In this episode of The Next Next, host Jason Jacobs interviews Mike Redbord, an expert in customer success with significant experience at HubSpot. They discuss the changing nature of the customer success function in the context of AI's growing influence. The conversation covers Mike's journey in customer success, the operational dynamics within organizations, and the role of AI in reshaping customer support and engagement. They also explore the future of SaaS and the potential for AI to create highly customized software solutions. Mike emphasizes the importance of hands-on experimentation with AI tools and provides insights into his work with Agent AI, a platform designed to democratize access to AI agents for professional use.
Navigating the Intersection of Customer Success and AI with Mike Redbord In this episode of The Next Next, host Jason Jacobs interviews Mike Redbord, a veteran in customer success and former VP at HubSpot. Mike shares his journey from being an inbound marketing consultant to holding various leadership roles in customer success. The conversation delves into how AI is transforming the customer success function and the broader SaaS landscape. They discuss the challenges and opportunities for CS teams in adopting AI tools and how these technologies can improve efficiency. Mike also highlights his work with Agent AI, a platform aimed at democratizing access to AI tools for professionals. The discussion provides valuable insights for leaders and practitioners in the CS space on how to navigate the evolving technological landscape and leverage AI for growth and efficiency.
00:00 Introduction to Today's Guest: Mike Redbord
01:19 Exploring the Customer Success Function
04:16 The Evolution of Customer Success Roles
06:37 Operational Dynamics in Customer Success
11:31 Challenges and Best Practices in Customer Success
21:45 The Future of SaaS and AI's Impact
26:04 Navigating AI Disruptions in Customer Success
27:22 The Push for AI Adoption in CS Teams
30:36 Leveraging AI for Operational Efficiency
33:09 The Role of Rev Ops in AI Integration
36:52 Personal Insights and Future Directions
39:49 Introduction to Agent AI
44:47 The Future of AI and Professional Growth
47:53 Closing Thoughts and Reflections
Jason Jacobs: Today. On The Next Next, our guest is Mike Redbord. I got to Mike through Jeremy Crane, and if you recall, I did an episode recently with Jeremy. Uh, who's a longtime head of product, uh, talking about the product management function and how it's evolving in a world of ai. I then did an episode more recently with Ben Blumen Rose, uh, who's a longtime designer, uh, who is now a managing partner of a venture firm called Designer Partners that backs, uh.
Designers and, uh, people that are leaning hard into design to build technology products, uh, about how AI is changing the design landscape. And so I thought that it was only fitting to have a similar discussion with Mike, who is longtime customer success. He was early at HubSpot, where he came in as an inbound marketing consultant in 2010.
So it was super early and uh, when he left, he had held roles like. VP of Global Customer Support and Technical Services, vice President of Customer Success, general Manager of the [00:01:00] Service Hub. Uh, and now he's doing a couple things. He's consulting with scaling startups around customer success, and he's also helping out at Agent ai, which is an agent AI platform and marketplace where he helps builders build AI stuff.
So we had a great discussion about the customer success function, which I had historically not known that much about, you know, where it sits in organizations, what types of people it attracts, what types of work they do, what types of tooling they have, and, and how AI is impacting that world. And then we also talked directionally about how that function will evolve over time, how SaaS in general will evolve over time and, uh, what's happening in the agentic AI world and, and how those are starting to intersect and what the future might hold. This is a great one and I hope you enjoy it. But before we get started....
i'm Jason Jacobs, and this is The [00:02:00] Next Next. It's not really a show, it's more of a learning journey to explore how founders can build ambitious companies while being present for family and not compromising flexibility and control, and also how emerging AI tools can assist with that. Each week we bring on guests who are at the tip of the spear on redefining how ambitious companies get built, and selfishly the goal is for this to help me better understand how to do that myself.
While bringing all of you along for the ride, not sure where this is gonna go, but it's gonna be fun.
Okay, Mike Redbord, welcome to the show.
Mike Redbord: Thanks so much, Jason. Happy to be here.
Jason Jacobs: Happy to have you. So I got to you through Jeremy Crane. And it's funny, I did an episode with Jeremy about product management as a function and how AI might evolve it. And then I did one that actually just published. [00:03:00] Yesterday at time of recording and that is with Ben Blumen Rose to have basically have the same discussion about design.
He was a long time designer and now he runs designer fund in, backing design founders. And he's been digging a bunch into these tools and, and I haven't done any with customer success and to be honest, nor do I know a ton about the customer success function. And you're a steeped customer success guy who now is is spending a bunch of time I.
Working with ai at least with one of your hats on. But I it was clear from the talk we had off camera that that we have a lot more to talk about on camera. So I'm grateful for you to come on and chat about it and teach me.
Mike Redbord: I'm thrilled to be here. I think we try to apply AI to every possible sphere of business or personal life or whatever, like it starts to become clear that some of those nooks and crannies are just like less well understood, like in general, like on planet Earth than others. Right?
And people have written a lot of books about sales and like even design and stuff like that. And like CS is it's like the. Dirty [00:04:00] basement that like nobody ever goes into and we know there's like stuff going on in there and it's important, but like people don't hang out in there.
And I think, there's a lot of like interest in what AI and Cs are gonna look like together, but also a lot of question marks. 'cause it's a weirdly understood field, right? It's fun.
Jason Jacobs: Maybe that's a good starting point. Maybe talk about you, how your career and how you've come up in, in cs. And also just how you know, if someone asked you what is cs? How would you answer them?
