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The Margin is a podcast from MGI Research that explores the evolving world of business monetization. Hosted by MGI Managing Directors Andrew Dailey and Igor Stenmark, the show features candid conversations with founders, CEOs, product leaders, and industry experts at the forefront of pricing, billing, and revenue operations. Each episode dives deep into the strategies, technologies, and trends shaping how companies generate, capture, and grow revenue—from subscription and usage-based models to AI-driven monetization. Whether you're in finance, product, or IT, The Margin offers practical insights to help you navigate complexity and drive growth in the digital economy.
Andrew Dailey: Okay, let's get rolling here. Welcome to The Margin. Today's guest is Noel Goggin, and one of the reasons that we're very excited to have him on the pod is he is uniquely positioned to comment on the challenges facing CEOs and management teams today. Across his career, Noel has led teams and organizations through major transitions, from companies struggling with slow growth, application vendors needing to rewrite core technology, or inject innovation into a stagnant product line, to businesses bleeding cash. Noel has the scars and the experience that come with navigating through the stormy seas and leading teams through good times and bad. Most recently, Noel served as CEO of Conga, where he transformed the company culture, made a number of acquisitions, and essentially delivered Rule of 40 growth and profitability. He also brings experience as a board member, notably having served recently on the boards of Anaplan, the planning and analysis tools company, and Auctane, a global shipping and logistics software business. Noel, welcome to The Margin!
Noel Goggin: Thanks, Andrew. Good to be with you.
Andrew Dailey: So, you're looking awfully tanned and rested. Let's start with the personal side of things. What have you been up to?
Noel Goggin: Well, it's been nice to be out of the firing line of the CEO job for a while, so I've been traveling a fair bit, and trying to improve on my so-called golf game and doing a bit of skiing, which I've not had the opportunity to do a lot of skiing over the last couple of years due to work obligations, so it's nice to get back to some health and wellness for oneself, which is nice.
Andrew Dailey: Terrific. You were describing in the lead-up to this conversation, you were describing, actually, your flight coming back from Europe from that trip. Do you want to share with everyone? What happened?
Noel Goggin: Yeah, I guess I was in Chamonix last week. We were skiing, and we got great ski conditions and everything else, but it's a bit of a hike back, so it was about a 10-hour flight back to Atlanta. And I was pretty wiped out because we got great snow and we had a fabulous event. But I ended up finding myself immersed in the latest version of Claude, and even though I was tired, I went from one thread, to another, to another, to another. That kind of led me from satisfying, frankly, my own curiosity initially, and then using it just to kind of organize my own thoughts around a number of things and one thing I was going down, I was kind of creating a pricing model in the current environment for SaaS-based companies who are kind of predominantly seed-based, and how should you be thinking about consumption-based models and how do you make the transitions, and how do you navigate through that, which is a pretty tricky navigation to do. Another board I'm involved in, and I was trying to figure out how could I help show them how to write requirements. for agents in another space, and so I spent a bunch of time writing requirements for a bunch of agents to try and get the construct across to the Chief Product Officer. Then I got really curious, and I said, well, it's been a while since I've written code myself, so am I capable of writing some agents? So then I wrote two agents on the plane, and even though I was flying, whatever, 25,000 feet, the bandwidth was perfectly fine for me to be able to kind of get pretty responsive performance from Claude in terms of actually creating two agents that I basically put to work while I was in the air, so from what I was hoping to have a five or six-hour snooze on, I basically spent about seven hours on Claude, totally immersed in it, so it was a lot of fun in the end.
Andrew Dailey: So this hits at what's ripping through Wall Street literally today and that's this belief that SaaS companies and the SaaS model are fundamentally broken and we're going to be able to do, as individual contributors, in a matter of hours or maybe weeks, what used to take months and millions of dollars to do. So, are you telling me that, as a CEO, you would go to your Board of Directors and propose that instead of hiring an outside consultant or pricing expert, that in a matter of hours, you'd rework your price books using Claude?
