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

How SaaS CEOs Should Navigate AI-Native, AI-Augmented, and Bolt-On AI Strategies to Protect Revenue and Reduce Churn 

Guest: Ken Lempit, President & Chief Strategist at Austin Lawrence Group  

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AI is not just another feature cycle — it’s an inflection point for SaaS.

In this episode of SaaS Backwards, Ken Lempit steps into the guest seat to break down what AI really means for SaaS companies, especially mid-market and enterprise software vendors trying to protect revenue while planning their next product evolution.

Ken draws a powerful parallel between today’s AI shift and the early 2000s transition from client-server to cloud — arguing that this AI cycle is moving faster and carries even greater competitive risk.

He explains the critical differences between:

• AI-native SaaS products
• AI-augmented platforms
• Bolt-on AI features

And why the wrong strategy could quietly increase churn, shrink pipeline, and erode relevance.

You’ll also hear:

• How to diagnose whether you have a GTM problem or a product relevance problem
• Why “vibe coding” poses real risk to mid-market SaaS vendors
• Short-term product and pricing moves to survive the next 12–18 months
• Lessons from BackEngine’s pivot from conversation mining to revenue enablement
• Why your AI narrative may matter more than your marketing spend

If you’re a SaaS CEO, founder, or go-to-market leader wondering how aggressive your AI roadmap needs to be, this episode is your strategic wake-up call.

Get a free SaaS GTM Checkup: https://info.austinlawrence.com/saas-gtm-checkup 

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Transcript

Welcome to SaaS Backwards, a podcast that helps SaaS CEOs and go-to market leaders to accelerate growth and improve profitability. Our guest today is our very own Ken Lempit. He's usually the one hosting the show, but today he's stepping into the hot seat to share some thoughts on AI.

Jason Myers: Ken, welcome to the podcast. 

Ken Lempit: Thanks. Appreciate it, and it is a hot seat for sure. 

Jason Myers: 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?

Ken Lempit: Sure. Well, you know, I was talking with a couple of our colleagues about the work we do in 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. You know, that the, the challenges that they're facing are more being in the wrong place at the wrong time.

Being in the, 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 two thousands, 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 a hundred thousand dollars, 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, a moment with one of our clients, software company called Thunderhead, where the CEO stopped all development of the client service 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 and 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, you know, 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, you know, 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, 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, to generate. It was very difficult. So I think we're in that same inflection point in the software business.

That we saw 20 ish years ago, and that's why I wanted to have a chat about this because I think it's, it's a teachable moment for all of us who do go to market work that we, we have some challenges ahead of us, depending on where we are in our migration to cloud native, cloud augmented, or co-pilot kind of bolt-on solutions.

Jason Myers: Great. 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? 

Ken Lempit: 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 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 gonna be a lot of time still ahead of us where these solutions are gonna 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 gonna vibe code your way into an SAP. But I think that, you know, if you're selling to more entrepreneurial customers or you're selling a smaller solution. Something maybe a little bit bigger than a point solution, but not that much bigger, those two paths. So a mid-size prospect base, you know, your ICP is mid-size or smaller firms, or your solution is not all that complex.

I think your customers are gonna be tempted to vibe code their way to solutions, even if they're partial solutions. In fact, you know, we have a podcast 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, you know, most user organizations are using, you know, 5, 10 or 20 percent of what the thing can do, and they come to the realization, Hey, I could make this myself.

 I just think there's, there's risk there in the mid-market, whether you're a mid-size solution or you know, your prospects are mid-sized.

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

Ken Lempit: So, look. I'm no expert, but I'll give my, my working definitions on these things.

So AI native is, you know, kind of what it says on the tin. You know, 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, within the product. So it's semi-autonomous at, at least in places. And the, the nature of the underlying technology is completely new.

It's not it's, it's not built on a five or 10-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 gonna be replaced by a completely new system on this new architecture, and it's gonna provide remarkably different set of capabilities, not only to the software vendor itself, but to the users where they're gonna end up being able to chat their data.

Much like we chat to build content today, they're gonna be able to chat into their data, analyze their data in natural language, and, you know, manufacturing, if it doesn't do anything else besides create products, it creates an awful lot of data. So the, the ability to, have a natural language interface to do decision support against manufacturing Data, you know, 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, you know, 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.

'cause it'd be a completely unstructured you know, the ability to manage against completely unstructured or structured data. So that's AI native, it's a thing unto itself. So AI augmented to me, these are, you know, this is gonna be a little bit of a heterogeneous term, if you will. When we, 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 differed 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. They're, they're features, they're 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, is a little different and 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 compliments what else is happening on that application?

And the Bolton is a great strategy because it allows you to build kind of on the side without disrupting your mainline product, and whether it's a kind of a, an add-on product or a true bolt-on the, the thing about these is it gives the sales to people 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, you know, in the mid two thousands, 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, that's how I kind of view it, why I view it that way. I'm sure other people are gonna have definitions that are different, but you can certainly see that it, 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, you know, it's a big win. You know, if you're a NetSuite, you know it'd be a big win if you could get 20 or 30 or 40 percent of your customer base to level up to a new version that has some kind of bolted on, you know, AI data interpretation 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, I think there's some real opportunity here. 