Mike Redbord: I'll start with the first question 'cause it's an easier one. So for me, I think I found my way into CS land like a lot of people do, which is that you, you fall backwards into it and you often fall backwards into it by like doing a little support, doing a little operations, doing some like post-sale stuff. 'cause you're not wired like a seller. You're operational, process driven, and you're not really wired like an engineer. You're not really gonna build like tools for yourself. So you're like a process creature who likes talking to people and helping folks out. [00:05:00] And I was at a company called HubSpot for 10 years.
I started off working with customers. I ran support and then I ran customer success 'cause that made sense at the time. I found myself managing half a billion in revenue and, doing a whole bunch of awesome stuff. It was just an absolutely amazing ride. But like when I was 20, did I ever think.
I'd be like a customer success person or a customer person. Heck no. That wasn't even like an option at career day. It's just something you end up doing. And when I talk to CSMs or CS Ops people or CS leaders, most people have the same story, right? Very few people had a deliberate like career thrust where they wanted to be in that seat. And so it's got a lot of different of people with a lot
Jason Jacobs: It sounds a lot like recruiting. Sounds a lot like recruiting, by the way, Mike, no one grows up saying, I'm gonna wanna be a recruiter when I grow up. But then that, that's where I started my career, so
Mike Redbord: it's a great
Jason Jacobs: yeah.
Mike Redbord: do think it's similar. And you've got a real kind of diaspora or very diverse, set of people in those seats, and I think they bring different sort of mindsets and, and tools, right? As far as what CS is, it's such [00:06:00] a different answer. Operationally speaking at different types of companies at different stages, it's hard to draw a clean circle around it. The cleanest way I could express it is it's like everything that needs to happen post-sale in order to retain and grow revenue. So to me it's a revenue function and it's got two, those two verbs in there, that, that can involve a lot of different things, right? That can involve some, professional service elements that can involve some intense account management. It can involve more of a product led motion for, companies that are like human and have lower ACVs. So super diverse kind of space.
Jason Jacobs: And you've seen a lot of these organizations over the years, so I'm curious just do there tend to be parallels across, do there tend to be differences in each and are those parallels? 10% or 90%. And then what kinds of differences do you see, and I have a lot of other questions too, but I'm gonna, I'm gonna start with that one.
Mike Redbord: Yeah, this is even a good one when we [00:07:00] start talking about like how AI can help CS land because you have to understand the shape of the thing before you can, take it apart and put it back together with ai. So I think, look. Structurally, there's a lot of similarities from one Cs team to another. Operationally, what happens within those structures is often quite varied from company to company or even customer to customer. So like structurally speaking, you gotta do a few things with your customers, right? You gotta get 'em set up quick and get fast. Time to value. You have to hit like a good stride in terms of usage. You gotta figure out how to keep that usage going. You gotta retain them, you gotta expand them, and you have to help them along the way when they have questions or need help. But each one of those actions, take onboarding. Sometimes you wanna fly out to a customer with your whole team and get a bunch of desks in their office, on their exec floor and do workshops for weeks to get things set up right.
That's a very enterprise emotion. Sometimes you wanna not talk to your customers at all and just let the product do stuff. So the way that you execute any one of those kind of pieces of the post-sale process can be [00:08:00] super, super different from company to company. And so when people talk about Cs, they often are talking about a lifecycle that has, humans, people like me in the loop rather than is totally, product led. But it can really encapsulate like a bunch of those different approaches.
Jason Jacobs: Where does it typically sit? In an organization, 'cause it, I'm through one lens, it feels closer to the sales organization, but one could argue it, it would be closer to support as, as well. Do, where does it typically live? And is it, is that also something that you see a wide range of perspectives and structures for?
Mike Redbord: Yeah, that's a. a wide range. And honestly a somewhat thorny question for a lot of people that are actually reading CS teams. 'cause they have a lot of opinions about where they ought to sit and who their peers like ought to be. Often I think it gets grouped in with support, or maybe better to say CS is often born out of support.
So if you look at early stage B2B companies, you often have a support style [00:09:00] leader who's like doing some CS stuff. But then as the company grows and the function becomes more important because the revenue grows and the retention of that revenue matters more to the, just to the top line then. CS gets spun out on its own. It'll sometimes go to sales in a world where there's a lot of selling to do to your customers. If you're an enterprisey business and you have lots and lots of different things you could sell for lots of money, then CS often resembles a sort of, more sales driven function.
But if you're more of a kind of product led, single skew kind of thing, then CS often looks more like a support function because there's less selling, there's less opportunity management, there's less sales craft to the way it comes together.
Jason Jacobs: When you look at things like selling models, so for example, is it bottoms up or tops down? When you look at things like, who do you sell to? Is it. Is it SMBs, is it big enterprises or is it certain verticals? When you look at stage of company for each of those questions I'm curious so maybe for that first set, [00:10:00] so take away stage for a minute for that first set, are there some types that are more conducive to this function and others where it makes less sense?
Does the, should the structure vary widely depending on the. The selling model And I also ask, because the selling models are also probably gonna be changing with AI as well. And so I would imagine that's gonna have trickle down effects to every function, including cs. Yeah.
Mike Redbord: on that last bit, I'm super excited to see what the bullet effect of changes in go to market. Have on changes in CS and on customers too and just on, on human behaviors? Both. I think you often find cs, organizations sprout up like after engineering other go-to-market functions and support.
It's often kind of one of the waiter links in the chain when you need to professionalize and basically execute better on revenue from your customer base. And so you see a lot of companies are having really good go-to market success actually under optimize in cs, especially early on because they're like, their [00:11:00] revenue's doing really good.
So why, why go spend the effort to make a great CS team? Conversely, in the enterprise, if you're selling really up market, you need to have really strong post-sale experience really early on because you're trying to show up and feel bigger than you are, right? You're trying to compete with the incumbents, and you need to provide really high level of service. So different folks just sprout out different places, and there's almost always like a triggering event that's like a board level problem, right? Where a big customer left or, oh shit, we didn't onboard these guys. Something like that is often a catalyst to making it happen.