Noel Goggin: I would definitely. You can create enough of a construct, of a thesis that you want to test for pricing, in my mind, and you can bring it to market and test it very quickly, rather than going down the traditional path of outside consultants, which takes a long time, and then they want to build you a bunch of tools. On the other side of it, I think you can bring things to market much quicker. I think you can bring that intelligence into your own organization. Frankly, most companies should have a lot more sophistication around pricing themselves in-house. And the fact that you can build the models now quickly, you can test them quickly, and then you can iterate through them very quickly as well. Plus, you can compare what other companies are doing around their agent pricing, their consumption-based pricing, etc. And more and more of that data is public domain now, in terms of at least the list price of things. And then you can run scenarios about how does this kind of degrade your existing business? And for a lot of people, I think there's a lot of analysis that needs to be done about kind of three things in the seat-based licensing model. I think, one, is how many seats are sold. Two, how many seats are actually provisioned out of what are sold, and three, how many are actually used. How many are actively used. So, let's say you've sold 1,000 licenses of something and many of them are actually provisioned and assigned to a user. And then, out of those, how many of them are actually really used? And there's huge risk in a lot of people's seat-based licensing, and it's only a matter of time before people wise up to that and start to negotiate harder at time of renewal for downsells on existing seat-based licensing, so being able to take that dollar amount and protect it in some capacity with something that you can, instead of it, or a swap, I think will be important. And people have renewal cycles going on every week, every month, that they can test those new models in, so you can get to a faster answer quicker, I think, and a more accurate answer quicker than these kind of big, long, longer-term pricing projects that the problems half the time, they don't get implemented effectively in organizations either.
Andrew Dailey: It's interesting, we're working literally right now on a piece of research that kind of decomposes the argument and looks at it in terms of assessing kind of value or, elasticity, price elasticity by kind of different workflow types. So if you think about it, there's kind of human judgment seats, there's throughput seats, and then there's kind of what we call collaboration seats, a tool like Jira or Slack, for example, and there's different value that you can ascribe to those seats.
Noel Goggin: Yeah.
Andrew Dailey: And the software attached to it, so that the kind of binary approach that Wall Street has, saying that all SaaS companies Are going to struggle. Everyone's going to struggle to a certain degree, but certainly the price impact may be quite varied depending on the type of software and the type of seats that are being sold.
Noel Goggin: Yeah, and I think part of it is going to be very determined a little bit on the mission criticality of software as well, so things that are mission critical, that are anchored around a proper system of record data and particularly the stuff that's more vertically oriented, is going to be more protectable than simple use cases that are very horizontal in nature. Those are the products that are going to be in really, really at the mercy of being disrupted in a pretty material way, I think.
Andrew Dailey: What do you think about, outcome-based pricing? Do you think that will get real traction, or is that short-term novelty?
Noel Goggin: I think that the construct of outcome-based pricing has been around for a while, and I think there's different people experimenting with it. Salesforce experimented with it, call center apps are all experimenting with it in terms of calls served and stuff like that. Where it's defensible, it has some potential. The question is, how do you get it through the gates of the CFO when you're trying to come down to a model which was seats were very simple? It's a very simple model where you can tie it to employees and people using your systems to outcome-based and how do you do it in a way that does not just elongate the sales cycle? And I think that's going to be tricky for people. So, and again, in simple, routine call center apps are probably easier to do, because there's just it's calls in, calls out type of thing. But in other applications the use cases are not going to be as easy to quantify, and the agents could be depending on how the granular your agents are, if they're simple and very micro-agents, or they're macro agents doing big, sophisticated things, or groupings of them we'll see. So, I think the outcome-based pricing model for me it needs to be proven, I think, a bit more in a broad-based cases across the industry. Something that's tied to transactions will help, for sure, because you can count them. But again, you have to be able to defend it from a value proposition as well, which is going to put more pressure on product marketing functions in companies, and product management functions in companies because also, the cost to process is not free. Depending on the complexity of it, is if those go unbounded it can have a massive impact on your gross margin profile of your business, and that's a pretty secure set of cash flow for existing enterprise SaaS companies today, because SaaS revenues, gross margin SaaS revenues are really high, you start eroding down the material away then you need to be very careful on that. So that's where it becomes complex, I think.
