WEBVTT
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You saw the MIT study where 90 % of these projects
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are not showing ROI. That's because you launched
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it as a point solution to a problem that's not
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well -defined inside of a system that isn't architected.
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That's fine. I can't even imagine watching the
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AI train kind of come by my station and deciding
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to stay on the bench. I mean, the only thing
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worse than missing a train is missing a fast
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train, and the AI train's going really fast.
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I want to get on that thing. You'll be automating
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everything that's predictable. Yes, AI is going
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to take that. And it's going to give you a chance
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to humanize all the exceptional stuff, the stuff
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that only you could do. That's where we want
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to live. Welcome to Artificial Insights, where
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we have conversations with leaders and thinkers
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in AI about how AI is changing economics, work,
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and us. I'm your host, Daniel Maneri, and this
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is Season 5. Today, I am joined by Dave Boyce.
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Dave Boyce is an American entrepreneur, educator,
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and author renowned for his expertise in product
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-led growth and AI automated sales and marketing.
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He's got a track record of building and selling
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five SaaS companies. Currently, he's working
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for a firm called Winning by Design, which I
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encountered because of their unique way of thinking
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about the sales cycle as continuing past the
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sale and all the way up until the customer renews.
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or leaves. From his position at Winning by Design,
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Dave's able to see the life cycle of sales at
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companies as big as Canva and some much smaller
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than that. Dave, could you tell us a little bit
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about who you are and what you do? Thank you
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so much for having me, Daniel. I'm Dave. I'm
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Dave. I'm a father. I'm a software executive.
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I'm an entrepreneur, an adventurer. I really
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am drawn to figuring out You know, taking steps
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into the unknown, which is why I keep going back.
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You know, we'll get a we'll get a company landed
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and, you know, good, bad or ugly. We'll get it,
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bring it in for a landing. And I've done this
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seven times. And then I go back to the beginning,
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like and then you figure it out or go, you know,
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go into the unknown in some way or push into
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that. That's who I am dispositionally. And I
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think it keeps you, you know, just stay curious.
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You can stay young, stay engaged. That's that's
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what I try to do. Where are you now? And then
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how did you get there? Could you just give us
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a version of that journey? Totally, yeah. Just
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like time -lapse. I want to start with, you know,
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I was born the oldest of eight kids. We'll skip
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forward to, you know, professional life. I've
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probably done it more than eight times or seven
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times, honestly, if you count all the kind of,
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you know, failure to launches. But, you know,
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four companies we built as an operator and got
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them sold. One was ProfitLogic, which sold to
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Oracle. One was Inside Sales, which sold to Aurea,
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which sounds similar. One was Zenprint, which
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sold to private investors. And one was Marks,
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which sold to Amazon. And then as a kind of active
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investor advisor, three other ones. Now I'm at
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Winning by Design. I run product and I'm executive
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chair. And Winning by Design is a chance to kind
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of click up a few clicks in altitude. observe
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the success patterns across the industry. Because
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when you're in a business, you know, you're heads
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down, you're trying to pay attention, but really
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you're working hard, heads down. Now I've been
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able to spend the last four years kind of a click
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up, got a book published, got contributed to
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some really cool framework developments at Winning
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by Design, working with some of the smartest
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people in the industry. And so that's where I
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am right now. Very cool. And have you held different
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roles across? that journey? And what have those
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roles looked like? It's a great question. It's
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interesting because there's certain things that
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you're good at, right? Like dispossessionally
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good at. There's certain things that where if
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the job is that Daniel is your person, like,
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you know, superpower, that's who you need. And
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you know it. And the people who are closest to
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you know it. It's true about me. It's true about
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you. And then you get into a company and stuff
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happens, you know, and so there's a battlefield
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promotion or a battle. field scope expansion
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or a last man standing kind of like assignment.
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So the stuff I'm really good at are product and
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strategy and positioning. Like I'm really good
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at that and I'm good at representing it externally,
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which makes it natural for me to be, you know,
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an evangelist or to lead partners or channels
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or analyst relations. I've done all those roles.
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Also, I have, I've also run customer success
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and I've also been a CEO. Let's see, what am
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I missing? So I've run marketing, product, product
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marketing, customer success. I've never run finance.
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I have run business development and I've carried
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a quota. So there you go. More things you've
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done than you haven't. Yeah. And that's what,
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so being a CEO at this point does feel natural
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to me because I've been in many of those seats.
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It's almost like, you know, if you're going to
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become a general contractor and you. learned
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multiple trades you can just do a better job
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you know if you learned plumbing and framing
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and finished carpentry and painting it's like
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okay maybe you didn't do electrical maybe you
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didn't do excavation or concrete but you've done
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a lot of those jobs and now you can be a good
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kind of overseer of a project i feel that way
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as when i'm in a ceo seat very cool and there's
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a question on everybody's minds with a podcast
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like this which is dave are you an ai I'd be
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much smarter. I'd be much wittier if I were.
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I'd be better looking too. I've seen some of
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those AI kind of brush ups. No, I'm very, very
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human. You and I shared stories about kids and
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families and, you know, and that comes with all
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kinds of bumps and bruises and apologies and
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do -overs. That's who I am. I appreciate that.
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Never had an AI apologize to me genuinely. I
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don't think it has to, but it never does. With
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that journey, now that you're seeing the industry
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that you're working across in a new way, you
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called it a click up. What is the industry that
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you are seeing at that big level? Yeah, now we're
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getting into it. So Daniel, when I first started,
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the first of all those startups that I mentioned
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was very much a sales led company. Our average
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ACV annual contract value was $1 .2 million.
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We were hosting these very large machine learning
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models in colo facilities, which eventually became
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known as the cloud in a model that eventually
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became known as SAS. This was in the, in like
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2000, I think SAS, this term SAS was coined like
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a year later, $1 .2 million. Then the next I
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left, we sold that company to Oracle. I left,
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had a global VP role there for almost five years.
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I left, go back to the beginning and do a startup
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called Fundly. And the. The median, I'm not going
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to say average because the average was a little
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higher than this, but the median ACV at Fundly
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was $1 .2 thousand. That is a completely different
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business. So I had to learn, I had to learn like
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how do you, how do you set up self -service?
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How do you get your unit economics to work when
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you've only got $1 .2 thousand of kind of total
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possible margin? And if you have any decent margin,
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it's going to be a fraction of that to work with
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on cost of sales and cost of marketing. Okay,
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got it. So we figured that out. We ended up getting
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that company sold. And now, Daniel, where we
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are now, so fast forward, we sold that company
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in 2010, I think. Now fast forward to what's
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happening now. Everything's changing again. Like
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AI is making itself useful in purchasing. I just
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read that over 50 % of B2B purchases. Start with
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an LLM inquiry. Okay, that's new. That's new.
