GTM 172: Why Old Playbooks Are Breaking and How AI Assisted GTM Actually Works

Dave Boyce is a go-to-market–focused technology executive, author of Freemium, and longtime SaaS operator and board member. Over two decades leading and advising B2B companies, he’s helped founders navigate the shift from sales-led to product-led, from gut-driven to instrumented, and now from manual GTM to AI-assisted systems. Today, Dave teaches revenue architecture and PLG through Winning by Design’s Growth Institute, works with leadership teams on Bow Tie–based growth models, and is all-in on how AI will reshape GTM as a true engineered system.

Discussed in this episode

Why classic sales & marketing playbooks haven’t caught up to how modern buyers actually buy.
How the Bowtie model exposes the real levers of growth that funnels hide.
Why PLG-style thinking is now essential even for sales-led and enterprise motions.
The 3 first principles of freemium: empathy, generosity, and metrics.
Where AI can reliably outperform humans across the customer journey, and where it absolutely shouldn’t.
How to design hybrid human + AI workflows using a clear data model, not vibes.
What RevOps should own in a modern revenue architecture (and why it can’t just serve the CRO narrative).
Hard-earned founder lessons from Fundly on reinvention, calling bets early, and letting go of old branches.

Episode highlights

00:00 — GTM is still running 20-year-old playbooks
Watch: https://www.youtube.com/watch?v=oPi9-LPXNoM&t=0

01:29 — “Sales, marketing, CS… the last unengineered engine”
Watch: https://www.youtube.com/watch?v=oPi9-LPXNoM&t=89

03:20 — The myth of “just add more heads”
Watch: https://www.youtube.com/watch?v=oPi9-LPXNoM&t=200

05:50 — The Fundly story: reinvention, too late
Watch: https://www.youtube.com/watch?v=oPi9-LPXNoM&t=350

08:30 — Why Freemium had to be written
Watch: https://www.youtube.com/watch?v=oPi9-LPXNoM&t=510

11:01 — Three first principles of freemium
Watch: https://www.youtube.com/watch?v=oPi9-LPXNoM&t=661

15:25 — Mapping AI across the entire customer journey
Watch: https://www.youtube.com/watch?v=oPi9-LPXNoM&t=925

19:29 — “Automate the predictable, humanize the exceptional”
Watch: https://www.youtube.com/watch?v=oPi9-LPXNoM&t=1169

25:18 — What the Bowtie exposes that funnels hide
Watch: https://www.youtube.com/watch?v=oPi9-LPXNoM&t=1518

27:25 — Building a “minimum viable Bowtie
Watch: https://www.youtube.com/watch?v=oPi9-LPXNoM&t=1645

Key takeaways

1. GTM is finally being treated like an engineered system.
Most GTM teams still run on analogies, habits, and heroic reps instead of explicit design. The companies pulling away are treating revenue like manufacturing or aviation—instrumented, monitored, and continuously improved, with AI as “robots on the line” rather than random tools.

2. Your buyer already did the discovery.
Old rules like “never demo without discovery” and “never give price until you reach the budget owner” assume information scarcity on the buyer side. Today’s millennial and Gen Z buyers know the options, the pricing, and the reviews before they ever talk to sales, which means GTM has to meet them where they are, not force them into dated motions.

3. Assistive AI sets the floor, not the ceiling.
Email writers, meeting summarizers, and AI coaching are table stakes and will quickly become the new baseline for individual productivity. Durable advantage comes from engineering growth as a system and then assigning “robots” (AI agents) to specific jobs within that system.

4. Robots need onboarding too.
You can’t just buy an “SDR bot” and expect magic the next day. Just like humans, AI agents need a designed system, clear responsibilities, training data, guardrails, and ongoing coaching—once that’s in place, they don’t forget, don’t tire, and can scale in ways humans can’t.

5. PLG is just empathy, generosity, and metrics at scale.
Modern freemium playbooks start with deep empathy for the end user’s job to be done, give away enough value generously to build habit, and then rely on product analytics to observe, learn, and tune. If you’re not instrumented, you’re guessing—and guessing doesn’t scale.

6. Self-service is a one-way trend.
In your own life, you rarely talk to a human to buy gas, groceries, or apps. Your customers feel the same way about software: if your competitor lets them trial and succeed on their own while you gate everything behind a demo, you’re training them to choose the competitor.

7. The right side of the bow tie is where compounding lives.
Most leadership attention, budget, and headcount still cluster around “calling and hitting the bookings number.” The bow tie model makes it painfully obvious that renewals, expansion, and customer-driven growth loops on the right side are where long-term compounding really happens.