Andrew Dailey: I want to go back to one of your earlier points about, kind of the threat of AI, speed, and the fact that you're seeing these companies with tremendous growth rates and it seems like there's more and more pressure now on leadership teams to turbocharge growth which is really hard to do in any kind of environment, let alone today's macro environment. First of all, do you agree with that thesis that there's just going to be this massive and growing pressure on teams to accelerate growth or else?
Noel Goggin: Yeah, I think there's going to be, for sure, on growth. Absolutely on growth. But I also think there's going to be big pressure on existing software companies to retain their existing customer base. I think you have both pressures. You have “how do I manage the retention of the existing solutions that I have?” And products that were so-called sophisticated a couple years ago are struggling today with renewal rates and that's happened pretty quickly. So, you have a renewal rate irrelevancy problem to be preserved for the existing customer base, to keep the existing products and spend current, that they feel you're getting value from. And then, on top of that, how do you have more AI-first, more relevant, more differentiated solution sets that are able to capture the mind's share of people and really move the needle on competitive win rates, right? There's a battle to be fought on both sides for existing companies, in my mind.
Andrew Dailey: So it sounds like it's going to stress more areas of the organization. You mentioned product marketing as an example. Renewal teams, sales, it's going to stress the organization across the board, not just in, say, R&D, or engineering.
Noel Goggin: Yeah, and I think the hardest thing to do in any organization is to get functions working cross-functionally well, right? Reduce the friction in the system cross-functionally. And when you compress dramatically the time to build products and deploy products, I mean dramatically, from what it was, let's say, a year ago, even two years ago, even more so, a lot of companies haven't got a really good new product introduction muscle, right? They're not good at introducing new products to markets in general because, again, it touches pricing, services, support, partners, sales, engineering, customers, I mean, it touches everything. So when you compress the time down, the ability to work cross-functionally quickly in a low-friction environment is really, really key to be able to capitalize on those opportunities, and that's hard work to do. That's the change management, in many ways, that companies are going through today and there's just a lot of inertia built into organizations in the main that people have to overcome, that startups are liberated from. They don't have that inertia, that kind of legacy of customers and old products and everything else that they have to deal with.
Andrew Dailey: So, you're an acolyte of the author and researcher Jim Collins and in his seminal book, Good to Great, Collins outlines the principles that he discovered, or he found, the principles that allow companies to transition from being merely good To being consistent, consistently excellent. What is it about, as you were going up through the ranks of management, what is it about Collins' research that really spoke to you personally.
Noel Goggin: Yeah, I got to know Jim in the late 90s, he did Built to Last first, which, to me, was something that I really was kind of energized by, and I did a startup with the intent of building around the practices of Built to Last. And frankly, it was a massive learning experience for me, just because we failed massively. It was the dot-com bust of 2000 and I got to know Jim at that time, and he was actually going to come on our board, but then he was trying to finish Good to Great, but Good to Great then, for me, became a much, much more tangible book. There's a lot more companies that want to transition from Good to Great and I think in the current climate, where companies have to fundamentally transform from where they're at today, it just requires huge effort and huge change management in organizations. And you think about the principles of Good to Great, disciplined people, disciplined thought, disciplined action, and there's a couple of constructs in that, for me, that have always spoken to me, and they're more relevant today than they ever have been. The construct of level five leadership, this kind of oxymoron of this humility with this indomitable will of “we will prevail, we will find a way, we'll get from A to B.” Then you look at things like the Stockdale Paradox, of dealing with the current reality. What is the current reality? And not being afraid of it, not being able to say it out loud, and use it as a vehicle then to kind of galvanize an organization, mobilize an organization, go on a path to doing the hard work that it takes to get from A to B. It's not any one day. You use the analogy of the race to the South Pole of the 20-mile march. One team does what that could every day, another team that did 20 miles every day and then the team that won was the team that did 20 miles every day. So that discipline and rigor every day, but that kind of will to kind of succeed, but dealing with the current harsh realities of things I think is more relevant today than ever, in terms of what's in front of CEOs and companies to make that transition.
