00:21:56 · Paul Todgham, CJ Gustafson
[00:21:56] S0: We're in the Wild West right now. Right? I think I'm a parent of four kids, two in high school, one in middle school, one in elementary school. And how I think about AI at work and AI and improving hiring processes versus AI to educate my kids, dramatically different. Maybe relevant for our audience. If you've been using take home tests before and you haven't revised your take home test in the world of AI, absolutely, you're getting mostly LLMs at this point. So for me, I was hiring for a role recently. And I still gave a take home test. And in fact, said, it's Okay to use AI for it, because I want my people to use AI when they when they come. But then the interview was all about the discussion. The debrief of it was less about the conclusions and more about the process they used and what they questioned. And, again, it's more about sort of how people think and how they use the tools to help them than the actual final output, let's say. [00:22:40] S1: It's hard to fake it if you show up to talk about what you created and you just have a blank stare. Paul, I wanna transition to talk about business model transformations because you you joined Greenhouse as CFO of what most people would probably describe as an ATS company, but the product is changing pretty dramatically with AI. What's hardest for finance when the business model starts to change underneath you?
00:22:40 · CJ Gustafson, Paul Todgham
[00:22:40] S1: It's hard to fake it if you show up to talk about what you created and you just have a blank stare. Paul, I wanna transition to talk about business model transformations because you you joined Greenhouse as CFO of what most people would probably describe as an ATS company, but the product is changing pretty dramatically with AI. What's hardest for finance when the business model starts to change underneath you? [00:23:04] S0: Before I complain about how hard things are with finance, maybe I'll just start by saying I actually feel really fortunate. This is my first SaaS business. You know, I was in retail for a while, Ross Stores, Levi Strauss and Company. And then I was at a, you know, integrated hardware, software, industrial technologycom... Called Cognex for the previous four years. So I started by just feeling pretty fortunate that two of the things that take a lot of time and create a lot of headaches for finance folks are real estate and inventory. And I'm used to spending a lot of time working on those things. Right? Should Levi's open a new store in Times Square at a cost of roughly the cost of naming rights for Levi's Stadium, you know, stuff like that, not having to worry about those things. [00:23:41] S1: This is great perspective. I love this.
00:23:44 · Paul Todgham
[00:23:44] S0: So so I start with that. Right? Like, I actually feel like I have more time to focus on how do I partner, you know, with the business, with, you know, the key things that are gonna drive success for Greenhouse, which, you know, we're a tech company. So it's usually some combination of your go to market function and, you know, your product and R and D function. Know, how to... How can I best enable those those teams to do their best work, make the best database decisions, you know, where to allocate our investment and so on? So I start with a position of kind of optimism, and, like, it's a privilege to to get to do that. That said, it is challenging. I mean, I think balancing, you know, short term performance with long term strategic change is always hard, and it's super important to to do it well. You know? You definitely don't wanna be in either extreme, right, overly focused on the short term that you're blindsided by AI or other, you know, other changing technologies. And by the time you've got your act together, you know, your competitors are nine months ahead of you. And you can't just be kind of taking taking swings at the fences in the in the future, you know, for what's gonna what's gonna pan out. My my private equity investors would not keep me at my job very long if, you know, every forecast we missed or or, you know, we were constantly under delivering. I think decision making under uncertainty is super critical for finance during, you know, a changed dynamic or a time when there's this much change. But individuals comfort with that, and their skill with that can vary dramatically. I was kind of fortunate. Came of age as a consultant. You know, I spent the first eight and a half years of my career at BCG. So I learned that skill pretty early. We had to do a lot of making recommendations with both quantitative and qualitative factors, some of which were more certain, some of which were less. Accountants often maybe learn that skill later, if at all. And so being able to take your team through that, I think, is important. One of the things I tell my team, in the absence of us getting involved, a decision will get made. And almost certainly a less good decision will get made. We may have only a little bit of information. We may only have two hours or two days.