8. AI can finally make complex products feel simple enough for self-serve.
AI isn’t just about speeding up existing workflows; it can also de-complexify entire product categories that used to require months of human-led onboarding and configuration. That unlocks PLG-style motions even for heavyweight enterprise tools that were previously “too complex.”

9. Revops has to become the backbone, not the reporting team.
If RevOps’ job is just to make the CRO look good in board meetings, the system will never get truly instrumented. To run AI experiments, bow tie analytics, and continuous GTM tuning, RevOps needs to be the objective owner of the data model and truth, not a political function.

10. Leaders must shift from “I know the playbook” to “we’ll figure it out.”
There is no 15-year-old manual for AI-assisted GTM or PLG + agents; nobody grew up selling this way. The best executives are showing up as “chief figure-it-out officers,” bringing smart people together to run experiments rather than recycling war stories from a different era.

This episode is brought to you by our sponsor: ZoomInfo

ZoomInfo is the GTM Intelligence Platform built for sales, marketing, and RevOps.

By unifying data, workflows, and insights into a single system, ZoomInfo helps revenue teams find and engage the right buyers, launch go-to-market plays faster, and drive predictable growth. With industry-leading accuracy and depth of data, it gives your team the intelligence advantage to win in competitive markets.

It’s trusted by the fastest-growing companies and has become the category leader in GTM Intelligence.

Learn more at zoominfo.com.

Recommended books

Freemium by Dave Boyce
A practical playbook for building and transforming into self-service, product-led, and freemium models—especially for companies that weren’t born PLG.
Competing Against Luck by Clayton Christensen
The definitive exploration of Jobs to Be Done and how customers “hire” products to make progress, which heavily informs Dave’s approach to empathy and product design.
Playing to Win by Roger Martin
A concise strategy framework Dave leans on for making clear choices about where and how to compete.

GTM 172 Episode Transcript

Dave Boyce:0:00

GTM has not gotten that overhaul. We’re still doing what we learned to do 20 years ago, and now we think it’s still gonna work today.

Sophie Buonassisi:0:06

In this episode, Dave Boyce, who scaled winning by design’s revenue architecture practice, breaks down the steps to rebuild the go-to-market engine for an AI forward world. He explains why PLG principles now sit at the center of AI-driven revenue and the shifting buying landscape.

Dave Boyce:0:22

Buyers are in control, sellers are not in control.

Sophie Buonassisi:0:25

We cover how to map your customer journey into a measurable architecture, where AI can reliably outperform humans, and what hybrid human AI workflows actually look like when well designed. We also dig into the Bowtie model, which maps the entire customer journey, both pre-sale and post-sale, in a single connected system. All right, let’s get into it. Dave, welcome to the podcast.

Dave Boyce:0:58

Thank you so much, Sophie. I can always tell when it’s a pro.

Sophie Buonassisi:1:02

We are gonna have some fun. And I have so many things to ask you. So let’s dive right in. Rock on because you set at this really interesting in intersection where you could just see a lot of different companies. And so given the patterns that you’re seeing across dozens of go-to-market organizations right now, what part of the go-to-market engine is structurally breaking? What’s changing? And what do leaders really still think is fine, but we should be evolving towards?

Dave Boyce:1:29

In my opinion, you know, talk about that as like sales and marketing, and I would include customer success and account management, is um is one of the last kind of major economic engines that hasn’t been engineered.

Sophie Buonassisi:1:46

Yeah, and we hear all the time about playbooks, right? Some are becoming outdated. And I know ourselves on the investor side, it’s super interesting because we’ll help facilitate uh the connections between a lot of go-to-market experts and portfolio companies. And the playbooks that worked for some operators no longer apply to every single company. They all require their own unique nuance. So I’m curious, like from your perspective, also seeing a lot of different companies, what is that evidence that people are still clinging to these kind of outdated models and not crossing the chasm that you said?

Dave Boyce:2:18

I mean, gosh, Sophie, like I mean, this is gonna reveal my age, but you know, I was taught um never demo without discovery. Like always figure, always do discovery first and then a demo. That’s one piece of kind of power that you have. Never give a price without getting access to someone who controls a budget. Um, those are things I was taught. That ain’t true today. Like buyers know how to find what they need, buyers know what they need when they need it. We’re not in control, sellers are not in control, the company’s not in control. But then what’s the evidence that we’re missing that signal? I mean, literally, like last week, a CEO came to us all excited about engineering GTM, brought his brand new CMO and brand new CRO with him. They want to get from 100 million to 200 million in ARR. Okay, great, amazing. How are you gonna do that? We’re gonna add 15% to our headcount, we’re gonna spend more on marketing, and we’re gonna get and we’re gonna move up market.

