In this episode of GTM Innovators, I sit down with Mike Redbord to explore how AI is transforming customer success and support. From leveraging automation to streamline operations to the evolving role of CS professionals, Mike shares insights on balancing technology and human connection in modern B2B SaaS.
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Transcript:
Kyle James 00:00
Hello, hello, hello. Welcome to another episode of GTM innovators by 3Sixty insights. I’m your host, Kyle James, and today we have Mike Redbord, who is going to talk to us about all things Customer Success and Support and how it relates to the modern GTM teams and B to B. SAS, so with that, like, welcome Mike.
Mike Redbord 00:26
Thank you, Kyle. I’m excited to be here and have a fun chat together. Yeah, looking
Kyle James 00:30
forward to it. So Mike and I are old friends. You know, he’s former VP of Customer Success at HubSpot and Regal, among a wealth of other companies, but he’s built cutting edge customer success and technical support teams at those organizations. Today, he consults, advises and invest in startups and scale ups, bring his customer success, lifestyle or life cycle experience, and shares that with him. So I don’t know that that’s kind of like my intro, but I would love to, like, hear your take and your background and, like, Well, you talk about it, so like, fill me in on one of the gaps you have. Yeah,
Mike Redbord 01:05
happily so I’ve been in, you know, customer success, post sales, account management, I don’t know whatever you want to call it for gosh, like, you know, 20 years now, which is basically my entire career. And I think, like a lot of people who find themselves in this line of work, you probably didn’t intend for that to be your, you know, vocation, as it were, you went to school for, who knows what. For me, it was philosophy and International Relations, a little computer science. And eventually you get a job. Because, you know, you’re lucky to get a job with, you know, no particular training, and you know, you sort of end up working with customers. And you know, I had the very fortunate experience of working at a company called HubSpot and growing with it and leading a big global team, and it was a delight.
Kyle James 01:45
So I think you stayed there much longer than I did. It
Mike Redbord 01:49
was 10 years, and it weren’t a lot, you know. And I think you know, you kind of find your way into that line of work. And if you enjoy working with people, and you like working with the subject matter that you know, customers are using, whether it’s a product or a, you know, or data product or an AI product, whatever it is, then then, you know, it’s a great place to be. So for me, I’ve always loved, you know, working with the human factors and combining it with the sort of software and data stuff is really my thing, and it’s been a joy. I will share One fun fact, Kyle James, your host for today’s show, was, in fact, my first boss at
Kyle James 02:24
HubSpot. Maybe I was your first. That was a long time ago.
Mike Redbord 02:29
You told me a lot of things. I’m very I’m very thankful for it, and I’m also thankful that Kyle didn’t fire me back when I didn’t know how to work customers or use a phone at all. This is 2010,
Kyle James 02:39
thank you, Scott, yeah, I mean, and to that, you know, you and I go way back, so, like, I’ve got some extra details about your background, like your superpowers and whatnot, which you kind of mentioned people and the technology and data and all that. And, you know, obviously the big interesting topic right now is AI and how it’s affecting teams and with your experience and kind of, like the CS and support roles. Like, I know you have that, like, dual superpower, which is always challenging, of, how do you keep good customer success or customer service, but also, like, do the data and automation elements? Like, what? What is your trick to that? Yeah,
Mike Redbord 03:17
I don’t know if I have a trick, but I’ll share my perspective anyway. Like, good customer service, or good customer experience is it doesn’t fundamentally have anything to do with technology or with or with people, even right? Like, that’s a matter of the perspective of the customer. It’s a feeling right. It’s an evaluative statement of, hey, this company was good to me, and they provide a good service. And so we have to unpack what the statement means. And to most people, it means, you know, I got what I needed, and I got it fast. And, you know, as a plus one, it maybe put a smile on my face or something. But if you don’t get the first three right, you don’t get the smile right. And so, you know, if you can give the right answer and you can do it quickly, then you know, you you’re in, you’re in a good place with regard to sort of, quote, unquote, good customer experience. So people and tools can further our efforts to be accurate and to be fast. And I think AI is just, you know, it’s another brick in the wall. It’s another step in that evolution.
