In this episode, I follow up with Ross Koenig, Chief Data Officer at Shore Capital Partners and one of our earliest podcast guests. Ross reflects on how his role has evolved over the past three years, from leading the Data Center of Excellence supporting portfolio companies to focusing on Shore's own investment process data and cross-portfolio insights. He shares why Shore's longstanding commitment to process and documentation has become a powerful foundation in the age of AI, and how nearly 1,000 codified executive hires now enable pattern recognition that few firms can match. Ross also discusses why "garbage in, garbage out" matters more than ever, how AI is breaking down walls between executives and data, and why business owners who ignore AI risk being left behind.

Key Takeaways:

• Build the data foundation first because AI only works as well as the process, documentation, and discipline behind it
• Codify what you know across people, processes, and decisions so you can spot patterns, learn from past outcomes, and make smarter choices going forward
• Recognize that AI's biggest unlock is accessibility, putting information and analysis directly in the hands of executives who used to be blocked by technical walls
• Engage with AI now regardless of size or industry because the cost barriers have fallen and the businesses that ignore it will quickly fall behind

Chapters:

1. 00:00 – Introduction
2. 01:36 – Building the Data Foundation
3. 06:36 – Codifying Knowledge at Scale
4. 12:15 – Accessibility and the Power of AI
5. 15:31 – Why Every Business Owner Should Engage with AI Now

Listen to our podcasts at:

https://www.shorecp.university/podcasts

You’ll also find other Bigger. Stronger. Faster. episodes, alongside our Microcap Moments and Everyday Heroes series—highlighting the people and stories that make the microcap space unique.

Other ways to connect:

Blog: https://www.shorecp.university/blog

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LinkedIn: https://www.linkedin.com/company/shore-university

This podcast is the property of Shore Capital Partners LLC. None of the content herein is investment advice, an offer of investment advisory services, or a recommendation or offer relating to any security. See the “Terms of Use” page on the Shore Capital website for other important information.

Welcome to Bigger.

Stronger.

Faster.

the podcast exploring how Shore Capital Partners brings billion-dollar resources to the lower middle market space.

In this episode, our 50th episode of the Bigger.

Stronger.

Faster.

series, I follow up with on of our earliest guests, Ross Koenig, the Chief Data Officer at Shore Capital Partners.

Ross describes just how much has changed in the three short years since we last recorded, not only in his role, but in the data field itself.

With the rise of artificial intelligence.

He also talks about some fundamentals.

That really haven't changed despite the emergence of AI.

For a data guy like Ross Shore's commitment to process and documentation for the last 15 plus years, long before most of us were thinking or talking about AI is proving an incredible foundation to demonstrate.

The real value of artificial intelligence, not only in our investment practices, but in the performance of each of our portfolio companies.

As a firm, we are working and learning and unlocking insights at warp speed that were built over many, many years.

But to do this well and responsibly, it's critical to have someone like Ross guiding the ship.

The data world has always been clear and repetitive, garbage in, garbage out.

So without the right data foundations, AI has the potential of just making more garbage.

And at a faster speed.

Ross's leadership and Shore's long time and constant commitment to good data and rich documentation are enabling AI just as AI is enabling us.

So Ross, for somebody who's listening, they probably assume or it makes sense that you're thinking about data in terms of dollars and cents and numbers and investments and returns and those kinds of things, which I know is part of the story.

What's the broader picture beyond just what the sort of investment numbers look like?

When you think about holistically the idea of an investment or holding a portfolio company, the dollars and cents are obviously top priority and up there, but there's so many dimensions of this.

I like to start from the very beginning of where these investments come from, which is Shore's process from the very beginning.

Identify an attractive sector.

There's a lot of information about a sector as an element.

What is the TAM?

What is the historical growth rates?

How concentrated is, these are all informational elements that we can grab.

There's a lot of qualitative stuff.

Once we go from sector, we identify a company that's a little bit more of the dollars and cents piece of it.

And then once we transact and partner with the business, I think a lot of the information that we're spending a lot of time on is people and talent.

Who is the CEO?

Who is the CFO?

When were they hired?

What did their resume look like?

These are all codifiable informational elements that I'm spending a lot of time alongside everybody who's in the field and on the front lines, making sure that we're collecting and being very diligent about where it's stored and how it's stored and how it's categorized.

So it's ultimately available for us to recall when someone has a question about what happened, but also.

Stored and available so we can answer some of those higher order ones.

And when you think about that talent, I think this, again, is something that's a bit of a broader definition than most people would think.

Again, I think similar to talking about investments, people would naturally go to what the dollars and cents are on the spreadsheet.

