The Achieve Podcast - Mark Stouse (Proof Analytics)

Welcome to another episode of the Achieve Podcast, the show where we highlight leaders, creators, and entrepreneurs who are building meaningful businesses, shaping their industries, and having an impact. I'm Taylor Baker Baker, your host. Today we're excited to be joined by Mark Stuus, the CEO at Proof Causal AI, and you can find them at proofanalytics.ai. Now Mark, we were just talking off camera, but But give us the backstory on how you got into the space and the problems you guys are tackling. Like, who are you helping and how are you doing this?

Yeah, so it all, for me, it started almost 20 years ago when I was a marketing leader at HP and I had a lot of interface back and forth with Mark Hurd, who was the CEO at that time. And Hurd was a very tough operational manager who went further than a lot. I mean, I think it's, it's fair to say that most CEOs ask, what am I getting from my marketing spend? Right. Heard really wanted to know. And he, he would get in our face about it. And, and so he asked me to figure out a solution And I'll be honest with you, at that time I didn't know even where to start.

Okay.

So I have a lot of empathy for people today who are in that same place. And we started— I kind of got reacquainted with my university math books and things like that and got reintroduced to causal inference and econometric analysis, things like this. And then that gradually just built and built and built and built into a very successful causal system.

Okay.

That was so helpful that we decided to make a company out of it. And that was about 8 and a half years ago. And, you know, then we started getting into AI and We were absolutely one of the pioneers in causal AI, which is now one of the 4 or 5 categories of AI. Wow. That's out there. And so today we still do a lot of go-to-market analysis for companies, but we also do a lot of other kinds of parts and functions within a company. And we're doing more and more of it through a fiduciary lens and a decision governance lens because the regulations and the court rulings on that have been very strong in the last 3 years. And so that's a driver. We work primarily with finance, but— okay, as the economic buyer of our stuff, but we're working across companies. When we actually do the work.

That makes a lot of sense. And how big is the organization now?

About 40 people. Wow.

Okay. And what would you say is actually driving growth for you guys at this stage?

Uh, I would say that it's really the, the volatility and, and high velocity change in the marketplace. You know, that the stuff we don't control is 70 to 80% of why things happen the way they happen, which is sort of an uncomfortable statistic for a lot of people. You know, it shows us that we're not really in control, um, you know, uh, that there is no such thing as a master of the universe, um, you know, in that sense. And so, uh, in the Wall Street sense of that. And so it's a, um, people really feel more incented today than ever to have accurate forecasts, to be able to look further and further down the road and occasionally around corners.

Yeah.

So that's, that's really the big impetus, um, that and the fact that there is now a proven mathematical approach to doing this. And, and, uh, This is something that's not new, but it's evolved considerably. Bayesian, I'm sure everybody at least remembers that word from college. Bayesian was sort of the first toe in the water on all this, and that's 500 years ago. And some of the algorithms that are still in use today were authored by Aristotle. So I mean, that should tell you something. People have been interested in this space for a long time. Some of them went to fortune tellers, and some of them went to mathematicians, right?

Yeah, absolutely. Now, for you, Mark, looking back, what turning points, positive or negative, have molded the way you think about running the organization?

Well, I'm a— a— and have been for a long time. I'm really, uh, about a people-first, uh, approach. That's certainly true inside, uh, our, our team, but it's also the way we think about customers.

Okay.

I mean, a customer is just like a, a team member, right? Has their own set of goals, their own set of fears, their own set of needs. Right. And that's really what makes the difference, is being able to meet them as much as you possibly can where they are.

Yeah.

You know, I think a lot of leaders tend to say to everybody around them, inside their company or outside their company, no, no, no, meet me where I am.

Right?

And, and that, that's just not, uh, that's not flying. Uh, I don't think it ever has really, but it, it's more obvious today than ever before that that's not working.

I love that, and that's a good distinction of good leadership. Meet me where I'm at versus let me meet you where you're at. What do you have? What are your needs?