Sophie Buonassisi:3:20

Heard that before? But AI is a big catalyst of that. And you’ve been, I mean, deep in the trenches of AI. We ourselves, we hear obviously a lot of excitement around AI. We see a lot of AI tools from more of a startup investment perspective, and we hear a lot of excitement across our LPs in in the operator side of the house. Everything from you know, email writers, meeting summarizers. I think I’ve seen multiple meeting summarizers just this week alone. But it seems like, and we both know the real shift is is far deeper than better tooling individually. Like, what are the biggest misconceptions anyone has about AI’s impact on revenue right now? Like, how should we be thinking about this?

Dave Boyce:4:01

We gotta start thinking about growth as a system.

Sophie Buonassisi:4:04

So if you had to actually describe the current state of go-to-market in one sentence, let’s say, what would it be?

Dave Boyce:4:11

Well, I’ll give you an aspirational statement. I think it’s more designed, more architected, more engineered, more closely monitored, more iterated, uh, more closely managed. That’s the operation, that’s the aspirational kind of statement. Most of us are not there, but the ones who are, like you look at the kind of growth hyperscalers, they are that.

Sophie Buonassisi:4:32

Yeah.

Dave Boyce:4:33

They are that. And many of them came out of a PLG background because PLG requires you to be that, because you have to program GTM into the process, into the product, into the systems, because it’s going to run on its own. Um, those of us who didn’t build PLG companies, you know, we were able to get away with kind of like, and then she’ll do something great, and then he’ll pick it up and he’ll he’ll figure it out, and then you know, I’ll hire someone who really knows how to do that piece of it. And it’s very kind of bespoke. Um so aspirationally, we’re more designed, more engineered, more calibrated. But in reality, I think most of us are on notice right now as we watch these hyperscalers, and we gotta retool.

Sophie Buonassisi:5:14

Mm-hmm. Yeah, it really has leveled the playing field, is what it feels like. And I hear you on PLG companies perhaps having an advantage because they’ve had to design these systems from the beginning. Yeah. But you yourself, you know, you you were a founder, you’ve built a company, Fundly, and it’s kind of one of those rare founder journeys where the the stakes are existential as most are. So take us back to Fundly. Love to hear a little bit more about what that reinvention looked like for you. And I know you had a crazy kind of time fundraising and everything. So love to hear a little bit more on that story.

Dave Boyce:5:50

So you want to pull me into my trauma space, huh?

Sophie Buonassisi:5:54

Yes, this is actually a counseling session. Okay, fantastic.

Dave Boyce:5:56

Let me pull up a couch. Um well, you know, it I think the experiences I had as a founder are not rare among founders, but it’s but very few of us step into that space because it’s scary. And once we do, we realize why we didn’t. Because it’s super scary, it’s super hard. Um, and you use the word reinvention. I I think um, you know what, funnily, we um I’ll I’ll say this. Did we reinvent? Yes. Did we get to uh good unit economics? Yes. Did we do it in time? No. Like, and it was because of me. It’s because I couldn’t call it soon enough. I couldn’t call balls and strikes soon enough. I couldn’t tell someone that the thing that they had built was gonna get disassembled and we were gonna build it anew. I mean, I did, but I just did it too slowly. Like, this person who we get hired for that reason is now fired, and now we’re gonna pull a new person in. Those are like human decisions. The work that I did to build kind of in that direction is not working, so I’m gonna disassemble it and we’re gonna go in a different direction. The money that I raised from those investors based on what I told them days ago or weeks ago, is now gonna be deployed in a different direction. But I’ll tell you, Sophie, I had a I had a hard time with it, which is why we did it too late. You know, we raised a seed round, it was super um super encouraging. Um we didn’t pivot after the seed round. We kind of like tried to straddle strategies. We’ll keep this one going because it’s making us money. While we launch our kind of next thing, we’ll try to not let go of the branch we’re on before we have a firm grasp of the next branch. It moves it moves faster that than that. I found a world. You just freaking you get a little money in the tank. This one’s not gonna be your future, you just let go. You drop it, you literally drop it, and you go to the next thing, and that is super scary. And when I did that after the series A, that’s when everything started working. Um, but we were a firm number two in the market, and we should have been number one.

Sophie Buonassisi:8:02

Yeah, that’s I mean, I’m sure you could write a book on all the learnings from the director, funnily.

Dave Boyce:8:08

What a good idea.

Sophie Buonassisi:8:10

But you did just write a book, freemium.

Dave Boyce:8:12

Yeah.

Sophie Buonassisi:8:13

And congratulations for some premiums. It is perfectly timed, as we’ve discussed, with PLG intersecting with AI, and as go-to-market teams really rethink cost structure too. What was the spark that made you decide that this book needed to exist?