Jason Jacobs: Huh. Are there are there certain trends in terms of the profile of the CEO that makes an organization more conducive to doing Cs?
Mike Redbord: So this is a hot take for me, but I find that technical CEOs, so CEOs who are, product or coders themselves have. much slower time hiring like quote unquote real CSMs. As opposed to more technical [00:12:00] ones. I think, it's the, when you have a hammer, things look like a nail kind of situation.
And then if you're really superb at developing software to solve problems, your initial instinct is, let's make software to do this. Frank, lemme be clear, this is not a bad thing. It's just an order of operations difference where if you have a sales driven CEO, which I think is, probably. Like the majority in a lot of cases then, they tend to hire people who work with people first to solve that same type of problem.
Jason Jacobs: And. When we had our initial discussion, you were telling me something I thought was interesting, which is that you're wearing two hats now. You're consulting on CS with companies and then you're doing some work with agent AI focused more on AI agents and the AI ecosystem, and that there's not a lot of crossover between those two pursuits.
So it'd be great to understand, what you're doing with each. And also I think I was surprised to hear that there wasn't more crossover so it'd be fun to talk about why you [00:13:00] think that is as well.
Mike Redbord: Yeah, I wish there was more crossover like, like I wish in general that CS land was faster to adopt new technologies and experiment and try stuff. But they're not, and probably the reasons for it are multifold. Like one is that like experimentation takes energy. And CS teams are often, staffed just as thin as possible to maintain revenue.
And they don't have a lot of fat in the system to go try new stuff, right? They can't really, endure a hard winter if you will to go try new technologies. That's probably number one. And number two is that there, there are not a lot of like very technical people in most. Most CS organizations. You do have sometimes, like technical folks in onboarding or sales engineering, which might be adjacent parts of a CS team. But in like pure CSM, that's really more of a commercial function or a, conversational relationship function.
And so you don't have people that are like on the vanguard of new technology. And these are in the adoption curve they're probably not [00:14:00] even the early adopters. They're over the hump. in terms of, late majority who are gonna adopt stuff. So they're not the people that are playing around with frontier models and hanging out and trying to sort out what new thing happened.
This week they're gonna be second movers on it. And they just don't have the time 'cause they're too busy dealing with today's fire too. Bigger organizations do a better job. I.
Jason Jacobs: And we'll get to the agent AI stuff, but on the CS side. So what are some examples of the types of companies that you work with and the types of engagements that you have been taking on?
Mike Redbord: Yeah it's pretty varied in, in, in a lot of ways. And for me it's largely about the people at this stage of my career almost in, in everything I do. And if I find people that have a certain magnetism, I'm like. I'm attracted to work with them almost independent of what the work is if there's, work that we can do together. Sometimes it's, it's PE-backed companies or companies that operate like PE-backed companies that are looking to, identify how to improve operations and lean things out and, [00:15:00] get more efficient while either improving results or whatever. And that can involve a lot of analysis and tuning. and other times it's, more of a VC backed, like fast growth, rocket ship type thing. And that's more about holding on and, developing minimum viable processes to just execute and get through the month or the quarter and hit your numbers along the way. The latter is arguably more fun, but I think they're both really interesting problem spaces that just represent different kind of stages of the journey.
Jason Jacobs: And f from what you've been able to piece together from your professional experiences and now from the consulting, are there best practices that you recommend when introducing these functions? And conversely, are there common mistakes that you see that newcomers who might be listening should watch out for as they're thinking about.
When and how and and the details on standing up their CS functions.
Mike Redbord: Yeah it's a good question and it's interesting to me, as you were asking and just thinking, [00:16:00] my answer to this actually hasn't changed much over the last 15 years, which is how long I've been in cs. Think that people need to really pay attention to the growth levers in their post-sale kind of, machine. And sometimes that's onboarding. And maybe your onboarding is performing poorly and your customers aren't getting set up and you should go attack that. That's the biggest lever. Sometimes it's renewal, right? And maybe that's a place where you're losing a lot of customers 'cause your contracts are, too adversarial and you're bad at negotiations or whatever. But there's a lot of folks I think that's set out to, fix problems that don't necessarily need fixing in an urgent way. And it's really critical that you find what the most urgent problem is in the customer journey and then attack that, and then one by one, solve it in a right sized manner. When you go to build or fix onboarding for the first time, you don't need to build the same onboarding program you're gonna have 10 years from now, or five years or after your next raise, right? You need to rightsize the solution to the problem. So starting with a big problem and then solving it in a sort of, in the [00:17:00] right size way honestly a lot of the game, and that really hasn't changed in the last decade.
Jason Jacobs: And is there a process or methodology or tips or how does one, or how should one go about finding that most urgent problem and determining. What to put their wood behind and what maybe to either either might be a false flag or something that, matters too. But should be not the most urgent priority.
How do you know?
Mike Redbord: I like the best I don't know, radar or like
Jason Jacobs: I.
Mike Redbord: system for this stuff to me is a cohort heat map chart of your revenue. these things you've probably seen 'em hanging around. They're triangular shaped and they have the monthly, or sometimes weekly or quarterly, but typically monthly cohort down on the vertical. And then what you're doing is on the horizontal, you're tracking the revenue retention of each cohort over time. So if that cohort started with whatever, a thousand dollars of monthly recurring revenue, then in the first month, how much of that [00:18:00] did you retain in the second month? Did it go up or down? That heat map will basically tell you where the. in the customer lifecycle are, and also if they still persist. A year ago maybe you had really a rough time onboarding customers 'cause your product just wasn't there. Now let's look at what happened last quarter in the last three months.