Kyle James 04:15
So how do you measure that, right? Like, it basically what you heard from my product background. Like, you identify the customer pain, right? Like, and make sure you tackle that as soon as possible. But how do you, how do you measure that? Or, how do you measure that across hundreds or 1000s of like, communications and interactions with people? Yeah.
Mike Redbord 04:33
I mean, it depends on what application, I guess, of service you’re looking at. And so you know, if you’re a customer success professional in a subscription business, then your yardstick for good service is probably net retention or renewal rate or something. And then it’s a little more sophisticated, or a lot more sophisticated, what? How you sort of get there, if you’re if you’re looking at sort of support, traditional support, like ticketing, which is a very transactional ticket. Comes in. Thing happens? Ticket gets closed. If you’re looking at that part of most businesses, then there are, I think, you know, a little bit clearer yardsticks for what good looks like, right? And there’s industry benchmarks around this. You know, your CSAT should be greater than, like, 95 your time to first response. You know, by channels. If you’re on the phone, it should be picking up within two minutes. If you’re doing email ticketing, it should be eight hours business day. If you’re doing chat, it’s pretty fast, like 30 seconds or something for first response, then you have resolution times too. So in support, I think there are clear yardsticks, and in this conversation about AI, I think when we can we can make clear yardsticks, we actually then earn the right to talk about how AI can help in a world where we have very squishy goals and we’re like, oh, we want to make the customer experience better, it’s hard to say, Oh, the answer is AI, because AI is not yet a cure, all right. It’s not yet a broad based panacea for sophisticated problems. It still is early days of this technology, and it needs to be applied in sort of small doses. So if we come up with a metric we want to move, we can very focused on it, then we can apply the right tools. And AI is a really great tool nowadays to do so
Kyle James 06:15
give me an example. I’d love to have an example of like, something that you’re like working with somebody on like, doing that, right? Like people could always understand tangible stories. Yeah. So startup, of about, I don’t know, 2022, folks nowadays. You know, small post sales teams, small everything Team Series, a business. And, you know, they, they get a fair number of support tickets, like I think every startup does, because your products early and trying to figure stuff out, some of those tickets are what I’ll call novel, right? They’re things that we haven’t seen before, or it’s like a brand new feature, and the customer just used it and they found a bug or something. It’s novel, right? But some other percentage, and as a startup, that percentage starts small and grows over time, is a percentage of tickets that are you’ve seen before, that are repeatable, and you’ve already answered and, you know, there’s a variation on a theme, but it’s all ice cream, whether it’s vanilla or chocolate chip or, you know, paramost will and so what we’re what I’m seeing, is that there are companies who have adopted AI, and the AI is enabling them to excel at that second category, the repeatable tickets and different vendors are doing it, you know, to different sort of quality points, like intercom is a different solution than Zendesk, and there’s all sorts of plugins and stuff you could use. But if you can find something that works for you, you can meaningfully reduce the amount of human effort you need to apply on that second category, which is very repeatable order, and you can also get faster responses back to your customers, because it’s just it’s a machine doing it, not a human. And so I think that that ends up being a kind of double win, where for the business, you don’t have to spend the money on staffing, you don’t have to hire as fast and support, which is great because it improves your your gross margin, and just, you know, pure headcount makes things easier in startups, but it’s also good for the customer too, because, you know, they’re basically getting a fast track to a solution, and they don’t know that their peer customer has already asked the question totally but, but, you know, we’re able to basically share that in a safe, AI driven way to get them an answer faster. So let’s, let me change it up and ask kind of a challenging question here, right? Like, because, so I totally get that right. There are absolutely those things that we hear over and over and over again, and we and we almost memorize the way we repeat the answer to them. And you could scale that where they could just search it to get a chat that would explain it to them, or send them to a help article or automate it, essentially. But like part of that too is like those things come up over and over and over and over again fixing the product, or build a workflow or a wizard or something to do it. And like, how do you find that balance of when to, like, go bang on the door of product and engineering to, hey, fix this thing, this thing is needs to get better, versus just like, you know, I don’t want to bother them. We’re just going to do it this
