Expanding from Consulting to Product: A Strategic Growth Journey with Lawrence King | Episode 202

Greg Alexander: Hey, this is Greg Alexander, from Collective 54. You’re listening to the Pro Serve Podcast. On this show, we talk to founders of boutique professional services firms, and we learn how they’re making more money, making scaling easier, and maybe someday getting to an exit as they figure out how to grow and scale their firms. On today’s show, we have a friend of mine, Lawrence King. He’s been a long-term member of Collective 54, and he’s gone through an interesting pivot in response to a changing marketplace that he operates in. He’s going to talk about how he went from a consulting firm to developing a software product and generating licensing revenue. The reason why I wanted Lawrence to be on, well, many reasons, but the primary reason is that I get asked this question all the time by Collective 54 members: “Hey, I think I want to become a product company. Should I do it?” Sometimes the answer is yes, sometimes the answer is no. But the point is, that’s a big question, and the answer is very nuanced. Since Lawrence just went through that, I wanted him to come on the episode today and share his experiences with that. So, Lawrence, welcome. It’s good to see you. Why don’t you introduce yourself and your firm to the audience?

Lawrence King: Yeah, sure. Great to be here, Greg. My name is Lawrence King. I founded a consulting firm called Head Storm in 2016, predominantly around technology and in the agriculture space. We had other sectors, but probably 60% of our business came from the agriculture space. I bootstrapped it myself in the beginning, and up until 2023, grew it to about a hundred people and scaled pretty well during the good years.

Greg Alexander: Yeah, I mean, the growth you had was amazing. It was great to watch that growth while it happened. Then, like a lot of our members and even people in your space outside of our community, you got punched in the teeth in 2024. Set the stage there because your pivot was in response to that. Let us know what happened in 2024.

Lawrence King: Yeah. In 2022, we grew every year. In 2023, we grew maybe 15%, maybe 20%. Then, in the last month of the last quarter of 2023, we started to have clients pull back in the shipping and logistics space and also in the agriculture space, with not a whole lot of pipeline to replace that revenue. We could see something was happening, but I didn’t realize it was going to take a downturn so quickly. The spigots were turned off, really. Fortunately, we had a lot of cash that we kept in the firm up until that point. In the course of probably 12 months, we had to make some really tough decisions about downsizing, all the way from a hundred people down to about 30 people. That’s hard to do. It’s hard to scale in the whole history of the company when it’s been up and to the right. But then to try to scale down is much harder. You have no tailwinds, no momentum, and all of your morale quickly evaporates. You’re really running on fumes but having to work just as hard, if not harder, to downsize and keep up with the reduction in revenue.

Greg Alexander: One thing that really blew me away while you were going through that is, you’ve got this really quick decline…

Greg Alexander: And you gotta right-size your business quickly, which again, is really hard to do. But in that storm, you had the guts to invest in an AI team and hold on to that AI team because many of your peers were just cutting everything and running for the hills. You leaned into that, acquired some talent, and that led you to where you are now. So tell us about that AI team and how you’re using them now for the next evolution of your firm.

Lawrence King: Sure, sure. So, being in the Ag space enough, we pivoted more into doing strategic work for a lot of these bigger companies. What we realized is that all of the technology being built in agriculture had no product market fit, and they were investing in the wrong areas. We were giving this advice in our strategy engagements, and when the market started to tank, we had to downsize generally. However, the folks that we kept were all of the machine learning experts and data scientists. I said, “Listen, I think there’s a way to build a product for agriculture and differentiate into recurring revenue.” So, what I did was say, “Let’s go do some market research.” This was for agriculture retailers and their agronomists. Effectively, the sales teams—the agronomists—are folks that sell chemical fertilizer, seed, and all of that to farmers. They are technical in nature, providing recommendations on agronomy, but they are also salespeople. Our tool is for them.

Lawrence King: We went out and did market research. We got 30 agronomists from across different retailers, conducted focus groups, and really understood the pain points they were having and where we felt we could benefit the retailer and the agronomist at the same time. Then we asked, “Could generative AI solve that?” Once we identified there was a business case and that we could demonstrate quantifiable gains to a retailer, we decided to build the product. We retained the folks with those capabilities—the product and engineering teams—and downsized the rest of the firm to keep up with the reduction in consulting revenue. For us, the unique nature of this was that we had such eminence in agriculture and all of the connections with these big companies that we didn’t need to invest as a product company in a sales force because these guys were already in my phone. We had that positional asset, the capabilities to do this, and the know-how. Now, we just needed to know if we were solving the right problem and if the product had market fit.

Lawrence King: That was the mission we went on. We started building it about 14 months ago. It’s been interesting because, in the development stages, in the beginning, you’re like, “Okay, do we have product market fit?” That is a really hard thing to find. There is so much out there to build that doesn’t have product market fit. We proved that out. Then, when you start to build this stuff, it’s like, “Okay, can we bend the will of these LLMs in a way to deliver on the use cases we were trying to build?” The enlightening part there was that it’s a lot harder than we thought. I don’t know if other firms or clients of firms think they’re going to sit down, grab ChatGPT, and just start wrapping code and developing stuff to put into production, but it’s really hard. It took us, like I was telling you earlier, Greg, probably 60% of that time to prevent hallucinations, deal with memory management, optimize prompts, manage context, and harness all the data together so it could orchestrate a meaningful response that delivers on the use cases we were setting out to build.

Lawrence King: From an execution standpoint, just building the software piece was something we didn’t realize would be so challenging. Next thing you know, our feature roadmap slows way down because we’re dealing with problems we didn’t anticipate. That was one of the challenges for execution. Then, once we had a product, we built it. It was important to create a minimum viable product. It’s very easy to say but very hard to do and have the discipline to stick to. We had to draw a line and say, “We’re going to market with these features.” Once you go to market and start engaging in sales, the sales enablement piece is very different from consulting. That’s something we’ve had to navigate and figure out how to handle. I’ll give you some examples, and feel free to dive in with any questions. On the sales enablement piece, selling SaaS services or software is a different sales model than selling consulting services, and sometimes they can…

Greg Alexander: Yeah, entirely. And you know, I’m going to jump in here. Thank you for allowing me to do so. When I’m getting asked this question by members who are services companies thinking about doing this, they think it’s the same sale.

Greg Alexander: and it’s an entirely different sale. So why don’t we dive into that a little bit. What’s different about it? Selling a product versus selling a service.

Lawrence King: Yeah, speed to revenue. So when you’re selling a service, you’re gonna get paid day one, and that could be integration and all of the work that goes into that. And so you’ve got a team. You’re able to estimate what that looks like. You’ve already got the business model in place, and you’ve got your rates and your structures. So you just put together a proposal, they sign it, you start billing, and revenue starts coming in. Software sales is a longer sales cycle. You have to demo your application to them. There are lots of hurdles and objections you have to overcome. Will it work in our systems? Will it work with our data? If you start charging from a consulting perspective on some of this, you’re introducing friction into the software sales cycle. While you may get some easy consulting revenue out of that, you’re probably jeopardizing the subscription sales because they’ll abandon using the product if it costs too much to implement. Coming up with a pricing strategy was a real challenge. That part was tough. Do you price it based on value and what you’re going to deliver and go at risk in some cases? Or do you come up with a licensing model that’s per month or per user? How do you benchmark that in the market when you’ve got something right now with AI that’s fairly novel and unique? Those were things we struggled with and still struggle with to this day, because a lot of this product has been self-funded by the consulting side. Are we pushing too hard on the integration dollars because it funds consulting and jeopardizing getting the close on the software? Or are we giving too much consulting work away for free on the integrations just to get the close on the software?