Janis Zech: Hello, and welcome to another episode of the RevOps Lab Podcast. I'm here with Philip, and our guest today is Laura Fu. Laura, welcome back.

Laura Fu: Thanks so much, Janis. Thanks for having me.

Janis Zech: Yeah. Great to see you again. I actually joined your podcast a few weeks ago. Now you're back.

Laura Fu: Yeah. You wrote a book. And, yeah. So it's, it's live. It's published in preorder.

Janis Zech: Before we dive into the book and digest, you know, what you wrote about, maybe for the audience who don't know you yet, like, who are you? What do you do?

Laura Fu: Yeah. So I'm Laura, and I currently work at DevRev, which is an AI native company. We are an AI platform that help connect all of your company data so that you can ask questions to the agents and make sure that all of your data is LLM ready. I run RevOps at DevRev and I also have the SDR function.

Janis Zech: Okay. Interesting. Well, that's — I actually didn't know that. That's a very interesting combination, and I think very timely with, you know, signal based outbound and agents you can build. But, that's not the topic of today. Today is essentially, you know, talking about your book, Designing for Excellence, Sales Enablement in an AI Native World. We actually had a previous podcast episode with you where we talked about sales enablement, best practices. And so, I mean, maybe let's kick off with, why did you write that book?

Laura Fu: Yeah, thank you. So I'm actually going to show the book right here too. This is the book. And I wrote this book because I was actually challenged earlier this year by Dheeraj Pandey, who's our CEO of DevRev. And enablement used to roll up into me as well. And now we've hired an amazing head of enablement. So one thing less off my plate. But at the time, he asked me, hey, can you write down your thoughts on what good enablement looks like and what that might look like in an AI native world? And I was anxious to do that. And when I finally started writing it sometime in April, I started realizing that this document or this white paper that I was writing was like becoming so lengthy. And I was like, I think this is a book. So I started like drafting out the sections and I was like, this is enough to make a book, you know? And I finished the first manuscript. I finished it in about six weeks, and then I doubled it in the next two weeks.

Janis Zech: Wow. Okay. Yeah. I mean, I think this is the way how to write books, right? You actually don't intend to write it, and then you start off and it becomes like a whole thing. So congratulations on that. Huge achievement by itself, I think. And as mentioned by others, it's up for preorder. So definitely we'll put the link into the show notes so people know where to preorder it. But one thing in the title I find very interesting and that is the second part of it, the AI native world. And I'm just curious, like, how would you define that actually? I had that recently someone coming up to me and said like, yeah, but what is AI native? What does that even mean? Like, you know, like digital native, AI native, so sort of like, what is the step that we are taking here?

Laura Fu: Yeah. So I think about AI native as where AI is built into the system so that we are doing things in a fundamentally different way because of the new AI capabilities that we have, not just that we're doing it faster or better, but we're doing it differently because of the systems and the situation that we have.

Janis Zech: Okay. Got it. So can anybody, any company turn to an AI native company in your mind? Or do you have to be sort of like started as an AI native company?

Laura Fu: That's an interesting question. I think that if you were building an organisation from scratch, you would, by definition, be an AI native company, if you adopted AI native tools and situations — you know, tools, systems, and processes. But if you are wanting to upgrade your existing systems, let's say you're upgrading your sales enablement program, and you want to make your go to market team more AI native, I think that requires some breaking down of your processes. So it might look like one part of it is AI native and the rest of it is still legacy. Maybe with bolt on AI, we're doing the same things. We're just doing it a little bit faster and better with AI. That would be how I would answer the question.

Janis Zech: Yeah. Okay. Good. Makes sense. I think also, in my opinion, any company has a chance to become like an AI native company if they really just — I think they do have the chance to. Certainly should be possible. I would be sad if that wasn't, like, possible. But maybe let's just dive into the book. Curious sort of, like, how did you structure it? You know, what are you kind of like trying to cover — particularly, I think in the first chapter, probably makes sense that we take it step by step and just go through the book. Kind of like one chapter at a time to understand sort of like the greater picture that you are painting with it?