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That's new. Like we haven't done like SEO type
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optimization on LLM. We don't exactly know how
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to trace the footsteps of how things get into
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LLM. We don't know how to make our companies
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visible on LLM. And yet, that's a place where
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a buyer can have a very, very rich conversation
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about my product, about my competitor's product,
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about my price, about their price, about my reviews,
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about their reviews. So we have to figure this
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out. And we have to meet our buyers where we
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are. So I think what we're seeing right now is
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some AI native companies who kind of grew up
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just assuming that everything would be AI -able
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and they built their whole go -to -market in
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a no -touch way. You know, if you think about
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OpenAI or Anthropic or Mistral, you don't talk
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to a human to buy that product. You don't talk
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to a human to learn how to use that product.
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You don't talk to a human to upgrade to the enterprise
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or Teams version of that product. No, it's very
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much feels like a PLG, a product -led growth
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journey, but inside of like a consumption -based
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kind of model and built by people who just assume
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that AI can bridge all the gaps. So they have
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real -time data feeding the machine, helping
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it. And those are foundation models. Then you
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talk about, you know, what everyone likes to
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talk about, Lovable and Replit and these companies
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that are setting crazy growth targets. But Daniel,
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that's like the new world we live in. If you
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wanted to meet today's B2B buyer who behaves
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like I just said, and by the way, 70 % of the
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B2B buyers, this is a different statistic. I
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don't know how they crosshatch. 70 % of B2B buyers
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are either millennials or Gen Z. Okay. All right.
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I'm not. So if I'm building a go -to -market
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motion for B2B and I build it a way that makes
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me comfortable as a Gen Xer, I'm doing it wrong.
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Like I got to meet Gen Z where she is. And I
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got to build a machine that makes her comfortable,
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that makes it easy for her to do business with
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me. So we're seeing these revolutions. They just
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happen, right? Like they happen kind of generation
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to generation. We're in the middle one. We got
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to put on our curiosity hat, channel our best
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problem solving, and not assume that the playbooks
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that got us here are going to get us there. Yeah,
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there's a lot to dig in there. But I'd like to
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ask you, with what you're seeing, you're seeing
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it through, I think, two different lenses. And
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one of those is you're currently working for
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winning by design. Yeah. How does winning by
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design get involved with, with all of those growth
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motions? Yeah. I'm super lucky at winning by
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design for the vantage. So we work with growth
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stage companies, typically 50 million to 2 billion
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in revenue. And in many cases, not AI native
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companies. I mean, in basically all cases, we're
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not working with a single. 50 million plus AI
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native company. We're working with some startups
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within a company that are building AI native.
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But what we do is we work with those companies
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who have tremendous enterprise value. They've
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got rule of 40, rule of 50, rule of 60. They're
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sitting pretty right. They've got high growth,
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high profitability inside a PE portfolio. And
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yet they see the same thing that you and I are
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talking about right now. They see the industry
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evolving in a different way and they know that
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they have to stay relevant. We use kind of our
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bird's eye view to help those types of companies
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build growth infrastructure, growth processes,
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growth architecture that allows them to keep
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doing what they're doing well, but also then
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evolve to their next stage of growth and to their
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next stage of buyer. So the way that looks is
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very basic things like putting in a data model
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or putting in an operating model so that we have
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visibility and consistency and so that we're
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AI ready. And then very kind of tactical things
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like figuring out which tasks within a customer
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journey should be performed by a human and which
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ones should be performed by a robot and how the
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handoffs happen from human to robot to human
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and where all that kind of information lives
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in an operating model. The very, very cool frameworks
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that we're using to just help those companies.
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Basically, you don't have to run faster than
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the bear. You just have to run faster. than your
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hiking buddy, right? So every one of these companies
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is under the gun and they have to keep up with
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the times and that this is a super exciting place
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to be. And before I dig into that even more,
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the other angle is you told me about a book that
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you've published. Yeah. What is that about? Rock
00:12:32.230 --> 00:12:34.750
on. Well, it kind of is about this journey, Daniel.
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It's called Freemium. I'm going to see if I can
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put it into the frame of the camera without messing
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us up. It's called, there we go, Freemium. Beautiful
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cover. We published it earlier this year with
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Stanford. And it is about this journey, this
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self -service journey. It's a long -term evolution,
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in my opinion. It goes all the way back to shareware,
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free software downloads. Then we had Atlassian.
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Then we had Dropbox and DocuSign and Twilio,
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defining kind of Gen 1 PLG. We got that all pushed
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out. We started becoming very used to software
00:13:07.820 --> 00:13:09.779
just being part of our lives on our phones, pushed
00:13:09.779 --> 00:13:13.570
out to mobile. very easy kind of emerge of kind
00:13:13.570 --> 00:13:15.429
of consumer and enterprise software. So in our
00:13:15.429 --> 00:13:17.409
work life, everything became, you know, easy
00:13:17.409 --> 00:13:20.690
to discover, easy to use. Document what that
00:13:20.690 --> 00:13:23.210
is, how we got here, where we are today, but
00:13:23.210 --> 00:13:25.590
then also how would you build it? Like, let's
00:13:25.590 --> 00:13:27.610
say you're one of the 99 % of companies who didn't
00:13:27.610 --> 00:13:29.710
start that way. Then what do you do? Like, how,
00:13:29.789 --> 00:13:31.549
you know, what, how would you, how would you
00:13:31.549 --> 00:13:33.470
retrofit it in? So I talked to several companies
00:13:33.470 --> 00:13:35.750
who already were north of a hundred million and
00:13:35.750 --> 00:13:38.179
then retrofit. some of these self -service motions
00:13:38.179 --> 00:13:40.179
into their company one of those companies was
00:13:40.179 --> 00:13:42.659
hubspot one of those companies was mongodb one
00:13:42.659 --> 00:13:44.840
of those companies was unity and then i'm and
00:13:44.840 --> 00:13:46.919
then we take a look at all right so then where
00:13:46.919 --> 00:13:48.419
is it going from here what does the internet
00:13:48.419 --> 00:13:50.600
of things do what is what is software embedded
00:13:50.600 --> 00:13:53.059
into physical things do what does that unlock
00:13:53.059 --> 00:13:55.139
in terms of self -service and then the and then
00:13:55.139 --> 00:13:57.980
the kind of the you know 50 million dollar question
00:13:57.980 --> 00:14:00.100
probably more than that 50 billion dollar question
00:14:00.100 --> 00:14:03.100
500 billion dollar question What does AI unlock?
00:14:03.379 --> 00:14:05.779
Like how much self -service does AI unlock and
00:14:05.779 --> 00:14:07.779
where does that take us? So that's what freemium
00:14:07.779 --> 00:14:10.059
does. Where have we been? Where are we now? Where
00:14:10.059 --> 00:14:12.700
are we going? And how do you implement that in
00:14:12.700 --> 00:14:15.120
your company? One of the things you mentioned
00:14:15.120 --> 00:14:19.000
was being involved with sales processes and trying
00:14:19.000 --> 00:14:21.620
to bring AI into that sales process. Where does
00:14:21.620 --> 00:14:24.539
it fit? Where does it hand off to a human? And
00:14:24.539 --> 00:14:27.600
where does a human fit? Could you tell us more
00:14:27.600 --> 00:14:30.000
about the experiments you've done and what you've
00:14:30.000 --> 00:14:32.419
seen there? Yeah. You want to get real tactical?