Andrew Dailey: When you joined Conga, the company at that time faced a number of challenges. There were customer satisfaction issues, there was a roadmap that had kind of zigged and zagged, there were a number of pressing issues with strategic partners like Salesforce. You chose to make culture and getting the right people in place a top priority. Why?
Noel Goggin: To me, I guess I've been a student of organization design, human behavior from when I got introduced into the 90s and I saw, when I had a startup—my second startup was saved by me getting to know Pat Lencioni and using 5Dys Functions as a team to get a team that was in India working with a team was in the US, and we were fundamentally broken that time. The people in the US thought the people in India were just terrible people. The people in India thought the US people were terrible people, and neither of them were, right? So, as we put to work, a bit of a last-ditch effort for me, because I was young in career learning by doing, more than anything else. And I just saw how applying the principles of five dysfunctional team to kind of get people to be able to come together, be able to communicate, be able to understand each other, and then be able to kind of create a baseline from which they could actually operate functionally. And that was really transformative for me in an early day. Every business goes through challenges whether it's growth, whether it's customers, whether it's competition, whether it's technology, every business has a myriad of things that are always coming at them. And the question is, how do you handle them? How do people handle them with a little bit of grit, a little bit of gumption, and a little bit of grace as well at times, where people don't get panicky, and they're able to be quite consistent and steady and even-keeled. And that creates followership, and I think followership is a great thing to kind of bind organizations together, galvanize organizations together, whatever the culture of an organization is. There's no point in having a culture if the behaviors don't match it, for me. So, it's not only what you do, it's how you do it in organizations. And I think it's a hugely powerful thing in terms of retaining the people you want, attracting the people you want, being a protector of who you want to come into an organization as well. And it takes a lot of work to build a culture, and it's very easy for it to kind of meander then with a couple of bad hires in a couple of areas, or a couple of inconsistent behaviors across the organization. Customers feel it as well and sometimes it can give you a bit of forgiveness, it can buy you some time. Well, you need to kind of either resolve things, fix things internally, etc, but it earns a bit of goodwill for you as well, if you're vocal about it, and it's externalized as well with your customers, because they want to see you successful generally.
Andrew Dailey: Absolutely. Let's just talk about the technology space and the software space for a minute. Although this applies more broadly, the majority of companies that are out there today are not AI-first or AI-native businesses. They have existing businesses with customers, with investors, with partners, employees, etc, and those businesses that are built with products and tools that predate AI. They're now under the expectation to deliver new products, new services, faster growth. What do you think it's going to take organizationally, culturally? How do you re-inject growth? How do you re-energize growth in an existing business?
Noel Goggin: If you look at it, there's a race on right now and it's a race for the startups to get the distribution because product development for them is generally easier. So how do they get the distribution and customer base versus existing customers to get the innovation? If I look at the two, kind of the two orthogonal things. Startups, able to move fast, no inertia, no legacy, full of ambition working their tails off, and trying to get as many customers as they can. And existing companies who've got what I term as “the treaties.” They've got data, they've got domain, they've got distribution but they're slow to innovate in the main, generally, for a variety of reasons. So I think, for me, there's a couple of things. I think, one, in the current climate, things have to be very CEO-led. It needs a full immersion of the CEO and the ELT, really leading from the top, to really kind of reinvent the company at the pace that needs to happen. That will help cut through the inertia, and the resistance, and the kind of the noise that will happen in an organization, number one. I would advocate that people need to make AI their number one objective this year, in 2026, to make the pivot in their organization, and there's three areas for me that I think are important. I think one is internal, the