Ep. 188 - SaaS in the Age of AI: Augment, Bolt On, or Become Obsolete

Speaker A: Welcome to the SaaS Backwards podcast where we reverse engineer the success of fast growing SaaS firms and explore strategies CMOs and CEOs are using to drive their businesses forward. Welcome to SaaS Backwards, the podcast that helps SaaS CEOs and go to market leaders to accelerate growth at improve profitability. Our guest today is our very own, um, Ken Limpet. He's usually the one hosting the show, but today he's stepping into the hot seat to share some thoughts on AI. Ken, welcome to the podcast.

Speaker B: Thanks, appreciate it. And it is a hot seat for sure.

Speaker A: So let's jump right into it. Like, I know you have a lot to say on AI in the world of SaaS marketing. I think you drew a comparison recently between AI's shift in the old client server to cloud transition. Can you walk us through that analogy and explain why you think this AI moment is even more compressed and maybe risky for SaaS companies?

Speaker B: Sure. Well, uh, I was talking with a couple of our colleagues about the work we do in uh, go to market for SaaS firms and I was starting to have this feeling that so much of it might not be as relevant as I'd like it to be. That uh, the challenges that they're facing are more being in the wrong place at the wrong time or being with the wrong solution at the wrong time. And that the market is very rapidly moving to AI native AI augmented or AI copilot kind of solutions. And it did in fact bring me back to somewhere in the early 2000s, 2005, 2006, where software vendors were almost entirely client server, meaning that the solution was sold as a package solution, the software was sold for a list price, let's say it was $100,000 and you would get the software and you'd load it on a server in your closet or in your mainframe, and you would run it on your own private network, not on a cloud or on the Internet. As we run almost everything today, there was a, uh, moment with one of our clients software company called Thunderhead where the CEO stopped all development of the client server solution and completely made a left turn to make it a cloud delivered software, stopped all development on the client server part of the application. And his team honestly thought he was crazy. There was a fair amount of discord among the management team in terms of where they were going and the resources they were putting on this unproven kind of unknown future. Well, it turned out that Thunderhead was remarkably successful as a result of this migration to the cloud. And it enabled this software entrepreneur to get not one but two exits out of Thunderhead. And he was completely correct, very prescient in his move. So here we are today, a little more than a year into the chat manifestation of machine learning and artificial intelligence and things are changing super quick. And there's a distinct lack of relevancy or certainly insecurity among software go to market leaders, CEOs and founders if they don't have an AI story that's compelling enough to continue revenue growth. And I think that's where the parallel is. Somewhere between 2006 and 2008, if you didn't have a cloud delivered solution, you almost didn't have revenue to generate. It was very difficult. So I think we're in that same inflection point in the software business that we saw 20ish years ago. And that's why I wanted to have a chat about this because I think it's a teachable moment for all of us who to go to market work that we have some challenges ahead of us depending on where we are in our migration to cloud, native cloud, augmented or copilot kind of bolt on solutions. Great.

Speaker A: And I think you mentioned earlier, you said many software leaders don't really have a go to market problem. They have a my product is broken in the age of AI problem. So how can SaaS CEOs tell which problem they actually have and what are the first steps if it's the product?

Speaker B: Well, I think you kind of probably can see the symptoms in the pipeline, right? So if you're still generating meaningful real pipeline on your current non AI product, well, uh, you're probably in good shape at least for now. And I think there are probably plenty of categories where that could be true. And what I'm thinking about here is really, you know, enterprise solutions that are just so ingrained within the organizations that they serve that there's going to be a lot of time still ahead of us where these solutions are going to maintain their dominance, be able to get incremental sales at current customers and you know, generate new sales as those requirements come up. You know, there's just sort of no replacing SAP or Oracle with other things. You know, you're not going to vibe code your way into an SAP. But uh, I think that, you know, if you're selling to more entrepreneurial customers, where you're selling a smaller solution, you know, something maybe a little bit bigger than a point solution, but not that much bigger, those two paths. So a midsize prospect base, you know, your ICP is midsize or smaller firms or your solution is not all that complex. I think your customers are going to be tempted to vibe code their way to solutions even if they're partial solutions. In fact, you know, we have a podcast, uh, episode coming up with one of our clients where, you know, he's vibe coded his way to a whole marketing department and he's building software. This guy's a non technical user building software to run a marketing department. And you know, if I was in the Martech business, I'd be pretty terrified right now that people would be creating good enough solutions to meet their actual needs as opposed to these very large solutions like a HubSpot or a salesforce where most user organizations are using 5, 10 or 20% of what the thing can do and they come to the realization, hey, I could make this myself. I just think there's risk there in the mid market, whether you're a mid sized solution or your prospects are mid sized.