Mike Redbord 08:57
way. Yeah, I think it’s a yes and not an either or, like, if you’re if you’re getting, you know, if you’re getting questions about a particular feature in your product, because nobody understands how to use it, you know you should both, like, understand the customer perspective, user perspective, do the research. Do the product motion like, ship code to make the thing better. Yeah, and you should write documentation so that, you know, folks who are committed and want to go that route. Can, you know, use it better. And you should have support, and you can view AI in it if it also helps you achieve your, you know, kind of customer service goals to, you know, to help people when they, I don’t know, can’t figure out the documentation, or prefer to talk to a person rather than search box. So to me, it’s all it’s all three. I’ll say that the AI elements, you know, do change the game a little bit from an operation standpoint, and support. So in a traditional environment, you’re looking at, you know, the amount of time, the number of basically, conglomerate minutes. You. Support Team is spending on a particular issue type. So you have your categories and you’re like, Oh, the team is spending all their time on this weird integration. We should really make that better in order to improve the efficiency and support. So that’s that still applies. There are still that’s still a valid type of analysis. But with AI, the new type of analysis is a more customer centric one, where it’s not about the minutes your support team is spending, but it’s about the effort that your customers are spending. And so you have to kind of look at your ticket volumes by category. And you know, even if the AI is like solving all of it, you still have to respect the fact that the customer stub their toe on that feature and it needs work.
Kyle James 10:35
Yeah, yeah, because we’ve talked a little bit about how to use AI and these different things, to me, I’ve started breaking out AI usage by companies and like, internal optimization and efficiencies versus like, how are we engaging with the consumer, and how are they like, can, can we do this in a way that is maybe automated, but doesn’t feel look automated? And what we’re talking about, it’s kind of doing both of those right with this flow. Like, do you find, like, I hear a lot of people say, like, start with the efficiencies before you start rolling that out externally. But it sounds like this is a great way to, like, start tipping the toes and like, being able to communicate and share that stuff and in a mass, commuted, automated way. Is that, right? Right? What do you think about it? You
Mike Redbord 11:23
think, you know, I would be reluctant to roll out kind of broad based AI to your customers without, you know, kind of having a human in the loop at the beginning, right? And so what does that mean? In practice, it means using the identical AI to sort of pre write your answers to a support ticket. So support ticket comes in. I as the support rep, I’m going to look at it. The AI is writing it, and I’m sort of sense checking, you know, does this answer make sense? And I say, Yeah, sure, ship it. And if you know the AI, then is good, whatever it is, you know, you have some benchmark, 90 plus or 85% plus the time. Then your humans handle the exceptions. If the AI gets it wrong, and you’re willing to accept a little bit of friction as you kind of make the AI better, got
Kyle James 12:01
it so there’s kind of like a check in place. It’s kind of like it’s a guard, like we’re reviewing this before we hit the submit button totally.
Mike Redbord 12:09
And I think human in the loop when you are kind of wading into the waters of AI is a really smart way to do it. But that said, some of these solutions nowadays are like, good enough that the human in the loop period doesn’t need to be very long. You know, if you do it for a week and you see 100 examples of an AI doing it perfectly out of 100 Well, then, you know, you’re ready to sort of remove the human from the loop and let the AI do its
Kyle James 12:30
thing. Yeah, yeah. Speed to market. Like, the customer gets answers faster, and they’re not waiting on us to, like, prove it and read it. Yeah, exactly. It’s interesting. Um, where do you think this stuff goes? Right? Like, you know, we just heard last week some of the reports coming out of CES that they’re thinking about, like, physical AI, when, when? What we’re talking about isn’t even the agentic stuff yet. And I don’t know how nerdy we’re getting in, like, futuristic, but like, this is the stuff that’s super interesting. Like, how do you implement this? But also, know, what is the shelf life like? Is this something we can use, and is it going to still be the same in even two or three years from now, much less six, eight months, right?
