Scaling AI: Using AI to Scale to $250M and Beyond

How can you use AI to lead your technology service firm to new heights in 2025?

And how do you go from a linear scaling model to a model with a much steeper revenue curve?

And how can AI help you reach new levels of scalability and efficiency without sacrificing the personal touch your clients have come to expect?

Hi, I’m Courtney Baker, and this is AI Knowhow from Knownwell, helping you reimagine your business in the AI era.

For this episode of the show, we’re bringing you highlights of a recent conversation between Knownwell CEO David DeWolf and NordLight CEO Pete Buer.

They talk specifically about the challenges technology service leaders are facing in scaling their businesses and how AI can help.

Maybe we start off by asking a simple question.

Do you know where your next 10 million, your next 25 million, your next 50 million, your next 100 million dollars is coming from?

I know when I started 3Pillar in 2006, those questions would have sounded absolutely nuts to me.

But as we scaled and we hit 25 million, 50 million, 100 million, the numbers just kept getting bigger.

It wasn’t about the next $100,000 deal.

It wasn’t about the next million dollar deal.

It was about scaling the organization and making sure that you were building a machine.

And that was hard.

And it’s inherently hard in professional services, in technology services.

As we hit these certain milestones, the challenges we faced as leaders and as an organization changed.

It was always a new problem that we were solving.

And personally, I found that I had to reinvent myself about every 18 months, just to remain the leader that the company needed.

And I think that is a very common thing for entrepreneurs or for leaders that have scaled organizations.

As your company evolves, so do the challenges and what it takes to overcome them.

And when you start layering in the type of transformational change that we’re seeing in this market, it’s not just the scale of the business, but it’s the moving terrain around us that we now have to navigate.

Reaching these numbers is a remarkable achievement.

There aren’t a lot of 50 million, 100 million, $150 million businesses in technology services.

There tend to be the huge players and the boutiques.

But scaling beyond 200, 250 is exponentially harder.

The data in fact shows that there is a precipitous drop off of companies that are able to go from the 100 million dollar mark to a billion dollars, which you have done and have been a big part of.

And so at this stage, firms face growing pains that traditional methods can’t solve.

It’s the stereotypical, what got you here won’t get you there.

When you’re in the mid to upper eight figures, you face challenges that are new to you.

You’re going head to head with market leaders.

You’re not just kind of a niche boutique that’s able to win sole source opportunities anymore and win that next small deal.

You’re competing not just for clients, but for talent as well.

I remember at Three Pillar the first year where we had to hire a thousand people.

And that just hit me like a ton of bricks, right?

It was one thing to have to hire a dozen.

It was another, you know, I could tap my network to get to a friend of a friend to get to 25.

But then you hit 100.

And when we hit a thousand, we had to build a machine to bring in and recruit a thousand people.

That was amazing.

And it’s one of the challenges that is very unique to all professional services businesses.

But then you have the high touch relationship driven nature of the business model itself.

Just pure sales isn’t enough.

Most technology services companies grow through land and expand.

How do you navigate that?

What does that look like at scale?

That becomes more and more and more difficult.

And of course, what’s so core, and I know you’re passionate about in a professional services firm, especially company culture matters a ton and is really, really important, but it becomes harder and harder to scale because you have more and more and more people.

So there is so much that you have to overcome to break out to 250 million.

At the same time, you and I are testaments that it is possible.

I can tell you that it’s achievable.

And that’s why I’m so excited to be here with you, to put our brains together and to share what you and I have come up with as not just with the experience of navigating through these inflection points, but with our deep expertise, now diving into this area of AI, what is it that AI enables as we go through that transition that we’ve never had before?

Awesome.

Thank you, David.

My understanding of our roles today is that you are the fun color guy and I’m the dorky stats play by play guy.

There’s no truth in that.

So let me stay true to my assignment and give a little bit of backup to the stories that you just told.

On the graph, on the slide here, a histogram of distribution by revenue tier of company sizes in tech professional services.

And so working from left to right, you know, the smallest firms, 0 to 5, 65% of the market.

And slowly but surely, as you move right, you fall off a cliff to the point of not even 250.