Dave Boyce:8:30

Oh man, I you know, it is perfectly timed, but I didn’t think so. Like I we got when I so I saw a long-term trend. Um I believed that self-service buying is a long-term trend. It started kind of in the digital era with e-commerce, and then it came to then e-commerce kind of pushed onto our mobile phones, and then and then SaaS became a thing, and then SaaS became self-service through PLG, and then PLG, you know, extended into more and more complex use cases through AI assistive kind of buying. I could see that trend. I could see it coming, and I knew that the definitive book had never been written, even though we had companies like Atlassian and Canva and Twilio and DocuSign and Dropbox that had built amazing companies based on self-service kind of PLG models. No one had written the definitive book, and I was like, can that really be true? Like I called my friend Mark Roberge and I’m like, I’m I’m teaching this now at uh in the MBA program at BYU. I’m gonna write a book. He’s like, okay, great, you should write the book. It needs to be written for people who didn’t build that way, not people who did build that way, because we had a lot of kind of blogosphere stuff going around PLG. You almost couldn’t turn around at Silicon Valley without hearing PLG. This is three years ago, so if you like, and um he’s like, and I’m like, okay, cool. So which cases should I use? And he’s like, Yeah, there aren’t really any cases out there. I’m like, what? Like, I go into Harvard Business Publishing and I just search on PLG and I come up empty. He’s like, Yeah, because it is empty. Like, here’s a case on Dropbox that you can kind of repurpose. Here’s a case that I’ve written that has that’s not in there yet. And other than that, you’re on your own. This is three years ago. So I’m like, it’s crazy because we actually know how to do this, but no one’s written the book. So that was the spark.

Sophie Buonassisi:10:17

Incredible. I’m glad it worked out.

Dave Boyce:10:18

Yeah.

Sophie Buonassisi:10:19

And glad we can talk about it today. No, we’re big believers here at GTM Fun that strategy alone doesn’t win deals, execution does. But 85% of sellers are stuck managing their books in spreadsheets because their data’s scattered across dozens of tools. ZoomInfo Copilot Workspace fixes that. It’s the first workspace where sellers actually work. Complete buyer context, your REM data, and AI-powered insights in one place. Execution wins deals, everything else is really just preparation. You can learn more at zoominfo.com forward slash copilot. It’ll be in the show notes. What are the most foundational parts of it that you think if you could tell any go-to-market leader or founder listening, hey, remember these three things or two things?

Dave Boyce:11:01

Yeah. Well, for the first thing, just to get your attention, I’d just ask you like, when’s the last time you spoke to a human to buy gas for your car?

Sophie Buonassisi:11:10

I try to avoid it at all costs and all that.

Dave Boyce:11:12

Of course. Like, why would like why of course? And then you think, well, what about all the other stuff in my life? When is the last time you spoke to a human to buy groceries? When’s the last time you spoke to a human to buy an app on your phone? When’s the last time you spoke to a human to buy a piece of software that you use in your work life? Like, it is less and less frequent over time, less and less frequent. That’s just a long-term trend. So then you think, all right, well, my competitors, or sorry, my customers are versions of me. So how much do they want to talk to me? How much do they want to schedule a demo? How much do they want to get on like get on a phone call, a discovery call? Like zero. They don’t either. And if they and if if they can figure out a way to kind of trial their way into success with my competitor’s product, or just get their hands on my competitor’s product where I’m putting up walls, they’re gonna go with my competitor. That’s just the way it works. So then so if if we can kind of like break out of this notion that it matters at all how we’re used to doing it, because it doesn’t. The market cares zero.zero how we’re used to doing it. The market just wants what what it wants and it’s gonna get it wherever it can get it. If it’s from me, great. If it’s from my competitors, great. Market doesn’t care. So here we are kind of holding on to the old ways. Here’s our competitor saying, Oh, you want it that way? Cool, here, try it. Then you just got to think about what are the first principles that would help you build in that way. And now I’m asking, finally answering your question. These are three first principles in the book, and then we go into all the details for how you engineer this into your product. But the first is empathy, and that’s and unironically, empathy, like literally, I want to understand the end user of my product. I want to understand how she defines progress in her life, how she what her jobs to be done are, and where and when it would make sense for her to hire my product or service to help her make progress. I really want to understand that what are the words she uses, what are them, how does she measure it, how does she experience it. Okay, cool. So now once I understand that, I want to build something that’s going to kind of meet her where she is. I’m not going to require her to meet me where I am, I’m going to meet her where she is. And the next principle, again, unironically, is generosity. Oh, that’s the problem? That’s what you’re trying. Here, try this. Cool, what do I owe you? Nothing. When do I pay you? Don’t worry about it. Never. Like, let’s just see if it works. Like, and then once she’s rolling and she’s and she’s developing habit around that, eventually I will, of course. She’ll get to a point where she wants to do a longer than a 40-minute Zoom call or score store more than 90 days of Slag messages or or two terabytes of storage or whatever. She’ll get to a point, she wants one access to templates or you know, whatever. Great. And then she’ll be happy to pay. So one is empathy, two is generosity. And then the third thing is if we’re building our products this way, we need metrics. Because we don’t have a human in the room here observing how she’s experiencing the product. We don’t have a human in the room kind of coaching her to do the next thing, training her, um, teaching her. So we’re relying on the product to do that work, and the only way we know if it’s working or not is by metrics. Like we will see the click tracks, we will see the success paths, we will see the dead ends, we’ll see where where she’s not discovering something we thought she should have discovered, and we’ll see that all in the metrics and in the analytics, and then that’s how we will fine-tune our product over time. So empathy, generosity, metrics. There’s a whole bunch of science behind how you do that and cohorted-based kind of measurement, and but if those are the three first principles I’ve got to get into place if I want to meet today’s buyers where they live.