Laura Fu: So the book is actually structured in five sections. And the first part of the book, section one and two, cover the guiding principles of sales enablement and also core outcomes and metrics. And we talked a lot about those in our last podcast last year, which is just around sales enablement in general, like the principles of sales enablement, how we get a sales organization up to speed and running and ongoing, like what are the things that we should continue to do? So that in a sense has nothing to do with AI. These are just like best practices of the program itself. And then we go into section three, which is the enablement flywheel. And the enablement flywheel talks about the five pillars of the flywheel that are required for it to spin and kind of move. And one of those pillars specifically talks about sales engagement tools and the new things that AI native companies are doing and what those tools look like. And so like starting in section three is where we start talking about how AI actually changes the way we think about all of these pillars. Section four is around what you need to get there, like what actually needs to change? What kind of platforms, what kind of systems you might need to adopt. And then the last part is just the closing around sales enablement in the AI native world, like why you want to do it and why that's going to be important for you moving forward, like a closing section.

Janis Zech: Awesome. Maybe let's start with just as a recap of our last conversation, what are the five core principles that define effective sales enablement?

Laura Fu: So we're gonna go there. Okay, so the first one is around enablement driving the performance through systems. And I think we talked about this before, where enablement is not a single person. It's not like the onboarding program. It's actually all of these things in combination. And it's actually the ecosystem. One thing that I think we tend to forget is that the ecosystem of marketing, R&D, all of those things are actually part of go to market enablement as well. And then the second part is around enablement requiring motivation and reinforcement. And that you can lead a horse to water and you can give all of the amazing assets that the rep needs. But if they're not motivated because they're not inspired by where the company is going, they're not going to consume it. So it actually requires a lot of motivation and continuous reinforcement because people need to hear something seven times right before they remember it. Third one is reps deserve clarity and that brings confidence. I think a lot of times, especially you know in startups, the industry is changing, the message is changing, the focus changes quarter over quarter. If we as leaders are not bringing that clarity on what's most important and telling the reps, here's what's most important, here's what was important last quarter, here's how we're threading the needle to this quarter, that's actually gonna bring them more confusion. And confusion impacts confidence. I think a lot of what we talk about in sales enablement is about how to get the reps inspired and confident about the thing that they're selling. And then the last one here is that rep development is owned by their managers and supported by enablement. That it's not just something that you outsource to enablement, like you got to own it.

Janis Zech: Yeah. I love all these. I mean, I think, like, the principle of, like, you know, communication, repeating something seven times, reinforcement learning. Right? Like, not just saying it once. And I think that's also why the managers play such a big role because if they don't do it day to day, right, like, you as a, like, enablement function will find it very hard. So I think the enablement of the managers is one of the key glues to actually drive performance. And similar, right, like, if you're a manager and you don't care about that, like, if you're not motivated, similar to the reps, right, like, it's just impossible to get you there. And I think we've all had these situations where we had colleagues that just go and they want to learn more and they want to go and they're motivated. And then we have people that are just not that way. Some of them can still be very successful because they've been there and done this for a long time, but generally more fun to work with the kind of other half, I'd say, at least for me personally. I think we could obviously dissect all these things. Right? Like, we don't do that today because we've done that in the last one. But I think you have this idea of, okay, let's assume you do all this. What do you focus on in terms of metrics that you measure and what are metrics that matter and what are metrics that are maybe just lagging indicators of success. Right? So I think you have that section in your book. To me, this has always been — obviously, the end goal is sales productivity, or AI per head or CAC paybacks. You can look at it in different metrics, but these are all lagging indicators of success. What do you measure? How do you know that these enablement programs actually bear fruit?

