Philipp Stelzer: Hello, and welcome to another episode of the RevOps Lab podcast. I'm actually alone today, but I'm super excited to welcome Alex Müller back to the podcast. Alex was actually our first guest ever. We've recorded over a hundred episodes by now, and, yeah, so super good to have you back. Thank you.

Alexander Müller: It's so great to be here, Janis. And I have to say I felt a little bit old when I looked at our podcast that was already two and a half years ago, and it's crazy how many things changed in the meantime.

Philipp Stelzer: Oh my god. I actually didn't know that's two and a half years ago.

Alexander Müller: Yeah. I do feel very old, personally, but that's a different story.

Philipp Stelzer: No. Look, I mean, so actually Alex and I, we are almost neighbors, so we see each other quite regularly, and we regularly talk about, you know, how is AI changing RevOps. And so today, what we want to do is we want to jam on AI use cases and then basically draw back to how is that changing the team composition of RevOps. So in a topic where I think both Alex and I would say we have a lot of opinions, but it's all a bit unclear. And in the works, right, so it's work in progress of what the best practices will look like. So I'm super excited about this episode. But maybe for the guests who don't know you as well as I do, you know, like, and for the audience, like, you know, who are you? What do you do? What have you done in the past?

Alexander Müller: Sure. So I run a small Berlin based consultancy for revenue operations. We are really like a boutique consultancy. We help companies to set up their revenue operations. We take over any kind of projects that they, well, can't do by themselves because they need extra workbench or because they don't have the right people in place. It could be that, for instance, they have a very, well, tool minded team, and they need somebody who is a little bit more people driven. So then we come in. Or also the other way around, they need, like, a specialist for new tools, and we help them to set this up. And the company is called Revenue Enablement. As said, very small helping B2B companies. About myself briefly, I have been in the B2B space for the last fifteen years. Originally started in banking, then came to Berlin to the startup scene and built up a lock tech company here in Berlin. Fortova was one of the first twenty employees there, stayed with the company until we were, I think, valued at two point three billion, took the COVID hockey stick for us as an ecommerce close company. And then I joined Cognigy as their Vice President of Revenue Operations where I owned the global SDR team, the global revenue operations team, and the data team. Cognigy was sold to a US company called NICE in the customer service sector for one billion, the largest AI exit in Europe to date. And, yeah, now for the last three years, I've been working on my own consultancy here and very excited to see many, many different companies, how they scale, what they do with AI, how their tools are changing and the processes are changing.

Philipp Stelzer: So for everyone who is not in Germany, because I think most people in Germany know you by now, but, like, for everybody who's not in Germany, go to LinkedIn, follow Alex. He also creates some good content and is overall just a super awesome human being to hang out with. But enough of the praise. Let's dive into something even more interesting, our topic for today, AI use cases. So, I mean, maybe let's kick off with, you know, the first use case. Like, you know, what's hot right now? Where is AI really changing go to market?

Alexander Müller: Yeah. I think right now over the last couple of, well, twelve months, the RevOps role changed so dramatically when we speak about AI use cases. Right? We spoke, I would say, in mid 2025 a lot about Clay. Clay was kind of the name of the game. Right? Everyone wanted to enrich data, find new contacts, automate email and SDR. And I think right now, this is changing a little bit. We see more people working directly with the providers such as Anthropic or OpenAI. And I would say when I was at an event just recently, the most discussed tool was actually Claude and Claude Code, of course. And yeah. So I think the use cases here are pretty broad. I would say for RevOps, it's still a lot about data enrichment. That's the number one, I would say, and typical RevOps responsibilities, that you say. But it's changing, and I think that's a very interesting part. You know, it comes along that the orchestration of agents now is also one responsibility that we see with many RevOps teams, and that I think is really a big, big change going forward.

Philipp Stelzer: Yeah. A hundred percent. I mean, I think every company in the world has an AI transformation mandate. And suddenly, there are AI workflow and agent builders out there, and which tools should be better positioned or are better positioned than RevOps to take those and actually make them work. I mean, maybe going a bit deeper into this, maybe we can stay top of funnel, but we can also go into other directions, right? Like, what are workflows and agents you know, you've seen, you know, being super interesting?