adoption of AI for internal use with the objective that should be to create capacity. How do I create capacity that I can kind of then take and reinvest back into the business, wherever it ends up being, whether it's into sales, into product, into wherever it is, it doesn't matter so much. But how do I kind of start applying in and building a fabric and an expertise internally that I can start just knocking off use cases that are going to create capacity in the organization, whether it's in support, in services, or wherever? That's a great learning to—that's a petri dish. People can learn in that in a very safe environment. The second area for me is really around the software development lifecycle has to be transformed, fundamentally. Various people are in kind of an agile masking, some waterfall-y type of a product development cycle today. And this is a massive shift, and it's a particularly hard shift for engineering, because engineers tend to be probably the most resistant to change, particularly on things they don't want to debate, or they want to negotiate for their own personal ideology, or whatever. And everybody wants to be the AI czar as well, so you get this political dynamic that goes on in product and engineering where, “well, I want to be the AI czar,” so to speak. So the transformation of the software development lifecycle, which is very different now, once people start using tools to be able to move much quicker, and get much closer to customers, and engage customers in their process as well. And the third thing then for me, which is around product innovation. And this has to be really taught from the bottom up, versus a veneer. I think a lot of people are going through, “how do I apply AI as a veneer around the fringes of the products, and a little bit of here and there?” I think people have to really fundamentally rethink their products, whether it's kind of rethinking, redesigning, rebuilding existing core components, whether it's building new products that do genuinely integrate with existing products. Because there's three things people need to look at, I think, when they're looking at product innovation. I think number one is, what are the things they need to do to ensure they have renewal rates for existing customers? It has to be a very clear set of things that are going to keep the existing customers engaged and educated on what the existing products do. Because once that starts to erode, it becomes problematic. The second thing, then, is what are the things that are going to drive win rates, competitive win rates? Because if I can support retention rates, and I can drive competitive win rates, what are the things that are material enough to that? And then the third thing then for me are what are the things that can drive incremental bookings, that can move the needle for net retention rates, and they have to be interesting enough, too. So you have to have a spread across the freedom. You can't just kind of go to a bunch of free agents that are in the product, because you need to do more. So those are probably the three things that I would say in that, and for me. I think the companies that will be most successful will be the companies that kind of go back into founder mode. If you go back to founder-based companies, there's a drive, there's a galvanizing of the team, there's a “won't take no for an answer,” there's a charisma about them and but it brings a sense of urgency, it brings a sense of ambition, of fearlessness. And going into founder mode, then we'll start to hopefully start to remove the spur-draggers, as they would say in Texas, people pulling things back, and I think it was 2 weeks ago, I was with the CEO of Ivalua. And he's a founder-based CEO, and he's done a phenomenal job building that organization, but he removed himself from the CEO chair and put himself into the Chief AI Officer chair. So he himself went back into being an innovator back in the business to kind of get the pace of innovation, going in the organization as well. That's what I mean a little bit about founder mode. If things are just kind of at the pace of a little bit faster than the old pace of development, people are going to find themselves very, very far behind very quickly, in my mind.
Andrew Dailey: Well, it's interesting, we saw, what was it, just a week ago, roughly, Anil Bushri came back as one of the founders of Workday to retake the CEO seat.
Noel Goggin: And he's a product guy.
Andrew Dailey: And he's a product guy. You mentioned the need to create capacity. For a lot of the existing businesses. They don't have a lot of spare capacity, and all of the investment, attention, and more importantly, capital. is going into AI-native companies. Talk to us more about how organizations can build or create capacity, because we've got to find the investment dollars somewhere.
