Ep. 1 - How AI Is Reshaping SaaS Pricing

Guest: Pat Meegan, Senior Partner & Pricing Practice Leader at Investor Group Services 

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AI is changing SaaS pricing by forcing companies to rethink how they capture value, protect margin, and package new capabilities. 

In the first episode of ProPEllant, Ken Lempit talks with Pat Meegan, Senior Partner and Pricing Practice Leader at Investor Group Services, about how AI is changing the economics of SaaS. As companies introduce AI features and agentic capabilities, traditional seat-based pricing often no longer reflects either the cost to deliver the product or the value customers receive. 

Pat explains why pricing, packaging, product, and sales can no longer operate as separate conversations. The discussion covers how SaaS companies are thinking about add-ons, premium tiers, credits, and hybrid pricing models as they work to align monetization more closely with usage and outcomes. 

The episode also explores what private equity firms, operating partners, and software leadership teams should be watching for during diligence and post-close value creation, especially when pricing has not been revisited in years or new product value is being added without a clear monetization strategy. 

Key takeaways

•  Why AI is putting pressure on traditional seat-based SaaS pricing 
•  How pricing and packaging need to evolve alongside product strategy 
•  Why credit-based and hybrid models are getting more attention 
•  Where operators and investors may be missing monetization upside 

Transcript

Speaker 1 0:06

Welcome to Propellant, the podcast about how operating partners help companies grow inside private equity and venture capital portfolios. In each episode, we explore how experienced operators support founders, drive execution, and help companies scale. And now, here's your host, Ken Lempit.

Speaker 2 0:26

Welcome to Propellant, a podcast for private equity operating partners and the leaders building the next generation of SaaS companies. Each episode features conversations with operating partners, investors, and experienced operators about how value is created inside software companies before a deal, during diligence, and after the investment closes. My name's Ken Lempit, president of Austin Lawrence Group, an advertising agency that specializes in SaaS Go-to-Market, and your host of the Propellant Podcast. Our guest today is Pat Megan, senior partner and pricing practice leader at Investor Group Services, a consulting firm that helps private equity and investor clients make better investment and strategic decisions. Welcome to the podcast, Pat.

Speaker 1:13

Thanks, Ken. Great to be here.

Speaker 2 1:15

Yeah, I'm really excited to have you here and to see you again in the podcast format. But uh before we get into our planned conversation for the episode, could you tell us some more about yourself and IGS?

Speaker 1:29

Yes. So I am a senior partner leading the pricing team here at IGS. IGS has been around for over 25 years. Do everything from due diligence, market diligence, and go to market and pricing work. So we're going to span the the pre and post-close period of the hold. So kind of the whole life cycle. And my role there is to get in and figure out where monetization opportunities exist and then help companies realize the EBITDA gains from those opportunities.

Speaker 2 2:00

Which is really important, right? EBITA gains mean valuation increase, right? So that's right. That's what it's all about. That's what it's all about. And like digging into the company and its and its place in the PE ecosystem, it's kind of interesting because you're working with the PE firms both in the front end in diligence and post-close value creation. For listeners that may not know firms like yours exist, can you explain how consulting partners like IGS plug in to PE firms and what problems they're hired to solve?

Speaker 2:33

Yes. So as you imagine, when a firm is going to buy a company, they want to make sure that the growth is real and they want to make sure that the market has potential. You know, this is an attractive asset. And oftentimes there's there's a third party that comes in to help those teams so that they have an objective look at it. Also, there's a lot of activity that happens around a deal. So the third parties can can uh provide additional capacity there. But you know, during diligence, it's is this company's growth real? How attractive is the market? What are the challenges? What are the risks? Obviously, things like identifying technological AI displacement risks, things like that. And then after the deal closes, there's a host of things that come up. And often these kind of value creation levers can be evaluated to some extent before the acquisition. But after the acquisition, the focus really is how do we accelerate growth? I mean, what are the big buckets of opportunity and how do we go attack them during the hold period? And so that's where a firm like IGS has capabilities to assess the sales team and assess the comp and quota structure and the pricing and all those things and breaking it down into the different areas and then applying expertise to it to then, you know, accelerate that growth.

Speaker 2 3:45

Yeah, I mean, even successful SaaS operators, SaaS founders, they have blind spots, right? And or they've attempted things like a pricing change, pricing strategy, only to get some bruises as a result. So they might be reticent without greater support to take on these challenges, either for the first time or or again. Has that something you've seen in the engagements you've worked in?