Is that still a problem? If that's been a consistent kind of. Hotspot and like that chart is bright red in the first couple months. Then that to me is how, to attack that. it's really by going by like numbers like that, that you can, I think, apply some rigor to this stuff like we were talking about before, finding the right problem and applying the right level of effort and the right solution rather than trying to solve the entire journey all at once.
And that discipline to approach one problem space like at a time. I think comes largely from experience and recognizing that you just can't do it all at once in a resource constrained, time constrained universe.
Jason Jacobs: It is funny. Earlier in this discussion I was asking you whether CS [00:19:00] sits closer to sales or to support, but it seems like there's a couple other functions I'm curious about and that's finance and sales operations. Does it straddle all four of those.
Mike Redbord: Often. Yeah that's why some companies hire a chief customer officer and put it, reporting into the, to the CEO. 'Cause it's unclear where the space can live. I will say with finance, good CS teams are good friends of finance and finance are good friends of CS teams too, because you're solving the same number and you really need to interrogate your data with new lenses and sometimes creative lenses in order to figure out where to. What's CS program to run and like finance is not only the holder of that data, but they're also the measuring stick at the end of the day if it worked. So the better a CS leader and a finance leader get along, I think the better the results tend to be.
Jason Jacobs: What do the relationships tend? And I know every case is different, but but if there were a typical between sales and cs, are they best friends or is there. Do they frustrate each other?
Mike Redbord: Yeah, I [00:20:00] think healthy tension is good in the same way you want a healthy tension between your support team and your engineering team, right? So to extend that analogy, sales or engineering should really be pushing the envelope in some ways engineering should be off making the new thing, not just fixing, the entire, like backlog of bugs. Sales should be off trying to expand your addressable market, not just doing the same thing every day for most companies, right? And so that. Innate desire of sales. Let's talk about them to expand and do things differently is going to have downstream effects on cs. Just like in engineering, if you're always developing new stuff that has downstream effects and support. So sales and CS is to me, good fences make good neighbors. You have to have strong agreement between the leaders about what that operating philosophy, not cadence, not like the details, but philosophy looks like the CS leader needs to understand their place in the org, which is to like, make it work as the org is gonna shift and change and the sales leader needs to understand their place, which is to be, to push the envelope, but not the ridiculous shit that is unsustainable and really [00:21:00] gonna screw up the machine by just throwing like a grenade kind of down the river. So it should be good fences make good neighbors, which is another term for a little bit of tension is a healthy thing. And I think when you have some tension, not too much, but some, then it's a sign that the company is like, like the cylinders are firing in the right way. If there's no tension and they're too buddy. I. I actually view that as a yellow, pink type of flag, where it's you guys should be pushing each other to do better and do different. You shouldn't just be like all hunky dory and happy all the time. There should be some amount of conflict and friction in here because sales and CS should be pulling away from each other at all times.
Jason Jacobs: It, if you look. On Twitter or XI guess it's called, or you look in in the trade rags if there are any at this point. But it, there are these big, bold proclamations about the, the future of SaaS and how the models under thread and how all software is gonna be custom and personalized because AI is making it, cheap on a path to [00:22:00] free to to build.
H how do you think about what the future holds for software? How much of that, how far down, down the path are we at this point in time? Snapshot towards that. And what do you think the implications will be directionally on on CS.
Mike Redbord: Yeah, so I, I think you know the future here is not. As bright as the past was for SaaS. I won't go as far as say SaaS is dead. 'cause it's not dead. Like it's obviously alive and well look at the earnings, look at the multiples. It's doing well. But like I think you're nuts if you start a pure SaaS business today. Like that would be like trying to, take venture capital, start a lemonade stand. It's just, it's an old model that isn't going to perform the way that it did over the last, decade or two. For existing SaaS companies, especially big ones, I think the prize is there is to lose, and we've seen a lot of them do, I think a pretty.
Good job at trying to modernize and, pushing AI in a, not just a narrative [00:23:00] way, but actually in an execution capacity to like, modernize and get features to customers and use it internally. So like I, I think when we look back on it, maybe I. I dunno, years from now or whatever, after the AI c freezes on the downturn, look back at SA SaaS businesses as something that there were a lot of them that were created in like the two thousands and 2010s, but as we got into the 2020s, fewer them were created.
But I think a lot of the old ones are gonna be able to make the turn. The ones that aren't are probably going to get snapped up and. Merged or just head out to the wood chipper sometimes. So I think if you're gonna end up with very much a tale of two eras, where you have the old ones that some of them make it, and then, newer companies that are just not created in a pure SaaS way, they're gonna be AI first, or, just not sell themselves like those old school SaaS companies do.
Jason Jacobs: And do you have a clear perspective? We, so it sounds like reflecting back that was the model and there were a lot of them and now it's starting to be a dated model and the big ones [00:24:00] should be able to turn the corner, but you wouldn't start a new one in that regard. Do you have a clear perspective on where we're heading and what the next wave will look like and what type of model?
M might be the new and fresh one that gives SAS a run for its money.
Mike Redbord: I think there's a lot to perspective that, size fits one software is back. At the beginning of any large technology cycle. If you look back to Web 2.0 Molo code or mobile local, all that stuff. Or you look back even further to, that's like Web 1.0. There was a moment in time at the beginning of those technological curves where it was a great time to be a consultant to build one size fits one stuff, to not try to build very large horizontal PR platforms, but build reasonably deep vertical ones.