Speaker A: And I know you distinguish between AI native products, AI copilots, and bolt on AI features like we see in tools like HubSpot or Grammarly. So how do you define each of those and why does that distinction matter for product strategy and messaging?

Speaker B: So look, I'm no expert, but I'll give my working definitions on these things. So AI native is kind of what it says on the tin when you look at how the product was built. It's relying on machine learning, artificial intelligence, foundations. Probably agents are doing the work within the product. So it's semi autonomous, at least in places. And the nature of the underlying technology is completely new. It's not built on a five, uh, or ten year old tech stack. It's built on new tools. In fact, we have a client building a new version of their software to help manufacturers and that product's being built entirely on Palantir. So there is no legacy code base of any kind. And they're making that solution. Like I talked about with Thunderhead in the past, they're making the decision that we are jettisoning our old tech layer. The whole thing is going to be replaced by a completely new system on this new architecture. And it's going to provide remarkably different set of capabilities, not only to the software vendor itself, but to the users where they're going to end up being able to chat their data, much like we chat to build content today. They're going to be able to chat into their data, analyze their data in natural language and manufacturing. If it doesn't do anything else besides create products, it creates an awful lot of data. Uh, the ability to have a natural language interface to do decision support against manufacturing data is pretty revolutionary. So that first category of solutions, AI native, they're built on different stuff, let's say like Palantir, they're able to present both a structured engagement with the data and application. So your workflows can be related to what you might be used to. They're probably more efficient, a little less demanding of human resources. But the AI native solution, it's a coherent, complex implementation that takes everything to the next level. New development environment, technology layer, augmented improved workflows and the ability to inquire against your data, decision support against your data in ways that just aren't possible today because it'd be a completely unstructured, you know, the ability to manage against completely unstructured or structured data. So it's AI native and it's a thing unto itself. So AI augmented, to me these are, you know, this is going to be a little bit of a heterogeneous term, if you will. When we brought up HubSpot before and as you know, many people probably know, we're a HubSpot partner. HubSpot is a really complex big application now, you know, it used to be a much smaller, simpler piece of software and they are implementing AI throughout this software in many of its functionalities. But their levels of AI capability differ depending on where you are. So whether it's data augmentation, where they're doing the work of trying to fill in the company profile kind of information in the company object, or are they trying to write your email for you as a salesperson. So each of these things is a different level of sophistication, but it's definitely an augmentation. You know, it's, they're looking at what is the low hanging fruit in their user base, where do their users use the application the most and where can they augment its capability through these AI features, if you will. Their, their features or featurettes, very little of it's running in the background and it's not really independent of the workflow that people had before. And then the bolt on is a little different in that from my way of thinking. If you had something like a Salesforce or a HubSpot, you would be adding new capabilities that never existed before. You would bolt on some new thing that you want Salesforce or HubSpot or NetSuite users to have that kind of complements what else is happening on that application. And the bolt on is a great strategy because it allows you to build kind of on the side without Disrupting your mainline product. And whether it's kind uh, of a add on product or true bolt on, the thing about these is it gives the salespeople and marketing people a story to tell that's contemporary and allows them to differentiate both in the sales environment and the marketing environment from competitors that haven't done that work yet that can't make that claim. And when we look back at what happened with the move to cloud in the mid 2000s is even being able to make the claim of a cloud solution was enough to box out other competitors. Especially if you're trying to defend, you know, against your own clients being churned on you. So that's how I kind of view it, why I view it that way. I'm sure other people are going to have definitions that are different, but you can certainly see that uh, you don't have to reinvent your entire product to have a contemporary go to market for your sales and marketing and as well to have an experience that's augmented or bolted onto in a way that gives the users some satisfaction. There's a big risk here, especially for a large software company like a HubSpot, a salesforce, you know, netsuite, these mid size applications, big risk is being churned out, you know, losing a customer. So it's not always about new logo. I think as any real student of the SaaS business model knows, you know, sometimes you know your best revenue is the revenue you get from your existing customers. So protecting churn and growing them, um, you know, it's a big win. You know, if you're a NetSuite, it'd be a big win if you could get 20 or 30 or 40% of your customer base to level up to a new version that has some kind of bolted on, you know, AI data interpretation, uh, layer as an example. I think that's the opportunity and it's, and it's not that hard, it doesn't take that long to be able to make those claims. So I think there's some real opportunity here.