I mean, when you’re at 50 to 100 or 100 to 250, you’re already down into low single digits proportion of the market.

And then depending on who you talk to at the 250 plus level, it’s hard even to size the relative bar size.

I should, at this point, draw your attention to the source of the graph.

This data actually does not exist in any single source out there.

And so what you see is a result of me kind of beating the daylights out of ChatGPT all night long, spent a long time trying to find the specifics, and it’s not publicly available.

But I believe we did a job, you know, crawling through everything else that’s out there and weaving together a good sense for what the market looks like.

I believe it, it lines up with the anecdote and the punchline whether these numbers are off by a point or not is that 250 plus is rarefied air.

It’s lonely at the top.

And Pete, I have to add in, you sound a lot smarter when you don’t say you beat it to death, but we call that prompt engineering in the AI world.

So.

Fair enough.

Well, so let’s talk for a second about why this is the case, right?

So I did some massive oversimplification of the plight of professional services and growth based on my hour experience in trying to grow these kinds of businesses over time.

And I think we can draw attention to some pretty simple inputs and outputs.

And so I’ll start here with the first element of the equation, which is the notion of specialized talent, the kind of people professional services firms field are unicorns, if not gold plated unicorns, right?

They are deep in a subject matter area, and they have to be at least sufficiently deep in all aspects of tech in this particular sector.

They’re top-to-top communicators, they’re able to not just deliver, but to sell their talented problem solvers.

They had trouble off at the pass and they’re creative in coming up with solutions.

They are all those things, and also are good at process and project management.

And this becomes a limiting or gating factor in professional services growth, simply because these people are hard to come by, they’re hard to acquire, they’re costly to acquire, they don’t always line up culturally with the rest of the organization, they’re hard to keep, and they require a fair amount of infrastructure around them to keep the business going.

So let’s call that part one of the equation.

And I love, Pete, that word unicorn that you use, you know.

A lot of people can imagine, you know, we’ll take technology services, imagine engineers, right?

Real practitioners and great engineers are crafts people.

But that versus the skill set that you described of being a consultant, they’re two fundamentally different things.

How do you scale that?

What does that look like?

It is so difficult.

And we used to talk about these unicorns at Three Pillars so much that we’d often say, you know, is this a true unicorn or do we have to settle for the pony in the party hat, right?

Like, it was literally front and center that we knew we needed it, that we came up with vocabulary around it because these are unique people.

The engineer that you can have working in a product company that doesn’t have to talk to anybody, you know, isn’t necessarily who you need leading an engagement of a professional services firm.

And it becomes a more and more multi-faceted individual that is very unique and hard to find.

Yep.

And there are coping mechanisms, you know, to put them on teams where some of the skills are complemented.

But all of the coping mechanisms, at least until the current era of AI enablement, they are all costly, you know, and they add complexity to the model.

That’s right.

Yeah.

I mean, one of the very common ones that everybody here has heard of, you know, the two-in-the-box model, right?

Where you’d have a practitioner paired with that consultant overseeing the account and the management, so you get the commercial and the practitionership.

And this is in addition to your sales individual, right?

Well, that’s a costly model, right?

Just think about two-in-the-box.

That means you have two people doing what you hoped one person could do, right?

And I think that says it all in exactly what you’re speaking to.

Okay, so specialized talent is our first mitigating growth, mitigating factor, bespoke complex delivery, let’s call the second one.

You have heard this from your teams, and we know it to be true, regardless of whether it’s an excuse for not hitting goals or it’s just the reality of the business.

But every customer is different.

Every implementation is unique.

There are endless swaths of customer contact to be in touch with, to wow, to personally serve.

All of that brings with it significant operational complexity, the infrastructure, to keep this work going.

And then when you start talking about growth and moving to new markets and new regulatory barriers to overcome and new talent to acquire, like all of the inner machinations of the business, like the weight of the delivery mechanism itself and its support is burdensome to the business.

Pete, the example that comes to mind as you say that is probably one that’s not front and center in most people’s minds.

You think about the linear growth that you’re talking about due to this bespoke delivery, right?

