Foundational Ops to AI-Driven Go-To-Market with Justin St. Louis Wood

Outline Summary: Foundational Ops to AI-Driven Go-To-Market with Justin St. Louis Wood

Intro

• This outline distills a deep dive with Justin St. Louis Wood, a veteran revops leader known for building scalable revenue systems in fast-growing SaaS companies.

• Core themes: start from first principles, build foundational systems, then layer in advanced techniques; balance strategic thinking with hands-on execution; and leverage AI to create proprietary, high-leverage tools.

Center: Core Frameworks and Practices

• Foundational mindset: Don’t chase dashboards or hype; establish principles and measurements before adding complexity.

• Discovery approach: Early in a new company, focus on the field — reps’ realities, territories, lead quality, and the limiting factors of current tools. Build first, then iterate with stakeholders.

• Three pillars of the modern revenue system:

  • Volume (activity): Depth and quality of AE meetings, discovery, technical assessments, buying‑center engagement.

  • Accounts (quality): Tiered targeting (Tier 1–3), appropriate seniority, clear pain points, prevention of “bloat” in pipeline.

  • Accuracy/validation: Clean, categorized book of business; automated checks and handoffs to VP/Sales leadership for cadence enforcement.

• Go-to-market articulation: Map how Marketing, BD, Sales, and CS feed one another; ensure quality opportunities move from discovery to value-stage gates.

• Single source of truth: Move from disparate dashboards to a data skeleton that spans corporate north stars down to functional, then activity-level KPIs.

• Northstar vs. functional KPIs:

  • Northstars: ARR, net revenue retention, customer-centric metrics; investor-adjacent targets.

  • Functional: pipeline quality, stage progression, account tier health, and cadence adherence.

• Operational choreography: Define clear “gates” for deal progression; emphasize the quality of opportunities over sheer volume; establish a disciplined cadence to avoid misalignment.

Key Tactics and Tools

• Prototype-first development: Build V0/V1 internally to solicit quick feedback, then iterate with stakeholders; avoid building in a vacuum.

• Data skeleton vs. dashboards: Use hundreds of metrics in a single, automated system; enable cross-functional visibility without drowning teams in reports.

• AI-first evolution: Move beyond marginal efficiency gains; design AI so teams perform tasks previously impossible at scale.

  • Build proprietary hubs (e.g., MSA extraction) in Replit with Python plus AI overlays to push data into HubSpot, Zuora, and downstream systems.

  • Transform outputs into agent-ready formats (JSON, structured prompts) for downstream automation.

  • Keep humans in final approval and oversight, but maximize automation in the mid-to-late stages.

• Human–AI collaboration: Delegate low-leverage tasks to AI; combat theater–level leadership risk by redesigning workflows around AI-enabled capabilities.

Examples and Outcomes

• MSA extraction tool: AI+code stack reads PDFs, extracts contracts, revenues by year, contacts, clauses, and feeds results into CRM and finance systems.

• Operational impact: Reduced manual handoffs, improved forecast accuracy, and a scalable, auditable data backbone across GTM functions.

Outro

• The conversation closes with a personal arc: Justin’s career—from finance to startups to RevOps—built on systems thinking, disciplined experimentation, and continuous learning.

• He underscores the value of stage-fitting processes, compound gains from small, consistent improvements, and mentorship through sharing lessons on LinkedIn.

• For those seeking to connect, LinkedIn is the best starting point; Justin welcomes coffee chats to exchange ideas and accelerate growth.

Summary in One Line

• Build foundational systems grounded in first principles; measure with a layered, single-source cockpit; elevate with AI-driven, proprietary tools while keeping humans in the loop for validation and strategic judgment.