Sophie Buonassisi:15:00

Mm-hmm. Yeah, I love the simplicity and it it sounds simple the way that you framed it, however, it is super hard in practice.

Dave Boyce:15:07

Super hard, yeah.

Sophie Buonassisi:15:09

Yeah. And how does AI come into play with the book? Because if we map the buyer journey from end to end, AI is touching more and more of it. What is AI’s impact on go to market right now? Like, where is it most visible that you’re seeing and that you’ve written into the book?

Dave Boyce:15:25

If I think about that customer journey, and in you know, PLG terms, we’re gonna talk about um we’re gonna talk about awareness, we’re gonna talk about acquisition, activation, first impact, then we’re gonna talk about habit, then monetization, then retention, then engagement, then retention, then expansion. So these are kind of like stages in the customer journey. Okay, cool. So we’ve mapped that all out. And then you think, all right, well, where could AI help? It can help almost anywhere. Like, you know, I’m let’s say I’m active, I’ve created my account and now I want to activate. AI can can be almost like a guide to help me do the things. It can make guesses for me. I don’t know if you’ve ever used any kind of vibe coding platforms or like an AI automatic website generator. You tell it a few prompts and it just says, here, let me guess. Here’s some pictures, here’s a logo, here’s a um, here’s a uh login kind of um I want to say dialogue. Um it just pulls it out of its library, pulls it out of its memory, does pattern recognition and kind of fills in the gaps. Cool, that’s a way faster activation process than if I had had to kind of read something and go do it myself. You can think about it for onboarding. You think about it for um for feature uh discovery, like hey, I’m I’m chugging along in Canva, and Canva identifies that I may be able to benefit from templates, or I may be able to benefit from a background eraser, or I might be able to benefit from something that I haven’t discovered yet. Boom, it can suggest it for me. Um and then you can think about long-term engagement, like it just helps me, AI just helps me um accomplish what I want to accomplish. Uh helps me know when I should upgrade to the next tier, it helps me know when I should expand this to team usage, etc. etc. The other thing that AI, so that’s just basic. That’s basic, Sophie, just basic kind of automating the predictable of all the way across the customer journey. The other thing that AI can do though is unlock product categories that were previously not automatable. So now I have a very complex kind of, you know, think about the most complex software there is, you know, like uh process manufacturing ERP. Like, oh my gosh, like super complex. Um, lots of configuration, lots of connections to uh machines, etc. Well, AI can kind of decomplexify that for me too, like literally tutorial kind of help me connect this machine, help me configure this uh dashboard, help me interpret these metrics. And AI can literally just help me self-serve my way into success where I may have needed two or three humans and two or three months to do it in the past, and now a previously too complex to be self-service product becomes self-service.

Sophie Buonassisi:18:20

Which is incredible. And we’re seeing it happen more and more. I mean, you go to these Yarmy websites, even you can see these product led growth, these PLG flows in place.

Dave Boyce:18:30

I mean, even S I I was just in Saudi Arabia working with a manufacturing firm, they’re doing that same thing. Even SAP, you know, largely thought of as the most kind of most monolithic and complicated software, they’ve got agents all throughout their go-to-market, and they’ve got like almost a PLG kind of flow where you first start in a sandbox that has their data, then you go to an offline sandbox where you’ve uploaded your uploaded your own data, then you go to like a cloud environment where you can be more real-time, and then you go to um like the fully kind of permissioned you know, SAP instance. But that’s like a self-service onboarding that you would never have thought of five years ago for SAP.

Sophie Buonassisi:19:14

Yeah, exactly. So what what tasks should AI immediately own, and which ones should humans protect? Just it sounds like we’re moving further and further along the spectrum, which we know we are, but how should people be thinking about that divide?