Alexander Müller: Yeah. Yeah. I mean, I think it's good if we start, like, from the front of the funnel, right, because that's the most obvious use case, and then we go a little bit deeper. I mean, I already mentioned the work of the SDR changing. Right? Responsibilities for revenue being transferred to RevOps. So actually, the name revenue is also, like, really has a different meaning now for us, a different responsibility, and we can also discuss that in a minute, a little bit closer with one top ad that I saw quite recently from xAI. And there, I mean, let's face it. I think customer questions, customer interactions that you're collecting information from your customers is something that we will see more and more agents do, that they respond automatically, that they give discounts where it makes sense, that you especially give the long tail, the smaller customers that you probably can't serve with a human being because it's too — or it's not cost efficient enough — that you hand over those to agents. And, I mean, that's something that I have seen quite a number of use cases, not necessarily really AI SDRs calling yet, right, but more AI SDRs responding to emails, writing better LinkedIn outreach, and so on. However, being said, that requires still a lot of training and human in the loop. Right? But I think we can also get to that. Then on the next step, we have also seen more customers going through their database and collecting information from existing customers or from closed lost opportunities so that they could reuse them better in the future. And I think that's, for me, still one of the underlying requirements for any other use case — that you enrich your data, that you have the right data at hand. Then when we see, or look at, the education part, I think there we also see quite a dramatic shift from how content is produced, of course, but also how content is consumed. Right? That buying decisions are more taken with LLMs, with chatbots, that you brainstorm, that you gather requirements together with them. And then I think at the closing, that's still a lot — well, I would say, especially for enterprise, human related, where human beings are still building up relationships. But also that, of course, is changing. And then going beyond the close part when we look at expansion, at churn, that we make better use of the signals that we already have, that we, for instance, gather different signals from customers. So let's say they are not logging in anymore or as much anymore. In the past, there was, like, one thing: no user logged in for the last week. Right? And then you would know, like, would go, okay, what should I do with that information? And now with agents, you can basically summarize the information from different tools. You can combine it, you can actually push it through Slack, through whatever channel where your existing teams are, back to the users. And I think those are, like, just a few use cases that I've seen recently and all not too crazy also to get started with.

Philipp Stelzer: Yeah. Yeah. Yeah. I mean, obviously, as you know, we are — I mean, we're basically building in this space, so we see a lot of stuff. And, I mean, I think what I find so interesting is that, you know, the kind of using agents to orchestrate actions is something that is essentially giving RevOps the power of resource allocation. And I think if you think about the best companies in the world, they are really good at resource allocation and thinking about where should you spend your time. And I think top of funnel is a great example. Back in the days, we started with people manually creating lists, then enriching them, and then essentially doing the outreach. And that's basically the first wave of the SDR role being specialized. Then you come into, okay, there is a workflow automation tool, like a sales engagement tool, that helps you automate certain steps, but still you have to write the message. You still have to think about who you go to. And now it's basically you take all your first party, third party, second party signals. You layer them into an orchestration layer. You then go a lot more detail into micro campaigns, and you automate basically the things that can be automated. So I think that very interesting trend I'm seeing there right now is you automate a lot of the messaging, the emails, the LinkedIns, but then still calling is done manually with often parallel dialers, so you have higher connect rates. So obviously that's not AI, but what I think is essentially really interesting there is you do what is the highest value work for an SDR and you have to do that really, really well and everything else gets automated. And I think that is a common theme we're seeing. It's not that the AE is going anywhere, but all these fifty percent to seventy percent of non selling activities, those can now be automated. And I think that's just so powerful. Curious what you think about that.

Alexander Müller: Yeah. I think too that the time that you actually spend with value generating activities is increasing. However, I also have to say that I think we are still in the process of finding the right limits there. Right? So it doesn't make sense that you're cross checking every email five times and run it through three different agents because one formalizes your tone, the other one improves it for your reader to sound less salesy. And then, I don't know, you let it run through another one just before you send it out. Right? And suddenly you need for an email that would have taken you five minutes, fifteen minutes because you let it cross check several times, still need to read it. And the other extreme, of course, is that emails, messages go out unchecked. Right? And what I'm saying with that is that we can also keep ourselves quite busy with these kind of things. And I think we still need to choose. And where do we approach customers if we look at the outbound side? And how do we approach them? Because the interaction between the human beings will be even more important. Right? Because, I mean, you and I, we can probably already tell which LinkedIn message is AI generated by now. Right? That you're receiving every day. And you're like, okay, I'm not even reading that anymore. Right? And I think there we really need to keep the human in the loop. And that's also why I'm thinking the teams will not completely go away. The roles of sales, the roles of CSMs, the roles of SDRs, they will not go away, but they will be better. They will also be a little bit more fun for us, I think, because we can actually do the tasks that we like doing. But we can also get, like, immediate responses to our customers. We can finally start working with all these signals that we are seeing. What do they really mean? Who should we actually talk to? And yeah.