And so I think we're at that time in ai. I think that if you want something that today is a good fit and could turn into whatever the future ends up being, it's a good time to make one size fits, one tools, learn this [00:25:00] toolkit, be on the vanguard of what is like out there. I. Then, as things settle down in the next, I don't know, year or two, it won't take five, just, it'll be faster than the last cycle.
I think it'll become clearer what the like emergent model is. That's a half answer to your question. That's what I think folks should do today. And then, tomorrow it'll become clearer how to adapt that one size fits one software shop to something that's a little more scalable.
Jason Jacobs: Given the uncertainty at the highest level about what types of companies will thrive and what types of companies will be under threat and what types of business models will standardize around and things like that. Then as a as a CS practitioner, is it just treading water and not trying to.
Make any commitments that could potentially make a mess directionally, or, how does one carry themselves, or how should one carry themselves? Given that level of macro uncertainty,
Mike Redbord: Yeah. Interestingly, these CS teams that you know I mentioned before are [00:26:00] not particularly
Jason Jacobs: i.
Mike Redbord: Often tech-minded. They're actually gonna be some of the first ones. They're a bit of the canary in the coal mine in terms of seeing when the cancellations happen due to ai or due to, new incumbents.
It's a weird time to be a CS person because you're trying to create steadiness in the business and one of the choppier macro environments, to your point. So the thing to look out for then is like, where is the chop coming from and what is new in the environment for you? Is it that your product, you rewrote your product to maybe do some AI stuff and it was buggy.
That's different than your customers leaving your product for, one size fits one or new entrant type solutions. And so CS folks have to, I think. Understand the various like applications of AI to their world and their world is broad, right? They have to understand those applications in order to pull apart like the, the opportunities and risks that are presenting themselves to them every day. Because if you're seeing like that ladder thing happen where you, your customers are getting pulled [00:27:00] away from you because of new entrants that are AI first, like that's a real issue and something that needs to be discussed in like a serious way. And I think that's probably starting to happen to some of the more incumbent SaaS players that they're losing business to competitors who are newer, cheaper and doing this stuff better and faster.
Jason Jacobs: And I just had a thought and it slipped my mind, it'll come back to me. Oh so you mentioned before that cs to some degree doesn't attract people that are tech forward and hasn't been leading as hard into ai, and you wish that they would lean harder into ai. There's, similar to that drumbeat about, SAS is dying, there's another drumbeat, which is everyone needs to be experimenting with ai.
Everyone needs to be, getting their hands dirty with the tools. Everyone needs to be wrestling with them. There's these big proclamations like Duolingo where we, where AI first, Shopify, where. AI first, right? Like Aaron Levy I think that's how you pronounce his [00:28:00] name. Maybe I'm wrong, but front the box.
CEO, all he talks about now is ai with his public persona. Do you think that is customer success a function? Where it's not as relevant. Or is the, are the ex existing DNA that staffs them? Will they be left behind? Or what's going on here? And how do you reconcile the mismatch between what it sounds like the urgency level is in cs with the urgency level that seems to be coming down from the top in more and more companies.
Mike Redbord: Yeah,
Jason Jacobs: Yeah.
Mike Redbord: On one hand if I see another like memo from a CEO or manifesto, maybe I should say on LinkedIn, I'll lose it. But on the other hand, like I, I agree with them. Like I do think that. Folks need to be pushed to change. Look at the way people are during change. You do need to create some exogenous order to get over like the hump, right? like I do think the CS teams are gonna need to push. Now the question is where will the push or maybe better, I. optimistically put, where will the help come [00:29:00] from? Like I, I think when you see those CEO memos, like they don't come with a lot of assistance. They come with an edict and they gaslight your employees into being like, Hey, you should listen to this podcast and do this stuff and all these things.
Okay, fine. And so where will the help come from? And historically, CS teams get help in, in sort of bits and bobs from Rev ops or CS ops. And those folks I would actually put a bet on. Because those are the people that can speak Cs but also can speak tech, and they're the tinkerers, like they're the people that are out there experimenting with stuff and they're they're like tinkerers with strong intent too.
They're not just playing like they have a job to do and their job is to, I. Know, help improve those same metrics CS teams are after. So I would put a bet on the rev ops folks to lead the way. The challenge there is any tools that are secure and will pass compliance and blah, blah, blah. At a lot of companies the companies that maybe are a little faster and looser might experience a near, near term advantage over the ones that require, 50 step compliance checks. there's no version of SOC two for ai [00:30:00] really. And I would put a bet on them to help CS out. I think in some CS cultures or maybe company cultures, there are innate advantages around experimentation or, if they bake in 20% time, if anybody does stuff like that anymore economically, like those are healthy things. That can give a little bit of breathing room to CS people who with the right push, they're creative generalists like they might. Go ahead and figure it out. I think they're individually going to be less likely in a probabilistic sense than rev ops folks. But I think and investment in rev ops are the places where if you've done those things historically, you have a better shot at winning in the future.
Jason Jacobs: Uhhuh And from your seat how much have you been playing with the tools and then are there clear areas where you think they could help where you're sincerely excited or do you just feel like it's the brave unknown and I'd be irresponsible if I didn't try to figure it out, but there's nothing obvious to me.
Mike Redbord: A, a year ago I felt exactly like the last part of your question there where I was like, man, if I'm not like, swimming in these waters like every day, like I, I'm just getting left behind on the [00:31:00] current and that, that's why I just jumped in and started doing stuff. I think for CS teams, like This technology ought to help them. And if you look at some of the core capabilities that AI, or I'm just talking about, gen AI in particular has demonstrated itself to be good at, it's good at summarizing, it's good at like reading lots and lots of stuff and extracting insights. It's good at going back through unstructured data and coming up with like next actions. And that's a lot of where like admin time is spent in CS land. A CSM is gonna spend a lot of their time, hopefully during the day on calls. They're talking, they're generating, reams and reams of transcripts and after the call, they gotta get off the phone and say, these are the next steps, these are the actions.