Speaker A: So next I got a scenario that I think a lot are dealing with right now. So imagine a CEO of a uh, 10 year old SaaS company who's maybe 12 to 18 months away from an AI native offering. But they have to survive the next five quarters. So what should that CEO be doing with their uh, product pricing, positioning and marketing right now in the short term?

Speaker B: Product pricing, positioning and marketing. All right, let's start product because that's where we just landed. Well I think you have to look for the low Hanging fruit, you know, and probably for many applications, you know, business intelligence layer not only really make the product more interesting and valuable, but it might allow some of your, uh, customer base to reduce their costs elsewhere. Right. And make you more of a platform for them and allow them to reduce costs like so they're not considering a subscription to a software like Tableau, for example, which is not an inexpensive so product. Definitely look for the low hanging fruit on the bolt on or, you know, augmentation route. Augmentation could look also pretty simple. Like for example, if you're selling a call recording solution, can you bolt on an interpretation layer? Can you augment it with an interpretation layer where you have an AI agent or a chat that allows your customers to inquire of the repository of conversations that have occurred on your platform. So if you're like a Zoom or competitor of Zoom or Otter, competitor of Otter, it'd be great to be able to do that and come up with insights from that. So I think you have to look at what are the things that would make your application more valuable and get a product story ginned up quickly. And you're gonna have to invest in the resources if you don't have them to be able to build that capability. So that was product. I think from a pricing standpoint I'd want to give away my new stuff to keep my clients from churning in the short term. Because you want to message them that it's a journey that they're going to go on with you and you want to invest in them because in the future you'd like them to reinvest with you. And I guess it also depends on how defensive you feel. If you're not feeling all that defensive, charge for the augmentation charge for the business intelligence layer, but charge it at a fraction of what their alternatives are. For example, a Tableau or something like that. Let's say that you have a piece of software like a NetSuite and you're charging $50,000 a year for the privilege of running your company on NetSuite and you build a new BI layer that's run with artificial, uh, intelligence, charge them another 3,000 a year. Seems like a very small upcharge, but it's all marginal cost of delivery near zero. And it'll get your customers accustomed to thinking about your product in a new way. So as you build more capability into the product, you can again go back and sell them. So maybe give some free period of time, give an introductory period where you're giving the product away for free. But people have to know they're going to end up having to pay for it. Messaging, I think this is the one that's pretty straightforward. Everybody's in the same boat. So it's not like it's 2015 and you finally have a software, uh, as a service offer 10 years after the first one came about. So if we're in the party, if we're joining the party in 2026, I think we can talk futures now. Like if we're developing something now, we can talk to our customers under NDA and we can make something about it. We can make some excitement about where we're taking this product and how we have embraced the future of AI. I think that's very important. If I'm trying to grow my business on the back of your software, I want to know that you're going to take me into the future and the future is AI enabled. Whether it's a completely uh, native solution, an augmented or a series uh, of bolt on solutions around a core capability. The future of growing my business on the back of your software has to include AI. And if you're not going to give me the AI capability to do it, I'm going to find it for myself with or without you. Right. I'll either find something that integrates onto your platform or I'll find something that's shiny and new and I'll churn or I'll build my own thing, which is possibly the most dangerous of all of them. Because if the word gets out that every user or 50% of the users of NetSuite are vibe coding their way to a business intelligence layer, that can't be good for the future of the business. I realize I'm picking on NetSuite. That's part of a big company in Oracle and I don't mean to pick on them because I don't have any inside knowledge, but if you're the product and go to market team for a piece of software like that, you don't want to be struggling behind an earned reputation for being a laggard. So I think the risk is being a laggard. The opportunity is to secure your customer base, maybe even make them more loyal by building the things that they think are going to help them build their futures and, you know, set the path for yourself to have, you know, a really exciting future as a future AI native product.

Speaker A: I know you mentioned Ellie Portnoy from Back Engine who was a guest on the SaaS Backwards podcast about nine months ago, I think episode 168, but anyway, his uh, company is Back Engine and they were evolving from a simple conversation mining to real time sales coaching I believe. So what does that story teach us about finding or refinding product market fit in the AI era? And how should other SaaS founders think about similar pivots?