Dave Boyce:19:29

I love it. There’s a um there’s gonna be a weird way to start this answer, Sophie, but there’s a a Polish fantasy fiction author named Joanna. She says, I want AI to do my dishes and laundry so that I can do art and writing. I don’t want AI to do art and writing so that I have to do dishes and laundry. I really think that is how we should think about it. We want AI to automate the predictable so that we we can humanize the exceptional. So anything that’s Predictable. Fill out a form, fill out a field, process an order, build a do research on an account, you know, respond to a trigger with uh with some sort of a you know just a mechanical kind of acknowledgement. All of that should be automated. AI should be doing all of that for us. And then the human stuff, if you’re thinking in terms of go to market, the the human, it’s where we want to show up as a real human, like the emotional stuff, like many times on the other side is a is a real human. This is me and you talking, like if I’m the if I’m let’s say I’m with the selling company or with the buying company, you’re trying to accomplish something. Like you’re not just out here for fun and you’ve gotten a certain way amount of the way on your own, and now you’re trying, now you’re trying to have the courage to put this forward as the standard platform within your company. Okay, courage is something that we can work on together. I can help you connect with people that have done the same thing. I can use my judgment or my pattern recognition and and then and also my trust building with you and and help you get to the point where you’re managing your stakeholders in a way that makes sense for kind of what they’re gonna need in order to approve that as a go forward thing. That’s a very human experience. It would be very hard for you to trust a robot who was kind of trying to coach you on stuff like that. But when we get to the point where all the automated stuff, all the predictable stuff is automated, then that means we have to show up as like superhumans. Like we gotta show up like really, really human, like aesthetic and intuitive and helpful, and in a how can I help you mode, not a what can I sell you mode. And that’s that’s the version of the future that I want to believe in. Well, the first thing you need, um, you need a theory of the case, Sophie. Like, um, and I think that all starts with the data model. Like we described the PLG data model from you know, awareness acquisition, activation, first impact habit, you know, blah, blah, blah. I need a theory of the case. I need to know what that looks like. I need to and I I need to have kind of mapped it out into what I would call a data model. Winning by design uses the bowtie data model. That’s the PLG version of it. The sales-led version of it would be um awareness, education, selection, commit, and that’s like the traditional sales and marketing funnel, kind of narrowing as you go. Awareness, education, selection, commit. Commit is the narrowest part of the funnel. I have it turned on its side, but it’s still a funnel. And then that’s we that’s like the knot of the bow tie, and then I’m gonna start opening out from there. Onboarding, retention, and expansion is gonna hopefully kind of make my long-term lifetime value with that customer actually expand over time as we deliver impact. Okay, now if I can define each of those um stages and I can define what needs to happen within each of those stages, you can imagine that that is like a um almost like a manufacturing process, right? Something happens during this stage, something happens during this stage, there’s a handoff, something happens during this stage. There’s success criteria, there’s activities in stage, there’s success criteria to exit the stage. And then once I have it defined like that, that’s kind of like bare minimum, and it’s not a lot. Like that’s bare minimum for me to be able to start running experiments. Then I can start A-B testing human versus robot on this task. Did it help me improve my conversion rate? Oh, it didn’t? Okay, then that’s probably not the right place to be using a robot. How about or maybe I tune it a little more and see if I can get it there, tune it a little more, eventually I get it there. Oh, cool, cool. The robot can help me there. It helps make my reps more efficient, it helps make my conversion rates better, but I gotta be able to measure it, which means I need a data model. And that data model needs to not be a political data model. I’m sure you’ve been in board meetings, I’ve been in uh board meetings where it feels like I’m just on the receiving end of like a commercial and the operators are just trying to kind of convince me that nothing stinks in this business. That ain’t that’s not what we need. We need a very clear light of day, consistent data model so that we can run experiments, because we’re just gonna run experiments and we’re gonna get AI deployed everywhere that it works, and if we deploy it and it doesn’t work, no harm, no foul, we’ll kick it out and we’ll try it somewhere else. So I think data model first, kind of theory of the case. What are we trying to accomplish? And then we can start choosing where we’re gonna deploy AI, and we’ll treat it as an experiment until it’s proven that it’s a permanent thing. In lean manufacturing, this is like one of my favorite things about uh sayings in lean manufacturing that you never hear. You always hear about continuous improvement or the and-on cord or uh just in time, but here’s here’s a very cool one. Don’t bolt down what you can’t tape down, and don’t tape down what you can’t hold down. So basically, I don’t need to go bolting the AI in place when I still don’t know if it’s gonna work. Like, let me just hold it in place. Let me test it. Oh, that looks like it’s gonna work. Now let me tape it and let me step away for a second. Oh, it still looks like it’s holding. Okay, now we’re convinced AI can do that job consistently, reliably, cool. We’re gonna bolt it down and move on to the next experiment.