I think when you go to talent, you think, okay, well that's talent evaluation data and so forth, which again, is part of the story.

Mm-hmm.

But it's also a bit of pattern recognition with talent that you're building.

Will you talk a little bit about that perspective that comes from a portfolio?

Yeah.

And not about evaluating an individual performer.

Yeah.

It's been one of the more impactful ways, I think, that we've spent a lot of time here.

Evaluating talent is difficult.

A person has a million dimensions and I give a lot of credit to Justin and Conor Leamy.

Who were kind of at the originals of this idea of putting structure around this idea that is ultimately infinite, and with these structures in place on an individual level at a portfolio company, it's not just about, oh, who is this person and who did we hire and what was their background?

We can take a step back and say, okay, here's all the people that we hired and we have some kind of comparability across them, right?

This is one of the most exciting parts about kind of where we sit.

It's not just about this portfolio company needs to hire the best CFO.

We can say, all right, of all of our 80 odd platform investments, we've made a lot of CFO hires, we all have them codified.

What are some similarities in the ones that did the best?

What are some similarities?

This is often overlooked.

What are some similarities in the ones that didn't go well?

Maybe we should be a lot more diligent and intentional about avoiding those.

Given where we are, we have that viewpoint to, again, it's not just about the individual hiring process, which don't get me wrong, it's very important, but we want to enable a more informed individual hiring process by taking a step back and looking at all of kind of our corpus of action.

Yeah, and I think the way that I've framed it to people talking about it, because it's hard to understand the portfolio effect until you say, we have 60 C-suites.

Oh yeah.

We have 60 VP levels.

Yeah.

We have 60 boards of director.

We have six.

I mean, then you start going.

Oh, that is a talent thing.

Yeah, we, yeah, I mean we, I think as of the last, like we have unbelievably clean data on almost a thousand executive hires.

In the world of big data we're not quite there.

Yeah.

But, and I think that's again, one of the most exciting parts about being at Shore is our volume.

Right?

We do more transactions and by the nature of doing so many transactions, we have more people that we hire, right?

And we keep getting bigger and we're collecting more and more data elements and outcomes that we can again, feed into this idea to help us get smarter.

Yeah.

Well, and even with that level, you know, we've been over the last 10 years, I think the most, or at least one of the most active and most in acquisitive private equity firms in the country and in the world even.

And I gotta believe that that pattern and that data also comes to the founders and businesses and so forth that are gonna make successful partners with us as well.

A hundred percent.

It's so dynamic, right?

And the world is changing underneath us in so many ways, but we have a live look into what that looks like.

Yeah.

Right?

We can see things that don't work.

And it's part of the reason why I, I'm so attracted to Shore in the first place is, you know, most private equity firms and previous to Shore I worked for private equity fund of funds.

We met a hundred managers a year.

Very few people have this many portfolio companies, do this many transactions.

And somebody who would be in my seat at one of those could run a lot of the same analyses but, you know, every, every statistician knows, you know, the bigger the N, the more likely you are to find a significant result.

And we're here with the ability to have, I would love a million executive hires, but that's not realistic.

But you can start to do things when you get into the volume that we're looking at nowadays, and it's only getting bigger.

Yeah.

Well, and speaking of that, you and I talked back in 2023 on a podcast.

We've talked since then.

Yes.

But we had a podcast discussion back in 2023.

Who has more gray in their beard since then?

That's, yeah.

Thank goodness this is just audio.

Yeah.

Uh, you are leading the Data Center of Excellence.

Yeah.

How has your role evolved since we talked back in 2023?

Yeah, I have a different job.

When I first joined Shore, the focus was with the Center of Excellence team making data work for our portfolio companies.

We would get plopped down into one of our physician practice managements and work with their practice management systems and look at visits and really kind of for each portfolio company, getting the infrastructure right and everything like that.

Getting the analytics right, getting the reporting right, getting the governance right.

And that was 80 different projects across all of our different portfolio companies.

Since then, about a year, year and a half ago, I was always interested to apply a lot of those same concepts to quote unquote the mothership.

We're still collecting information, we're collecting a lot of the same information, not quite at the unbelievably minute level of detail of something like a codified visit or, but there's still a lot of information that's coming into these walls and now I'm focused on, you know, looking at these cross portfolio trends.

What do they show us?

And also I, I think would be remiss if I didn't mention Shore is second to none in process.

And process in and of itself if implemented with the rigor that process should be is a data feed and we've spent a lot of time, it's probably better addressed in in one of your earlier questions, but we're spending a lot of time with Shore's process data.