00:14:32.860 --> 00:14:37.059
Oh, I'd love to. Yeah. Okay. So we have seen
00:14:37.059 --> 00:14:39.659
a lot of experiments. I've run, I launched personally,
00:14:39.919 --> 00:14:43.320
Winning by Design launched five agents last year.
00:14:43.360 --> 00:14:44.860
I was personally involved with four of them.
00:14:45.080 --> 00:14:47.259
And we, and a lot of our clients are launching
00:14:47.259 --> 00:14:49.679
agents to either an assistive agent to help a
00:14:49.679 --> 00:14:54.200
human do her job better or, or like an agentic
00:14:54.200 --> 00:14:56.299
kind of AI approach where they actually take
00:14:56.299 --> 00:14:58.750
over a piece of the customer journey. have a
00:14:58.750 --> 00:15:01.049
conversation with a customer, like front lines.
00:15:01.529 --> 00:15:03.009
And then what we're eventually going to get to
00:15:03.009 --> 00:15:04.929
is orchestrative, where we're actually kind of
00:15:04.929 --> 00:15:07.450
orchestrating workflows. I haven't done any of
00:15:07.450 --> 00:15:09.129
that yet. We're working with one client who's,
00:15:09.149 --> 00:15:10.850
I haven't done any of that at Winning by Design.
00:15:10.909 --> 00:15:12.250
We're working with one client that's doing it.
00:15:12.629 --> 00:15:14.769
But here's what you need. You have to have a
00:15:14.769 --> 00:15:17.090
point of view. Like you have to understand a
00:15:17.090 --> 00:15:18.950
theory of the case in order to do this. We've
00:15:18.950 --> 00:15:22.350
seen, you saw the MIT study where, you know,
00:15:22.350 --> 00:15:25.789
90 % of these projects are not showing ROI. That's
00:15:25.789 --> 00:15:28.529
because you launched it. as a point solution
00:15:28.529 --> 00:15:31.970
to a problem that's not well -defined inside
00:15:31.970 --> 00:15:35.129
of a system that isn't architected. That's fine.
00:15:35.269 --> 00:15:38.710
Guess what? Getting 10 % success, I think, was
00:15:38.710 --> 00:15:41.370
it 85 -15 or 90 -10? I can't remember. But let's
00:15:41.370 --> 00:15:44.909
say it was 85 -15. So getting 15 % success in
00:15:44.909 --> 00:15:47.370
those environments, pretty good. That means like
00:15:47.370 --> 00:15:50.850
15 % of the time, you know, even a blind monkey
00:15:50.850 --> 00:15:53.809
can hit the bullseye. But what we need to do
00:15:53.809 --> 00:15:56.309
is have a theory of the case. So what we want
00:15:56.309 --> 00:15:59.820
to do... is establish two things. One is a data
00:15:59.820 --> 00:16:02.740
model. So we want to understand, I'll go from
00:16:02.740 --> 00:16:07.000
left to right, how a prospect makes it from awareness
00:16:07.000 --> 00:16:12.240
to education to selection to commit. And if you're
00:16:12.240 --> 00:16:15.220
in PLG, you're going to actually not even ask
00:16:15.220 --> 00:16:19.500
for a commit until after acquisition, activation,
00:16:19.840 --> 00:16:22.879
and first impact, and maybe even habit development.
00:16:23.000 --> 00:16:25.250
Then I'm going to come ask for a contract. But
00:16:25.250 --> 00:16:27.129
then I want to know after I've got them in there,
00:16:27.169 --> 00:16:30.730
what's my ongoing engagement and what's my expansion
00:16:30.730 --> 00:16:33.590
and what's my, what's my retention and what's
00:16:33.590 --> 00:16:36.269
my expansion. If I can document all those stages,
00:16:36.330 --> 00:16:37.730
we talk about, at Winning by Design, we talk
00:16:37.730 --> 00:16:40.370
about a bow tie. So it's a traditional sales
00:16:40.370 --> 00:16:42.409
and marketing funnel, wide at the top, narrow
00:16:42.409 --> 00:16:45.629
at the bottom. Turn that thing on its side. The
00:16:45.629 --> 00:16:48.409
narrowest part is the, is where, you know, the
00:16:48.409 --> 00:16:50.909
customer first makes her commitment and then
00:16:50.909 --> 00:16:54.860
it starts to widen out through usage. and renewal
00:16:54.860 --> 00:16:57.120
and expansion. And the right -hand side of the
00:16:57.120 --> 00:16:59.100
bow tie is where all the compounding happens.
00:16:59.379 --> 00:17:01.580
That's the whole customer kind of magic. That's
00:17:01.580 --> 00:17:03.179
where all the impact gets delivered. That's where
00:17:03.179 --> 00:17:06.579
all the recurring revenue is. But let's say I've
00:17:06.579 --> 00:17:09.079
structured that and I've defined each of those
00:17:09.079 --> 00:17:12.220
stages and I can watch cohorts of customers come
00:17:12.220 --> 00:17:14.980
through and either succeed or fail. Amazing.
00:17:15.180 --> 00:17:18.460
Turns out it's very hard to do that. CRM wasn't
00:17:18.460 --> 00:17:20.720
originally built to track the right -hand side
00:17:20.720 --> 00:17:23.599
of the bow tie. Turns out... Because of recurring
00:17:23.599 --> 00:17:26.579
revenue, there's a lot of recursiveness in a
00:17:26.579 --> 00:17:29.259
go -to -market motion, like these renewals and
00:17:29.259 --> 00:17:32.500
these expansions. And if you want to get really,
00:17:32.579 --> 00:17:36.319
really honest about it, these models don't compound
00:17:36.319 --> 00:17:38.619
until you have advocacy going from the right
00:17:38.619 --> 00:17:40.619
-hand side back over to the left -hand side,
00:17:40.720 --> 00:17:42.779
meaning your existing customers are so happy
00:17:42.779 --> 00:17:46.160
that they're helping pull in new customers, not
00:17:46.160 --> 00:17:48.539
in a cold start way, like a cold outbound way.
00:17:48.680 --> 00:17:50.400
And I want to track that too. That's called a
00:17:50.400 --> 00:17:52.309
growth loop. I want to track all of that. So
00:17:52.309 --> 00:17:54.130
now I have it all kind of modeled out. Cool.
00:17:54.210 --> 00:17:56.829
I've got a theory of the case. I know my conversion
00:17:56.829 --> 00:17:59.349
rates that need to happen from left to right.