Laura Fu: Yeah. My number one measure that is lagging for sales enablement, but leading for the go to market organization is pipeline. The ability for the rep to create pipeline and the activities associated with creating that pipeline. It's really interesting, right? Because we think sometimes when we become a rep that works, we get all this pipeline from the SDRs. And I'm going through that right now, since I own the SDRs. But if you think about it, if pipeline is lifeline — we've heard about that, pipeline is lifeline, right? But then we're leaving our lifeline to these newly graduated SDRs or people that have a lot less experience and we're expecting them to develop some of these conversations that wouldn't happen without them, it's kind of crazy. So I actually think that pipeline, the ability to create pipeline is owned by the rep. And if they are able to actually do a good job with opening up those conversations, establishing the pain that the company has, the rest of the stuff like closing the deal and getting the technical demonstration done and validation and the POC — those things, they're gonna have a lot of help with. The SE is gonna help them. The entire executive team is gonna come on board and negotiate. That's all for me lagging stuff. The leading stuff is, are you actually able to have a conversation, open that up and get a customer to buy into the evaluation cycle? To do that, you're going to need to have a really good handle of the product, the solutions itself, and the ability to be creative in terms of what your customer needs, identifying their pain, and then matching those two things together. So that for me is if they can do it, they're productive. Productivity for me is can they build pipeline? And then the measure is okay, well, how much pipeline do they need? Usually we just say, okay, well, for the next quarter, they need 3x of what their quarter is. Are they meeting that number? That's my key number one metric.

Janis Zech: And I mean, so this is very much not what I expected. So that's great. I love it. I totally get it. But, like, what does it mean to create pipeline? Does that mean that they basically should spend eighty percent of their time on prospecting? What would be your expectation towards, let's say, a MQL? Do you mean, like, the conversion of an MQL? Let's say, you know, it's loosely qualified, it comes inbound, or it comes via an SDR, and then they use that, and then they basically convert that into a qualified opportunity? Or are you basically talking about them going out and doing the SDR job or doing the marketing job? Or maybe a combination of both. Yeah, I think super curious what you think about that.

Laura Fu: It's getting a lead to a qualified opportunity stage, right? Whether getting that lead is getting it from the SDR, it's getting it from marketing, it's orchestrating the combination and the interaction of the lead attending an event and then getting followed up by the SDR or them calling the lead themselves or meeting them at networking events or whatever. It can be a variety of things that they're doing upfront to get it to a qualified opportunity. And at DevRev and in many other playbook companies, there are specific qualifying activities that we do. Two of them are, one is the discovery meeting, and the other is the new business meeting, which is like the echo back to the customer on what their pain was and how we might be able to help them. And if we can train our reps to do those two things really well, then that becomes pipeline opportunity. Those two things are the leading activity that gets to the pipeline number.

Janis Zech: Yeah. And just curious about that one. So how did you identify those as being the leading indicators? Was this because, like, here, you saw basically the biggest break off in the conversion funnel with — in quotation marks now for our listeners — bad reps, or reps that are not performing that well versus reps that are really high performing? Or how did you identify this? I mean, just to be clear, right, it makes total sense to me listening to it. I'm just curious if there was a quantitative system underneath it to get to this.

Laura Fu: So it's actually a metric that I've used over the last three or four companies that I've been at. And we developed this actually at Sprinklr where we talked about how productivity is actually — how pipeline is equal to productivity in the beginning. And that leads to eventual performance. And we actually did do an analysis. The reps that were able to create their pipeline and were able to execute on these activities upfront and earlier in the cycle, they were actually closing business in the sixth, seventh month of their ramp compared to the ninth, tenth month. And a lot of it is just because sales cycles, they take a long time. And if you're not staying on your new reps and you're forcing them into productivity, they're just going to lag. By the time their ramp period is over, they won't have enough pipeline. And they're focusing on, let's say, closing a deal that maybe inbound marketing brought them, right? But then they're not training, they're not honing the skills that are actually needed to open those conversations, which is what they're gonna need to do on an ongoing basis.

Janis Zech: Yeah. No. I mean, I fully agree. I think it's one of the hardest things I think to do as a rep. Right? I think it's building your own pipeline and expanding it. I think it always depends very much on the company that you work for and like the type of product and the stage and so on. But, yeah, a hundred percent, and I think this segues nicely into section three because, like, basically, if you're saying, okay, this is the really the leading behavioral indicator, and, you know, what you outlined earlier when you gave an overview of the different sections, like building an enablement flywheel that is really focused on coaching and content. Right. So this is where what it's all about. Like, you have to sort of, like, qualify them, understand their problem, understand their pain, reflect that back to them, really understand the product, and be confident about it, so you can also speak confidently about it to the prospect, which then again projects, you know, confidence for them into the product and so on and so on. Just curious. So how are you basically building an enablement flywheel then for these leading behavioral indicators?