Mike Redbord 13:12
Yeah, I got it. I think two or three years like the world will change again and again. We’re probably, you know, two or three rotations by 2028 or whatever, right? So I think you know the things that you use today you should implement make your numbers better, hit your goals faster, but also continually be looking for the better thing. And I think you know there are companies that are sort of, if there’s a company that’s founded today in 2025 that company is going to have certain structural advantages when they look out at the market of tools they can buy and use and test things from today onwards, and they don’t have any kind of historical baggage. Whereas a company that was founded five years ago, they looked at was the market in 2020 and man, it was very different. Ai, was totally different world, right? And so, you know, they are sort of retrofitting and trying to catch up. So I think there are real structural advantages to founding a company today that can use the best of AI, but that will also be the case two or three years from now, because then they’ll be able to use the best and most modern, yeah,
Kyle James 14:08
so let me put you on the spot a little bit, right? Like, because, because I know you work with multiple companies. Do you come in with, like, your preferred stack that you come in and recommend, of like, how you want them to do, you know, customer success. It could be account management, it could be onboarding, it could be support. Like, do you have kind of a preferred stack that you roll out and, like, get people trained up on? Or is it more of like, find the right fit to the company?
Mike Redbord 14:37
It’s much more the latter. For me, I suppose I have preferred processes, and I’m sort of people in process first, and technology, you know, fits in and supports those. And, you know, I just think the technology, the technological application, is very, very different by company, because the customer experience is truly unique by company. Like, that’s the definition of companies or customers are different. And so if you’re a, you know, really high. Volume shop that’s selling something, you know, like, for one time use, not on a subscription basis, and whatever, like, you know, your post sale process is going to be very different, and your customers tolerance and sort of the needs for service are also going to be very different. Whereas, if you’re, you know, your Oracle, right, you’re selling a giant implementation, huge sales cycles and multi year contracts. It’s just a very, very different process and people, and therefore a very different set of tools that should probably be employed. Doesn’t mean there is an overlap, right? It doesn’t mean that you shouldn’t be using AI to help with tickets, like we were talking about, but that might just not be top of the list for an Oracle versus somebody selling, you know, single use item online,
Kyle James 15:45
yeah. And like, what do you what do you recommend to, you know, CS support, account management leaders and and professionals like, how do you recommend? They get up to speed with this stuff and be comfortable with it, and, like, make decisions on what tools to use going forward, right? Like, is that just get into play? Or do you have, like, I
Mike Redbord 16:09
mean, I think that’s actually a legitimately challenging thing nowadays. So you know, if you’re ahead of customer success at a Series A or B, startup, you got a lot going on, right? You got the board, you got a CEO, you got co founder, like, you know, you got customers fires to put out, whatever those problems structurally are, this is sort of the same as they ever worked. It’s probably been the same for 20 plus years, 30 plus years, and now it’s like, man, the tool landscape is evolving faster than ever. And so, like, you know, your nights and weekends job now is like to research, you know? Oh, is it should I use the the Zoom note taker? Should I use fathom, or should I use, like, firefight? It’s just, there’s, there’s so much out there. And so I think that as a leader, you need to be picky and need to be strategic, and thinking about what tools would help you hit your goals faster. Because back to think we were talking about at the very beginning, start with your start with your process, start with your goals, start with your customer, and then choose, all right, we need to make this thing better. And it feels like this is a good application of Gen AI. We need to, you know, like, go after this problem really hard. It’s the strategic priority for the department. How can AI help us? And I think if you take it tops down like that, rather than bottoms up and sort of responding to any AI STR that goes into your inbox or the next thing you see on LinkedIn, you won’t spin your wheels as much, because there’s just so much out there. It’s a full time job just to, like, explore the landscape. And in fact, there are people who job that is, is to map the landscape. But what will work for you is going to be, you know, unique to your business and to your customers, yeah,
Kyle James 17:41
yeah. I got a feeling we’re probably going to see more of that, right? Like, every company is going to have like, a full time person who’s just, you keep in touch with the newest technology, like, in a way that we’ve never seen before, right? Like, historically, it’s like, that was IDs job, but now it’s, it’s almost like a specialist that’s focused on just, and, gosh, there’s different kinds of AI that do different things
Mike Redbord 18:02
totally. I do think this is a place where, you know, external folks can be helpful to, you know, your, if you’re a founder, you’re a head of a certain function, like, you know, by working with peers and sort of pattern matching. Hey, what are you using? Oh, I heard this tool come up five times. Think that’s good, you know, asking your, you know, folks on your board, maybe see across multiple companies, you know, they might have some bias in there, if they’re investors and something, but still, like, you know, something’s working, it’s worth checking out. And so, you know, think about how you can skip the line and shortcut a little bit so you don’t have to just like, Google, like, AI note taking things, or, like, the last perplexity, like, tell me what these are, because that’s really the long road.