We have heard Justin and the, and the investment team talk several times about nine innings of sector valuation or four quarters of getting from LOI to close or three periods of all the steps you need to do to get something ready for exit.

Each one of those processes is core to how Shore the investment firm operates.

So I've been able to dig into those in the way that I used to kind of focus on, you know, the patient onboarding process for some of our portfolio companies.

So it's a lot of the same structure and framework, it's just applied to the investment operations of Shore rather than the non-investing operations of our portfolio companies.

Yeah, and that work still is happening with our portfolio companies, we should say.

Somebody else is leading that now.

Yeah, it's one of the greatest points of pride for me was standing up the Center of Excellence and for the data team, my first hire, Chris Pennoyer, who was actually had an internal job at Shore on the finance team join me and is now taken over.

And I remember when, when we were talking about this transition saying that there is no better human being that is suited to take this over than Chris 'cause he knows it.

And frankly, a lot more technically savvy than I ever was with all of the other parts to match.

So we are still at the end of the day, and we would harp on this foreveris Shore Capital isn't successful unless our portfolio companies are successful.

So.

It was very, very important as part of this transition that we weren't gonna leave these folks without a go-to data resource 'cause it's still super important.

But yes, the transition is completed and Chris is doing a great job building out that team.

It's great to see.

Was your transition just a natural evolution?

Was there something that Shore started thinking differently about, or you started thinking differently about?

Talk about why that shift.

I think it came for me was, you know, all of a sudden when I joined, it was in the dozens of portfolio companies, and then a year or two later, it had doubled.

And we have unbelievably smart, hardworking, detail oriented people that work at Shore, and you can kind of get away with it for a really long time if you have those resources.

I think there was a bit of a tipping point, whether it was, you know, an investor request or something that's relatively simple that just we could do it.

The sheer volume of it became a bit of a blocker, and I think that started to impact some of the investment folks.

At the end of the day, we wanted to make data more available, and it was getting in the way of our ability to do, you know, of those resources, do what they were supposed to do was just making investments and evaluating, talking to their portfolio companies.

I also would mention there was always this underlying belief from our leadership that digital transformation had to take place and that that energy and effort around that was being done by a lot of different people as kind of the second and third priority.

Our COO, Jeff Williams, you know, led a lot of the technical first steps that we've built on since then, but he was doing that alongside the million other things that A COO does.

And when we got really, really serious about digital transformation, I think it made a lot more sense for this to have, you know, somebody like me think about it all day and all night.

Yeah, I mean some of it just mirrors Shore's growth that's gone along sort of behind this whole story anyway, and I'm curious, you know, part of this, I can't help but think about part of that story, that growth story is also like those processes and, you know, 15 years or so of investments were initiated and lived a lot in the first wave of leadership's heads too.

Yeah.

And I have to believe there's some level of codifying and sharing institutional knowledge, confirming or dismissing institutional knowledge in this process as well.

You're spot on Anderson and for a really long time, we were small enough where everybody could join the Monday morning meeting in person in one room, right.

And those are the discussions and the note sharing.

And everyone had time to read all of the really long IC memos for every platform and every add-on, and that's so, so valuable.

To hear that discussion and get those reps and read those words.

Fast forward, there's a lot of good stuff that's happening in our healthcare investment committee discussions that our food and beverage team simply isn't gonna participate in those in the same way because you know, there's only so many hours in the day.

So that is where you can look to technology to bridge that gap, make those information, make those key learnings that are happening more available to the guys who could use it.

But it's just kind of logistically harder without the right tag.

When we talked before, back in 2023, I think we just tangentially talked about AI.

It was at that time still this thing that was coming and we didn't know what you might have known what it meant.

I sure didn't know what it would mean.

How does this story and this evolution and the topic of data relate to what we're all experiencing now.

Not as a thing that's coming, but as a thing that is here and now and ever present for most of us.

Yeah.

Right.

How, how long were we into the interview before we end mentioned AI?

No, it's the right thing to be talking about.

I think the AI force, that is happening everywhere.

Touching our portfolios and our lives has changed the way a lot of things operate.

Right?

It's not like it used to be, and data is absolutely in that world.

What's most exciting to me is I feel like AI has been a vehicle to get folks, whether they know it or not, more excited about data and information than they ever have been.

It was almost, when we talk about things like data warehousing or some of these technology tools like business intelligence or writing code, there's so many unbelievably intelligent executives or, you know, hard charging, ambitious executives who rightly would just say, that's not my domain, right?

Like, I, I'm not gonna write SQL code.

And so there's this almost disconnect between the data and accessing it that now AI and especially in the last, you know, two, three months, like that wall is down, these executives are now even more trumpeting like, you know, what are we doing here?