00:17:59.490 --> 00:18:03.069
I know how that's going to result in stacking
00:18:03.069 --> 00:18:06.869
ARR, annual recurring revenue. Now within that,
00:18:06.950 --> 00:18:09.690
I need to figure out what happens within each
00:18:09.690 --> 00:18:11.450
stage. Like what is the customer experience?
00:18:11.650 --> 00:18:14.509
How does she progress from awareness to education,
00:18:14.730 --> 00:18:18.069
from education to trial, from trial to conversion?
00:18:19.020 --> 00:18:21.480
And I'm going to build kind of the steps in there.
00:18:21.559 --> 00:18:24.099
And some of those steps, as we said before, might
00:18:24.099 --> 00:18:26.940
be executed by a human and some might be executed
00:18:26.940 --> 00:18:29.579
by a robot. By a robot, I mean like an AI agent.
00:18:29.640 --> 00:18:32.940
Okay, great. Or it could be a human who's getting
00:18:32.940 --> 00:18:36.000
assistance from a robot. Okay, all good. I got
00:18:36.000 --> 00:18:38.099
to define those things. I got to get them kind
00:18:38.099 --> 00:18:41.559
of documented. And then we're going to talk about
00:18:41.559 --> 00:18:43.700
an operating model. So the bow tie is the data
00:18:43.700 --> 00:18:46.640
model. The plays inside of it are just, you know,
00:18:46.680 --> 00:18:48.789
that's just like process definition. But the
00:18:48.789 --> 00:18:52.190
operating model is how am I translating information
00:18:52.190 --> 00:18:55.150
from one silo to the next silo? Like what's the
00:18:55.150 --> 00:18:58.390
common framework that allows me to follow a through
00:18:58.390 --> 00:19:00.190
line all the way across, even through multiple
00:19:00.190 --> 00:19:02.970
handoffs, even through human to agent and agent
00:19:02.970 --> 00:19:05.309
to human handoffs. And that's what I'm going
00:19:05.309 --> 00:19:07.089
to call an operating model. You can think about
00:19:07.089 --> 00:19:10.150
in technology, a codec and encoding and decoding
00:19:10.150 --> 00:19:12.430
algorithm. That's kind of like how video is going
00:19:12.430 --> 00:19:14.970
over the internet right now between you and me.
00:19:15.029 --> 00:19:17.539
It goes from. You know, an optical image, it
00:19:17.539 --> 00:19:19.920
gets coded into something that's very compact.
00:19:19.980 --> 00:19:22.640
It gets translated into bits and bytes, goes
00:19:22.640 --> 00:19:24.960
over the, you know, to a satellite. And we're
00:19:24.960 --> 00:19:27.660
all, we almost feel like real, and it gets decoded
00:19:27.660 --> 00:19:30.099
on your side. It's, it's like magic. Well, what
00:19:30.099 --> 00:19:33.039
a similar amount of rich information is contained
00:19:33.039 --> 00:19:35.980
in customer interactions, like conversations,
00:19:36.259 --> 00:19:39.779
but I can't take the entire conversation out
00:19:39.779 --> 00:19:41.819
of my brain and stick it into my colleague's
00:19:41.819 --> 00:19:43.680
brain so that she understands how to onboard
00:19:43.680 --> 00:19:45.869
a customer. I can't do that. It's impossible.
00:19:46.089 --> 00:19:49.190
So what I need to do is I need to encode it into
00:19:49.190 --> 00:19:53.230
a minimum viable set of information that's very
00:19:53.230 --> 00:19:56.990
important that can then get handed off. So that's,
00:19:56.990 --> 00:20:00.009
we use, when we design, we use a operating model
00:20:00.009 --> 00:20:03.210
called SPICED, situation, pain, impact, critical
00:20:03.210 --> 00:20:06.430
event, and decision. And situation, pain, and
00:20:06.430 --> 00:20:09.369
impact are always, always important. It doesn't
00:20:09.369 --> 00:20:12.359
matter. If you're considering a purchase, you're
00:20:12.359 --> 00:20:14.599
purchasing, you're in the middle of onboarding,
00:20:14.599 --> 00:20:16.759
you're renewing or you're expanding, awareness
00:20:16.759 --> 00:20:20.000
of the situation and the pain of the customer
00:20:20.000 --> 00:20:21.660
and then the impact that the solution can make
00:20:21.660 --> 00:20:24.119
is always important. So we take all those interactions,
00:20:24.279 --> 00:20:26.200
whether it's an interaction with a product, interaction
00:20:26.200 --> 00:20:29.279
with a human, or interaction with an AI, we reduce
00:20:29.279 --> 00:20:32.680
it into that very compact kind of delivery mechanism,
00:20:32.819 --> 00:20:35.519
and then we use that to hand off from agent to
00:20:35.519 --> 00:20:39.059
human. So now you can imagine that. famous onboarding
00:20:39.059 --> 00:20:41.000
call that you or handoff call that you always
00:20:41.000 --> 00:20:44.160
want to make happen between sales and cs that
00:20:44.160 --> 00:20:46.579
you know that basically sales always no shows
00:20:46.579 --> 00:20:49.259
for and then cs is always like left holding the
00:20:49.259 --> 00:20:51.259
bag we don't have to do that anymore we can actually
00:20:51.259 --> 00:20:53.519
codify this like we can we have an agent named
00:20:53.519 --> 00:20:55.460
celeste at when anybody's and it listens to every
00:20:55.460 --> 00:20:59.079
single sales call and maps them back to both
00:20:59.079 --> 00:21:01.980
contacts and opportunities and then and so when
00:21:01.980 --> 00:21:04.220
that opportunity closes all that information
00:21:04.220 --> 00:21:07.269
can is automatically available. I can literally
00:21:07.269 --> 00:21:09.390
go on Slack and talk with Celeste and say, hey,
00:21:09.390 --> 00:21:11.569
catch me up on this account. And she knows everything
00:21:11.569 --> 00:21:14.029
about that account. So that's what I mean by
00:21:14.029 --> 00:21:15.789
an operating model. But just those two things,
00:21:15.869 --> 00:21:18.230
a data model and an operating model can really
00:21:18.230 --> 00:21:21.930
set you up for an AI forward go to market. Wow.
00:21:21.990 --> 00:21:25.769
Yeah. That's a lot to set up before you're equipped
00:21:25.769 --> 00:21:29.849
to even apply AI to the situation. Well, it is.
00:21:30.839 --> 00:21:32.859
It is. That's a great call out. So does that
00:21:32.859 --> 00:21:34.599
mean we don't run experiments in the meantime?
00:21:34.859 --> 00:21:36.059
No, I think you should be running experiments.