No, I think you have to, you know, this is actually one of the reasons why being a, a CEO can be the hardest job in the company is that the, the reality of the job is that you are working for everyone else in your company. Yeah, you are. Your whole purpose is to bring the, the positional power and other kinds of power that you have to bear on their problems. In other words, to smooth out the road in front of them, to make it possible for them to be more effective, more productive, right? That's your main job. And, you know, they're usually— if you encourage, particularly if you encourage it and show them that you really mean it, they are— they're going to tell you what they need from you, right? And when you provide it to them, you are serving them.

So true.

And so, you know, so I mean, it's, it's, uh Again, that's, that's not the popular idea, or hasn't been the popular idea of leadership, uh, particularly in the United States, but I think it's the only one that, that is going to take us where we need to go.

I like that, that servant leadership. I like it. That keeps the focus where it should be.

Now, actually, that's really what servant leadership really means is that the— it's not all about you. Yeah, that, that is— that little phrase, it's not all about you, is probably the most important thing that we could have in our top of mind right now. Is it doesn't mean that you're not important. Yes, it's not all about you. Okay, so it is about you, but it's also about a lot of other people.

Yep.

And I think that's really important.

So true. I like that distinction. It's not just about you, you're included in that, but wow. Now, as you guys try to grow and expand, what have been some of the biggest bottlenecks or obstacles that have shown up and gotten in the way?

People. Seriously, the hardest part of life, of leadership, right, is the stickiness with which human relationships get entangled. Right. Yeah. And so, you know, there's a part of every— and I think actually that most CEOs, most leaders today are not comfortable with this at all. It's one of the reasons why they hold most people at a distance.

Mm-hmm.

Right. But it is— you've got to be able to deal with people as people, particularly when they are inconvenient to your goals and objectives. Right. When they're they're being who they are and who they feel like they need to be. And that's not, that's not working with whatever it is that you're trying to achieve. You know, you— there's a number of options there, obviously, including separating, right? But, but it is a— you have to see it humanly before you can see it operationally. And I think that we have inverted that.

I like that distinction. See it humanly before you see it operationally. That's a good line.

And so now we have been obsessed with the idea of scale as a doctrine, right? That we— I'm talking about companies all over the place, right? It's all about scale. It's all about efficiency, right? Not about effectiveness. And The human being is the cornerstone of your organization's effectiveness. It's not your systems.

I like that. Now, when it comes to you guys' messaging, your outreach, your marketing systems, what has been working well and where do you fill the gaps and frustrations?

Well, I think that again, right now is, is a, is a really pivotal time. You look at go-to-market Uh, it's been in a steady state of decline in terms of effectiveness since about 2018. We, we track this and publish a report every year. Um, and this past December we did the report on 2025, and it was the first year where more than half of go-to-market spend— so that's marketing, sales, product, and and customer success, that's kind of a classic definition. It can be include more things than that, um, has— is now 53, 54% ineffective.

Really?

Yeah.

I mean, say that again, that's important.

Yeah, it's, you know, 53, 54% ineffective. And marketing in particular is a multiplier, a nonlinear multiplier on sales Effectiveness, which is sales is a linear value creator. So that's— they're very complementary and very different in their action. And both, you know, all of them are way down, um, in terms of their ability to produce. And you see it. I mean, the easy one, you still need causal analytics in order to be able to figure out what to do about it. But you can see the problem very evidently, uh, when you just say, okay, CAC continues to go through the roof, okay?

Yeah.

And yet deal volume is down now for 3 straight years. Average deal size is down 3 straight years, maybe 4. And average deal velocity continues to slow and has been slowing since you know, COVID, really. So it is, uh, different reasons for that at different times. But right now we're looking at the thing that's uppermost in everybody's mind in B2B when they go to buy anything is risk. I mean, there's just not even a close second place. And so that is— that's a big challenge for everybody, and that would include us, right? I think it's never been more important to have a really clear understanding of who your ICP really is. And it's not about stuff to be done and functions and all this kind of stuff is nearly as much as it is somebody who is thinking about the problem in a very similar way to the way we are. And who is ready to go through a mindset shift, right, that is, uh, always hard. I don't care who you are.

Yeah.