Noel Goggin: Yeah. So I'll give you an example. So, if I look at—from my experience, there was a lot of toolings that came about for go-to-market, right? AI, sophisticated, data science-driven intent-based sensing, etc, but on marketing ops and sales ops side of things. And what ends up happening with a lot of those tools and systems is they're half implemented, they're less than half used and they create more confusion about what goes on. So whether it's the Six Senses, the Larry's, the User Gems, the HE Insights of the world, the Salesforces of the world, the OpenReaches, Saleslofts, I mean, there's a whole massive grouping of go-to-market tools that kind of came up in the last number of years that were all interesting, but they all started to converge on each other in many ways. So one of the things that we did was we simplified our stack dramatically. We took a lot of tooling out. But we had a lot more work at getting people to use them. and integrating them into the workflows that we're actually having. And we used, at that time, we used, kind of, OpenAI as our framework to kind of start to kind of build our own use cases, some of which were our user interface, some of which were plugged into Salesforce or Service Cloud or whatever. But by just simplifying our processes, getting consistent in our processes, and getting a lot more rigorous about the implementation of them, and the use of them. Just gave us back capacity, where we were just burning a lot of calories and a bunch of things. If you look at the area of FP&A data analytics, data models, BI, analytics, etc. I mean, what you can do today on that compared to a year ago is dramatically different. You don't need these big groups of people doing all this reporting stuff. So you can start creating a much better insight into your business, again, freeing up capacity or taking your current headcount and having them do more valuable things in the business as well. The same in support. There's a bunch of things that you can do in there. The same in services implementation, the same in preparation of QBRs for customers, right? QBRs for customers are manual. PowerPoint-based decks, teams were not super coordinated on it. You can generate really nice QBRs, professional QBRs right now that tells a much more thoughtful story, more relevant story, that's more coordinated that your team can look better and show better as they're presenting to customers. So there's a lot of things you can create capacity in an organization and the same in engineering. It's dramatic in terms of what can be done around core development and testing as well.
Andrew Dailey: So, historically, when we've gone through these periods of technology and business dislocation. The one question that comes up is, “should organizations be building more, or looking to buy technology?” Where are they going to source the technology? Where's the innovation going to come from? Given the nature of generative AI and agentic AI, do you think we're going to shift more towards a period of internal build for organizations, so a consolidation of software spend and more internal spend building things? How do you see software budgets and spending changing?
Noel Goggin: From a customer perspective?
Andrew Dailey: Yeah.
Noel Goggin: I'll give an example in retail. I spent a big chunk of time in retail, and the retail were kind of deployers of package technology. IT departments were deployers, right? Then along came digital commerce. It kind of rattled everybody because it took off like crazy. Sometimes in the marketing department, sometimes in the Chief Digital Officer, kind of was outside the bounds, generally, initially, of the CIO. And because it was growing, it was generating revenue, and it created all this kind of change management conflicts within retailers. So then you saw retail start to adapt change from being kind of the CIO, where I'm implementing and maintaining third-party solutions with a bit of integration from third-party integrators, into a CTO role. They started going from a CIO to a CTO, where they were clearly going to have packaged software for sure, but they're also going to start to kind of build more of their own fabric and their own internal engineering capacity. And a lot of really good companies did, like Nike was famous for doing that. It was a bunch of companies that really doubled down on that as a domain, as an expertise. Some of them even built their own e-commerce stacks, because they couldn't get the responsiveness that they wanted from some of the third-party tools. I think you're also going to see a little bit of that's going to creep now into IT departments. I think IT departments are going to get retooled to be not only, kind of, implementers and maintainers of existing product, to being able to have rapid responses to being able to kind of build agents that kind of connect together disparate systems, disparate data sets, work around the limitations of older tech that they have, or older ERP systems that they have, etc. So I think that's going to happen. So I think agentic frameworks will get applied more broadly within the enterprise for a lot of customers as well because the cycle time is reduced dramatically. You can now start to produce a lot of stuff fast without having multiple layers, multiple savings of work, multiple coordinations across multiple parties, where there can be a lot more self-sufficiency to start showing some short-term value, while maybe core systems don't have to change immediately.
Andrew Dailey: Listening to a lot of what you're saying, Noah, what you're really saying is where we started at the top, which is “is the SaaS model is still relevant?” But everything that you're describing here is actually that cycle times are just going to get a whole lot faster.
Noel Goggin: Yeah.
Andrew Dailey: And it's maybe less a question of the business model. And more a question of which organizations are going to be able to adapt to a much faster clock speed.