Full Transcript

Today we have Justin St. Louis Wood, a true operators operator. He's built and scaled bisops revops at some of the fastest growing SAS 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 blends strategic thinking with deep technical execution. Justin, so excited to have 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, um, 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 stacking on top of that. And 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. So I 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 I guess the first step is when I joined the company uh roughly after series B there's a lot of momentum that was happening in terms of volume for sales teams but everyone was doing their own book of business their own prospects there there's not systems in place to measure and keep the account 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 account executive at the 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 months with the uh CRO to establish a new system kind of revenue intelligence, revenue operations intelligence uh to really systemize the way that account executives were measured. And it starts at the forefront of you know measuring the activity of account executives into different categories of activity that we would want to measure. Uh 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 um and building your book of business as an account executive that you need to be able to systematically assess. Uh so there's all these kind of predictive insights that we're building through these foundations to assess really the success um and and what it takes to be successful as an account executive in that seat. And you'll 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? 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 I can't say I'm the the best at being, you know, 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 to see what's the limiting factor of the existing systems. I learn through doing, not necessarily through asking different people questions. I'm just trying to get a feel for how things are being done. um the limitations and the breaking points for the existing platforms and I'm just trying to get to work in you know creating things from scratch. Um 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 it's really my core instinct to just try and build things as quickly as possible. Um, after that, I think it's easier to 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. Got it. I like it. Ask for uh ask for forgiveness, not permission. So, Exactly. No, and I think I think there's something to be said about like it's really tough to build new things in a vacuum. And I think what tends to be helpful is if you build a V 0 V1 first, give people an opportunity to poke holes in something that you built, then that gets the conversation going in a better way and you're at least giving someone a starting point rather than trying to ideulate from scratch. Yep. Yep. I strongly believe in that. I mean you get the vision of other people who have been in the company so they'll have the knowhow and they'll know the context of the business really well but you as a builder simply need to create a like a working prototype. um 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 were missing. But if you start from zero and you're you're constantly asking for things uh without you yourself learning how you operate it you're you're kind of operating from behind I feel. Absolutely. Absolutely. Something you mentioned during our prep um 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 I 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. 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. uh 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 toz you'll need to have different types of meeting whether it's discovery meetings first and then technical assessment which are kind of more technical deep dives eventually an economic buyer meeting etc. So you have to cover a real depth of activity. Um, 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 you know how many deals is that? how much in terms 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 um your book of misses in terms of not just how many accounts you're you know talking to but what's the quality of these accounts themselves. So it's understanding the tier of the 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 the quality accounts are, you know, 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 uh that you're speaking with in this case the the meetings that you have are you having meetings with people who are senior enough to drive decisions. Um, in terms of validation, it's are is your book of business, you know, categorized in 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 back stops 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. So that's like the final piece which is the back stop 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 le scale we work with series A BC companies are scaling they're growing um they're investing quite a bit in go to market and what we have found is one of the biggest misses is account planning and figuring out that taring like you mentioned tier one two three and figuring out no-fly zones like hey we do not serve these companies so let's not waste a single marketing scent on them and get hyperfocused 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 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 prey. 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 and know what you should be measuring along the way to have the best chance of getting that deal closed. So, I really like 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 choreog choreography right, then it's really really tough to know like what's off in the whole process. I agree. Yep. 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? Um what tools are you using? What metrics are you measuring? Are you measuring those metrics in dashboards or you know spreadsheets or BI? like 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 you know laying the groundwork and what are you trying to build before you find the platform to build it is you know simply how much can I compile into a single dashboard a single system because I 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 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. Um it's stuff that's most important to the success of the the team uh 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. Um, 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, 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 uh themselves instead. And you have like 50 60 metrics on one system. Let's say it's a spreadsheet in this case. um you're having everything in one place and you'll start from level one which is like your corporate metrics uh things that are you know very important for the board your investors um 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 uh a deal starts so it's marketing and BD when you're trying to initiate create leads after that it's assessing for the KPIs with sales after it's customer success, product engineering, um finance and then HR. So you're having a full scope front to back. Um and you're essentially for each of these functional areas, you'll have six, seven, maybe sometimes up to play 10 KPIs for u each of the teams, but each of them is kind of interconnected in lock step with one another. If marketing doesn't bring in the leads, 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 doesn't have much to expand uh to improve their, you know, gross revenue retention, net revenue retention, their expansion pipeline. All this stuff is kind of in lock step to one another. So you have to build a very comprehensive skeleton. um 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 