And so to me, there's a very reasonable, match between gen AI and, meeting summarization, next steps, blah, blah, blah, blah, blah. I think that those capabilities need to pierce deeper into the CS kind of tool stack, like into the CRM or the customer success platform and help. Take work off of CSMs, [00:32:00] right? And do the work for them. And that's a bold effort on the part of either the Rev ops team who's building the CRM or the CRM company or the CSP company. But I think that represents a big opportunity in, in, in CS land. Similarly, there's a lot of like operational work that happens in order to help prioritize CS team time. So one example is like you gotta take all your contracts and digitize them into a way where you can talk to your most highest price customer first and your next highest price second, and all that. As it turns out, like that's a pretty good use case for AI too, and digitizing contracts has been around for a long time, but it's getting cheaper and better and more effective. Similarly with health scoring. Like people spend a lot of time trying to rank their accounts by health, oh, which one's healthy? Which one's less healthy? All that, and AI, in theory, at least, haven't seen it in practice, but there's some startups out there doing it and I'm bullish on them. In theory can take all of that unstructured data along with the structured data pattern match.
Do the old school. Know, clustering stuff, but also apply the unstructured data in a novel way to figure out like, all right, this is the [00:33:00] account that you have one hour today. This is the number one account that you ought to call first in order to improve whatever KPI you're going after. So I'm pretty long on it, obviously.
It's just I also think it's a long journey before adoption and kind of execution is really going to match the nuance of what CS teams do every day.
Jason Jacobs: If there's a. Wide range of CS teams out there of, different sizes and stages and industries and, top down and bottom up and, oh, whatever, however you want to cut the, sort the, or categorize the CS teams. There's different levels of urgency. Like one message if saw the head of that team or the CEO would be hey I know it's not obvious, but it's coming and you should, do your flossing. It's just take your medicine and, a little bit a day and just, and there's another message which is yo, you need to go from not rotated to going the other way, or you're gonna get left behind because the time is now and do not look back and however bold you think you're gonna be is not bold enough.
What, which message do you come down on? And or does it [00:34:00] depend? And if it depends, what does it depend on?
Mike Redbord: Do I have to choose one of the extremes?
Jason Jacobs: No.
Mike Redbord: Okay. But then I would take a, I'll take a slightly different route, right? Which is that I think the. The sort of strategic approach of CS probably is not going to change. Your job is to retain and grow revenue. Now, the way your pricing operates might change with ai, like there might be a lots of change in there, but fundamentally retain and grow revenue.
Okay. Then like your toolkit is being able to observe that revenue and identify the biggest problems. Is it an onboarding? Is it in renewal? Where in the customer journey For what segment of customers is it? The place where I would say folks are not being bold enough is solving whatever that thing is, whatever the leverage point is, solving that in an AI first way where AI can give you leverage. So it's not always the case that AI is going to give you leverage and faster or better solutions to any problem, but like it should, you should at least be asking the question like, is this a good problem space for ai? And pursuing that until you've exhausted the answer. [00:35:00] Only then building it in like the old way. I think what a lot of companies are doing is still practicing good CS fundamentals. They're finding the problem in the lifecycle, and then they're applying a solution that's worked for 15 years. I just don't think that's good enough. I think you really need to reexamine the nuts and bolts of the process and retool it with ai. And at the least what that's going to do cause you to execute faster. At the very least, just by using some gen AI in your solutions, it's going to cause you to be able to ship what you would've taken a month to change process wise in a week. And I see companies that are really AI first doing this all the time and their pace of innovation and pace of kind of scaffolding and building a CS is radically faster.
I. If you're just applying like the old playbook and not interrogating, you know the question of what can AI do for you here, then I think you're gonna fall behind in a pretty short order to companies that are gonna be able to build like a heck of a lot faster in an AI first mindset, AI first way.
Jason Jacobs: I, in your experience, the, the companies that are really leaning into [00:36:00] AI from the top, does it tend to correlate in terms of how much they're leaning into it as a company and how much they're leaning to it in cs? Or is CS always gonna be a laggard relative to the, the the front lines that might come from other places in the organization?
Mike Redbord: It's both. It does correlate and CS is gonna be a lagger compared to peer
It's just the revenue leverage in CS is significant, but it's hard. It's a slower game. It's a long game, not a short game. Whereas in, in sales, I think you can test things faster and you can get that leverage faster.
So sales is typically gonna move faster on this stuff than Cs 'cause it's just just wired better for it. I think the companies that are pushing really. Hard are basically doing the right thing, like I said before, but like you need to understand, that different functions just have different capabilities and constraints and that's gonna define their ability to, jump and how high.
Jason Jacobs: Uhhuh and in our initial discussion, I think you said to me, and correct me if I got it wrong, that while you believe [00:37:00] that helping these CS organizations lean harder into AI and get native faster is a worthy pursuit, that's not where you derive energy personally. Did I get that right? I.
Mike Redbord: Yeah. The, what we've been talking about so far in this pod is, what we think is true in the world, right? This is more about what what drives Mike and what makes me tick. But yeah, like absolutely the places where I really derive a lot of, enjoyment out of, like my work on the CS side come from working with. People who I think, are just brilliant. And the further along I get in my career, that matters increasingly more so a lot of the discussion matter that we have when I am advising or coaching in CS capacity is about ai. And there's AI stuff in there. But I. My selection process for whom to work with is almost entirely driven by the personalities, right? And so I wanna work with people that give a shit. I wanna work with people that, I'm feeling lucky to have intersected with their trajectory and regardless in a way of how helpful I am. Five, 10 years from now they're gonna be doing something amazing, right? And I just am happy to be the sidecar on their [00:38:00] motorcycle for for a couple years here in 20 25, 20 26.