Jason Myers: So next I got a scenario that I think a lot are, dealing with right now. So imagine a CEO of a 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 product, pricing, positioning, and marketing right now in the short term? 

Ken Lempit: Product, pricing, positioning, and marketing. All right, let's start product. 'cause we, 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, a business intelligence layer, not only will it make the product more interesting and valuable, but it might allow some of your

customer base to reduce their costs elsewhere, right? Send, make you more of a platform for them and allow them to reduce costs, like so they're not continuing a subscription to a software like Tableau, for example, which is not inexpensive. So product, definitely look for the low hanging fruit on the bolt-on, or, you know, augmentation route. Augmentation, you know, could look

also pretty simple. Like for example you know, if you are selling a call recording solution, you know, can you bolt on an interpretation layer? You know, can you augment it with an interpretation layer where you have an AI agent that allows, or, 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 a 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, 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 wanna message them that it's a journey that they're gonna go on with you and you want to invest in them. 'cause in the future you'd like them to reinvest with you.

And I guess it also depends on how defensive you feel, right? If you're not feeling all that defensive charge for the augmentation, you know, 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. So let's say that, you know, 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, you know, run with artificial intelligence, you know, charge 'em another 3000 a year.

It seems like a very small upcharge, but it's all, you know, 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, you know, give an introductory period where you're giving the product away for free.

But people have to know they're gonna 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 as a service offer 10 years after the first fund 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. You know, we can, 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 gonna take me into the future, and the future is AI enabled, whether it's, you know, a completely native solution, an augmented, or a series 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 gonna give me the AI capability to do it, I'm gonna 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.

'cause if the word gets out that you know 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. And you know, I realize I'm picking on NetSuite and it's part of a big company and Oracle. And I don't mean to pick on that 'cause I don't have any inside knowledge. But you know, 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 gonna help them build their futures and, you know, set the path for yourself to have, or, you know, a really exciting future as a future AI native product.

Jason Myers: And I know you mentioned Eli Portnoy from BackEngine, who was a guest on the SaaS backwards podcast about nine months ago, I think episode 168. But anyway, his company is BackEngine 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 refining product market fit in the AI era and how should other SaaS founders think about similar pivots, 

Ken Lempit: Eli's business BackEngine?

You know, was a real eye-opener when we first met him, you know, 9 or 10 months ago. And, you know, the short story at the time was that he could mine every conversation on Gong and other platforms and be able to tell you as a management you know, 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 eyeopening at the time, the problem I think for BackEngine was, you know, market leaders like Gong and others have the resources to be fast followers if they're not gonna be the market leaders in these capabilities. So, I think Eli's strategy was pretty brilliant and that he said, well, I have these insights and they're good and valuable.

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 is integrated it with the something more, and what he built was extracting the value of those insights. In the moment they're most valuable, right? They're, 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 Eli's tool now operates. So he is crossed domains from a business intelligence or sentiment or marketing research tool to a sales enablement tool. And I think for vendors of marketing tech and marketing advisors like us, we, 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 new and valuable capability, but he is aiming himself where the budget is. So I think that it's really instructed from that standpoint, and you know, when we met him. You know, we 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, and buy the software.

But I think once you can demonstrate that you can enhance revenue in a real way. 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 'cause people like me and how much is that because my solution is really valuable.

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

Ken Lempit: Yeah, I, I think, you know, the, the news has been really

it, it's been really disturbing. Like if you, if you hear the news coming outta Wall Street. You know, last week the stock market was punishing software as a service, publicly held software as a service companies because of the ability of Claud 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 gonna be replaced by people, or smaller software companies, vibe coding their way to a point solution. But I don't think any worthy bit of software is gonna be completely vanquished by what non-technical users can build, at least not in the short term.

I think that it's time for, you know, mid-size 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, you try to avoid the subject of how AI is gonna change your product.

You're gonna lose those customers sooner than you might imagine. The rate of change is greater than it was for cloud, you know, from client server to cloud. This rate of change is gonna be, you know, a year or two at most before almost every piece of software has some mix of the kinds of capabilities I described. And I think it's a big opportunity now for software vendors to, you know, augment bolt on and make a plan to completely reinvent their software as AI native, I think that's where the money's gonna be. And yeah, we need to still do go to market around all that, but I almost think that, absent coherent AI narrative, you know, investment in go-to market is gonna 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 gonna spend money trying to grow your market.

Jason Myers: Great. And if listeners have questions or need some advice about how AI may incorporate in their go to market or their product itself, what's the best way to get ahold of you? 

Ken Lempit: 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 austinlawrence.com. My email is kl@austinlawrence.com where you can reach our Jason Myers at jm@austinlawrence.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. 

Jason Myers: 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 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, 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 backwards podcast wherever you get your podcasts. We'll see you next time. Ken, thanks for leading up the charge on AI. 

Ken Lempit: Hey, thank you Jason, this was a lot of fun.