um 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, uh we use it for certain purposes too um because it's essentially userfriendly. people really know and understand spreadsheets, but you have to augment to a level that it operates like a business intelligence 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 a 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 uh you know spreadsheets get a lot of shade thrown their way, but I think they tend to be effective. Especially depends if you're like a uh productled 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 and go that route. But I mean a lot of companies I worked at a Fortune 1000 company. I was leading revops at a massive company. The entire company was run on spreadsheets. The 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. Um, if you don't mind, I'd like to back up real quick. So, you mentioned three levels of metrics. Uh, you have northstar KPIs, stuff that your investors are looking at, then the functional levels, then like hyperspecific activity level, maybe based on people. What are some examples? Hey, you know, some people are not as familiar or they they think they're measuring the right things, but maybe they're not. Um, what are some examples of Northstar KPIs like, hey, you better be measuring these specific things? The northstar would be very kind of investor and board adjacent. So, it'll be things like number one, your revenue. So, your ARR will be most importantly, usually your number one target to aim for. You have a certain kind of target AR you would want to hit for the next year. um that usually is is pretty high up on the list. Things like net revenue retention for CS is deeply important. Net promoter score for uh product engineering in terms of the quality of the code. There's ways of assessing that through uh you know bug rates uh deployment efficiency things like that. For HR it's all about talent retention. Uh because at the end of the day the the talent that you have the people that you have is the company that you build. So as a north star you you have to essentially have a way of measuring the talent that you have and not losing that talent elsewhere. So those are kind of four or five ones that are you know very important to our company and and for most company in the venture space I'll say within maybe B TOC of having four or five targets that align the entire company itself in that direction to essentially push for you know what it's trying to achieve and solve the the goals to for the most case uh both the company and the investors tied to that company. I won't make you go through all the other ones because I know as you go a level deeper it balloons out into more um 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 functional 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. Uh marketing obviously obviously has a very high KPI for the amount of volume in terms of lead generation uh the size of the accounts themselves. So if your target for marketing is creating a 100 million in pipeline, uh that's very good for one because you have the ability to you know 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 you know 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. Um, so I think a real benchmark or like 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 it 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's generated from BD past a certain gate until the deal process. So it's moving from the discovery phase into what we would call pro value. So the value of the account has been proven enough to be a qualified opportunity. When it reaches that gate will have metrics on qualifying pipeline creation. Um things like the number of 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 um and also within the quarter you have the ability to close that not just within a year but within a certain time frame but that's a real gate for us that we assess and that's something of a of a high metric that we would push for sales uh you know in terms of the data skeleton in terms of dashboard wards that we measure is something that's really foundational to quality itself, not just the quantity. Yeah, I 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 AR 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 salesled growth motion, I do like having the definition being sales had their disco 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? We make sure that we're not just bringing in good fmographic 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 goto market operation 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. Um, 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 northstar, 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. Um, I think bringing all that into one place too is really helpful. So you can see those tiers all in one screen, all in one area. And then you can start to cross-ch checkck 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. Um, 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 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, you know, 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. Um, and it's a really difficult thing to do, but I think you have some really interesting approaches. So, how do you then just throw fuel on the fire and get your team hyperproductive with AI? Yeah. So my approach would be setting the the foundations clearly not just kind of embedding AI into existing workflows um because it might you know help to a certain capacity or efficiency maybe 10 15% more um 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 uh 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 tools for enrichment, tools for prospection. Uh you have so many tools in one area of the business. And instead, if you think about AI as maybe you have the ability instead to build foundations, you know, custom proprietary to the, you know, 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 pro kind of product requirements documentation creating the full scope of the product of the the tool itself I'm trying to build and then I'll build it through replet. It's 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 bucks on a go to market tool, another 200 on another tool. So over time it's two 300 400 maybe a thousand a month and you're compiling this cost over time that you're able to create this platform for 150 bucks maybe less than $200 on replet and you're eliminating the need to to have this really convoluted really kind of terribly 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 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 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 hypers scale hyperrowth startups that doesn't matter too much. If you can get a little bit more efficient, you're not going to realize these massive multi-million dollar gains by helping your salesperson get like one to five% 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. Um, 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 my team is realizing the benefits of it. A really solid system that we've built that we were we would not be able to do before is with the ability of replet and code. So code and replet. I essentially was able to create what we call a contract or MSA extraction tool. Uh so what it's doing is you're building the logic in replet to have over time you build a really tight extraction system or extra kind of extraction protocol and you do it by first building the instructions in Python and then over top you had a a layer of AI on top of it using code with a direct API integration 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 air is. 