Uh, in order to get to the other side of the river. And that is, you know, uh, it's a small— getting bigger, but it's a small ICP. And quite honestly, we're— we've been pretty happy with it being what it is right now.

Wow. So have you seen these stats across multiple industries, or is it more niche, or it's kind of just universal?

No, it's a— it's a— so we track, uh, in our basket is I think 458, uh, Fortune 2000 companies. So they're— they cut across many different industries, levels of maturity, all this kind of stuff. Right. So these are big companies though, right? So it's around 53%, 54%-ish in that zone for them. If you're a startup or a scale-up, it's way worse. It's more than 70% ineffective. Wow. Wow.

And as far as scale-up, when do you kind of cross over that threshold from the— you're in your analytics into that and it starts to lower down a little bit?

So I would say that it— that's not actually the way that we would look at it.

Okay.

The way we would look at it is what are the different kinds of risks that different size businesses are trying to mitigate.

That makes sense.

Because if you're a small company, you know, It's a— the major risk to ineffective spend is always cash flow.

Mhm.

In a big company, it's more about opportunity cost. What you— because you're not going to run out of money, probably, if you're a really huge company, right? But you can definitely show that you've wasted a lot of money and what you could have done with that money All those kinds of questions are under greater and greater scrutiny by shareholders today.

That makes a lot of sense.

Shareholders are starting to realize that, A, courts are moving that way, the SEC is moving that way, and for the first time, at least to most of them, right, there's actually a way to calculate it.

Oh, that's nice. Now, I'm sure you guys are using the AI. Like, what are some of the ways you guys are using AI internally, and where do you still see opportunity?

Well, we use AI. So if by AI you don't mean causal AI, you mean GenAI, so like LLMs and all that kind of stuff.

Yeah, just any AI. There's a lot of buzz around AI automation, and people don't fully understand. They just use that term openly and loosely. It's like there's specific—

yeah, so it's really, it's starting to become really important to be clear about what kind of AI is being discussed.

Yes.

Now, for the last 3 years, if you said AI, pretty much everybody knew you were talking about ChatGPT and Claude and all those products, right? Mhm. That's, that's getting to be not necessarily something that could stand up anymore, right? So you have to be a little more specific. We use GenAI tools, LLMs and things like that, all the time internally. We have one of the first really large custom LLM chatbot-type interfaces on our website homepage where you can go ask incredibly specific questions like, hey, I'm the CFO of an energy company. We have the following challenges. We're trying to figure it out. Can you help us? And oh, by the way, why the hell should I trust your system? Right? And it will give you a very long and very customized answer. So we use it there. We use it the same ways that a lot of people use it, except I think we're probably much further ahead in terms of being able to use it. Another cool thing that we started doing about 3 years ago when this capability came online is that wargaming has been— and I'm using this in a non-military context, okay? Yeah, wargaming has been around for a long, long time, uh, even statistical wargaming models but they were very, very expensive because you had to create all the synthetic data by hand. 3 years ago, the major LLM tools— ChatGPT, Claude, etc.— you started to be able to say, okay, I want to, to achieve this in the future, and that's never happened before. What would it take? So clearly this is a series of prompts, right?

Yeah.

Uh, what would it take to get there? Like, what's a strategy that would achieve that? What are the underlying operational layers that deliver that strategy? And then what are the tactical pieces underneath the operational layers? And then you can say, okay, I need a, a thousand observations, meaning in my— I need all these datasets and each one of them has to have 1,000, a strip of 1,000 measurements over time, right? Wow. And it has to be— it has to accurately represent the scenario, right? So that's a lot of ability piece. And then it's purely synthetic, right? And so it allows you to then put all that into Proof Causal AI and explore things that have never happened before and what you might be able to pull off, what works. And it actually works exactly the way you use a GPS on your phone or in your car, right? So being human, we all tend to put ourselves at the center of everything. And so we can think of a GPS as starting with where we are on the map, but it actually doesn't. When you plug in the address that you want to go to, that's where it starts. And reverse engineers and reverse engineers it back to where you are and then gives you 3 choices on the best way to get there, right? Wow. And then as you're cruising along and circumstances change just like it does in the marketplace, right? Circumstances change. And let's say there's an accident, lots of traffic backing up, and it says, hey Taylor, you know what? This was a really great way to get to your destination, and now it totally sucks, right? Stay here, you'll be 2 hours late to your meeting. But if you exit here and you go right and left and right and left and whatever, right, you'll be 10 minutes late.