Kyle James 18:41
Yeah, well, and I think we’ve seen definitely in the last decade, right, as more and more SAS has been adopted by companies, right? This, this growth of, like, how many different tools you use, right? And I think it feels like just in the last few years, companies start to get in control of that a little bit, right, where they’re starting to reel in some of the, you know, what you don’t need Canva, because we’ve got figma right here, and we don’t need two different skews in our, you know, the finances, complaining about or whatever. But now, like, we’re hit that S curve, and is it about to grow again? I think
Mike Redbord 19:18
it’s ricocheting back the other direction, like last, the last, you know, maybe eight to 12 months like this is a sort of recent phenomenon, like the proliferation of, you know, ai, ai imbued tools that are, I would say next generation is just like, so fast, yeah. And these are both startups, by the way, and established players. And I think in particular from the established players, this might be a bit of a similar bit of a cynical perspective, but especially from the established players, it’s hard to tell what’s fluff and what’s real, because they have so much marketing muscle that when they bring something to market, when you’re going to hear about it, they’re going to have good case studies, because they found, you know a customer for whom it works, that you know you need to be like a. Picky and work your network, I think, to figure out what, what’s going to work for you the era of going to g2 and saying, like, oh, top app in this category. Like, we all know that’s kind of all done. And I think you need to be skeptical of established players and open minded to new ones in this in this moment.
Kyle James 20:16
Alright, so let’s do a little plug. Like, I know you’re spending a lot more time playing around with the agent AI team. So y’all are probably seeing a lot of stuff being built in interesting ways. Is kind of, and I think most of that’s probably people building on top of HubSpot, right? And kind of thinking how they leverage it with workflows and stuff that’s beyond the system. But you know, are you seeing anything that’s like, really blown your mind, or something that’s just, like revolutionizing or is it more of like incremental steps
Mike Redbord 20:44
still? Yeah. So for context, agent.ai is like a general purpose AI marketplace for agentic solutions. So go make a thing. You know, it can be lifestyle. I made one for, like, my daughter, BEDTIME STORY reader, right? Makes up a new bedtime story gives but it can also be, you know, for, you know, kind of revenue professionals and HubSpot or Salesforce or whatever it is. So I’ll tell you the things that have been surprising about what we’ve seen created and used. The most surprising thing to me is that the most popular AI agents are number one, very simple. It’s like one prompt or maybe two in a basic chain of thought, like, it’s not the really complex stuff that you’re reading about if you’re subscribed to, like blogs or really in this it’s simple stuff that, number two, feels more like a web app than AI. So it’s less about a chat interface, certainly not the new like, you know, open AI voice thing. It’s not synesthesia with video, right? It’s like, drop down, drop down, output, maybe another drop down, or, you know, kind of reiterating the output. And I think that you know, those two things, you know, are testament to the fact that the technology has moved really, really fast, and the capabilities are crazy. And if you’re in this whole day, you’re like, you’re living in the future. But for mere mortals, for the average human being, you know, that is sitting in front of a computer trying to do a job, their mental model has not shifted, yeah, so they’re still looking for kind of an app that does a thing. It’s just that now the capabilities of apps are more sophisticated. I can give it, you know, 100 page research paper, and it gives me back what I need, whereas before I had to, you know, buy a solution for that and pay a bunch of money. Now that’s that’s gone to free, right? So, like this, the surprise to me is that, yeah, the tech man’s moving crazy fast. And yeah, if you’re like, ahead of a function at a startup, you need to be in that a little bit and use this tech. But for mere mortals, for the rest of us, you know, we haven’t changed the mental model on the paradigm yet. Yeah, I think that that’s an arbitrage that, you know, I think smart people will figure out how to explain that’s
Kyle James 22:50