00:21:36.400 --> 00:21:39.539
Like figure out, we launched a bunch of agents
00:21:39.539 --> 00:21:41.460
before we had anything like that plugged into
00:21:41.460 --> 00:21:43.700
them a bunch. I told you we launched five, but
00:21:43.700 --> 00:21:45.640
like one of those agents is Jack. You can see
00:21:45.640 --> 00:21:47.019
it on the Winning by Design website. You can
00:21:47.019 --> 00:21:48.460
have a conversation with Jack. When we first
00:21:48.460 --> 00:21:50.460
launched Jack, he couldn't even push information
00:21:50.460 --> 00:21:54.299
into the CRM. Okay. That's horrible. But then
00:21:54.299 --> 00:21:56.539
he could. And then he couldn't do research on
00:21:56.539 --> 00:21:58.660
you while you were talking to him, but then he
00:21:58.660 --> 00:22:00.180
could because we connected him up with Clay.
00:22:00.599 --> 00:22:02.259
And then he couldn't, and then we didn't have
00:22:02.259 --> 00:22:04.539
it kind of going and despised in the way that
00:22:04.539 --> 00:22:07.259
we wanted it to, but then it could. So I say,
00:22:07.279 --> 00:22:09.420
start where you are and learn along the way,
00:22:09.480 --> 00:22:10.859
but just know what you're shooting for. Have
00:22:10.859 --> 00:22:12.380
a theory of the case, know what you're shooting
00:22:12.380 --> 00:22:16.640
for. Yeah, man, that's a lot to take in at once,
00:22:16.700 --> 00:22:19.900
but I want to keep going deeper. I'll listen
00:22:19.900 --> 00:22:22.480
to this again myself and relearn from it as well.
00:22:22.859 --> 00:22:25.700
You mentioned that some of the AI experiments
00:22:25.700 --> 00:22:29.440
that you've either seen firsthand or at customers.
00:22:29.980 --> 00:22:33.140
Some of them work and some of them don't. What's
00:22:33.140 --> 00:22:34.880
the differentiating factors that you've seen?
00:22:35.500 --> 00:22:38.779
Yeah. Well, I mean, if we're in figure it out
00:22:38.779 --> 00:22:42.000
mode, then we're going to expect some stuff not
00:22:42.000 --> 00:22:45.279
to work. So like in the PLG world that, you know,
00:22:45.279 --> 00:22:47.700
that I had to figure out when I went, you know,
00:22:47.700 --> 00:22:49.859
and launched that company called Fundly that
00:22:49.859 --> 00:22:54.099
had a $1 .2 thousand median ACV. If we're in
00:22:54.099 --> 00:22:57.640
figure it out mode, we were by definition launching
00:22:57.640 --> 00:23:00.799
experiments almost weekly. Well, in PLG, you
00:23:00.799 --> 00:23:04.079
are launching multiple experiments weekly, and
00:23:04.079 --> 00:23:06.819
you're retiring experiments weekly too that might
00:23:06.819 --> 00:23:08.420
have launched a week ago, two weeks ago, three
00:23:08.420 --> 00:23:10.960
weeks ago. And every time you retire an experiment,
00:23:11.019 --> 00:23:13.180
it's either with a successful outcome or a not
00:23:13.180 --> 00:23:15.579
successful outcome. If it was successful, you
00:23:15.579 --> 00:23:18.099
take whatever the MVP version of that experiment
00:23:18.099 --> 00:23:21.480
was, and you hard code it into the experience
00:23:21.480 --> 00:23:23.339
so that it's going to be part of the machine
00:23:23.339 --> 00:23:25.339
going forward. If it was unsuccessful, you just
00:23:25.339 --> 00:23:27.319
deprecate it, like get rid of it. So it's like
00:23:27.319 --> 00:23:29.960
an A -B test, you know. A worked, B didn't, keep
00:23:29.960 --> 00:23:32.380
A, kill B. And we're doing that relentlessly,
00:23:32.420 --> 00:23:35.180
like week after week. So now we're in a mode
00:23:35.180 --> 00:23:37.579
where you say, like, what are the distinguishing
00:23:37.579 --> 00:23:39.519
characteristics of a successful or unsuccessful
00:23:39.519 --> 00:23:42.359
AI experiment? One thing that's not successful
00:23:42.359 --> 00:23:45.900
is don't start. I can guarantee you how much
00:23:45.900 --> 00:23:48.359
success will come out of that. But I can also
00:23:48.359 --> 00:23:50.240
guarantee you that if you're aggressive, that
00:23:50.240 --> 00:23:52.000
some of your stuff will work and some of your
00:23:52.000 --> 00:23:54.720
stuff won't. The problem is when we talk about
00:23:54.720 --> 00:23:58.410
experiments, if you don't have measurement, How
00:23:58.410 --> 00:24:01.210
will you know? How will you know? Is it just
00:24:01.210 --> 00:24:03.950
gut feel like that feels pretty good? You need
00:24:03.950 --> 00:24:06.769
to understand where that experiment is supposed
00:24:06.769 --> 00:24:09.589
to make a lift in your customer journey. Is it
00:24:09.589 --> 00:24:11.789
supposed to make a rep more effective? Is it
00:24:11.789 --> 00:24:14.029
supposed to close a deal faster? Is it supposed
00:24:14.029 --> 00:24:16.269
to speed up qualification? What's it supposed
00:24:16.269 --> 00:24:18.670
to do? Okay, cool. Now let me set a baseline.
00:24:19.589 --> 00:24:21.930
Now let me launch that experiment. And now let
00:24:21.930 --> 00:24:23.970
me see if it delivers better results or not.
00:24:24.210 --> 00:24:27.150
And then relentlessly, if it doesn't, I'm either
00:24:27.150 --> 00:24:28.650
going to tune it a little more and see if I can
00:24:28.650 --> 00:24:30.289
get it to do it or I'm just going to kill it.
00:24:30.529 --> 00:24:33.230
I think the number one way to fail is to not
00:24:33.230 --> 00:24:35.589
try. The number two way to fail is to not measure.
00:24:36.089 --> 00:24:40.210
And after that, every single failure is actually
00:24:40.210 --> 00:24:43.230
positive. Like if I launch something with a theory
00:24:43.230 --> 00:24:45.529
of the case and it didn't hit what I thought,
00:24:45.670 --> 00:24:47.730
cool, I'll never do that again. That's going
00:24:47.730 --> 00:24:51.650
in column B and I'm going to keep trying things
00:24:51.650 --> 00:24:55.029
until I get things to move the needle. And you
00:24:55.029 --> 00:24:58.720
mentioned, That some companies are, I think you
00:24:58.720 --> 00:25:02.799
used the term AI native. And you said to me before
00:25:02.799 --> 00:25:07.180
that those growth rates can be significantly
00:25:07.180 --> 00:25:11.160
larger than, I guess, the older SaaS model of
00:25:11.160 --> 00:25:15.640
product -led growth. Why is that? Is that what
00:25:15.640 --> 00:25:18.299
you're seeing? Yeah, I've reported elsewhere.