Every single dollar of revenue requires another head count.

I remember having to scale our talent organization and our culture.

And I remember over and over again having to, just like you do with so many other things, you have to break existing processes.

You have to break what exists in order to get to the next level of the scale, right?

And I used to use examples like the head of operations that we needed when we were nine people sitting in a same room was wildly different than the head of operations that we needed when we were ten different countries around the world and thousands of people, right?

And people got that.

But that’s also true if you think about that same situation when it comes to culture and how you have a culture.

You know, one of our values was collaboration.

How do we actually work with each other to build on each other’s ideas?

Well, as you can imagine, what that looks like when you are a nine-person organization sitting in a room is wildly different from what it looks like when you are a multinational skilled organization.

And so that level of cohesive collaboration, yes, the value stays the same, but the way it plays itself out in the organization has to change as you go through these inflection points.

And I think that’s a great example of one of the non-obvious areas of scale that really doubles down on this point and shows what you are talking about, about how that cost comes along with it.

Because you are scaling with people, it’s not just the cost of people, it’s the cost of having to reinvent how you live your values even on a day-in, day-out basis.

Well, David, you have previewed the result of the math, and so I will take us there next.

The result that you get is a linear contour or slope to the revenue productivity line.

So I think about for each increment of revenue that you’d like to acquire as a professional services firm, there’s a commensurate additional increment of cost that you need to deploy.

And while they might not share a one-to-one relationship, like the 45-degree angle or slope to the line implies, it is linear, right?

There’s not a ton of leverage in the model.

And that’s some of the pressure on getting to highest levels of growth when you have to keep throwing resources and customized work at the next marginal customer or segments that you’re bringing to the organization.

You just kind of never get ahead of the people need, of the delivery need.

And that’s where we’d like to focus today.

It’s to start with this image of the linear ramp as our taking off point.

So from the linear traditional reality, the transition would be to something that’s more exponential, right?

For every dollar of cost, I don’t want a commensurate dollar of revenue.

I want 10x, I want 20x, I want 30x.

And I want that to continue to grow as I get smarter and more efficient and as our work processes and intelligence and the way that we do work, that reinforces and strengthens the model.

That’s where AI comes in, right?

The premise of our conversation today is that getting from the linear to the exponential view, while enabled by other processes in the hard work that companies out there are doing already, AI offers a unique and accelerating way to bend that curve.

Why?

I think these are things that we all understand already, but with the automation of time-intensive tasks and further the automation of real knowledge work, we could have the technology working a whole lot harder for us at marginal cost and allowing the people to get on to other aspects of the work.

We don’t need experts.

We don’t need everyone in the business to be expert.

They can be augmented by AI as a technology to make them expert, expert at the touch of a button anyway, that at least serves in some settings.

The ability of AI to do all its other things, digesting massive amounts of data in order to create new value, new insight for customers and for the team.

All of it together works to improve the view of the graphic on both dimensions, right?

It’s tightening the efficiency of your cost use and it’s pushing up revenue at the same time.

And so this is the ideal that we’re gunning for in having this conversation today.

And it’s exciting because we can bend this curve for the first time, Pete.

I have to say though, it’s also a little bit scary, right?

Because when you step back and you look at this is possible, we’re talking about the reinvention of an industry that candidly is one of the few that did not get really disrupted and reinvented during the digital era, right?

In the digital era, it was all about how we take and provide these digital offerings.

But the one thing we couldn’t figure out how to digitize was knowledge work.

And what is it that we do as technology services organizations?

We do knowledge work.

And so, yes, it’s super exciting.

It’s also scary because there is disruption right there that we can see.

And there are organizations that are thriving and figuring out how to do it.

But there are also organizations that haven’t been able to take that step yet.

And what we want to do today as we walk through these four strategies is we want to talk about the different areas you should be looking at, the different strategies, because too many of them and too many organizations are focused simply on, oh, how do I replace my worker?

How do I have workers do work more productively?

I think there are more diverse ways to think about that.

And so let’s talk about these AI driven strategies to scale.

What are they?