Jason Jacobs: Yeah, it almost reminds me one of the things that we talked about in the Ben Blumen rose discussion was, hi. His point was, people talk about the one person unicorn in a really aspirational way, and he's do they actually realize that if you get there, like your day to day will suck?
Like it's just gonna be you, like you're not gonna have anyone to share it with. You're not gonna have any sparring partners. Is that actually a world that. You want, like why are we lionizing this? And I thought it was a really interesting point that it's yeah, dollars and cents and that makes sense.
But at the end of the day work is work and it's a livelihood, but it's also another life experience and you spend a high percentage of your time doing the work. And so you want to create an environment that is actually enjoyable for you. And that I get that means different things to different people, but I think maybe a lot of the people that are aspiring to build these one person unicorns.
Don't realize maybe how lonely they'll feel if they ever tried to do it that way.
Mike Redbord: I [00:39:00] think there's a lot of wisdom in that. Yeah, one person unicorns, I don't know. That's a cool future, but to your point, I don't know if that's what I would want for myself. I'd rather have some friends along for the ride and yeah, like my take on that would be we as a founder, but I think you're gonna enjoy the journey like a hell of a lot more. There's some other people in the car with you, and building company is just hard, right? And doing it alone is really that's expert mode. That's like super hard mode. And so I think, if you can find folks that you derive energy from and that make the journey more enjoyable, like you gotta do whatever you can to hold onto that. 'cause that's where you're gonna remember, from now. In any event, it's hard to bet on whether or not you're gonna actually turn up to that unicorn, but you can for sure bet on. Did you have fun today and are the people you're working with good people?
Jason Jacobs: So switching gears for a moment I'd love to learn more about. Agent ai since I know that's a project that you've been quite involved with maybe talk about what it is, how it came about and and where you [00:40:00] are on the journey with that.
Mike Redbord: For sure. So Agent AI is a marketplace for AI
Jason Jacobs: I.
Mike Redbord: It's a place to, find, discover, and build to your to AI agents. We've got, I don't know, about 1500 agents on the marketplace. A million and a half users tens of thousands of people building stuff all around the world. And the best part about it is it's all totally free. The belief structure here is that a lot of these tools, the tools being frontier models or some of the other plumbing you need to make these these LLMs into more agentic forms of the tech. A lot of these tools are, somewhat in walled gardens.
And if you work at a fancy company, with fancy people and fancy venture capital dollars, then you get access to them. And you see these companies, they're buying like whatever Perplexity Pro or quad for everybody. It's awesome. more concerned with the folks that are the rest of the 8 billion people on Earth, right?
And if you really believe that this AI stuff is gonna change humanity, regardless of your thoughts on a GI or whatever. But if you just believe, like fundamentally in the same way the internet changed things or the iPhone changed things or [00:41:00] whatever, or the computer changed things even before that, you believe AI is like that, then we have some. Duty to democratize access to these tools. And you shouldn't need to work at, a unicorn or a public tech company. Get access to 'em. So we have everything for free. Got all the models you could ever wanna go play with. You can mix and match 'em, you can. Pulling data from various places.
There's those programmatic hooks. And it's been a blast. It's a place where, we have a very strong community, I would argue the most positive and welcoming one in AI land, which has been a blast to just work with. And the team that we've built is, maybe it's maybe 20 folks, right?
It's a small team of delightful humans, all of whom have often some shared background. And that's caused it just to be a lot of fun on the day to day.
Jason Jacobs: What are some use cases for anyone listening that it's oh, that seems like the perfect kind of thing, that you'd go to open AI or Agent AI to build not open ai.
Mike Redbord: Yeah. So we tend to focus on more professional agents. There are like lifestyle agents, you can make images or one, there's one that [00:42:00] teaches you to play the ukulele in like great detail, which is a personal favorite, but really agent AI is about professional agents like fundamentally. And so if you think about, things you do at work that I. You don't like things that are repetitive that are process oriented, that are good fits for ai? Agent AI is a great place to find tools to take that work off your plate and buy you back some time in the day. So you can either live your life better as a human or do other work things that you would prefer to do.
Things like summarizing YouTube videos sending follow up emails, creating icebreakers at for a zoom meeting when with somebody you don't know, right? Like agents, AI has agents for all of that and a heck of a lot more. And if there's not an agent for something that you really want, then you can go build it yourself also for free. So we hope, and it's doing this today, is that it becomes a place where people see if there's something that matches their needs professionally, and then if there's not, they can roll their own and build something pretty quick in, an hour that, that saves them multiples of that over the course of their professional journey.
Jason Jacobs: And do you need to know how to code to use agent ai?
Mike Redbord: No. And that's like [00:43:00] on purpose, right? A lot of these AI agent building tools are very code first, and that's cool for one population. But we're focused on a different population. We're focused on people who, have like good computer skills, if you will, that can, build a filter in Gmail, and if you should, can build a filter in Gmail, you ought to be able to take advantage of this next wave of tech.
Jason Jacobs: Now is Agent AI a company?
Mike Redbord: Yes. Yeah.
Jason Jacobs: Is it a, is a for-profit business?
Mike Redbord: It is, but we don't make any money because everything is free. We are associated with HubSpot, and so we have a little bit of a longer runway to figure it out and bring these tools to the masses than most kind of standard startups.
Jason Jacobs: Huh. So HubSpot is the primary backer.
Mike Redbord: Yeah. Yeah. That's the right way to think about it. Yeah.