Speaker 4:12

Yeah, I mean, it's uh it's always fascinating to work with founders and to see the things that they're incredible at. And then also, you know, just being human, there are things that uh, you know, their backgrounds or their strengths may not reside in. We've seen some founders that are fantastic at monetization and pricing. What's more common though is they tend to be you know fantastic at the uh at the product and the selling part of it. So when you think about monetization, there's often latent opportunities like you know, the legacy deals, the first logos they got. How do you better monetize the install base or having a strategy around capturing more of the value through packaging or looking at changes to the product over time and maybe we should revisit the pricing model and the pricing metrics? So, what often happens in the challenging cases is thinking about price as kind of just the price point and changing that, you know, a price hike. There are often many other levers that should be considered to improve the monetization of the product.

Speaker 2 5:09

Yeah, and I I want to I want to dig in a little on the pricing opportunities and careful listeners will remember that we had you on SaaS Backwards, our other podcast in January of 2024. And we we did a pretty deep dive for that format on pricing strategy in SaaS. But maybe now from where you sit working closely with PE value creation teams, what's changed the most in pricing conversations over the last year? And we probably should dig in a little bit about what the impact of AI is having on pricing as well.

Speaker 5:44

Yeah, you hit topic number one. It's it is the impact of AI. And so, you know, you as you would expect, and as we've seen, technology companies are focusing on identifying ways to bring in AI, augment their product, or of course you have AI native products as well. So across that landscape, the question of not just what do we do from uh satisfying our user needs with the AI capabilities, but then how do we monetize it becomes an important part of that question. Because if you think about traditional SaaS, you have next to no marginal cost for each additional user or use case. In most instances with AI, it brings marginal cost. And so it forces a discussion around okay, how do we ensure that our margins can be recouped? But then a bigger question is uh, you know, that's kind of the floor question. How do we make sure that we can we can maintain margin? But the the ceiling really is how much more value does this provide? How do we align the value of this AI capability to the monetization model, the pricing metrics, the packaging? And there's a whole discussion to be considered around that where you know, should this be if you're an existing product, should this be an add-on or should it augment an existing package? Should it be its own premium package? Should you have a model that's that's based on you know seats anymore? Does that fit at all? Or should we be thinking about something that has some of the like a credits type system that allows you to be flexible across different agents? So those questions are pushing teams to think differently, and some exciting things are coming out of it.

Speaker 2 7:18

Yeah, the credits thing is really where we have to train a whole like the entire market that there's a consumption cost because they've not had that experience in in vendor-supplied software, right? I imagine that's one of the big hurdles to jump for companies that are like bolting on new capabilities that it do create marginal cost to them.

Speaker 7:41

That's right. And then also just to make it more complex, you know, if you have multiple agents, they could have different value and different cost and different consumption of compute. And so you have to think about those things. How do I keep my model from becoming this long list of different variables and nickel and diming the buyers? Often a credit system can allow for more of that variable nature of the consumption to be easy to buy and predictable to buy. So if you think about, you could use an example of like going to a fair where you buy, you know, a block of 20 tickets. You know, you might be doing skee ball that takes a ticket or two, or one of the teacup rides that takes five. You have to kind of set some of the tiering in there and the way that you're going to allow the buyer to buy, but then make it easy for them to use across the agents that they value.

Speaker 2 8:30

Yeah, I imagine that's going to take some time for the best practices to really emerge and be tested on how to package the tickets, right? The tokens and how to train users and make them feel good about the consumption of that resource, right? Because again, right now, if I get on most of my software products like Microsoft Office, it's, you know, spell check your heart out, right? But on some of these other products, like the CRMs, they're beginning to put intelligence in that do require credits. And it's a little bit of a black box right now, I think.

Speaker 9:04

Yeah, you know, you're right, but it's also kind of an exciting space to invent and try things. And and yeah, teams that are putting the thought into it are finding ways that work. Mark Benioff mentioned it, Dreamforce. You know, he's the leader of Salesforce, of course. He talked about how, you know, there's an extreme partnership is required as you think about this AI world and these models because it's it's not like it's been figured out and people are trying things and they're testing and learning, but no one's got it figured out. I think his point is really is like, you need to be thinking about this, you need to be putting the work into it versus kind of saying, you know, we're going to leave our current model and just try to get more volume on the platform.

Speaker 2 9:42

Yeah, I love that idea of extreme partnership. I mean, it's sort of saying to your customers and prospects, let's go on this journey together, let's build a reasonable future that you can afford and we can afford to deliver for you, right? It's kind of a nice, a nice approach. And so much in AI seems like this inevitable calamity, and it might be nice for it to be, you know, a shared build of a future, right? So it's kind of a cool way to think about it. I want to move on a little bit. One of one of the themes in our prep call was that operating partners, in your experience, are still thinking about the product and monetization as kind of two separate worlds and conversations. And as we sort of hinted at just now, you know, AI kind of breaks down that barrier, right? That approach sort of falls apart. So why do SaaS companies need to design pricing and packaging at the same time as they design the product itself? I think that's the first time I've heard that.