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 1, year two revenue, sorry, year 1, year two, year three revenue, uh the contacts, the billing contacts, if there's exit clauses for legal, if there's different advisory that we're doing for this customer, all these different things that you'll find in the order form 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, 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 and pushing out to your financial platforms like Zero. So you're able to get what was highly highly useful information from uh 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 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, agent capabilities where the models themselves are solid enough that you could create this logic and be really really precise um on how much you can get out of it. and the tools that you build with it. And I imagine the amount of manual work it would take to do preAI 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 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. Um 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 pushing information to all the channels that you want. So it's eliminating the need for multiple teams, you know, to not having, you know, to do this stuff anymore and have it done automatically by a system. No, there's so many and that's such like a 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. Um, you know, we've internally here at Leanscale, 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 tools. We've um 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 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 the next piece of this if you're building the hubs so in this case the kind of customuilt applications or pieces of software that you're building um 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. M 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 you know push information to different channels but you have to first transform the inputs so that the agent can take the leap. Um so if you're using N8 end or using computer use or headless browsers like um browserbased or manusi 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 that's you know easily read and transformed 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 nannn like manis 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 Instead of having a, you know, person doing it, instead of having an accountant do the invoicing process, 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 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 that will finally do the final push and you know make sure everything's 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, then 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 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 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 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. Um I think we're still getting used to that. And I think 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 form like them, what am I going to do? Um, but it should be creating even more work for you to do and higher leverage tasks. So just 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 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 that I think you know you should execute on for the you know 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 opened up the opportunity for so many people to get more hands-on, get more creative. Um, and I only think it's a good thing. This has been incredible. I think just going from 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 I think everyone is striving to get to this part. Um, 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 this type of work? Like what's your what's your background? Were there some early signs of interest in this type of uh this type of work or these type of companies? My whole kind of career has been first starting with finance. So, I graduated first in finance. My degree in academics was in finance. 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 I went for was a 10 15man startup in the marketing and analytics space. Um that was a really good first experience as a you know a new graduate to one 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 myself. So it got me thinking into one okay all the gaps I need to learn to you know be able to execute at a 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. strategic partnerships, but it's understanding ecosystems, understanding the deal side of things, kind of external facing. And then all the experiences after that were all kind of ops focused. So I had a good background in kind of the operational efficiency piece, understanding how things work. Uh it was special projects, revenue 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 uh by you know creating new systems from scratch from building you know automations workflows that solve deep issues um across the board. Uh 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 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 that's what it comes down to so you know learning the muscle quickly early of you You 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 and you know doubling down where you're very strong at and then you know leaving 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 you find your weak spots and over time I think you you build a pretty good depth of experiences in the op space by u being asked to do stuff at small companies uh 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 thinking I carry in my my personal life it's the same thing of you know thinking in systems like the 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 any anything from the workouts, the the mixed martial arts that I'm doing. It's it's discipline, it's consistency, it's systems, and it it definitely pours into my work. It it's just the way that I operate. And for me, it's it's natural to me. So the op space for me, it speaks to me because it's the natural line kind of language for operations and it's kind of my natural language for life and how I think about how myself how I 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. Uh 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. Um, I've done mixed martial arts in the past too. Spent a lot of time wrestling and you know, ops does uh, 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. Um, 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 in ops for a startup, um, I think that's when you realize the impact. And I I agree with you. I like, you know, having some experience at a small company, having someone at a big company, seeing what works in either. What is, we talk about this a lot, like what stage fit, what's 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 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 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 everything is compounding in your work, compounding in your life. And that's the way I think of it. 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 going to, you know, get uh 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 ops at different levels. Um, sharing a little bit about your personal life and approach to that, too. I think this is going to be helpful anyone who's in in ops right now, especially if they're kind of coming ops for the first time and looking for some information. I think you've armed them with a lot of really good frameworks and thinking. Um, 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 some ideas, where can they find you? You can find me on LinkedIn. I think that's the the platform I'm I'm most used to. You can find me on LinkedIn on my 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 of the the kind of the engagement of back and forth both learning from people and you know giving learnings to others. Um 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 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.