interesting. You put it that way, because the way I’ve seen and played with that is, to me, like there’s nothing that I’ve seen in those agents, you know, this that I couldn’t go do in a chat GPT, right? But, but what it’s done is it’s like, it’s simplified the prompts you know you’re writing up the scope of work, of what you want it to go do for you in a way that, like, is plug and play, right? Like, it’s a little app like and and it has made these things much more approachable for the mere morals are just trying to do their job, and they don’t have the time to go, or they don’t have the capacity to go get creative and think about how they might want to do it a different model, because they just want to do the thing right. So it’s hot. It is making these things much more approachable for the masses and not the crazy weirdos like us who like to play and build new and interesting things just because we can,
Mike Redbord 23:42
yeah, I mean, I know you well enough, and hopefully know myself well enough, like we’re tinkerers. And probably the people listening to this too are also tinkerers, to a degree you have to recognize that that puts you in a very small minority. Most people don’t care. They don’t care to learn something new. They’re sometimes even allergic to learning something new, right? It sort of rubs their brain the wrong way. They just want to get through the day, get the thing done, and get on to, you know, their family, or, like, whatever it is that’s more important than the than the new tech, right? And so we, I think, we live in this bubble. And, you know, it’s to our credit, we’ve created the bubble in certain ways. And it’s, you know, it’s exciting and it’s fun. I love it. But recognize that, you know, 99.9% of planet Earth is not in the bubble, yeah, yeah. And it took how long for them to adopt, you know, at marketplaces on their phone, right? That, you know, I remember at marketplaces back in 2008 well, it didn’t really become a thing that, you know, normal folks use until probably 510, years later. Yeah, so that curve, we’re in that curve again.
Kyle James 24:43
Yes, we are definitely in that curve. I can I feel that because I tell my wife, love her to death. She’s a business manager for a small cyber security company, but, like, I’ll tell her about some of these things she built. She’s like, whoa, you could do that. And then I’m like, You just had to play with it. And she’s like, I need to play with you know? How long we’ve been having that conversation, for like, three months now, but she hasn’t played with it yet. She needs just, there’s purpose built things for and I think that’s probably what we’re going to see with a lot of things and but do those become their own companies? Right? Like, totally going off on a tangent here, but that’s interesting to think, like, is that the next wave of companies that get put out is this little, you know, the next wave of like apps in in, you know, these integrated apps in large ecosystems. They’re just AI backends on them.
Mike Redbord 25:29
Yeah. I mean, I think you heard this from CEO of Microsoft, where SAS is dead. And what he actually meant was that, you know, the sort of incremental value of new SaaS, and the speed to which you can redevelop existing SaaS is going to zero. It’s going to compress rapidly. And so to your question, you know, are these things the next generation of companies? Well, like maybe, but also they’re just foremost, going to replace the last generation of companies that are no longer defensible. If your moat was just that you had a lot of code and it took a while to rebuild it. That mode is getting eroded. And you know, the enemy is at the gates, right? The enemy, the enemy is AI, and, you know, probably an agent cloud that’s going to eventually put you out of business. It won’t happen in 2025 these SaaS businesses are durable as recurring revenue, whatever. But you know, if I was going to start a company today, you know, I certainly would look for the for the AI angle, not just pure SaaS, because the speed to redevelopment, or speed to replacement to pure SaaS, so fast,
Kyle James 26:28
yeah, yeah. And that is will have major implications on like UI, right? Like user interface can be totally different, much more customizable than it ever was, and that’s interesting,
Mike Redbord 26:41
yeah, and that way custom solutions are back, right? What’s old is new again. You know, look at the beginning of Internet technology, back in the 90s or something. Everything was like, hyper custom. It was so gross, right? And then SAS kind of happened, and things got a little more, it’s took 10 years, things a little more stamped out, right? Like, when you walk into, you know, whatever, Gmail, everybody’s Gmail, kind of basically looks the same customization, whatever. Now we’re kind of back in custom land, where the the expense and the time necessary to build something custom has gone, you know, from whatever it was to dramatically lower. So now we have a lot more one size fits, one software coming out. Kind of a good time to be an independent development shop. Kind of a good time to be a buyer of somewhat esoteric software products, because the cost of those things has gone way down.