Speaker 10:45

Yeah. Well, and there's been some dialogue in the in the pricing world about this for a while. But what's great about AI is it it highlights the need more than ever. And if you think about it, you know, the reason to build a product is so that you can increase the value to your buyer. I mean, setting aside things like fixing bugs, if you're going to build a product, it needs to add value. So the question is, how does it fit into the monetization plan you have? Is it something where you can you can charge additionally for it? Does it increase the value of what you already have in some distinct way? Because you're you're gonna be adding cost. And so the question becomes far more poignant where you're saying, hey, if we're gonna add this cost and users are gonna be able to incur it, we need to be able to get more for this. And so what's great about it is the ideal is when you're you're early in the process, you're thinking about, okay, what are the value drivers here? And how does this augment the value we can deliver to our existing or new customers? So then it starts to force a conversation on, okay, well, how are we going to sell this? And what are the things we need to either understand so we can build it the right way, or that we need to be thinking about in terms of our model so that at launch, either to existing or new customers, we need to position it in a new way. And to your point you made earlier about the partnership, is that some of these things are seismic shifts enough that it changes that selling conversation, even with existing customers. Hey, you know, that this is a different product. We're going in a different direction. The old ways we've done things need to change. And so you have this conversation that's focused around value and the buyer's needs that's really healthy. And so the question is, hey, how do you how do you do that in a way that you can you can share the additional value that you're generating appropriately?

Speaker 2 12:24

It may have been a best practice before, but it sounds like having the sellers, like sales leadership involved in these conversations early on is gonna be important.

Speaker 12:35

Am I kind of reading that right? Oh, I think there's that makes a lot of sense, right? So anytime you have siloing across the organization, you're gonna miss out on some things, but especially when sales is saying, okay, how are we gonna take this to the market? What do we know? And obviously there's a there's a role of research because your sales team has, you know, phenomenal contact with the customers and prospects they're talking to, but they aren't talking to the whole market. And it and for you know, a new need set that you're addressing with an offering, you may need to do some deep dives with customers or prospects and understand some of the unique value of what you're bringing. But the sales team plays a huge role on you know, how does this fit into the story they're going to be telling and from the reactions they get from buyers and they they know a lot about what's valued and what's not. And so they they play a really good role as a check in that process.

Speaker 2 13:21

Yeah, that's cool. I I think it's an opportunity also then within the organization, right, to get alignment around the new product and you know, probably energize your whole organization as you move into, you know, a new technology delivery model.

Speaker 13:35

Years ago, I was a sales rep and then later a sales strategy leader. And I remember in the product development process, it started to pull sales in. And the conversations that we had were were really revealing in terms of where the gaps were in the process and also what are the things we need to be thinking about several months out before you get to GA on these kinds of things. And everybody benefited from the push and pull of the different functions discussing this launch.

Speaker 2 14:00

I want to move on to this value creation, SaaS value model. And this traditional model that we've had, the seat-based pricing, you mentioned it before, really doesn't fit too well in an AI heavy or AI native product offer. And I think that investors are probably just starting to grapple with this, right? That they're beginning, they see the market threat, but I think they're now looking at the the actual the value model valuation pricing. What are the big mistakes you're seeing early on here when companies are trying to price the AI features? And you know, how does that roll up to the investing community?

Speaker 14:38

Yeah, and it's an important question. So one of the biggest misses is just, I would say generically, not enough time spent thought about the the pricing and the pricing strategy and the model. And that's something that within the pricing community, people have been saying forever. What's exciting is that it's it's getting more traction these days. So if you think about oftentimes it's tempting to just say, hey, we're not going to disrupt our model. We're going to try to kind of make this just kind of a nominal adjustment. But the companies that do well in these types of launches are kind of breaking it back down to the basics and saying, should we be thinking about a wholesale change to our pricing model? And so if you think about seats, seats are you know a fixed point. They don't vary over time. Oftentimes it's it's uh user basis, but you know, users could have widely variable consumption of compute based on their usage. And so you're you could easily be pricing some companies out, you know, low, low usage companies would be priced out of the market for what you're offering with a fixed rate in a seats model. And those that would be using a lot of it are often underpriced and you're you're missing out on the monetization opportunity. And so we're seeing models that come through where you have, say, a hybrid approach where you have some seats. In some cases, seats still make sense. Often when you can't draw a clear line between what the AI does in terms of driving an outcome. There are examples out there, you know, like intercom where you have this number of resolutions. It's really clean, it's nice, but not all products do that. And so they have to think about okay, well, maybe there is some a role for seats, but there's a variable or token element or a credit element that sits on top of that license and that captures some of the upside of the requirements based off of how much is consumed. So, you know, getting creative there, thinking about different models, looking at what others are doing, all those practices end up becoming useful because while you don't have like a one standard way to do it, it's also important here is that not all AI inclusions in a product behave the same way. It all depends on the type of SaaS you have and the type of AI you have to be able to think through what fits in this case. And then does it align with the value we're delivering? Does it impinge on adoption? You certainly don't want to be doing that, that you're kind of tripping up your the adoption of your AI. And so there's there's some balances that you have to work through to get to the right model.