Laura Fu: So the flywheel is structured — it's actually three components plus a fourth one in the middle. Okay, so the four pillars are content, which is the foundation. Like we gotta have the right content, right assets. That's all clear, right? Okay, second is programs. The programs are the thing that continuously deliver the content and how that works. There could be a sales onboarding program, it could be an ongoing learning program, like what those programs are. The third pillar is the sales engagement tools. Like what do we use for sales enablement? And how do we make sure that it helps the rep engage within the flow of their work? Sometimes we think about tools as some place you go to do something versus something that goes into the work that you're already doing. So that's the third section. And then the middle section, which is like the engine, I guess, it's the analytics pillar. And how does that actually measure and make sure that we are on the right track?

Janis Zech: So just like, how does a rep use this then basically on a day to day basis? Right?

Laura Fu: So, okay. So if you think about it in these three things and the analytics, okay, the idea behind an AI native flywheel is that this whole thing is a closed loop. It's in real time. It delivers the feedback to the rep and the manager in a way that can be adjusted so that it can actually impact outcomes. So example, there's a new product that we are launching. And as part of the new product that we're launching, we have some marketing assets and we've got some enablement assets that we want to train the rep on. First, the content is created and it's delivered to the rep in a way that is popping up in their day to day work. So maybe it's part of their Slack channel when they open it up. It's like, hey, your enablement tidbit of the day. It's something that they can consume in a very easy way. And it's not like they have to block off time in their calendar and they have to go through the whole thing. It's real consumable, real easy. Maybe it's even in a deal that they're filling out. They're like, oh, they just had a customer conversation. And the system should be able to identify that gap and say, oh, you might be interested in this new piece of product that we're launching. And it shows it to them. They'll read it in the moment because right now, at that moment, it is relevant to them. But the content has to be good. So that's where programs come into play. Like how are we delivering that content to the rep in a moment that they're actually going to care? Most of us, when we go to enablement sessions, we're like, I might learn something. But it's always like, I got to take time out of my day to do it. People learn when it's absolutely in that moment relevant to them. So it's like within the deal cycle or they're talking to their coworkers in a Slack channel. Like it would be great if they had this thing. Hey, sounds like you're talking about this. Have you heard about this thing? Okay, then part of that program could be, hey, do you want to practice? Do you wanna practice pitching it? How would you say it to your customer? A little AI avatar can come up and talk to them and they can pitch. Well, this is what I'm thinking. The tool itself is the system of enablement — it's a real life avatar that can actually coach them through what they're going to say. They can practice it. What are the things that the customer is going to do or whatever? And the analytics portion can be related to that, which is where the analyst can tell you, well, you talk too much or you should revise these things. And it can also feed back, let's say, all of these conversations that we had around this new product that we were trying to release that you were trying to educate the reps on. It should feed back to the product marketing team and tell them these conversations worked really well when they said these things. This asset didn't work so well. One of the tools that is actually in my book here too is a self updating deck, where based on your conversation with the customer, the deck actually just updates. Here's what we hear, here's what the solutions should be, etc. I think that is so cool because that's what we need. It's like before going to the slide where like, oh, well, maybe this isn't relevant anymore, it changes so that you can actually have a relevant conversation.

Janis Zech: Yeah. I think it's so interesting. I mean, as you know, we're in the AI notetaker space, so we have a conversation intelligence product. Right? So, like, you know, basically, after the meeting, every meeting gets scored, you know, against your sales methodology. So you have, you know, self coaching feedback immediately built into the conversation, and reps go there anyways because they, you know, wanna send a follow-up email, which is then AI generated, or they wanna update some Salesforce fields, which is automated with AI, and they have the summary. They might check out, you know, the specific pricing conversations or so. So it's basically in the flow of their work. They don't need to do any additional things. And I think where this is all heading, right, is very much like, you know, why would I not have a pitching conversation with an AI avatar. Right? Like, why would I not have basically the entire product documentation delivered at the right time when it's actually relevant to me? Right? Like, and I think this goes further with the content that is created and so on. Again, I think in the podcast we recorded, in your podcast, we talked about the AI use cases. I think some of those things are still not available in the market. Some of these things are very much available. But I think that's where it's all going, and that's what's possible today. And I really love this — learn when it's relevant for you to learn because I get a question from a customer. I don't have the answer, but I want to know the answer. But I forget about it like two days later. So ideally, then it should be surfaced. Right? Or right after. And I think those things are things that are being built these days. And I think it's quite an exciting time now.