Kyle James 27:28
So let’s circle it back a little bit to kind of like, how do you see, how has this stuff changed? How you go to market with, you know, in the customer realm. But also, how do you see it changing go to market in the next but just keep it simple, two years, right? Because we can’t really see any further, because who knows what’s going to be out there two years from now? Yeah,
Mike Redbord 27:51
I think on the go to market side, you know, there was a brief fortation with, like, aisdrs. I remember going out, Oh, yeah. I remember going on LinkedIn. It was like, my entire feed, I’m talking to my friends, and they’re like, oh, this AI slot, BDRs, you know, whatever. And I’m kind of happy that flirtation was just that didn’t evolve into a long term relationship or marriage. And you know, now that we’re through it, it’s easy to rationalize like that was never going to work, and, in fact, just made it easier for executives to filter out the junk, because it’s kind of obvious when it was written that way, and not, not by a human. So I think, like we will continue to have what I’ll call experiments right in this and you’re seeing those experiments with voice too. This is more in B to C land, but you’re seeing, you know, call centers using voice AI to have conversations and to handle simple tickets the same we were talking about the same we were talking about the beginning of the show, you know, taking your simple tickets and using AI. Well, you can do that via text, but maybe when we talking about that, you gentle viewer, weren’t thinking you could also do a via voice. Well, that’s happening. And now when you’re talking to your insurance company, you might be talking to to an AI. And so we’ll see if that kind of, you know, works, right? And I think those experimentations are critical to sort of figuring out what will work, and the companies that are at the forefront of them stand to really benefit if they put the bet right. But in terms of predictions, Kyle like, you know, I don’t know what it will be if I if I did, I’d be off building it. I’m just extremely curious what you know, like to watch the watch the show, if you will, I just think it’s a fascinating moment in kind of, like, technology and software and go to market history, if you will. And like, there’s going to be a lot of really, really interesting companies made, and a lot of really, sort of interesting on the surface, but didn’t work out companies that you know happen and fail, because the thesis was wrong and they didn’t pivot, or they ran out of money or something. Yeah, it’s,
Kyle James 29:42
it’s going to be interesting. And I think the good news is we don’t have to wait long to, like, really sticks continue to see that. It’s like, what time is fast? Yeah, everybody’s got go to market. I mean, they’ve got aI somehow mentioned on their website now is their offerings and integrated into their SaaS platform in different ways. It’s just like. How does that really start taking off, and what comes next, totally,
Mike Redbord 30:02
and I think in customer success, man, which is a form of go to market, right when I expand to customers and drives revenue. You know, we talked about support and ticketing and AI, and I think that’s been a sort of early and maybe more obvious kind of application of AI customer success. I think it’s, it’s sort of less obvious, because the actions are both more complex that CS professionals take, and more tailored to the business. Like, yes, people do onboarding, but your onboarding might last a month or might last a year, depending on the nature of your company, whereas a support ticket, you know, it’s a little bit more like controlled and tidy. And so I think, you know, you need to look at kind of the core verbs, if you will, of customer success, like onboard your customers, keep them healthy, renew them, expand them, these verbs, right? And think about where AI can be most useful. And some cool applications that I’ve seen are just in making those things like less of a headache to be blocked. So when we talk about doing renewals, you know, one of the first things you have to do with renewals is just get your contract data in order. And man, is this a headache, because if you got 500 customers, you gotta go through 500 paper contracts and, like, write it down and like, you know when, when’s the end date, and put it in a spreadsheet, and then data, load the spreadsheet on the Salesforce, and you’re gonna make mistakes, right? And so you know now there’s a variety of companies that can take those contracts and digitize them more effectively, pull out your custom clauses. This, again, always kind of existed, but the cost has gone way down, the accuracy and quality has gone way up, and that makes the renewals, sort of operations process a lot easier. It should mean CS teams can do a better job so they’re less stuck in the muck on digitizing contracts, for instance. Yeah, so I see applications like that that sort of help with those core things, but at the end of the day, are sort of small helpers along the way. They’re not doing the renewal for you on the phone or anything. So
Kyle James 31:48
I totally agree with you. It’s like it’s taking all the onesie twosie things that people have to really pay attention to and not mess up. Does that mean that like the persona of the people that you hire for the it almost feels like the persona of the persona of the people you have for these roles has to be more of the like, extrovert, super, super like relationship building type, which is kind of what you want anyway, but they don’t have to have all these soft skills of like, just being good at data anymore, right?