Speaker 2 16:59

You know, as you're speaking, I was thinking about we keep looking at like the pitfalls here. Like it's this human nature, I guess, not just in this conversation, but founders and uh that I speak with either as prospects for our business or you know, on our other podcast or you know, even clients, they're looking at it almost the downside, but I think there might be an upside here too, right? In terms of the monetization, as we talked about, and therefore expanding actually the value, the enterprise value of these companies. Can you talk briefly to that?

Speaker 17:32

Yeah, if you think about what an agent can do versus traditional SaaS, there are opportunities that a buyer has to be able to solve problems in a better way. And so if you think about that and say, this is actually generating value incrementally and potentially more so than what the platform did before, that's where a lot of this stems from. It's not just kind of this cute thing that we're tacking on that says AI. It's something that that enables outcomes in a better way, in a faster way, and can have implications across the organization in terms of where they put human resourcing as well. And so this is part of what the diagnosis is for a company is to say, well, you know, how does this generate value? Because oftentimes, and we've worked with organizations where they're they're building out AI that trumps what they used to provide just in SaaS form, right? And so it's a miss to not claim some of that value via pricing.

Speaker 2 18:24

Yeah. So that is both a pricing exercise and a customer education exercise. Have to train them almost on how to use the product differently to get that extra value so that it's worth paying for.

Speaker 18:35

Yeah. And then the sales team is charged with telling that story and helping the buyers and prospects to understand the value they're going to get from that. So that's again, goes back to that point of having them included in how they're going to position it.

Speaker 2 18:47

I do want to, as we kind of land the episode, sort of tell the cautionary tale also here. So when you step into portfolio companies after a deal closes, are there some common monetization or pricing issues that just seem to be pervasive or just very frequent? And perhaps highlighting those might help listeners who are looking at deals, you know, try and see if those things exist in these potential investments.

Speaker 19:14

Yeah. So uh one of the first ones that comes up is that pricing hasn't been touched in years. So, you know, often there are escalators included in contracts. It's a really basic one, but whether that's tracking to just kind of some flat rate or if you've been making significant improvements to your product over the time, but you're not capturing much of that value through changes to either the price levels or the pricing model, that's one of the big ones that we see. The other part is one that I mentioned briefly earlier is you may be taking prices up for those who are new coming in the door, but you aren't doing anything to strategically increase the monetization of the install base. And there's ways to do that poorly and ways to do it effectively. It isn't just about just ratcheting up prices ham-handedly. There, there are many cases that we've seen of others doing that. And it doesn't tend to work well. But if you think about it from a segmented and differentiated perspective in terms of, you know, what are the churn risks and what are the indicators we have of strong adoption and satisfaction? How do I have a phased and piloted approach to make sure it works really well? You can have a lot of EBITDA gains from improving the monetization of your base. The other part, too, is you know, aggressive product roadmaps, but you're leaving a lot of that value from being considered, or it's just rolling right into your packaging and you're not thinking about how much additional value it's bringing, and/or you're not over time looking at your packaging and say, maybe we should rethink this, start again from the drawing board and rethink our packaging, especially when you're bringing in agentic capabilities or or you know, AI in some form, be it whatever form of AI that you'd be bringing in the product, should be asking the questions, how should we think differently about our pricing and packaging model?

Speaker 2 20:57

So kind of to sum it up, if you haven't touched it in a while, regardless of where you're looking at the company, whether it's pricing for new customers, pricing for existing customers, pricing for kind of foundational capabilities you're adding, or if you're adding AI and not thinking through the pricing implications, there's at least four places where during diligence, someone might look and ask the questions, hey, what what's the thinking on pricing here, right? That's right. Well, and I think that's a great place to land our episode. Pat Megan, thank you so much for being on the propellant podcast. If our listeners want to reach you, how can they do that?

Speaker 21:39

Yeah, you can go on to uh igsines.com or you can look me up on LinkedIn. Easy as that.

Speaker 2 21:44

And if people want to reach me, I'm on LinkedIn slash in slash Ken Lempit. My advertising and Sasco to Market agency is Austin Lawrence. We're at AustinLawrence.com. And the propellant podcast is being distributed wherever podcasts are. Are offered and as well on YouTube. So, Pat Megan, IGS Insights, thanks so much for being here. Thanks, Ken. It's a pleasure.

Speaker 1 22:07

Thanks for listening to the Propellant Podcast, where we explore how operating partners help companies grow inside venture capital and private equity portfolios. If you found the conversation valuable, please subscribe and share the episode with another operator, founder, or investor. And we'll see you on the next episode.