Laura Fu: Just — I think the ability that AI gives us right now is speed, right? It's like capturing on time. We could do all this analysis at the back end, which is what we used to do. We would download everything and we do all the data and we look for trends. And now we don't need to do that anymore. Like the system can just tell us what's good and what's bad. But then we have to actually act on it. We have to be set up to receive that feedback in a real time manner that does something different for the rep. So for example, if we are getting all of this information about which assets work really well and which don't, which is confusing, the teams themselves have to be agile enough to be able to address that, right? And like change the content, change pricing maybe, you know? And we know that those things traditionally are like three quarter projects, year long projects, right? So if we're not actually set up for the AI native system — and actually that's what section four of my book talks about, which is like, what does it actually take? And a big part of what it takes is, well, the organization itself has to be set up for it, because otherwise it's a waste. You get the feedback, but then you're not able to adjust and be agile in real time.

Janis Zech: Yeah. I mean, I think this is generally a huge challenge. Go to an enablement session, and basically someone who is maybe not in the day to day weeds shows you something that is not backed up by data to basically say, hey, you know, you do this, you're more successful. Every rep out there wants to be more successful, but they need to believe that it actually drives success. Right. And so, right, like if it's detached from their day to day reality, it's actually really hard. So obviously, right, like from an enablement perspective, leveraging the core transcripts, leveraging the data that you have to actually identify what works, what doesn't work is I think the feedback loop that has been also missing, right? To basically inform the programs, inform the content, inform the best practices. But maybe going back to what's needed to get there — right, like this is obviously fundamentally changing, right? We're basically before the SKO season, right? Like, okay, let's do it once a year and then, you know, we're done. Just joking, right? So what needs to happen to implement what you're outlining in your book?

Laura Fu: Yeah, I think a lot of what we talked about in section one and two, which is around the best practices of enablement — like first that has to be done. You know, if we're not thinking about enablement as a whole company problem, or just like pipeline as a whole company problem, then fundamentally, we can have the best systems. You could set up like this best amazing flywheel with all of the great tools that you have. It's just not going to have the same impact. So the mindset around enablement, I think has to change fundamentally. The second thing is just like I think any kind of AI mindset shift — the organization itself has to get ready for AI and be ready that things are going to change faster. And they have to be ready to say, hey, I know I may have used this legacy tool for a long time, but it's actually not going to be working anymore for what we want to do as an AI native process. And it could be like the CRM. I'm not saying CRMs are going away, but I am also saying that I think that my eventual vision is that we live in a CRM free world because any kind of data that we want, we can just ask the AI and the AI will tell us, well, this is the answer. This is your average sales cycle. You don't actually have to do any data analysis. And making those kinds of decisions are tough, right? Because you have to convince everybody that you don't need something or you want to invest in something else. And if you're not willing to give up on what you have today, I think moving forward in the next direction is difficult too.

Janis Zech: Yeah. I mean, I think it's always super hard. Right? Like, I think as soon as you want a cultural shift, it just takes a lot of effort from the entire leadership team and company to get there. But I also feel like this is something probably where you can get to step by step, right, like building a flywheel like this. You probably don't wanna do that in, like, one big — I don't know how to call it —

Laura Fu: Like tomorrow. Very aggressive, you know.

Janis Zech: Yeah. Exactly. Yeah. Like one big heavy lift motion, like you actually want to maybe take it step by step and iterate, and also because there's so many tools and models out there that you can actually try out, and then iterate on, and then, you know, quickly discard, and then move to the next step. But that's actually sort of like, you know, where I wanted to get — that is, you know, if you have — since you've been going through this, right, and you wrote a whole book about it, what is like a piece of advice you would give to, you know, sales enablement teams that are currently, you know, looking to get there, looking to apply AI in their everyday life. And, yeah, I think that would be very helpful.