Noel Goggin: Yeah, clock speed is a great term, actually. When I think about, kind of, the 3Ds I mentioned earlier on, they've got customers, they've got distribution, right? So, they've got data. that they understand, and at the end of the day, that data's generally unique to customers, particularly where it's mission critical and where it's vertically oriented. And they've got the main knowledge, so they understand the business processes, right? And there's a lot of complexity to a lot of these business processes inherently, and there's not that many people who understand them end-to-end. So that's a huge advantage for people if they have the courage and the bravery to kind of go on, we're going to kind of deprecate ourselves, we're going to kind of reinvent ourselves. And there's a couple of things for me that I would advocate. I think one is people need to do a hard shift in product and engineering for existing customers. They should move 25% of their people onto agentic application development, new application development. The existing stuff, there might be a bit of noise from customers in the short term, there might be a few obligations that need to be met, but in the main, customers are going to like more what you're going to build versus this incremental addition to the existing products, number one. Number two, the AI-first products, and I'm talking about new products that are built AI from the ground up that may still interact with your old products, but what is the bookings that's generated from those new products as a percent of your total bookings? Because if that's not becoming material faster, you're losing in my mind. Because again, you're in this incremental inches game versus yards game, and people have to make that pivot, in my mind.
Andrew Dailey: Interesting. I want to shift gears here a little bit. Software valuations are approaching, or at cyclical lows. There's a trillion dollars plus in private equity dry powder and strategic buyers are under increasing pressure to accelerate growth, as we discussed. So a lot of these are basically the necessary precursors for a very healthy and active M&A market. At Conga, you made a number of acquisitions. You were instrumental in the operational integration of Conga and Apttus. Prior to Conga, you were involved in roughly a dozen or more, both acquisitions and integration initiatives. First, put on your analyst hat. What advice would you give to technology buyers as it relates to vendor M&A?
Noel Goggin: It's a tricky one. I think people are going to be a bit more thoughtful for this year. I don't know they're going to see huge quantum of M&A this year. I think a couple of things have to settle out. I think things that are mission-critical application software that are vertically oriented are going to have a really good chance of doing good things. And, if I go back to the analogous model which I've been through, as you mentioned, scar tissue. I went through the on-premise to SaaS transition and there's still companies doing that in the middle of that transition, right? I mean, the S4HANA movement is that in spades, but SAP didn't kind of lose the game in that transition. In fact, they've kind of continued to gain momentum in that transition, because of mission-critical core data, core system record data. Now, the cycle will compress, it'll be a quicker transition just cycle around. But it doesn't negate the knowledge, the IP that exists in existing software companies. But they won't be able to wait forever, either. So I think people who don't activate will struggle. So, I'll be interested to see how many APOs are this year. I'll be interested to see how many real M&As that are done this. I think there's a bit of tentativeness in the market at the moment given there's this notion of the bubble, the AI bubble is about the burst, and the “SaaS-pocalypse.” For me, the highs are never as high as people make them out to be, and the lows are never as low as they make them out to be. So I think it'll come back to some happy medium and I think there'll be a lot of existing software companies that will emerge, that will have an opportunity to kind of advance faster and kind of reinvent themselves, recast themselves. And you'll need to have lots of startups that are going to kind of challenge them to go do that as well. And there's going to be a bunch of losers. There's going to be a bunch of people that will get unraveled, for sure.
Andrew Dailey: What happens to the late-stage privately held companies that are either late in their term as a business owned by a private equity owner, or late-stage venture-backed companies that don't have a super sexy AI story but have a fundamentally good business? Let's say they're Rule of 20, Rule of 30, Rule of 40 companies, but just don't have the added AI zest, let's say. What's going to happen to those companies?
Noel Goggin: I think for them to kind of get their valuation, to get valuations that they'll accept, their runway will extend naturally, I think. I don't think there'll be a lot of choices there. And then I think there’s a broader base of knowledge in the business this February than there was last February in terms of the power of the tools, the applicability of the tools. There's still uncertainty, for sure, but I think in order to transact for assets that are held that are not that relevant yet, people are going to have to have traction for those to be able to get the multiples that they need to get in terms of an exit. Now, some people may be under a lot of pressure to transact and kind of move on a little bit as well, in terms of getting some returns for LPs, but I think private equity people who have the patience and expertise will probably wait it out, I think, a little bit, would be my sense.
Andrew Dailey: In closing here, share a prediction with us. Where do you think the enterprise software business is going to be 24 to 36 months out? Looking past the “SaaS-pocalypse” and the AI bubble, where do you think this lands two to three years out?