Philipp Stelzer: Yeah. Yeah. I mean, maybe just a few more, like, you know, additions to this. Right? Like, obviously, this is always in, like, context of size and segment. Right? So what I'm hearing, for example, is, like, inbound SMB, right, like, that's actually quite automatable. But, like, you know, outbound enterprise, that's still heavy human in the loop and actually, you know, like, calling below the line, finding information that you then present above the line. And those are all things that have been done. Like, they were true ten years ago. They're still true. And I just hired a SDR yesterday. And throughout that process, to me it was really a big — I mean, not a revelation, but it's like basically every SDR I talked to said, look, I mean, yes, we have a lot more tools, but in the end, what works best for me is calling. Right? And I think that's just really — I mean, it's not so surprising. It just basically tells us that all these other channels are highly crowded and very difficult right now because you can automate them, and everybody's doing it. So to a certain extent, right, then the question for RevOps is like, if you want to enable the SDR teams, right, like, okay, can you basically present as a unique research data point when people sit in their dialers so that they might have a higher success rate? The other piece I would say is the old openers and what you say in the first ten seconds, it's still true. And I think there's always been a disconnect from RevOps not necessarily managing that. This is typically the SDR managers coming up with the call scripts, with the email scripts back in the days. So I think there's kind of like — when we — I don't want to dig too deep, but when we think of the go to market engineering role itself, yes, one super skill is to automate all these things and make it more efficient and blah, blah, blah, but there's still the other element of the messaging and positioning. And so I think in the end, if you want to have real impact, real outcomes, and change the outcomes, you have to be good at that as well. And I think that's, to me, still a big question mark. Is that part of the — you know, role description for — you know, I always call go to market engineers modern marketing operations people. I don't know. Maybe it's the wrong way. Right? Like, and people will hate me for that, but, like, you know, but, like, clearly, to me, it's, like, part of the RevOps team. It's just a new role. And then where does this role start, and where does it stop?

Alexander Müller: I think this is becoming a little bit blurry over the last months, especially around, like, because, I mean, go to market engineers, they were quite hyped over the last year. When I always explain or look at how sales ops, RevOps evolve. Right? We start with in the seventies somewhere in the US, the first sales ops teams being founded. Right? Then kind of goes on like that in the mid two thousands, two thousand tens. This term revenue operations comes up, and then early twenty twenty three, twenty four, probably go to market engineers, kind of the new thing. And I think also I do agree with you that time selling with actual human beings will become more important. I think it's not about the blurbs anymore that we have seen in the past. Like, hey, you should say this and this about the customer. Right? Because if it's really mid market, you're probably that specialized. But most problems within the vertical, within the industry, they meet all of the customers that you're speaking to, and you don't have to go back and get, like, a summary of the LinkedIn history of the person that you're speaking. You can still do that quite quickly through scrolling. But I think we can make that whole process of actually doing the research much quicker. So for instance, I have a discovery bot that I always use that tells me everything about the company. Same stuff that I did in the past myself. Took me probably twenty minutes if I wanted to do it in a detailed way. Now it doesn't really take me five minutes. I get very good responses, and then I can dig a little bit deeper there about the person or about the company and so on and so forth. And to come back to your question, how does the role change? What I find really, really interesting is that suddenly we see responsibility for orchestrating agents as part of the job description of revenue operations. Now I've seen that. I've read that now. As I said with xAI, I've seen it with other companies as well. And I think that will be a very interesting trend because what does it mean? It means that the role of RevOps will change, will become not only more important in terms of who actually is responsible for the different AI tools that we bring in, but it also could mean how do we make them a channel. Right? And probably revenue operations will be rather responsible for setting these agents up. And then we will see who is responsible for owning them. Maybe there will be a new role around that. Maybe that's gonna be a go to market engineer or whatever. But the way that I see it is that there is just simply no other role in the whole go to market team that's better suited to own this than the RevOps team.