Sophie Buonassisi:25:18

What does the bow tie model actually expose that traditional funnels hide when winning by design is employing it?

Dave Boyce:25:24

That’s where the renewals happen, that’s where the expansion happens, and that’s where the growth loops are initiated that can pull new customers into the front of the funnel based on referrals from existing customers. All the compounding happens on the right hand side of the bow tie. Now we spent, if we if you grew up when I did or anytime, you know, if you built a company anytime before the last five years, you might have spent a ton of time in QBRs and planning sessions and board meetings talking about calling and meeting your bookings forecast. That’s basically what Wall Street tracks. That’s basically what you know most sales-led organizations track. It’s where we put our most expensive people, it’s where we put all of our executive attention, it’s where we put all of our focus in those meetings, is calling and hitting a bookings forecast. But bookings is the knot of the bow tie. It’s the beginning of the journey. Everything after that is the customer’s experience, and it if that customer experience is good, then renewals and expansion will happen, which means now I have the machine working for me instead of me working the other way around. So why wouldn’t I also be spending time and attention there? Why do I put all of my expensive time and resources and people and attention on bringing new customers in and I short sheet the right-hand side of the bow tie? It just doesn’t make sense from a systems or math perspective. And uh and the bow tie kind of just like brings that to life because once you start running cohorted math through that system, you start seeing like, oh my gosh, this short term, yes, short term, I will get a lot of benefit from sales, but long term compounding growth is all driven on the right side of the bow tie.

Sophie Buonassisi:27:07

Interesting with those cohorted systems are real. So, what would be if we take uh an MVP, for example, for inspiration on the product side, what’s like a minimum viable bow tie for a company to need to be able to run more of a hybrid AI and uh PLG or go-to-market motion?

Dave Boyce:27:25

Yeah. The hardest thing is connecting the right and left side of the bow tie. So all almost all of us have the left side of the bow tie instrument in some way, shape, or form. We have stage one, stage two, stage three opportunities. We’ve got it all in HubSpot or Salesforce. We kind of we we kind of know how to build our kind of bookings forecast and we manage it, and we have MQLs and SQLs, like we have that built.

Sophie Buonassisi:27:45

Yeah.

Dave Boyce:27:46

Where does the stuff on the right hand side live? Sometimes it lives in something like a gain site, sometimes we’ve written it back to our CRM, and sometimes it’s neither place, and you have to go get it out of finance. It’s literally tracked based on billings because we don’t have it in CRM and we’re not tracking it in a CS platform, and we literally have to say, well, when did we, you know, did we send that customer a bill or not based on whether they canceled or not? So minimum viable product would be getting the left hand and the right hand side stitched together all the way through the journey that I can see in one place. Once I have that, and if I’ve done it according to the winning by design uh bow tie data schema, then I can benchmark it. We’ve got 300 companies benchmarked, and you can cohort that based on companies that are similar to you or on similar motions to you, and then you can start benchmarking. But even if you can’t benchmark, at least you can compare yourself to last period on the period before. Now you’ve got like a baseline and you can start seeing if if you’re actually making improvements. But before you have that, I don’t think you can improve a human system. And I also don’t in a reliable, consistent, kind of ongoing way and like a continuous improvement in lean manufacturing way, and you certainly can’t improve an AI system or human AI hybrid system because you just don’t have the instrumentation to tell um what’s working and what’s not.

Sophie Buonassisi:29:07

So when teams are wanting to create this bow tie framework and they need to either take their data from GainSite or some other system, where are they creating the bow tie? Where’s this connectivity between the left side and the right side of covering?

Dave Boyce:29:22

So ideally you would write everything back to CRM. That that’s e way easier said than done. CRM wasn’t necessarily built for things like growth loops. Um very tough to do that. Certainly not built for things like um you know activation of an account pre um pre-payment. Um that’s a PLG thing, like very difficult. But let’s say you could get it into CRM. That would be my choice A. Um, but what we very often see is we’ll get it into like Snowflake. Um or we’ll get it into Snowflake and then a visualization layer like a um like a Domo or uh uh you know some sort of or Power BI or some kind of visualization layer. There are also some uh products out there that are doing this commercially um that are partners of Winning by Design that that will take that will visualize the bowtie for you, like a UNA or a Vasco. So like SaaStrack is a partner of Winning by Design, adheres to the bowtie data schema, builds the custom objects, it’s a it’s a managed package, builds the custom objects inside of Salesforce for you, and now you can just kind of manage it and use Salesforce Reporting. Amazing. That’s a SaaSTrek. Vasco and Una pull it all out, like if you have it in disparate systems and they give you visualization and a management framework. Or you can do it like I said, roll your own and put it in your own BI tool. But you do want that wiring to be um you want it to be wired, not just kind of like CSV kind of one-time pulls. Because you track it this month. The continuous exactly. Yeah.