Noel Goggin: I think for, again, transactional systems, mission-critical systems, I think you'll have one of two things. I think you'll have companies that will be able to regenerate and rethink and reimagine the current products and basically rebuild them in a much faster model with a much, much more, broader application of them, I think, number one. And then I think there's going to be a whole slew of new things that will be built on these datasets and connectors into other data that was always difficult before where I think people will start to build another set of applications that binds data together from multiple sources to create new types of applications that people could never get to before. They kind of might have imagined them or talked about them but whether those are built by IT shops, whether they're built by software companies, whether they're built by integrators, who knows? This consulting segment, I think, is going to be transformed dramatically 20, 22 months from now. I think you will start to see a real emergence of who the existing enterprise software companies are that are going to really survive through this period and thrive through this period. And then I think we're going to live in a world where we have to manage this proliferation of agents as well, which is also not easy to do. So we'll get this proliferation of agents that have to be managed, debugged, sustained, particularly whether interconnection of agents from multiple providers. And that'll be the reality of living in this kind of very decomposed world that we'll end up living in as well, which will create its own sets of challenges. I think there'll be things that will happen that will cause security to come back up to the fore in terms of data security in particular, as well, because I think there's a lack of maturity in a lot of organizations around data governance. And I think with all this experimentation phase that everybody's going through, I think there's going to be scenarios where people are going to have issues with data security. And if I looked back to my days of retail, look at all the hacks that happened in retail over at least my tenure, they were always the simplest of use cases. They were never that complex. And I think now, with data getting more open and more accessible to people, there'll be mistakes made, which will cause an increased focus on security again, which I don't know is as strong today as it was a couple years ago.
Andrew Dailey: Is there anything, in your view, that could derail the AI train?
Noel Goggin: So much of the growth in AI is on infrastructure and it's not yet in applications. And in order for the infrastructure to maintain over time, the applications have to be able to provide the value and be sustainable and commercially viable as well. I used to work in the telco industry back in when men were cavemen but during the whole fiber build-out area, there was such a focus on Level 3 and Qwest and all these guys to build, build, build, build, build, and huge capital investments, and the internet and et cetera is going to be just massive consumption of all this infrastructure. That never really fully transpired in terms of the valuation of those companies and they don't really exist today, those organizations, in reality. So, I think this kind of transition from the focus being on infrastructure for AI to being applications that are AI first, if that transition doesn't happen quick enough, I think, and there's enough commercial viability of the applications and the agents to sit on it, I think that'll create a bit of nervousness in a bunch of places, in terms of overbuilt capacity and underused infrastructure.
Andrew Dailey: Conversely, as a last question, do you think there's any opportunity where AI could fundamentally transform an industry, i.e., create an ability to do completely new things, change the cost structure of a business?
Noel Goggin: Absolutely. Healthcare is going to be massive in that area, as an example, in my mind. You're going to start seeing more and more physical AI now as well, in terms of robotics and the application of robotics, which are still early stages. You already look at what Claude, the latest version of Claude with the legal plugin has done for anybody who's in the legal segment, you know? And that's just here in front of us right now. So, you're going to have this kind of recasting of industry, rethinking of industries, a generation of a whole new set of companies as well that's going to come out of that. And then, so from existing non-tech-based companies, I think you're going to see a tremendous, and in many cases, for a good application of AI, the healthcare one, for sure, is going to a very good application of those models to do really good things.
Andrew Dailey: What's been unspoken in the AI conversation, but is so critical, is the reality that in the Western economies, given the demographics, we need to see some massive productivity gains to sustain economic growth.
Noel Goggin: Yeah, and every country's got an aging population at this stage as well. Yeah. And if you look at healthcare, we've got a growing healthcare cost as well that's going to, at some point, have to be dealt with, you know?
Andrew Dailey: Certainly, in the U.S, it's not sustainable to have 20% of the GDP being spent on healthcare.
Noel Goggin: Yeah.
Andrew Dailey: Yeah. Noel, this has been really fun. Thanks for your time.
Noel Goggin: Yeah, appreciate it, man.