00:32:37 · Paul Todgham
[00:32:37] S0: I worked with an AI to kind of develop my framework, you know, as I gave some ideas and pressure tested it. I had some healthy pressure testing back. But first, there's four things we're doing. First, just tracking AI costs. If you're going to understand the ROI, what is the I? How much are we actually investing? And how is that changing over time? There's some judgment there, by the way. It's not super definitive. Are you dealing with partial time allocations about people getting onboarded and enabled and stuff? There are some vendors where it's super clear, the LLMs. And there are some where there are vendors who've moved from seat based to hybrid. And the biggest thing I think we're doing that I feel like is kind of feel most conviction of is we've anchored on cost as a percent of revenue by function as our primary ROI metric. I believe that AI, if it's going to make us more productive, should make us growing more profitably as a business. The way to measure that would be your EBITDA percent margin and cost of percent of revenue is basically just the sum of that. But it's a it's a... It's something you can take to every single function. So I can, you know, I can't give EBITDA to the product team, but I can show them their cost of percent of revenue. I can show marketing their cost of percent of revenue. Over time, we should be seeing this metric go up. And it can go up in two ways. Right? It can go up by lowering the numerator, lowering your cost over time, or, you know, increasing the denominator, growing our revenue as a company faster. So although the first word is cost and it's coming from a CFO, so people mostly think I'm doing this just to reduce cost, it actually is about profitable growth. You have conviction that these initiatives we're doing are gonna increase your growth rate, and they're very favorable in the... In in that. The trouble with cost as a percent of revenue, it's a really big metric. There's a lot of different things that can affect it, not just AI. Right? So we've gone then sort of deeper into what, you know, the true AI activities and the AI metrics are just to help build conviction that these actions we're taking are ultimately gonna help improve our cost as a percent of revenue. So each function at Greenhouse is aligned on a a small number of AI impact metrics is what they're calling them.
00:34:24 · Paul Todgham
But they're basically like leading indicators or, you know, things that over time will lead to a better cost of percent of revenue. The easiest example to understand would be coders. Right? You know, r and d folk... Coders can code much more effectively or efficiently with AI than the past. So our R and D team is measuring, you know, both the kind of velocity and output, you know, how many PRs per per week or month are they generating, as well as a quality metric, our bug rate, which, you know, we want our efficiency to be going up or our product to be going up with no declining quality. And that's kind of the two metrics they're being held to. The fourth piece is sort of a set of impacts... AI initiatives. So we've asked each function to articulate, you know, what are your top three to five initiatives in each function that are helping drive those AI metrics and ultimately leading to lower costs as percent of revenue over time. So I kinda know from that level, what are the projects I'm looking at to say, hey. Is this on track? Is it meeting its goals? Are we focused on the right things where I have shared resources, let's say, in IT? Which of these projects should we devote their time to based on, you know, the perceived return? Then sort of the first level, am I seeing the metrics move that need to move? And then, ultimately, is it leading to, you know, a lower cost of a percent of revenue, which, you know, we've now built into our, you know, next year budget cycle? So this year for us was a lot about kind of leaning in, experimentation, enablement, and obviously next year is a little more doing those things and and and proving the ROI.
00:35:39 · CJ Gustafson
[00:35:39] S1: Still in budget season? My condolences. If you're only on v 40 of the plan, you're doing better than I ever did. Keep it up. Here's what I'd want in the trenches right now. Something I can hand a question to that already knows the context of my business. Not a chatbot that's never seen my p and l and not a bunch of AI features that are hiding in five side panels that I really don't know what to click. That's what Abacum just launched. It's called Abra, like Abra cadabra. I hope they let me keep that in. And it's one agent for your whole finance team. Ask it something like, what happens to runway if we push ad spend 25%? Or what changes has my marketing leader requested to their 2027 budget? Abra pulls together your actuals, your models, and your business context, calls in the right agent to model, analyze, or draft the board pack, and comes back with the answer, plus the trade offs in the next move. That's incredible. And for the control freaks, which is all of us, if we're being honest and you listen to my podcast, it shows its work. Abacum's engine does the math, not the AI. Abracadabra only sees what you're allowed to see, and nothing saves until you approve it. Here's the best part. It's included in Abacum as is. There's no extra charge, no add on. And I have known Abacum's founder and CEO, Julio Martinez, for more than three years. He and his cofounder, Jorge, came out of finance, and this thing shows their work. Most AI makes one person faster. Abra makes your whole team AI native. I could read you a short code to go type in, but honestly, just tell Jorge and Julio CJ sent you. They'll hook you up. Remember when pricing was simple? One product, one subscription, one invoice every month. Those days are over. The AI economy is changing how everything gets bought and sold. We're seeing usage based pricing, hybrid contracts, and something called a credit. The finance systems built for vanilla subscriptions weren't designed for this. This isn't just a rev rec problem anymore because outdated revenue architecture can kill a great pricing idea before it ever reaches the market. RightRev lets you recognize revenue in whatever shape it comes in. Whether you're launching consumption credits or testing entirely new commercial models, RightRev gives finance the flexibility to support whatever pricing model comes next.