00:25:19.019 --> 00:25:21.900
that ever since 2001, if you go back and look
00:25:21.900 --> 00:25:24.180
at the fastest ever growing software companies
00:25:24.180 --> 00:25:28.380
throughout ages of history, you know, the, and
00:25:28.380 --> 00:25:29.839
let me just, let me not say software, let me
00:25:29.839 --> 00:25:31.720
say SaaS. So we're not going back to ancient
00:25:31.720 --> 00:25:34.299
history. We're not going to mainframes. Salesforce
00:25:34.299 --> 00:25:37.019
was the fastest ever growing software company.
00:25:37.180 --> 00:25:39.460
And then it was VMware. Amazing. They were really
00:25:39.460 --> 00:25:41.859
good. And then it was Atlassian. And then it
00:25:41.859 --> 00:25:44.599
was, I can't, I think it might've been Dropbox
00:25:44.599 --> 00:25:46.839
and then Box and then Slack or something like
00:25:46.839 --> 00:25:50.289
that. So kind of every era has a fastest ever.
00:25:50.349 --> 00:25:53.190
They set new high watermarks. From Atlassian
00:25:53.190 --> 00:25:57.490
forward, 2001 forward, every single fastest ever
00:25:57.490 --> 00:26:00.730
-growing software company has been PLG. Then
00:26:00.730 --> 00:26:04.029
you say, yeah, but that's pre -AI. What about
00:26:04.029 --> 00:26:07.069
AI? And then you look at the fastest ever -growing
00:26:07.069 --> 00:26:11.410
AI companies. That's OpenAI, Mistral, Replit,
00:26:11.430 --> 00:26:14.750
Lovable. These are crazy, you know, get to 100
00:26:14.750 --> 00:26:19.579
million in 10 quarters. six quarters, four quarters
00:26:19.579 --> 00:26:21.940
in the case of lovable, like crazy, like one
00:26:21.940 --> 00:26:24.180
to a hundred million, what? And then you look
00:26:24.180 --> 00:26:26.240
at what is the, what's the kind of underlying
00:26:26.240 --> 00:26:29.960
mechanism that they're using? It is also like
00:26:29.960 --> 00:26:32.259
it's self -service, meaning you don't talk to
00:26:32.259 --> 00:26:34.440
a human to start an open, a chat GPT account.
00:26:34.779 --> 00:26:37.119
You don't start, talk to a human to upgrade your
00:26:37.119 --> 00:26:39.099
chat GPT account. You don't talk to a human to
00:26:39.099 --> 00:26:41.460
share your chat GPT account, your, your projects
00:26:41.460 --> 00:26:44.059
with, with colleagues. Like that's all just built
00:26:44.059 --> 00:26:47.880
in. But here's, but here's the truth. They wouldn't
00:26:47.880 --> 00:26:50.140
be able to harvest, and they have a ton of tailwinds
00:26:50.140 --> 00:26:53.559
because of the kind of AI nature of their products.
00:26:53.839 --> 00:26:55.940
They have a ton of tailwinds. They would not
00:26:55.940 --> 00:26:57.819
be able to harvest them if they were trying to
00:26:57.819 --> 00:27:00.720
harvest it the way that VMware or Salesforce
00:27:00.720 --> 00:27:03.859
did. If they were dependent on hiring, onboarding,
00:27:03.880 --> 00:27:06.380
training, managing, and coaching reps in order
00:27:06.380 --> 00:27:09.539
to turn inbound demand into paying contracts
00:27:09.539 --> 00:27:12.079
and expand them, they'd go way slower by definition.
00:27:12.079 --> 00:27:14.619
It just takes longer to identify a good rep.
00:27:15.069 --> 00:27:17.609
Get her hired and onboarded, then get her trained
00:27:17.609 --> 00:27:19.809
and managed and coached up to kind of productivity.
00:27:19.890 --> 00:27:23.250
That takes time. But it doesn't take a machine
00:27:23.250 --> 00:27:26.049
time as long as you can program it well. So AI
00:27:26.049 --> 00:27:28.589
natives is a very long windup. Sorry, Dan. But
00:27:28.589 --> 00:27:32.809
AI natives never even considered that other path.
00:27:32.950 --> 00:27:36.190
They just built thinking every single interaction
00:27:36.190 --> 00:27:38.289
is a piece of data that can feed back into the
00:27:38.289 --> 00:27:41.390
machine and help me tune it. Every single engagement
00:27:41.390 --> 00:27:43.920
with the customer. is a piece of her journey
00:27:43.920 --> 00:27:46.119
it fits here in the journey i'm going to track
00:27:46.119 --> 00:27:48.380
her over time i'm going to run a customer data
00:27:48.380 --> 00:27:51.220
platform not a crm i'm going to run real time
00:27:51.220 --> 00:27:53.539
not you know one month or one quarter delayed
00:27:53.539 --> 00:27:56.859
data i'm going to update my my processes as i
00:27:56.859 --> 00:27:59.480
go and so ai native even though they're not doing
00:27:59.480 --> 00:28:02.279
anything necessarily new it's kind of plg -ish
00:28:02.279 --> 00:28:04.720
they they just built from the ground up that
00:28:04.720 --> 00:28:08.720
way and now if you can imagine Using AI to analyze
00:28:08.720 --> 00:28:11.259
that data and make real -time changes and adjustments
00:28:11.259 --> 00:28:14.420
and launch real -time experiments just allows
00:28:14.420 --> 00:28:17.720
them to go even faster. So it sounds like the
00:28:17.720 --> 00:28:20.640
effect is real. Like it's really changing the
00:28:20.640 --> 00:28:23.099
way that these companies are able to scale. It
00:28:23.099 --> 00:28:25.359
is. Now, we don't know the end of the story.
00:28:25.839 --> 00:28:29.299
So here, we'll pick on Lovable. They're everyone's
00:28:29.299 --> 00:28:31.920
favorite success story. Four quarters to 100
00:28:31.920 --> 00:28:34.259
million. Guess what? Guess how many birthdays
00:28:34.259 --> 00:28:37.099
you've had? within the first four quarters? Zero.