Wow.

Okay, and that's really what Causal AI does for businesses. It, it is a GPS that tells them what they need to be aware of and what they need to do about it.

Live data that adjusts with the market and economy and everything's going on.

Yeah, and there's, there's a— probably the thing that's most exciting about it is the idea of what's technically called a counterfactual. So that's a really fancy way of saying, what if, what if we did this? So we've seen these changes, right? And those changes have kind of negated the effectiveness of what we were doing. Well, what, what could we do? How could we change our approach to regain effectiveness in the current marketplace? Right. And that— and so that's really what it comes down to.

Wow, Mark. So when you think about the next stages of the business, what are you most excited about?

Well, I would say that that is, uh, it's very much about the day you catch me on that. I mean, I think that like any entrepreneur, it's a, it's a strenuous thing. I think that a lot of people go into it expecting it to be other than what it is. And really what it is is a substantial amount of sustained suffering. That's the truth. That's the reality of it. Does it have its really great parts too? Absolutely. Okay. But so I would say some days the next big step that I fantasize about is selling it, you know, I mean, exiting. And then there are other days where I can't imagine not doing it right. And then it becomes more about, so how can we take this to the next level? How can we? And what we mean by that is how can we meet the needs of people better than we already are?

Yeah, right.

And so I think one of the biggest challenges here is that people feel— whether or not it's true or not is probably very individualized, right— but people feel that they've been lied to for a long time, and so they don't know what to believe and who to believe.

Yeah.

And so that's, that's probably the hardest thing that we deal with just in general. But one of the things that is really amazing is that when they start working with Causal AI, with the outputs of Causal AI, and they see it in action, you can see them relax. And that is a It's a very undervalued benefit. Yeah, right. And it's because when you work, when you run stuff in a causal AI model, you are actively de-risking the future on a rolling basis.

I like that, right?

And that is impossible not to appreciate. It's not that we are, you know, somehow dealing in prophecy, you know, or, you know, fortune-telling or something like this, right? We're not. This is a probabilistic mathematical calculation. Um, but because it's— the model recalculates like every week or every day, really depends on what the need of the customer is. You're constantly saying, we predicted this. And when the future became the present and it was measured for real, that's it. You know, it was either as forecasted or there was a little bit of a difference. And we could, because of the model, we can say why that difference existed, right?

Yeah.

And if you really, if you re— if it's really important to you to close that up, right? What do you need to do in order to do that?

I love that. I love that term, actively de-risking the future. Yeah, because we spend so much time concerned with what we don't know. Okay, so if you can eliminate as many of those unknown, those variables, and all right, I have these two problems, okay, I can deal with these three problems, I can deal with these, but right now I'm like, I don't know, you're actively doing it.

So, you know, there's that old saying that most of what we Worry about ever happens. Okay, what's really amazing is to see that maxim, that principle, illustrated in the causal model, in the math, right? So why is that? Why do, why do we, why is that our perception? And why is it actually reality that it, that's true? Right? Yeah, it's because 70 to 80% of the stuff that happens is dictated by things we have no control over and no knowledge of, can't predict under normal circumstances, right? And so, and I think also people are starting to say, wow, there's, you know, we actually, you and I grew up in a, in a world that was This is a relative statement, not an absolute statement. Okay.

Yeah.

This thought was far more stable and consistent year after year after year than it is now.

Yeah.

And so people are starting to say, man, you know, we're at war in Iran. And 6 weeks ago, I didn't even think that was ever a possibility. That's how much things change. That's how fast they change. And then the knock-on effects we're already grappling with, right?

Yeah.

Oil prices and all that kind of stuff, right?

Yeah.

So it's a— people are more aware of this than they've ever been before.

So true. Now, Mark, when you look at your own journey, the wins, the mistakes, what lessons stand out to you and what do you wish you understood sooner?