Mike Redbord 32:17
It’s a super interesting question. I think the answer is, like, is basically yes. If you look at startups and CS teams and startups, they tend to be generalists, because they got to do a little bit of technical support, a little bit of kind of product management, you know, some onboarding, like, whatever they got to do noodles, they have to go fly out, go to baseball game, have steak, whatever. And so they have, like, this really broad kind of buffet of things that need to be good at. I think the point you’re making is a good one, that if AI can handle some of these, especially some that are, like, kind of the outliers, right, the more technical, weird things, and then we can hire more specialists. I think that’s true. And I think that’s the phenomenon that you see when you compare startups with generalists to big companies with specialists. So as companies scale, you know, especially in CS, the roles sort of split. You end up with people that do onboarding, people do professional services, whatever and specialization happens now, if you think about it, you know, kind of multi dimensional way where they split. You know, onboarding versus professional services. But in the old world, the onboarding people had some technical baggage, and sort of, the Pro Services people, if you can, if you can turn the technical baggage over to AI, maybe you can actually have fewer roles and kind of, you know, specialize more in, like, you’re saying, just the extroverted communications layer and that type of person, yeah, does it
Kyle James 33:37
open up? That’d be great. You had to hire, like, the Walmart greeter, almost like, like, I don’t want to go that extreme, but Right, but like, that’s, yeah, that’s pretty interesting, if that’s where it can get to. I
Mike Redbord 33:47
mean, you know, ideally, if you follow the sort of theory here, which is, you know, maybe, maybe not, it would allow bigger companies to have fewer touch points for customers, because each human that works at the bigger company would, would not need to be, not need to have as many skills. Yeah, right. And they could, they can send word more processes that, you know, they could do both onboarding and renewals or something, or something like that. And that would be a terrific, you know, evolution of customer success for large companies and for smaller companies. You know, I think they could. They could, really, they could accelerate their move faster away from kind of generalists or a little technical, a little good at renewals, a little good at, you know, commercials, little good at customer stuff. And they could sort of work on those latter ones. And I think that would also be good for, you know, for their customers and for their revenue. So that’s an optimistic take. Yeah, that’s super
Kyle James 34:39
fascinating about that? Well, I don’t, I don’t want to keep you too long, Mike, this has been fantastic. Thanks for joining today. You know, it’s incredible insightful, and I’m sure this will be something the audience and all goes back and sit on, but you know, kind of coming back to you like, you know, how can people follow you? How can people help you? And what’s the best? Way to kind of plug whatever you want.
Mike Redbord 35:01
Yeah. I mean, thing that would help me if you made it this far in the show and you’re still listening and you agree or disagree or had a feeling about a thing like, let me know, let Kyle know. Love to hear your opinions. This is an ongoing conversation, and it’s moving fast, so hit me up. Just mike@redboard.com send me an email. It’s old school, but it works. Hit me up. Hit me up on LinkedIn, and it’d be a fun chat to have. If you are an operator at an early stage, you know, Series A, Series B, startup, and you feel like you’re in a kind of key moment of your company’s evolution, and you think I could be a useful helper along the way. Hit me up. We want to talk about this stuff. More
Kyle James 35:37
awesome, awesome. So to all the audience out there, if you enjoyed this episode, please subscribe, share, leave a review, and feel free to reach out. We’re happy to be happy to curious what other topics or suggestions you have, and you got a guest or somebody that you want us to talk to. Please let us know, and you know, thanks for tuning in. And we’ll be back next week with another go to market innovator. And until then, keep growing everybody you.