00:28:37.900 --> 00:28:42.019
So do we really know what their renewal rate
00:28:42.019 --> 00:28:44.460
is going to be? Do we really know what their
00:28:44.460 --> 00:28:46.740
ongoing ARR is going to be? Do we really know
00:28:46.740 --> 00:28:48.700
that it's going to keep compounding at that crazy
00:28:48.700 --> 00:28:51.420
rate? Are they going to hit some headwinds once
00:28:51.420 --> 00:28:53.599
they get out of the white hot center of their
00:28:53.599 --> 00:28:56.099
ICP? The answer is, of course, they're going
00:28:56.099 --> 00:28:58.319
to hit some headwinds and they're going to have
00:28:58.319 --> 00:29:00.740
to adjust. They have a smart team. They'll figure
00:29:00.740 --> 00:29:03.259
it out. But I don't think this is the entire
00:29:03.259 --> 00:29:06.410
story. They have a lot of journey left to go
00:29:06.410 --> 00:29:08.369
and they've got a lot to figure out. I think
00:29:08.369 --> 00:29:09.990
first principles are going to be their friends
00:29:09.990 --> 00:29:12.470
as they figure that out going forward. So can
00:29:12.470 --> 00:29:15.930
this unlock exponential growth? Yes. Will it
00:29:15.930 --> 00:29:19.829
follow a different set of laws of physics than
00:29:19.829 --> 00:29:22.109
the rest of the recurring revenue world? I don't
00:29:22.109 --> 00:29:24.990
think so. I don't think so. Like we're playing
00:29:24.990 --> 00:29:26.930
within a laws of physics and we're trying to
00:29:26.930 --> 00:29:28.650
optimize within it. Now we have a new set of
00:29:28.650 --> 00:29:30.589
tools. That's great. But we're still playing
00:29:30.589 --> 00:29:33.849
within like the same universe of laws of physics.
00:29:34.069 --> 00:29:37.289
Yeah. Now that's a good longer -term perspective.
00:29:37.670 --> 00:29:41.130
With AI especially, everybody seems to be a little
00:29:41.130 --> 00:29:43.690
fixated on the near term, and it's hard to remember
00:29:43.690 --> 00:29:46.809
that this is the beginning. We haven't seen much
00:29:46.809 --> 00:29:50.809
yet. So exciting though, right? I love it. I
00:29:50.809 --> 00:29:56.009
just can't even imagine watching the AI train
00:29:56.009 --> 00:29:59.670
kind of come by my station and deciding to stay
00:29:59.670 --> 00:30:03.829
on the bench. I mean, the only thing worse than
00:30:03.829 --> 00:30:06.170
missing a train is missing a fast train. And
00:30:06.170 --> 00:30:08.670
the AI train is going really fast. Like, I want
00:30:08.670 --> 00:30:13.650
to get on that thing. Yeah, absolutely. It reminds
00:30:13.650 --> 00:30:16.890
me that, well, one of the things you said, I
00:30:16.890 --> 00:30:21.250
think, being able to see each one of these new
00:30:21.250 --> 00:30:25.230
revolutions and even being able to see the invention
00:30:25.230 --> 00:30:29.630
of SaaS as a term. product -like growth as emotion
00:30:29.630 --> 00:30:33.289
that people actually understand. And it feels
00:30:33.289 --> 00:30:38.769
like we have a lot to build. Yeah. And you asked
00:30:38.769 --> 00:30:40.430
in the beginning, like, who am I? And I said,
00:30:40.450 --> 00:30:42.730
I'm dispositionally an adventurer and, you know,
00:30:42.730 --> 00:30:45.250
curious mindset. Not everyone's built that way.
00:30:45.250 --> 00:30:47.730
I totally get it, Daniel. That gives me energy.
00:30:47.930 --> 00:30:50.250
Like, I like stepping into the unknown. I like
00:30:50.250 --> 00:30:54.069
a reasonable chance of failure. For me, it keeps
00:30:54.069 --> 00:30:56.680
me on my toes. But that's not true for everyone.
00:30:56.759 --> 00:30:59.000
It doesn't give everyone energy. It can actually,
00:30:59.339 --> 00:31:02.259
for some personalities, you know, freeze me in
00:31:02.259 --> 00:31:04.940
my tracks. You know, I'm not saying everybody
00:31:04.940 --> 00:31:07.539
should be putting themselves in harm's way every
00:31:07.539 --> 00:31:11.180
day like that. I'm just saying if it's your predisposition,
00:31:11.319 --> 00:31:14.319
great. If you're the one who's going to keep
00:31:14.319 --> 00:31:16.900
everything honest and safe and understandable,
00:31:17.079 --> 00:31:20.140
then great. Partner with somebody who likes to
00:31:20.140 --> 00:31:22.480
go into the unknown. You'll be the one that keeps
00:31:22.480 --> 00:31:24.119
them grounded. They'll be the one that keeps
00:31:24.119 --> 00:31:27.000
pushing us forward. That's very often kind of
00:31:27.000 --> 00:31:30.319
a product and engineering partnership or a marketing
00:31:30.319 --> 00:31:33.900
and sales partnership. It's great. We need both.
00:31:34.099 --> 00:31:37.359
We absolutely need both. For me and my sanity,
00:31:37.519 --> 00:31:40.400
I want to be pushing into the unknown. And then
00:31:40.400 --> 00:31:43.160
if somebody's listening here who says, ooh, I
00:31:43.160 --> 00:31:44.740
like that, but I don't want to be that person,
00:31:44.799 --> 00:31:47.400
cool, I'll take the arrows and I'll come back
00:31:47.400 --> 00:31:49.380
to camp and you'll help me figure out, you know.
00:31:49.740 --> 00:31:53.259
how not to get shot next time. I like that. Is
00:31:53.259 --> 00:31:55.019
there anything that I haven't asked you that
00:31:55.019 --> 00:31:57.339
I shouldn't? It's been interesting to just think
00:31:57.339 --> 00:31:59.420
about the parallels between what's happening
00:31:59.420 --> 00:32:02.140
right now in AI and what's happened in the past.
00:32:02.400 --> 00:32:04.640
Yeah, here's something, Daniel. Like, let's say
00:32:04.640 --> 00:32:07.359
I'm listening to this and I'm freaked out because
00:32:07.359 --> 00:32:12.119
I got a job and now I'm thinking very, very existentially
00:32:12.119 --> 00:32:15.400
about me and my livelihood and the people I take
00:32:15.400 --> 00:32:17.890
care of in my life. and whether or not I have
00:32:17.890 --> 00:32:19.789
a job going forward, because I think AI is coming
00:32:19.789 --> 00:32:22.529
for my job. I think that's a very fair feeling.
00:32:23.210 --> 00:32:27.069
And I also think it is fair, a fair assessment
00:32:27.069 --> 00:32:30.970
that AI cannot replace you as a human. You asked
00:32:30.970 --> 00:32:32.769
at the beginning of the call, am I an AI? I'm
00:32:32.769 --> 00:32:35.349
not. I'm a human. You're a human. Every one of
00:32:35.349 --> 00:32:36.829
our listeners, well, that's probably not true.