Speaker B: Ellie's business, Back Engine was a real eye opener when we first met him nine or 10 months ago. And the short story at the time was that he could mine every conversation on um, Gong and other platforms and be able to tell you as a management, as a go to market executive or a sales executive what was the sentiment of these conversations? What are the things that keep coming up? And it was very eye opening at the time. The problem I think for Back Engine was market leaders like Gong and others have the resources to be fast followers if they're not going to be the market leaders in these capabilities. So I think Ellie's strategy was pretty brilliant in that he said, well I have these insights and they're good and valuable but they're not as valuable as they were the day I introduced them and they're decaying over time because it was a point solution in the end. So he's integrated it with the uh, something more and what he built was extracting the value of those insights in the moment they're most valuable. Right. They're interesting for sales leaders, but they're a lot more interesting to sales leaders if the salespeople can convert more conversations into pipeline. And that's where Ellie's tool now operates. So he's crossed domains from a business intelligence or sentiment or marketing research tool to a sales enablement tool. Um, and I think for vendors of marketing tech and marketing advisors like us, we've always realized that there's a lot more budget in the sales organizations to improve performance than almost anywhere else in most companies. So he's not only synthesized a, uh, new and valuable capability but, but he's aiming himself where the budget is. So I think that it's really instructive from that standpoint. And when we met him he had an initial, I don't know, 15 or so customers, but it was a struggle to get much beyond that. And he was in a founder led sales motion. But when he was going to the market at large it was a little harder to get people to open their wallets and buy the software. But I think once you can demonstrate that you can enhance revenue in a real way, you have much better opportunity. So I think it's the synthesis like this cross domain synthesis is the big learning for Young companies that maybe get their first 10, 20, 30 customers and kind of stall out. You have to ask yourself how much is that because people like me and how much is that because my solution is really valuable?

Speaker A: I think that's all the questions that I have. Is there anything else you wanted to add about AI and the directions and marketing for SaaS companies?

Speaker B: Yeah, I think you know the, the news has been really, it's been really disturbing. Like if you hear the news coming out of Wall street, you know, last week the uh, stock market was punishing software as a service, publicly held software as a service companies because of the ability of Claude code to you know, empower mere mortals to build software. And, and I think that that's probably being overdone. I think simple things are going to be replaced by people or smaller software companies vibe coding their way to a uh, point solution. But I don't think any worthy bit of software is going to be completely vanquished by what non technical users can build. At least not in the short term. I think that it's time for mid sized SaaS operators to make the change, make the commitment, share it with your customers because they'll reward you with more loyalty. And I think if you're quiet on the subject, if you operate from a defensive posture and you try to avoid the subject of how AI is going to change your product, you're going to lose those customers sooner than you might imagine. The rate of change is greater than it was for cloud, from client server to cloud. This rate of change is going to be a year or two at most before almost every piece of software has some mix of the kinds uh, of capabilities I described. I think it's a big opportunity now for software vendors to augment bolt on and make a plan to completely reinvent their software as AI native. I think that's where the money's going to be. And yeah we need to still do go to market around all that. But I almost think that absent a coherent AI narrative, investment in go to market is going to be less and less rewarded. So I guess that's my, the gauntlet I'm throwing down is have a good AI story. If you're going to spend money trying to grow your market, great.

Speaker A: And if listeners have questions or need some advice about how uh, AI may incorporate in their go to market or their uh, product itself, what's the best way to get a hold of you?

Speaker B: I'm on LinkedIn in kenlempit, our agency, which is a strategic advisory and advertising agency for software and AI. Firms is Austin Lawrence Group. We're at Austin Lawrence.com my email is KlostinLawrence Ah.com where you can reach our Jason Myers at jmustinlawrence m.com and we'd love to hear from you and learn of your experience as you're navigating these new waters. It's a really exciting time to be in the software business.

Speaker A: Just to add, for those of you listening who are thinking, hey, we might need to take a hard look at our own go to market process, we here at Austin Lawrence Group have put together a pretty robust go to market analysis that's designed to do exactly that. It's a, uh, focused go to market checkup and it's designed to help you diagnose like what's really happening in your go to market motion, like what's working, what's stalling, what needs to change to generate more consistent pipeline. If you're interested in that, we'll put a link in the chat. You would walk away with pretty clear, actionable insights around your positioning funnel performance and sales and marketing alignment. And you can take the self assessment on your own or request our help. Either way it's complimentary. And again the link will be in the show notes. And of course, if you haven't already, be sure to subscribe to the SaaS Backward Podcast. Wherever you get your podcasts, we'll see you next time. Ken, thanks for leading up the charge on AI.

Speaker B: Hey, thank you Jason. This was a lot of fun.

Speaker A: Thanks for listening to the SaaS Backwards podcast brought to you by Austin Lawrence Group. We're a growth marketing agency that helps SaaS firms reach reduce, churn, accelerate sales and generate demand. Learn more about us at www.austinlawrence.com. you can email kenlempittlustinlawrence.com about any SaaS marketing or customer retention subject. We hope you'll subscribe and thanks again for listening.