build the platform itself, you in this case, if you're building the MSA extraction tool, when it spits out the information that it extracted, you have to transform it into essentially maybe it's things like a JSON file, where it's able to read this information very coherently. And then it's able to take this information. And then in this case, it could write out an invoice for me, it could,

34:02 you know, push information to different channels. But you have to first transform the inputs so that the agent can take the leap. So if you're using N8 and or using computer use or headless browsers, like browser based or Manus AI, you have to first package the information so that the agent is essentially has the parameters established for it to understand very well what your information needs to be, you know, delivered to the end platform to the task you're trying to give it. So you do that. And then that's the part of the spokes is when you have that information

34:38 that's, you know, easily read and transform for the agent, then it's creating the mission for the agent. And you can do that through a bunch of different very good platforms, like NNN, like Manus that I've mentioned, and you're able to do the task that you're, you know, the downstream process that you're trying to solve after you've got all this information, and to doing all the other things, all the kind of, you know, missions, small missions that you want to, you know, do instead of having a person doing it, instead of having an accountant do the invoicing process,

35:09 writing out the manual invoices every time you have this information from a contract that you essentially close the deal from. And when you repackage that, you're able to give the agent the ability to write out the invoice and just let it sit in drafts. And then it's the human at the loop is right at the tail of this process, where it's just revising everything solid and then sending it out. But you you keep the human right at the end where they don't need to do the whole tedious task, you do the agents, the agents themselves should do this. And after that, it's the human

35:39 that will finally do the final push. And you know, make sure everything solid, that's where you want the human to be in. But you should really think about how much of the process can AI do. And if you can do the entire process with AI first, that's my first form of thinking. If you can't, it's thinking of human first and like AI as an augmented kind of augmentation tool. So that's my whole like, two way approach is path one is the maybe the hardest, but the most efficient if you get it right. Path two is how do I amplify the current process with AI? So if it can't really

36:14 be done easily, that's the second tier that I would look into. But you're solving it through this kind of framework that we're we're doing that I'm building in this company. And for any way that I'm thinking about problems for now, it's building AI first. Yeah, I think I think you have to reassess everything the company does everything you do. And it's it's, I mean, in a lot of ways, it's just similar to classic, like delegation. So if you're in a particular role, how can you delegate the lower leverage activities so you can focus on only things that you can focus on?

36:49 And how do you keep going up the ladder to have higher higher leverage impact and focus on higher leverage activities and delegate as much of the lower leverage activities as possible so you can get as much scale as possible. I mean, that's what it's the same process, we just have a different tool to do it. Now we have agents and we have AI to do it. But thinking about, like, what are the things that I'm doing that I shouldn't have to do. And it's not creative work that only I can do, it's stuff that I can easily augment. I think we're still getting used to that. And I think

37:23 it's pretty common with like new leaders, you step into a leadership role, you're so used to being valued on the execution, rather than like the management leadership and accountability side of what you're doing, that you tend to see not doing the execution as maybe a risk or threat to your value to an organization. And I do think some people are still struggling with that a little bit, like, if I delegate some of this execution work to some like AI forum, like them, what am I going to do? But it should be creating even more work for you to do and higher leverage tasks. So just

37:54 recreating it from the ground up, AI first, as you put it, I think is the perspective you need to have today, if you're going to survive and be competitive. Yep. And that's the exact full kind of framework and philosophy that I'm trying to adhere to. It's the whole thing of not building what's existing, but building things entirely from new foundations, because you have the leverage in the kind of amplification with AI, you need to start redesigning systems and rebuilding systems and that you were not able to do so a few months, a few years ago. So that's the frame of thinking

38:31 that I think, you know, you should execute on for the near future. And really, from now forward, it's building entirely different things that were not done before, because you have the leverage as an operator, as someone who's maybe less technical as an engineer, you have these tools to be able to go into different domains that you had no ability to do so a few years, a few months ago. It's definitely opened up the open up the opportunity for so many people to get more hands on, get more creative. And I only think it's a good thing. This has been incredible. I think just going from

39:07 foundationals, fundamentals, first principles of what you should be measuring, what the pillars are of your go to market engine, then instrumenting all of the measurements and KPIs and building the cockpit for yourself to see what's working, what's not working, and then taking it to the next level and seeing how you can create agentic go to market motions, and pulling all that together. I think everyone is striving to get to this part. But you got to do some of the homework first. I'm curious, you know, we've had an amazing conversation about this. How did you get into

39:41 this type of work? Like what's your what's your background? Were there some early signs of interest in this type of this type of work or these type of companies? All kind of career has been first starting with finance. So I graduated first in finance, my degree in academics was in finance. And then from then on, I had no interest in going into kind of financial markets, you know, big institutions, I like the underdog story. It was going into startups like the first company I went for was a 10-15 man startup in the marketing and analytics space. I was a really good

40:18 first experience as a, you know, a new graduate, to want to have the ability to work on, you know, very big important problems, because one, you're just such a small company. So whether you're the intern or the CFO, you have to make things work, you have to get things done really quickly and move fast. So I think that was a great foundation from, you know, you're you're young, but you have so much leverage because you're so tight on resources that you have to be able to build for yourself. So it got me thinking into one, okay, all the gaps I need to learn to, you know,