Okay, let’s start with improving commercial relationships.

And I want you to start by thinking about the fact that at the end of the day, the commercial relationships that you have, right?

The economic relationship that you have with your clients, this exchange of value that’s provided is the lifeblood of your business.

Your customer base is the core of your business.

And it’s why we talk about client centricity.

In fact, the data actually shows that companies are more profitable, they grow faster, they outperform in almost every single metric.

When they have a strong orientation towards client centricity, in fact, double click on that, if there is data driven client centricity, even more so.

And for technology services, it’s infinitely more difficult to be customer centric than other businesses.

Because of what we talked about, these relationships, we’re relational, we’re not transactional, and relationships inherently don’t scale.

In fact, there’s research on that as well.

Dunbar’s number, if you know Robin Dunbar, after a lot of research, really estimated that it’s about 150 individuals that you or I can maintain a relationship with.

And so relationships don’t scale.

It’s not only the people aspect, but it’s the person to person relational aspect of professional services that is inherently difficult.

At smaller scales, when I was a founder running a $7 million business, I could be in the center of every single conversation and know what’s going on and process and use my gut to optimize those relationships.

Well, that breaks down as you scale, and it becomes impossible at scale for you to manage in traditional ways the relationships that you had.

And it’s very expensive as well.

Even if you could find a way to clone the founder, the principal, the CEO of the business, it would be cost prohibitive to be able to do that.

And so there are many different things to think about, but I want you to think about not just these relationships, I want you to think about as a business scales, the number of client engagement scale, not just the people.

And then the number of stakeholders within those organizations, right?

You’re going from $100,000 engagements to million dollar engagements to $10 million engagements, right?

Naturally with that, you’re gonna have more and more and more of those relationships to manage and stakeholders in those clients.

And so, the many to many relationship becomes even more complex.

And then you take the next step.

There’s more and more channels every day in this information age.

We got emails coming in.

We got Slack coming in.

We got LinkedIn that we want to stay in touch with, right?

How do you keep track of all the video call transcriptions?

We’re now all getting these summaries of the calls and the notes.

But what are you doing with them?

No human being is able to keep up with the volume of feedback and communication that is.

In fact, if you think about how much that is, we all are overwhelmed by data.

But the reality is, this is over 85 percent of the data in your enterprise, and you’re not even tapping it.

You’re not even leveraging it.

There’s more data points flying at you about more companies and more people and more problems in your engagements, and it’s becoming overwhelming and impossible to keep up.

So let’s break down, what does it mean to have a strong commercial relationship?

Well, at Knownwell, we’ve actually done a lot of research on this.

We’ve done over 500 customer interviews.

We’ve poured through the data.

It really boils down to three things, and this will become obvious once you hear it, you know it and you live it already.

But it’s important to be specific about it and to decompose it and say, number one, it is the strength of the interpersonal relationships.

So how do you up and down the entire relationship B2B, look at every individual and make sure that you have healthy, productive relationships up and down that entire chain.

Number two, and potentially even more important, is you’ve got to manage the perception of service quality.

Well, what’s interesting here is that a lot of firms measure service quality, but it actually doesn’t really matter.

What matters is how is that service quality perceived?

How is it received by the client and how do you manage that?

And then finally, how do you calibrate the commercials so that what you are providing as a service is aligned to the strategic direction of your customer?

You don’t want to be delivering on an engagement that was an important priority two years ago, but is no longer in the top five, right?

You need to be staying abreast with all of the changes that are going on in leadership, with the budgetary pressures, with the shifts of direction, with new investments.

You’ve got to stay abreast of that.

And how do you do that?

Well, in this world where we have too many data points, it’s impossible.

It becomes so hard to manage that at scale.

But AI gives us an ability to process more data than we’ve ever had before.

It gives us the ability to digest these volumes and to synthesize it and actually to hone in on what is the true status of each and every relationship.

Not just who’s connected to who in the typical stakeholder map, but no, no, no, what’s the health of that engagement?

What relationships are actually in a negative place and have negative sentiment because now I can use AI to tease out the topics and to find those red alarms that are hidden and buried in communications, in phone calls, in video calls, in transcripts that I wasn’t able to see before, and how do I connect the dots between all of those?