Laura Fu: Yeah. I think the first thing that I'd say is have a look at your entire sales enablement process and the tools that you are using to enable each of these processes within the sales cycle for your reps. So I would do an audit first of that. And I would see whether these existing tools that you have, do they already have AI capabilities? If they have it, that's great. You can start by just turning some of those things on. That's gonna help us do it faster. Maybe not do it in a different way, but I think doing it faster is one way to start adopting the AI. But in that process of mapping out the systems and your sales process, find the — and for me it's pipeline right — but find the part that is going to make the most difference to your organization and see whether there are components of the AI native flywheel that can be applied. So let's say it's pipeline generation, and you want to focus on that part. Okay, what are the things we can do differently? Right? So one of the things that we've been talking a lot about is intent based and signal based pipeline generation versus just, you know, here's your ICP and just going after those things. Like, are there things that you can do to actually enable that part of the process? And if you find the most meaningful one, and it has impact on what that looks like, then the others will be a lot easier lifts. But the main thing is just like any kind of change management, you have to stay on it and you have to be all over it. You can't just turn it on and expect it to work. You have to follow up and ask, is it working for the reps? If not, let's change it. Let's do a lot of stakeholder buy in, that kind of thing.

Janis Zech: Yeah. There's one thing that I always have to think about when somebody talks about change management and process that takes a long time. We had a guest on the show who talked about the implementation of a MEDDPICC process in a company. Took them three years. And so I just wanna — just as a reminder to our listeners, right, like — or maybe it was two years, I remember wrongly, but it was pretty long. It was at least two years, and yeah, and it's just like, I mean, I would not expect, right, like, a shift to, like, a fully enabled flywheel with heavy AI systems to be something you can do within three months. It's just like, if you have, like, more than twenty, thirty people in your org working on that topic, like sales, I mean, then just forget it. It's like, it just takes some time. Yeah. And so it's so important to identify the thing that moves the needle and then, you know, essentially, you know, create quick wins so that the reps buy into it and the managers buy into it. Right? So that they see that what you are jointly working on as a team helps them become more successful. I think this cannot be stressed enough because it goes back to this, like, self motivation. Right? Like, if people — if it's just another thing on their plate, they're just gonna ignore it, and they will fight it. And if they see it makes them better and helps them be more successful. So I think in the end, you cannot go the way alone. Right. You have to basically align. You need to see that people say, yeah, no, this is great. I want to become better here. And so taking maybe top of funnel MQL to SQL conversion, if that's the thing where you feel like there's a lot of potential, or the signal based or intent based outbound — whatever it is, I think then honing in on that and creating those success case studies almost, I think that is super important. Almost thinking of it a bit like a product. Right? You have a product and people love the product and the outcome, and they basically tell their friends, suddenly it becomes a lot easier. Look, typically we ask all our guests what is a book you would recommend, but we talked about your book a lot. So obviously you'd recommend that book. Do you have any other book, research report, anything that RevOps people would appreciate to learn or connect that you would recommend?

Laura Fu: That's a good question. I don't think I've read any RevOps books recently. However, one of the books that I read earlier on this year, which had a big impact on me was this book called The Courage to Be Disliked. Have you heard of it?

Janis Zech: I haven't heard of it.

Laura Fu: The Courage to Be Disliked — it's not actually about being disliked. It's more about the ability to stand for the things that you really believe in and accept where you are right now, because then you can actually make improvements. I really recommend the book. It is actually the sequel to The Courage to Be Happy or something like that. And it's really reflective, I think, of this current stage where we're in, where it's like, it's okay that we're here in the beginning of the journey. As long as we recognize that and like, okay, here's where we need to be, how do we get there? And let's just make a path towards that. Some of our opinions are not going to be popular along the way. And that's okay. We just want to make sure that we're honest with ourselves on where we are and where we want to get to. And it's also okay if we decide, hey, making this huge AI shift is too much for us and our priorities in the next two to four years are just gonna be, let's adopt AI in a very lightweight way. That's okay also, right? So I think it's just acknowledging that we all don't have to be the same and we all can have a different opinion, but that it's most important to be honest with ourselves and our leaders and the organization.

Janis Zech: Love it. Yeah. Laura, thank you so much. This was great. Really enjoyed it. I learned a lot. Have a great day.

Laura Fu: Likewise. Thank you. Thank you so much.