Philipp Stelzer: Yeah. Yeah. I fully subscribe to this. I mean, Weflow is a revenue AI and orchestration platform. Right? We don't focus on top of funnel, but for, you know, essentially automating data capture across emails, meetings, conversations, field meetings, and then map that to your custom data structure. And I think what we are seeing is that unification of data across different systems is basically the infrastructure. And that's actually often very broken. And then you layer your AskWeflow.ai or your AI workflow and agent build on top of that to then basically orchestrate actions and automate repetitive workflows. And this is exactly your example — like, hey, you go into a meeting. You have a pre meeting brief. After the meeting, the CRM is updated. You get the follow-up email. You get the summary. You get the AI to coach you on your MEDDIC or MEDDPIC compliance. And I think that is already happening. And in my mind, it's like RevOps suddenly has a superpower to drive from, let's say, sixty percent non selling activities to eighty percent of the reps' time spent on selling activities. Instead of five to eight meetings a week, you do fifteen to twenty or even twenty five. And I think that suddenly changes. If you think that through, right, you have five hundred reps. That absolutely changes the game for the companies. That is, I think to me, that's basically what RevOps' future looked like and what everybody of us should embrace because that in my mind hasn't been — I mean, to a certain extent, that's always been the goal. It's just now the tooling has become a lot better to do it. It's not all about the tooling. Right? I think there's obviously other challenges, but I think we try to, for example, make Salesforce a system of truth for fifteen years. That's always been true, but now it's suddenly a lot easier and a lot more automated. And I think this was the hard way. We learned this the hard way because we started with something that was Notion-like initially, right, like a workspace on top of Salesforce, was still fairly manual. And what we realized is you just have to automate as much as possible because otherwise it just doesn't happen. And I think that was just such an interesting learning for us.

Alexander Müller: Yeah. And, you know, what I just thought about is also how we use those tools changes. Right? I think it's probably a little bit far stretched that we go to, like, seventy percent of our time with sales activities. Right? That would be such a huge bump. I think even if you would get it, like, five percent up, ten percent up, that's already, like, a great achievement. And I'm also thinking more about the typical RevOps work that we've done in the past when we had contacts where we didn't know if the number was still right or it was flagged by our SDRs that something is not right anymore. Person not working in the company anymore. Person having a different phone number or whatsoever. That was a process that in the past we did literally max every quarter or every half year. And now you can do that instantly. Right? You flag it in Salesforce or in your CRM. Your bot fetches that information, runs through it instantly, checks your first data source, checks your second data source, looks if there is maybe a change in the position, like different employer or different phone number, and then sends that back and ideally sends it back to the system that you're already working with. Right? So be it Teams or Slack that you use for in-house communication. You get a notification right there. Hey, by the way, this lead was updated in our CRM. Go back to the CRM and try to call them again. Right? And just think of the many good use cases of it, not only that you increase the reachability, but also that you find out where did your previous users actually go and what other companies that you should place higher in your target list because of that. And I think these are the things that every company can get started with. I think what's a little bit harder is then really, like, big automations because what we usually do when we come into a company is that we look at the data first, and in most cases, we suggest them to, well, do a little bit of homework first together with us often to improve the database.

Philipp Stelzer: I mean, I think this is so true. Right? Like, it's actually — I mean, if you could just hook, you know, Claude into Salesforce and it gives you the truth. Right? I mean, in theory, if it's a system of truth, it should be able to do it. But the reality is it just doesn't work. Right? And so I think you have to be really good at the data foundation, your custom data structure, right, like the quality of the data, and then also the consolidation unification. That is basically the infrastructure. And similar to you, I talk to a lot of teams about this because we also solve this, and it is almost always a problem and something that I think you need to get started on to be really successful in this transformation. And look, I understand it's actually way easier said than done. Right? Like, get that, especially if you have ten, fifteen years of, you know, technical debt in your system architecture. It's not just something you flip the finger and it's done. Right? Like, but, yeah, fully agree. But, you know, what comes to my mind when I hear that, the forward deployed engineer that Palantir and the likes have laid out — I think also forward deployed RevOps. And that's like thinking a little bit, of course, of the Weflow use case. Right? I don't want to make this an advertisement show, but I think that's becoming, like, super critical that companies, before they use a new system, actually get their data right. And if they don't have the capacity themselves, that they use, for instance, such a forward deployed RevOps team to fix it so that you can actually get started with the implementation of new tools.

Alexander Müller: And I think that's kind of a trend that I'm expecting — that new tools or new applications, they will probably partner with the providers of, what do you wanna call it, RevOps as a service or whatsoever, to bring such a forward deployed revenue operations manager in and help you set up a specific process. And yeah. So a lot of the groundwork I see also being done there, and that's kind of why I'm thinking overall the RevOps role itself will just be super interesting for the next, yeah, ten years probably still. Let's see. Always changing.

Philipp Stelzer: So maybe a final question from my side. So when you joined us at our first episode, you basically laid out how you're building or how you built the Fortova team, growing that to two point five billion dollars in valuation. And so I'm curious, if you would create a team today, how would you think about the skill set and the type of roles you would include in that team? And I think this is absolutely not set globally. That's very different and a lot of influencing factors, but I'm curious how you think about it.