Same thing with service quality perception, with alignment.

Go jump all the way to commercial alignment.

How do we stay abreast of all of the public information out there, the press releases, the new investments, the comings and the goings?

Well, we can really know, get to know our customers and the engagement experience that they’re having in a deep way that we’ve never been able to know before.

And so, really focusing your AI efforts on improving and scaling commercial relationships is a massive impact that I think organizations are overlooking right now.

David, a question for you.

I’m going to try to channel perhaps an audience member who’s wondering, is this a massive undertaking or is this something that’s in fact manageable?

Yeah.

Well, I think it’s manageable because what you see at the onset of this new AI economy is brand new products that are popping up, right?

And so, I think if you take this from the sales front, I think most folks are familiar with all of the data processing that like a sixth sense is able to do to give intent data around who’s buying in the market.

Well, there are absolutely solutions emerging.

Take a Gong, for example, which digest these video call transcripts and actually can highlight and synthesize, not just summarize, but give you intelligence about what’s going on in these calls.

And then, there’s more complex things that I think are just starting to emerge.

So, great example at Knownwell, we’re building a commercial intelligence platform that allows you to look at these three factors and really score the health of your commercial relationships to figure out where are they?

And then most importantly, understand the strengths and the weaknesses.

What levers do I need to pull?

What do I double down on?

What do I need to improve in order to strengthen that relationship to drive that retention and that growth that is the lifeblood of our business?

Right?

And so, I think these platforms are just starting to emerge and you will see more and more of them.

I would actually be careful not to build too much of this, but you’re going to see a whole new genre of platform that is coming out and really serving businesses to do some of these unique things that we could never do before.

I think that’s such an important insight because an awful lot of energy and investment right now is going into tying together every information source in the business and we may find soon that there are solutions that help sidestep all that and do the job of tying together for you.

If that first strategy resonates with you, you may be interested in finding out more about what we’re building at Knownwell.

Our AI-powered platform for commercial intelligence can help you uncover potential warning signs for your clients earlier than ever.

Go to knownwell.com to find out more and talk to our team today.

We will shift gears here from the first two practices that we’re about focusing on the customer to improve relationships, commercial alignments and drive retention and growth stickiness and start taking a look at the business itself.

Next two places we’re going to go are operations broadly and then the actual business model and so strategy three is the first of the two looking at improving the efficiency of our operations in the spirit of freeing up the human so that they can spend their time doing more important things.

We’ve taken a page from the process reengineers playbook to think about the right way to approach the application of AI to operations in the business or we sort of refer to it as AI transformation.

And there’s good practice going back through time, but it’s not grounded in the new realities of AI.

So consider this an update of your father’s old mobile or something to that effect.

And stepping back and trying to make sense of the graphic to get us into it.

There are across the business countless areas of operations that are pain points to the customers, pain points to employees, utterly wasteful, costly, so many, that in fact, you kind of can’t pick and choose among them without doing some serious groundwork first.

And so there is a work methodology referred to as value stream mapping.

And it’s grounded in the notion that there are five, six major process flows, workflows or value streams running through the business.

That basically take a request from a customer and get you to a place where you’re delivering something.

All of the major motions of running the business.

Only five or six of them.

But of course, they’re made up of cross functional departments that participate.

They have hundreds of handoffs.

They’re loaded full of data and they’re complicated.

And so, in order to do the right job of examining them and looking for opportunities to deploy AI, our recommendation is to start with the leadership team.

Because more often than not, in undertaking a work project like this, you discover along the way that the leadership team isn’t as up to speed on AI as maybe you thought.

No fault of their own, they’re doing their best to get smart at whatever pace they can.

But they are not 100% up to speed, and perhaps worse, they’re not 100% up to speed on where in the business the biggest opportunities for AI sit.

And so our recommendation is to do a leader led examination of the business, which can later be handed off to process teams or load bearing beams within the business.

But to start with leaders, to get to a place where you’ve got a common understanding across the business, where the most significant pain points and opportunities are in these value streams.