Sophie Buonassisi:31:00

Right, right. Super, super. Who owns that process?

Dave Boyce:31:04

Jeez, you are so mean. You’re asking all the hardest questions. Ums, it’s not very consistent. Ideally, you would have a function called RevOps that didn’t just work for sales. And ideally, that RevOps function would build those systems and would be the kind of impartial, objective arbiter of truth. In many companies, that’s not what RevOps is. In many companies, the RevOps does whatever it takes to make the CRO look good. And they help them prepare for board meetings, and they help them kind of scrub and whitewash numbers, and they help them kind of um maybe maybe uh in private they’re look looking for truth, but in public they’re whitewashing. That ain’t gonna work. That’s just not gonna work. So ideally we just we just step RevOps up into a kind of impartial arbiter of truth, and if not, then it can also be FPNA. But the problem with FPNA is they don’t actually understand the go-to-market well enough and the systems that run go-to-market well enough. So my ideal, Sophie, would be that RevOps steps into this role going forward and is the backbone of our modern go-to-market.

Sophie Buonassisi:32:19

I love it. And for other operators, not just RevOps, I have heard you say that. They need to become more chief figure it out officers, which I think is a really fun term. What does that role look like in the day-to-day? What does that entail?

Dave Boyce:32:35

Full attribution. That’s Ryan Sanders from Mercado. He gave me that term, and I’ve been I’ve been shamelessly reusing it. Chief figure it out officer, it’s super easy to remember. But it’s all the stuff that you and I have been talking about, like where would I deploy AI? I don’t know. I can’t pull that out of my bag of tricks. I can’t go, you know, rewind the clock 15 years to when I used to be an AE and tell you how I used to use AI. I didn’t use AI, which means we got to figure this out together. I certainly didn’t use it systematically. I can’t tell my RevOps person what we did 15 years ago to instrument or go to market so that we could run A-B experiments with human robot hybrid systems because we didn’t have that. So we got to figure all of this out as we go, which means if I’m if I’m a veteran, which I am, um if I’m a veteran head of revenue, I gotta get out of the idea that I’m just gonna teach people how to how to do it the way I did it. And I gotta get into the mindset of no, no, no, we’re gonna figure this out together. So I’m gonna grab smart people on my left and right, we’re gonna go in, we’re gonna systematically architect something that I can then run by instrumentation versus running in an Amelia Earhart way, like, ooh, it looks cloudy over there, I better steer left.

Sophie Buonassisi:33:54

So you’ve obviously spent a lot of time writing your own book. Are there other books throughout your career that have made a particular impact on you?

Dave Boyce:34:00

Oh, good question. Yeah, I’ve I my my author hero is Clayton Christensen. Um he’s amazing. I’ve read, I think everything he’s written. I think he is he’s a really good scholar. Uh may you rest in peace. Um I went to both of his funerals. He was a good friend too, but I knew him as a scholar and a friend. And um, so what I always recommend if you haven’t read anything of his is Competing Against Luck. It’s not his most famous book, but it’s a really good book for this moment. Um and that’s where he really unveils the jobs to be done theory. Uh I super like um Roger Martin’s book called Playing to Win. Um very, very good strategy framework. Um I could go on for for days about books that have made an impact, but that’s where usually where I start.

Sophie Buonassisi:34:52

Very cool. Great recommendations. And what about yourself? Where can people follow along your journey? Obviously, we’ll have a link for the book, but for yourself at all, are you on LinkedIn X? What’s the best place to follow you?

Dave Boyce:35:02

I’m not active on X. I would love for obviously you can find me here, and we’re gonna put that in the um show notes. I’m on LinkedIn, pretty active on LinkedIn. Uh I have a Substack uh which is just Dave Boyce. Um and uh and if you join the Growth Institute with Winning by Design, you’ll see me super active there. I run quarterly case studies, MBA style, kind of executive education case studies. You’ll see me on stage at the summits, and we will uh and we’ll go change the world together.

Sophie Buonassisi:35:34

Amazing. Amazing. Dave, this has been phenomenal. Thank you for the time. Thank you for the book on my bookshelf.

Dave Boyce:35:40

Yay!

Sophie Buonassisi:35:40

And uh yeah, really, really appreciate it.

Dave Boyce:35:43

Thanks so much, Sophie. Amazing. Let’s go do it.

Sophie Buonassisi:35:46

Let’s do it.

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