What are we doing here?

And the right folks who really understand AI quickly get down to, well, if we want this to work, how the executive needs it to, we've gotta figure out the data.

I'm just, you know, smiling from ear to ear.

It's happening at our portcos and it's certainly happening at Shore.

At the end of the day, AI can be used in any way up, down, and sideways, right?

But I think there are a lot of foundational things that people want AI to do that will really only work if your data is right.

Garbage in, garbage out has been around forever, but it's just, again, like in the same way, it's just brought way more to the forefront with these tools.

As I listen to you, the word that comes to mind is accessibility.

Yeah.

Right.

One of the things that when I came into Shore and, and we came in roughly the same era, you know, Shore had been process oriented from day one.

Had documented what we learned from day one on every deal, every hire, every, every everything.

Yeah.

We had volumes and volumes and volumes of documentation and learning and so forth, and it feels like it has been the transition really in the last six months, maybe a year.

That has actually become accessible and really usable to the majority of people within Shore.

Yeah, it's exciting.

I think that what initially attracted me to the data space is this idea of putting information, sometimes analytics, sometimes reporting in the right hands is such an exciting thing to watch unlock, right?

I've worked with sales executives who are dying for simple quantitative analysis and partner together and all of a sudden it's delivered and they can run their sales team in a strategic way that they never have before.

But getting there is painful.

Was so painful, and you'd have to set up infrastructure and design these things.

And it was all very esoteric in how and where.

AI has knocked down those walls and it's really fun.

Yeah.

From where you're sitting, giving everything that we've talked about, if you were.

Someone listening to this conversation and you have a company, maybe you're considering a partnership with Shore or otherwise.

Mm-hmm.

But you're a founder or you're leading a microcap business, why should you care about any of this that we're talking about?

Why does this matter to someone who may want to partner with Shore or may want to at least playing with the idea?

Yeah.

I do believe, and I don't know if this is a controversial take at this point in the AI arc, but the easiest answer is to say, if you aren't caring about it, your competitors are, and you are going to get left behind if you ignore it.

AI is new.

It's very, very similar to the internet.

It's very, very similar to a lot of the automation and data warehousing tools that have been, you know, that I've lived in Breathe for the last 10 years.

And that's true by the way, regardless of industry, regardless of size of business.

Well said, well said and that, that the big part with the smaller end of the spectrum is so much of the tech unlock, there was a barrier for cost, right?

Like to get so much of the automation or business intelligence or data science predictive models, you had to hire a technical person.

You had to stand up a super expensive infrastructure.

You had to build these things and find these people.

And oh, by the way, if that person doesn't work out because that happens you've gotta rehire again and spend more money.

And AI has just allowed that to be less.

It is way more accessible to the smallest of businesses in ways that it never was before.

I think you should care to know that like we have a lot of resources here are still trying to figure it out.

It's all very, very new and I think in this dynamic today where everything is changing every other day, if you're the CEO of a business, if you're thinking about partnering with Shore, this is my quick commercial, I can't help myself, but we have this internal AI team where these guys and gals are living and breathing AI in the changing landscape every single day.

You're a smart business owner.

Make sure you that you work with those people and they don't have to do everything for you anymore, right?

Because the technical roadblocks are largely reduced.

But there are pitfalls, right?

And there are ways that you can do it wrong.

I know it's all sold as the easiest thing in the world, and one shot prompt you've got the greatest app of all time.

It don't work like that.

Just just period.

But with a little bit of guidance with people who've seen it before, who've tried some things.

It's all building on that tribal knowledge that lessons learned.

That's the same.

But again, if you're not trying it, you're gonna be on the outside looking in real fast.

Yeah.

And I think that's why you should care.

Yeah.

Well, in that same spirit, when you think about the next year, the next 18 months, you don't have to predict the future, but what do you see on the horizon and what's exciting from you, from the data perspective that you think is gonna continue to transform not only perhaps Shore, in our process internally and our effectiveness as an investor, but also what we can do to support our portfolio companies.

Yeah.

I have two very specific answers to this one, 'cause I think about it all the time.

One, I would imagine every business owner or even every person who has worked in a business somewhere has felt the pain of having the information that they need available.

Having it buried in Susie's spreadsheet and you know, Susie's out sick, and I don't know where Susie saved it.

We are humans and we interact a lot with outputs and deliverables and reports, and I think that's for the first time ever, I have so many great ambitious projects that grinded to a screeching halt because all the information was in these old PDFs and God knows how long it's gonna take to get that out into a structure or a format where we can do anything with it, let alone, you know, some of the more exciting things and those walls are gone.