You know, our, our greatest strengths are always our greatest weaknesses. I mean, those things go hand in hand. I'm not the first person to ever say that, by a long shot. Um, I tend to be very methodical. I'm a chess player. Uh, one of the hardest things for me to learn is that things actually— this is actually remarkably similar to what we were just talking about— things don't change as much as you think that they will, particularly with people, until all of a sudden they change hugely.

Tipping point.

And when they change a lot, that, and you know, usually has a bad impact, right? And so I've had to learn to temper my optimism. And when, when a person isn't working out, I used to kind of give them a lot of opportunity to kind of change. I don't really do that anymore. And the, the, the actually the, what I really learned about that was taught to me by the rest of my team. And they would say, you know, they'd put it this way, but this is really what they meant: allowing that person to stay in the team is a tax that we all have to pay.

Yeah, right.

And that's not fair, right? And so that's really what it comes down to. So I would say in every case that I can really remember where I should have made an end of something, uh, I should have done it sooner than I did.

Wow. Mark, final question: if you had to pick one constraint to focus on and solve over the next 90 days, what would it be?

Myself. You know, when, when you, when you, uh, when you work for everybody on your team, you have to really work really hard not to be the choke point. And what that really means is that it's not that you're not the decider anymore, you're just the decider on a lot less. And when you are And I can say this objectively because I just know it to be true from a lot of different ways of thinking about it. I'm a really smart person, right? So really smart people tend to want control.

Mhm.

And it's a lifelong battle. It's, it's, it sort of reminds me a lot when I hear like different people talk about, uh, their substance abuse journey, right? And the fact that they, they never are cured, they're just always in maintenance mode, right? And that's what it really is like when you really desire control. You're having to beat it back constantly. And, uh, because it is in a sense a type of addiction, right? Um, it's gotten a lot easier for me, uh, but I still— the number one thing, thing that I always work on the most is me, because I'm the oftentimes the problem.

That makes sense. I can see that.

And as a CEO, as a leader, if I am the problem, right, I'm the problem at a magnified level, right? It's kind of like that old saying about the true test of a person is what happens when you give them power, because all of their impulses now have lots of power. So what they do with all that power tells you everything.

Wow.

But the hardest part is to balance it between what everything I just said and also realizing I'm still the decider on a lot of things and I still am smart. So you can, you know, we don't— as human beings, we don't do the middle of the road thing, the balance part, very well. We're kind of one ditch or the other ditch, right? And I've gone through that where, you know, I've gone from essentially being super, almost narcissistic, right, uh, to overly self-effacing.

Yeah.

And they're both bad.

Yes.

In a team context, they're not— neither is helpful.

Yes, I like that distinction. It's the middle of the road there. Well, Mark, I appreciate you coming on and sharing so much wisdom with us. And for those who want to reach out and learn more, they obviously find out on the website proofanalytics.ai, LinkedIn, anywhere else you want to send them.

LinkedIn is actually the best way to reach me if you want to talk to me as opposed to the company, right? Um, just either publicly in some, some underneath some post or whatever, that's relevant, tag me and say, hey, you know, or a lot of people will PM me, right? But it's, you know, that's the best way. My email is a disaster, pretty much as everybody's is. There's so much spam in it that it's just ridiculous.

Yeah, it's crazy these days. So thank you again, and thank you to our sponsors of today's episode, Achieve Fractional. Which provides fractional executive leadership for growing businesses. And if you enjoyed our episode today, share it with a friend, with a colleague. If you're wondering about the future of your business and what path to take, reach out to Mark and the team and they can show you a very clear GPS-guided mapped out plan. You don't have to guess. You can just have confidence in those decisions.

So— Yeah, actually, let me just add this real fast. One of the things that you can test a plan, a strategy, a proposal before you spend any money on it. You can basically run it through Causal AI and it will tell you what the odds are that it will be successful or not.

I love that. Well, thank you again, Causal AI. And you can find them at proofanalytics.ai. Reach out to Mark and the team, and we will see you on the next episode of the Achieve Podcast.

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