40:51 be able to execute at a higher level. And over time, it was different companies moving from that company, which was acquired fairly rapidly into, you know, bigger startup right after that, which was a 500 people startup, it was a different way of work, it was strategic partnerships, but it's understanding ecosystems, understanding the deal side of things, kind of external facing. And all the experiences after that were all kind of ops focus. So I had a good background in kind of the operational efficiency piece, understanding how things work. It was special projects, revenue

41:26 operations, kind of a right man to executives in some cases, where you're just trying to amplify the executive through data through, you know, thinking in terms of solving inefficiencies, by, you know, creating new systems from scratch from building, you know, automations workflows that solve deep issues across the board. That was my whole kind of, you know, last few years, it's going really from team to team, no real kind of existing team, whether it be a finance or go to market, you're just hopping to different places. And you're just trying to figure out the gaps,

42:04 what doesn't work, what's manual, what's inefficient. And then you're just a builder, you're trying to build systems, you're trying to build bridges between teams. And that's what it comes down to. So, you know, learning the muscle quickly, early of, you know, you have to build things, you have to be good at executing, not just being sharp on vision, but sharp on execution, really builds the fundamental to know, one, what you're capable of doing, and also your limitations, so that you're able to find the gaps over time. And just learn on the fly and, you know, doubling

42:36 down where you're very strong at, and then, you know, leave things kind of going away from the stuff that you're not necessarily as good as you double down on stuff that you're good, and you find your weak spots. And over time, I think you build a pretty good depth of experiences in the ops space by being asked to do stuff at small companies, being able to work at bigger companies where you have more structure, more process. I think that helps a lot. And that's kind of the same thing that I carry in my personal life. It's the same thing of, you know, thinking in systems,

43:10 like the entire way that I operate is systems for everything in terms of personal fitness, in terms of the goals that I have for myself. It's, I think in systems, I think in building notes that are outlines for what I'm trying to achieve. It's anything from the workouts, the the mixed martial arts that I'm doing, it's discipline, it's consistency, it's systems, and it definitely pours into my work. It's just the way that I operate. And for me, it's natural to me. So the ops space for me, it speaks to me because it's the natural kind of language for operations. And it's kind of my natural language for life and how I think about how myself how I,

43:52 how I just carry myself through life, I think in principles, I think in systems, and the two well, you know, they work really, really well with one another. So for me, that's, that's just been my experience. And, you know, generally how I think of things. No, I love that. I think it makes a lot of sense resonates with me a lot, too. I've done mixed martial arts in the past to spend a lot of time wrestling. And, you know, ops does, if you want to call it like a love language, but like following a process and like, Hey, the small habits, and like making small adjustments over time have massive compounding gains.

44:31 And you know, like anything, it just takes a lot of work a lot of time. But if you're doing the right things every single day, and making the right iterations in life, or an ops for a startup, I think that's when you realize the impact. And I agree with you, I like, you know, having some experience at a small company, having somebody big company, seeing what works in either what is we talk about this a lot, like what stage fit, what stage fit process for where you're at, you know, the workouts that I do wouldn't be the same workouts that a professional athlete would

45:08 do. That's not stage fit for me, like I should do something that's right for where I'm at. And I think in business and companies, it's similar, like, hey, are you just playing business and pretending like you're a fortune 100 company when you don't have to, or that's actually counterproductive to your progress? Are you doing things that are right fit for the right time? And I think if you just kind of continue to apply that to different areas, you'll start to see the momentum, momentum go. Yeah, it's it's it's compounding, right? That's the whole basis of

45:37 everything is compounding in your work, compounding in your life. And that's the way I think of it. It's small, consistent habits get you to where you want to be. It's not the big wins. It's the small wins that get bigger and snowball over time. 100%. Yeah, they're not done in like one day of you're not gonna, you know, get super muscular by having one incredible full day at the gym, like it's going every day for months and months, years and years. Well, Justin, this has been really, really awesome. Again, I appreciate you taking the time walking through those stages of

46:14 ops at different levels, sharing a little bit about your personal life and approach that too. I think this is going to be helpful. Anyone who's in an ops right now, especially if they're kind of coming off for the first time and looking for some information, I think you've armed them with a lot of really good frameworks and thinking. So I think last thing is just what's the best way, if people are listening, if they want to get in touch with you, if they want to jam out on some ideas, where can they find you? You can find me on LinkedIn. I think that's the platform I'm

46:49 most used to. You can find me on LinkedIn by my name, Justin St. Louis Wood. And yeah, if you want to get coffee, if you want to talk about ops in general, anything really that you would like to speak to, I'm open. I like learning from people. I like giving out the learnings that I've learned over time to other people to help in their own journey and their own process. And I like the kind of the engagement of back and forth, both learning from people and giving learnings to others. So yeah, you could reach me pretty easily. I'm on LinkedIn. I'm fairly active. So I think that's a good way of doing it. Amazing. Well, Justin, thanks again. Love

47:27 everything you've done. And I can't wait to continue to follow your career and see what you do next. Thank you for having me.