Jason Jacobs: Got it. And w why did it get going in the first place and what's in it for HubSpot to be involved?
Mike Redbord: Yeah, I mean if you believe in an agentic future where it humans have jobs, we also have like behind us, 50 or a thousand agents or whatever, then that causes you to believe that [00:44:00] the agent, gold Rush is one of the most important kind of professional toolkits that anybody can be involved in.
I think HubSpot's track record is fighting for the little guy, right? Small business, medium sized business, and certainly they're more at market nowadays, but they've done a great job of holding onto those core values and core. customers and if put those two together. Then what you end up with is a desire to create a place where small, medium-sized businesses, business owners, individual contributors at small businesses that don't have a rev ops team, have a place where they can go to create these agents at a cost that works for them, which is free, and also, they can share them with the world so that they can help one another.
And that community creates some resilience and opportunity from one creator to another. So I think that's what's in it for offs, Spott. It's the agent gold rush, if you will. An exciting place to be.
Jason Jacobs: And so as you look towards the future, Mike, do you do you think this portfolio approach is where you'll live personally for the next several years, or do you think you'll get back and and maybe get into one, small high growth operating company again, or how are you thinking [00:45:00] about the future for Mike Redbord?
Mike Redbord: Man, if I
Jason Jacobs: Yeah.
Mike Redbord: the world be an easier place for me? I think that it's a really interesting time, like we were talking about before, to have a bit of a survey approach while technology is changing so quickly, like I'm very thankful that some really cool companies have invited me in to work with them and I get to help them operate. I'm really thankful that with agents ai, I get to see across lots and lots of businesses and lots of use cases. So that approach personally is deliberate. At least in the short term. Like we said before, I think, on a year, maybe two timeframe, like things will be, I think, pretty different. And I'm very excited to see how that shakes out. I think once things do settle a bit and the emergent, whatever the emergent model is for the next wave starts to become a little bit more coalesced a little bit firmer. I think that's a cool time, personally speaking, to get back into the mix.
But right now, and it's, there's so much change. It's just a. It's a it's a treat to be able to watch this stuff happening and, be involved in making it happen. But in that sort of very horizontal, very kind of survey capacity,
Jason Jacobs: Yeah, not too dissimilar [00:46:00] from the approach I'm taking. It I felt reckless jumping right back into building another tech company without using this intermission phase to actually go, not go deep. Go the opposite, go broad. Because so much is changing and it's let me just spend time with a bunch of the different tentacles that are like keynotes, that have front row seats from different perspectives on what's happening on the ground and where things are going so that I can help inform my worldview before I anchor anywhere.
And I've really relished this time out. It's not forever like you, but but it's been invaluable and it's also just a lot of fun.
Mike Redbord: Yeah, great. Mind think it is just, it's cool to be able to live through these kind of, seismic level changes and there's an advantage to being mid-career when one comes around. 'cause the patterns and hopefully people like you and me, we can sniff out when we're starting to settle a little bit and when the, the, our footing is becoming a little bit more firm and that, that ought to, give us some advantage in terms of starting whatever the next generation of companies is.
Jason Jacobs: Yeah I think I'm getting close, but I haven't officially declared, but we'll see. I definitely have a, yeah, have a category that's got my attention and while I certainly don't know everything there is to know about. [00:47:00] Ai, I feel like I've at least gotten to a place where I am, where I'm where I've got my initial bearings and where I'm now on the treadmill of keeping up and getting my hands dirtier over time and utilizing in more aspects of my life and and things like that.
But but yeah, it's just changing so quickly that you can't blink.
Mike Redbord: Yeah, I think in this, like this moment right now, if your hands are in the dirt. You're working with ai, hand in hand like on a daily, weekly basis, you're doing the right thing 'cause you're improving your understanding of what this stuff can do for you. You're staying somewhat, you don't need to be on the absolute vanguard, but somewhat current on what's going on with it.
Like that to me is the very best thing that people could be doing right now. So that as it changes quickly, you're ready to execute fast on adopting those changes and pulling them into whatever you're, it is you're passionate about. If it's cs, cool, if it's whatever you're working on next, even better.
Jason Jacobs: Great. I definitely learned a lot from this one. Is there anything I didn't ask you, Mike, that you wish I did, or any parting words for listeners?
Mike Redbord: I think [00:48:00] I just gave him like, I think folks really have to play with this stuff, and I think the dividing line between people who are, using AI and actually know what it's. Capable of through experience. Like that line is getting to me brighter and brighter. It's like I can sniff it out in somebody in five minutes at this point, right?
You don't need to be a developer, but you need to like really spend time with it and understand its capabilities. I think by the end of this year, that's probably the biggest chasm that exists in like the professional world in terms of skill, right? And so I think you want to be on like the right side of that.
And you really wanna get your hands in the dirt and play with it. That's my only kind of on this stuff as we close out.
Jason Jacobs: All right. Great point to end on. Mike, thanks again for coming on the show and great to hear your perspective on what's happening in CS and what's happening in CI and how those are intersecting or not. Best of luck and looking forward to watching your progress. And who knows, maybe we'll find a way to collaborate in some form.
Mike Redbord: Awesome. Thanks so much, Jason. Appreciate it.
Jason Jacobs: Thank you for tuning into The Next Next. If you enjoyed it, you can [00:49:00] subscribe from your favorite podcast player in addition to the podcast. Which typically publishes weekly. There's also a weekly newsletter on Substack at the next next.substack.com. That's essentially for weekly accountability of the ground I'm covering, areas I'm tackling next, and where I could use some help as well.
And it's a great area to foster discussion and dialogue around the topics that we cover on the show. Thanks for tuning in. See you next week.