00:25:24 · Paul Todgham, CJ Gustafson
Maybe if we're lucky, maybe we have two weeks to work on this problem even if we think it's gonna take two months. My bar for them is not, are we getting to a perfect decision? It's, you know, in the time we had available to an... To to be involved, did we help make that decision better? And if the answer to that is yes, know, our teams feel great about what they're [00:25:40] S1: It's kind of a catch 22 for a lot of companies right now where they have this weird forecasting problem that they're super pumped that they're gonna launch this new product, but if something is actually that transformative, the historical data actually isn't that useful. So the question becomes, what do [00:25:55] S0: you forecast off of? I'm navigating that right now. Obviously, we just kicked off our budget cycle, and and we've got a couple technologies that I think are kind of right on that cusp of kind of inflecting and taking off. And I'm, you know, trying to navigate what's the best budget to build in that scenario. I don't think I have the right... All the answers. I mean, the approach I'm taking is put a little more confidence in kind of what I can bank and put a little more weight on that. And hopefully, the inflection is more upside. It starts with being in a fortunate position of my CRO and I and our team, you know, can get to a a good path that our board will be acceptable without betting on something that hasn't inflected yet. And then hopefully, that's the difference between us blowing the lights out versus having a good year.
00:25:55 · Paul Todgham, CJ Gustafson
[00:25:55] S0: you forecast off of? I'm navigating that right now. Obviously, we just kicked off our budget cycle, and and we've got a couple technologies that I think are kind of right on that cusp of kind of inflecting and taking off. And I'm, you know, trying to navigate what's the best budget to build in that scenario. I don't think I have the right... All the answers. I mean, the approach I'm taking is put a little more confidence in kind of what I can bank and put a little more weight on that. And hopefully, the inflection is more upside. It starts with being in a fortunate position of my CRO and I and our team, you know, can get to a a good path that our board will be acceptable without betting on something that hasn't inflected yet. And then hopefully, that's the difference between us blowing the lights out versus having a good year. [00:26:34] S1: Let's talk pricing. How the hell do you price AI right now?
00:26:38 · Paul Todgham
[00:26:38] S0: So we have a phenomenal director of pricing at at Greenhouse. He's one of the people outside of my org I speak with most frequently, Scott. Hire a really good pricing person. I'll start with that. We have a great pricing person. I think it... I also then would say start with really understanding your unit economics and your long term strategy. So we have a lot of AI features within Greenhouse recruiting where the economics still look a lot like traditional software. Like, yes, there's some token costs, but, you know, they've come down pretty dramatically. When you put engineers on it, they're able to optimize pretty well. So, you know, as long as users don't really abuse the feature, you're kinda fine. Right? So, you know, customers hate feeling nickel and dime. So if the marginal costs aren't breaking the bank, we're much more likely to build, like, a healthy amount of usage into one of our, you know, product tiers and then only go after consumption for that upper tail of the distribution. And folks who are in that upper tail often know it and are sort of more understanding and willing. Now, are other products where costs are higher and they scale pretty linearly with usage. So that model might not work. I'll give one example. AI interviewing, we we we talked a little about. We have this amazing product. I think it's the best in the market. And the return on it is a really easy case to make. Right? You could go one of two ways talking to customer. You could go the time saved argument. The average recruiting team spends about fifty minutes to an hour for a given twenty to twenty five minute phone screen. And we can bring that time down to about five minutes to carefully review the output of an AI interviewer. Right? It's pretty easy to value that time saved. A different way, the second approach, which we prefer to take, is go the kind of better talent argument. Point our vase... Voice AI tool at three times as many candidates you might have previously previously seen. So if you're gonna screen 20 personally, have the AI screen 60. And at least our our data so far suggests that your likelihood of finding strong talent deeper down your list is actually really strong. And and those folks are also more likely to get overlooked by other companies who are using superficial resume screens to choose talent.