00:37:53 · CJ Gustafson, Paul Todgham
So product teams can keep on innovating and finance teams can keep pace. If you're architecting revenue for the AI economy, learn how RightRev can help. Visit rightrev.com/cj. That's me. That's rightrev.com/cj. A wise man once told me that every startup is really just a loosely functioning disaster. Ain't that the truth? If your startup needs a little extra support, and let's be honest, who doesn't need a little help now and then? You're in for a pleasant surprise with Fidelity. Fidelity Private Shares helps early and growth stage companies stay investor ready with cap table, data room, and scenario modeling all in one place. A messy or missing cap table might not just slow you down, it could cost you your next fundraising round. VCs are flooded with pitches, and if your equity is confusing or missing, they'll move on fast. Fidelity Private Shares gives founders the structure and simplicity to focus on what actually matters, building your company. If you stay investor ready, you don't have to get investor ready. Check out fidelityprivateshares.com to learn more. That's fidelityprivateshares.com. So just to play it back, the cost as a percentage of revenue. Let's... I'm just throwing a number out there. Let's say my sales cost is 30% of revenue. You're gonna look at it and say, within that 30%, 6% is currently on AI tooling. That total can fluctuate by either spending less or the company gets more revenue, so the percentage goes down. [00:39:17] S0: Yeah. Absolutely. It isn't about I have to spend less in dollars on sales next year.
00:39:17 · Paul Todgham, CJ Gustafson
[00:39:17] S0: Yeah. Absolutely. It isn't about I have to spend less in dollars on sales next year. [00:39:21] S1: Yeah. The absolute can go up. Right? You can spend more dollars. Right? I've been [00:39:24] S0: at very low growth companies. I've been at companies that have shrank and had to obviously still, you know, do something to manage profitability while you're doing it. I'm thankfully doing it in the context of a a business that's growing, you know, pretty healthy, kinda, you know, low double digits. And so I think we have good choices. But our costs are growing, but our costs as percent of revenue are are coming down. [00:39:39] S1: What do you ask for in a business case when someone comes to you, Paul, and they say, Paul, I I need to spend more on AI?
00:39:45 · Paul Todgham
[00:39:45] S0: I've approached AI with... Especially this year, you know, with a bias towards enablement and experimentation. So we did green light a bunch of stuff without a business case. And I wanted to make sure, as a company, that, you know, we could fund a certain amount of incremental cost this year, which we solved. And I wanted to make sure that we had sufficient guardrails that, you know, we weren't gonna have the $500,000,000 month, you know, that makes the Wall Street Journal, $500,000 month would would would make my, you know, my my my board's attention or my TE sponsor's attention. So making sure we had the guardrail to kinda keep that from from happening. I actually didn't start with, before we spend money on AI, I need a business case. It's, this is revolutionary technology. We're going to learn a ton by doing. The cost of experimentation is really low. You often find out relatively quickly whether those are there or there. So let's let people do that instead of get kinda hung up in bureaucracy, provided, okay. The total amount we're gonna spend is comfortable. I've sort of solved that elsewhere and provided, again, no no one person has the ability to go totally rogue, which we've, you know, largely solved with how our plans are designed and, you know, tracking and optimizing token usage. That's kind of my first piece is like, don't necessarily jump straight to a business case if, you know, the time it takes to get that through review is better spent just on pushing that initial idea into proof of concept and showing, you know, what's what's what's there. That's kind of the cool part of AI that I love. I've never been a builder before, but I love seeing... You know, if I spend an hour here, am I meaningfully optimize, you know, an activity or improve the quality of something I I do every every month or every quarter? Now that we're getting to maturity, are thinking about a little more as a traditional business case. Right? Just what's the cost? What's the return? Do we have good cross functional alignment that this return is gonna be, you know, is gonna be realized? Just recognizing you can get further in proof of concept than with a lot of other potential investments.