Alexander Müller: Yeah. So I would still say that how I approach it in general wouldn't or didn't change a lot. Right? And I still have the three steps — for those people who didn't listen to that episode, I think I said something like get operations right, put your analytics on top, and then get your enablement done. Right? And that's still how I see it because operations is kind of you built the systems, you built the Formula One car, and now you have a very fast car, hopefully. Then you need the analytics people to tell you, hey, in this corner, you're losing a little bit of speed. And then the enablement people there eventually telling your sales teams, your go to market teams, whoever is customer facing, hey, hit the gas here a little bit earlier so that we save a few seconds on the overall lap time. And I think that hasn't changed. Again, coming back to what we just said because the process, the data is just as important. So we still need to get our operations right. But I think it changes a little bit with regards to whom I would hire first. And also when I look at the different teams that I work with, who they are actually hiring and how many people they are hiring. So I see smaller companies that were established quite recently having usually a bigger tool stack, having more different applications, and they therefore also need more people to orchestrate those. So I always tell the little anecdote — I was sitting at a dinner that we hosted with the CRO of a company that has approximately, I think, five hundred employees and another RevOps manager was working in a company with fifty employees. Even though they were ten x difference in size, the RevOps teams were almost the same size.

Philipp Stelzer: Interesting.

Alexander Müller: Now you could call that inefficient, right, on the one hand, but it comes back to what kind of tasks are they doing and how much quota do eventually the go to market teams carry. And I think here, the change is really happening — that smaller companies are hiring operations people earlier because you don't want — or it's more than just a side job to set up the CRM, to set up the other agents. And then you're having usually more tools that you can easily plug in at an early stage, right, where larger companies have just more to consider. And also whom we are hiring changes. In the past, I always said, hey, there's two types of RevOps in general, more the tool guys and more the generalists. And in the beginning, I said, hey, would first get rather generalists. Now I would actually say, try to pair them, get two people quite early on, get a generalist, get somebody who's tool savvy, who likes to play around. And then actually, I think together, they can be a real dream team. Right? Because you don't want to have a new tool every week. You actually want to deploy it well so that everyone can actually use it and you, by the end, improve your revenues with it. Right?

Philipp Stelzer: Yep. Yeah. Awesome. Yeah. I mean, I think that a lot of people I talk to, they're like, right, there's efficient growth. RevOps teams are fairly small compared to the company size. Now AI comes on top. It's really stressful. So for everybody who is stressed out, you're not alone out there. That's the reality a lot of people are facing. But I hope this episode today also brought a bit of ideal state vision where this is going and how RevOps can essentially have even bigger business impact, which I think is what helps you be more strategic and helps really to then also climb the career ladder and just puts more emphasis on the importance of the role, which I think we very much fight for and root for. Alex, before you go, any book recommendation, research recommendation, anything — can be RevOps related, can also be to become a better human being.

Alexander Müller: Okay. Great, great question. General, I think — I'm sorry. I was just — what am I reading? I'm actually reading right now a ten year old book from Aki Hinza, who used to be a doctor for athletes, and that's totally not RevOps related. So I was just thinking, should I tell you about it? I think still it's a great book because it has a lot of —

Philipp Stelzer: What's the name of the book?

Alexander Müller: It's called Overperformance. And I need to look that up if that is actually right. Let me just — sorry. I can't tell you what it looks like. I know who's the — it doesn't matter. It's The Core. It's — there you go. The Core. Performance, better life. There you go.

Philipp Stelzer: Look. I mean, I ask you for life advice. Right? So typically, we ask our guests before the show, like, you know, for a recommendation. I actually forgot that. To put you on the spot.

Alexander Müller: No. All good. I have to admit. Right? I read a lot of books because it's just — I like reading. I just finished a few books on politics, but I don't read that much on revenue operations or sales. Why? Because I have to admit, I think most of it is actually happening in discussions. It's happening for me on LinkedIn. It's happening at events that I go to. And because everything is changing so rapidly, I think you can actually do a better job with deep research. That's what I always recommend people who are starting a new job, a new assignment. Go to Gemini, go to ChatGPT, do a deep research on the thing that you're starting with, and then you can bounce your thoughts back and forth. To be honest, I think that's for me better than getting yet another book, and I have a big bookshelf here next to me. And yeah.

Philipp Stelzer: Yeah. Yeah. No. I think that's great. I mean, for sure. Alex, thank you so much for joining. It was awesome. As always, good speaking to you. Wish you a great day.

Alexander Müller: Thank you so much. Speak to you soon. Bye bye.

Philipp Stelzer: Bye.