And so typically the way the work unfolds is there’s some voice of the market analysis at the front end.

Conversations with everybody on the leadership team to hear from them, where they see the big pain points in the business or the big opportunities.

And what you find as a result of that process is that no one’s on the same page.

And you need to run through a process from there on norming and getting the group to the same place on what the big issues are in what order and what does a prioritized plan of attack look like.

We also often find in this process that the question comes up, what are we trying to build here anyway?

The nuts and bolts work of looking at operations within the business is a little bit of a fool’s errand in the absence of clarity across the group on what is our strategy?

And by the way, what’s our vision for the business?

And so inevitably, those are two pieces of corporate ideology and methodology that have to be updated in the act of doing this.

But once you’ve got the team in the same place and understanding where we’re trying to go as a firm and what our big impediments and opportunities are, then this work kicks in of you map these processes and break them apart into their task groups.

And you’re asking of each task, parenthetically, can I use AI here, right?

Evaluating it on the left hand side, you see the bullets.

Is it something that repeats?

Is it rote?

Is it data rich?

Could we be leveraging the data and doing something important with it?

How significant is the pain point?

Can we be creating new value for customers as part of this process?

Is it something that could just be process re-engineered?

Do we actually need AI here or not?

Could just be digitized, right?

And you get yourself to conclusions around the right horses for courses on application of solution to the problem.

And you can see those over on the right hand side.

Sometimes AI has no role, right?

There’s just a process that needs to be re-thunk or done away with because it’s been overtaken by events, it’s gotten old.

Co-pilot, are we augmenting the people in seat or autonomous decision making and execution by the technology?

Are we letting them take over workflows themselves?

Letting it take over workflows and are we moving the people to different and more important work?

My two takeaways from this that I just love, like the first one you mentioned, I think is so true.

It’s amazing how many executive teams aren’t informed on AI and thinking about it.

So that top-down approach of making sure you start with the education as you go into this, but then the deliberate nature of the value stream methodology that you propose.

So often, we see organizations being reactive.

And a lot of the AI adoption that’s happening is actually happening at the grassroots level.

In fact, a lot of research shows it is permeating organizations, and people are actually afraid to mention it.

But this top-down approach mapped with value streams, I think will drive the biggest transformative breakthroughs in terms of operational efficiency.

What we’re seeing day-to-day on the ground from the grassroots level is the 3% improvement here, 4% there, and all that’s adding up and being tremendously valuable.

But you can’t have the transformative change come from the grassroots.

You’ve got to have that top-down looking at what you point out.

And this is the brilliance of the method that you’re proposing, is you’re starting with the pain point, not the AI.

The big mistake we see is so many organizations, they want an AI strategy.

Right, let’s fix a problem.

What is the pain point?

And then C, does AI solve it?

Exactly.

Start with the problem, not the technology.

Make sure the problem is big, meaty and important to the plan, the direction of the business.

Make sure everybody gets it and is supportive and is on board and driving it.

And take on a small number that are going to make a difference rather than a thousand flowers blooming.

Because what you find is a flower blooming in one department actually runs into conflict with a flower blooming in another department, or at best, it’s redundant.

You know, we’re doing the same thing and buying it twice, right?

Like, there kind of needs to be some ring leading, I think, in this process as well.

If you’ve enjoyed this conversation with David and Pete, it’s actually part of a much longer presentation that they gave recently.

If you’d like to watch the full version and hear even more about scaling your technology service firm, you can get access to it at knownwell.com/scaling.

Thanks as always for listening and watching.

Don’t forget to give us a rating on your podcast player of choice.

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At the end of every episode, we like to get one of our AI friends to weigh in on the topic at hand.

So hey, Claude, what’s happening?

This episode, we touched on how technology service leaders can use AI to break the $250 million barrier.

So what do you think?

AI is a game changer for tech service companies trying to hit $250 million by helping them scale efficiently.

It frees up leaders to focus on strategy while automating the routine stuff.

And now you’re in the know.

Thanks as always for listening.

We’ll see you next week with more AI applications, discussions and experts.