You know, we really wanted to do a project where we looked at some trends across all of our investment committee memos.

And we got 80 plus platform investment committee memos, and we wanted to go and grab a few elements of information from all of them.

That would've been a month long project.

And now we rock it all up in AI tool of choice, and we've got it in a couple in an hour.

Like, so I think that piece of it where we're able to extract information from previously, kind of inaccessible outputs at scale, that's only gonna get, and then the only limitation is your imagination, right?

What kind of information, if you're a business owner, like.

What kind of information could you get if you looked at every single customer agreement that you've ever written?

Well, most smart executives have a long list of things that they would use to answer that question, and they would come to me and I'd say, okay, well let's find the agreements and let's get to work and I'll talk to you in a couple weeks or months, or, you know.

Now it's, let's prompt these things and get this information immediately in our hands and start cooking.

And I think that's only gonna get bigger.

And the other piece of it is kind of the next step of that.

Naturally, once you get all that information, what are you gonna do with it?

Data science has been around for a really long time.

Statistics is a thing, and you know, really, really heavily applied to sports and there, because there were box scores and all that.

Like when you start to get in these higher orders of statistical analysis, whether it's regressions or even beyond, you know, random forest and decision trees and all this.

Like these are really quantitatively intense concepts that are frankly out of reach for a lot of folks, just not due to brain power, just time.

And I knocks that down, right?

I can build statistical analyses in these higher order analytics in ways that I never could before.

And again, maybe we wanna add an element to this regression model.

Well before that's okay, let's go back to the beginning, let's pressure test it.

Oh, this is gonna change how we codify all these things and how we organize the project and all that.

No, no, no, no, no.

That's a couple of prompts and a couple of guidance, and all of a sudden you have a completely refactored model that takes into this new idea into account that's, again, only going to get more and more exciting as the technology grows and as people kind of get more comfortable.

First of all, building these baseline models that used to take forever.

Now, now they're here immediately.

Okay.

I kind of know what this is.

I'm getting more familiar with it.

Oh, can I add this?

Well, yeah, you can.

Before it was just such an arduous process to get everybody up to speed on what it was.

By the time we would be able to build these with our old technology executives have forgotten about it and had 20 more ideas, and that was such a struggle for a long time.

That's gone, and that's really exciting and I think that's gonna unlock most of the stuff.

It's like these phase two, phase three, phase four over months has now been compressed.

Yeah.

So just to play back the way I hear that it's, we've been talking a lot about unlocking and accessing information and insights like never before, but listening to you describe that you're also articulating creativity and innovation and rapid iteration like never before.

Oh yeah.

And what I've been so impressed, and again, early days of AI, when ChatGPT first came out, it was, we'll just call it what it is I would call it oversold.

Right?

They were interesting and it was exciting, but if you kind of read it down to details that fell short and last few months, I feel that that has changed.

And you really are able to have a conversation with these AI tools where you can bat these ideas around and really refine and develop them before you know, we are all under pressured timelines and we want to have a predictive model that thinks about what our revenue's gonna be at the end of the year.

Simple questions like that we can now spitball and iterate on maybe there's 50 things that I have in my head instead of kind of trusting my gut to pick the top 10 and the time it takes to think through that.

We can have a very detailed conversation with an AI to kind of get to the right place and then test it really quickly and, oh, that didn't really work.

Okay.

Hey AI, you know how I started with that 50, what's the next tier?

And then just keep going.

Yeah.

And it's really exciting.

I think you're exactly right, like the creativity, especially with these founders, these executives, frankly, anybody at these business who live and breathe it, they've got a million ideas.

They've been living this for a really long time, and now it's just a matter of articulating it and pointing it to the right place.

There's no development long pole in the tent.

That's not the limitation anymore, which is super exciting.

If you enjoyed this episode, be sure and check out our other Bigger.

Stronger.

Faster.

episodes as well as our Microcap Moments and Everyday Heroes series at www.shorecp.university/podcast

or anywhere you get your podcasts.

There you will also find our new video podcast series, Raw Talent.

This podcast was produced by Shore Capital Partners with story and narration by Anderson Williams.

Recording and editing by Austin Johnson.

Editing by Reel Audiobooks.

Sound design, mixing, and mastering by Mark Galup of Reel Audiobooks.

Special thanks to Ross Koenig.

This podcast is the Property of Shore Capital Partners, LLC.

None of the content herein is investment advice, an offer of investment advisory services, nor a recommendation or offer relating to any security.

See the Terms of Use page on the Shore Capital website for other important information.