00:32:36.869 --> 00:32:38.630
Half of our listeners are AI, but you guys shut
00:32:38.630 --> 00:32:40.710
it off right now. And for our human listeners,
00:32:40.950 --> 00:32:42.950
you know, we're humans. We can do things that
00:32:42.950 --> 00:32:45.339
are uniquely human. AI is not going to take our
00:32:45.339 --> 00:32:47.720
jobs as long as we're not trying to hold on to
00:32:47.720 --> 00:32:50.119
a job that AI should have. Like if I'm trying
00:32:50.119 --> 00:32:52.799
to hold on to an order processing job and that's
00:32:52.799 --> 00:32:54.759
how I define myself, that's where I get all of
00:32:54.759 --> 00:32:57.339
my self -worth. Yeah, AI is going to take that
00:32:57.339 --> 00:33:03.000
job. But if I am a human and I show up as my
00:33:03.000 --> 00:33:05.500
best human self and I'm interacting with other
00:33:05.500 --> 00:33:07.660
humans and I'm helping them manage fear, uncertainty,
00:33:07.759 --> 00:33:10.180
and doubt, I'm helping manage kind of stakeholder
00:33:10.180 --> 00:33:12.660
alignment, I'm helping manage strategy, I'm helping
00:33:12.660 --> 00:33:16.710
build. confidence. I'm helping chart kind of
00:33:16.710 --> 00:33:19.109
a course forward. I'm helping make connections
00:33:19.109 --> 00:33:23.109
between humans. AIs can't do those things. Just
00:33:23.109 --> 00:33:26.170
step into the upper kind of half of the realm
00:33:26.170 --> 00:33:28.829
of your job, lean into that and figure out how
00:33:28.829 --> 00:33:31.210
to outsource the rest to AI and you won't be
00:33:31.210 --> 00:33:34.930
worried anymore. Like you'll be automating everything
00:33:34.930 --> 00:33:36.730
that's predictable. Yes, AI is going to take
00:33:36.730 --> 00:33:39.990
that and it's going to give you a chance to humanize
00:33:39.990 --> 00:33:42.960
all the exceptional stuff. The stuff that only
00:33:42.960 --> 00:33:46.319
you could do. That's where we want to live. I
00:33:46.319 --> 00:33:49.079
like that call. Thank you, Dave. And is there
00:33:49.079 --> 00:33:51.839
anything that you'd like to say before we sign
00:33:51.839 --> 00:33:55.519
off? Thank you. Well, I'd love to see you online.
00:33:55.680 --> 00:33:57.119
I'd love to see the listeners online if you like
00:33:57.119 --> 00:33:59.579
this conversation. Winning by Design has amazing
00:33:59.579 --> 00:34:03.519
resources. One of them is free. I'll flash one
00:34:03.519 --> 00:34:05.880
more thing on the screen. It's the Growth Journal.
00:34:06.019 --> 00:34:08.800
It's a quarterly journal. It's like the HBR for
00:34:08.800 --> 00:34:11.340
growth executives. The digital version is absolutely
00:34:11.340 --> 00:34:13.340
free. You can also get the print version if you
00:34:13.340 --> 00:34:15.599
want, but that costs a little bit of money. I
00:34:15.599 --> 00:34:17.960
go to winningbydesign .com and sign up for that
00:34:17.960 --> 00:34:20.079
immediately. If you want to find me, I've got
00:34:20.079 --> 00:34:22.539
a sub stack and I'm on LinkedIn. Love to see
00:34:22.539 --> 00:34:24.360
you out there, especially all those courageous
00:34:24.360 --> 00:34:28.820
adventurers. I started this podcast because of
00:34:28.820 --> 00:34:31.940
an image that came to me in a dream. Back when
00:34:31.940 --> 00:34:34.079
cities had walls, everyone passed through the
00:34:34.079 --> 00:34:37.019
gate. Talk to enough people there and you'd understand
00:34:37.019 --> 00:34:40.760
the city. I wanted to stand at the gate and understand
00:34:40.760 --> 00:34:43.619
not just how AI was changing, but how AI was
00:34:43.619 --> 00:34:46.420
changing us. If you know someone building or
00:34:46.420 --> 00:34:49.039
leading with AI, or if this episode was useful,
00:34:49.320 --> 00:34:52.599
an introduction, a share, or a quick review on
00:34:52.599 --> 00:34:54.940
your podcast app all help keep these conversations
00:34:54.940 --> 00:34:58.559
going and help others find them. You can reach
00:34:58.559 --> 00:35:01.599
me on LinkedIn at danielmanary or by email at
00:35:01.599 --> 00:35:04.940
daniel at manary dot house. That's daniel at
00:35:04.940 --> 00:35:10.159
m -a -n -a -r -y dot Thanks for listening.
00:00:00.000 --> 00:00:03.600
You saw the MIT study where 90 % of these projects
00:00:03.600 --> 00:00:06.780
are not showing ROI. That's because you launched
00:00:06.780 --> 00:00:10.439
it as a point solution to a problem that's not
00:00:10.439 --> 00:00:14.300
well -defined inside of a system that isn't architected.
00:00:14.339 --> 00:00:17.519
That's fine. I can't even imagine watching the
00:00:17.519 --> 00:00:21.199
AI train kind of come by my station and deciding
00:00:21.199 --> 00:00:23.480
to stay on the bench. I mean, the only thing
00:00:23.480 --> 00:00:25.679
worse than missing a train is missing a fast
00:00:25.679 --> 00:00:27.760
train, and the AI train's going really fast.
00:00:28.489 --> 00:00:30.690
I want to get on that thing. You'll be automating
00:00:30.690 --> 00:00:32.530
everything that's predictable. Yes, AI is going
00:00:32.530 --> 00:00:34.630
to take that. And it's going to give you a chance
00:00:34.630 --> 00:00:38.670
to humanize all the exceptional stuff, the stuff
00:00:38.670 --> 00:00:41.250
that only you could do. That's where we want
00:00:41.250 --> 00:00:45.030
to live. Welcome to Artificial Insights, where
00:00:45.030 --> 00:00:47.109
we have conversations with leaders and thinkers
00:00:47.109 --> 00:00:50.670
in AI about how AI is changing economics, work,
00:00:50.810 --> 00:00:54.350
and us. I'm your host, Daniel Maneri, and this
00:00:54.350 --> 00:00:57.979
is Season 5. Today, I am joined by Dave Boyce.
00:00:58.299 --> 00:01:01.539
Dave Boyce is an American entrepreneur, educator,
00:01:01.700 --> 00:01:04.219
and author renowned for his expertise in product
00:01:04.219 --> 00:01:06.819
-led growth and AI automated sales and marketing.
00:01:07.260 --> 00:01:09.920
He's got a track record of building and selling
00:01:09.920 --> 00:01:13.280
five SaaS companies. Currently, he's working
00:01:13.280 --> 00:01:16.200
for a firm called Winning by Design, which I
00:01:16.200 --> 00:01:18.659
encountered because of their unique way of thinking
00:01:18.659 --> 00:01:21.959
about the sales cycle as continuing past the
00:01:21.959 --> 00:01:25.060
sale and all the way up until the customer renews.
00:01:25.290 --> 00:01:27.950
or leaves. From his position at Winning by Design,
00:01:28.209 --> 00:01:31.549
Dave's able to see the life cycle of sales at
00:01:31.549 --> 00:01:34.650
companies as big as Canva and some much smaller
00:01:34.650 --> 00:01:37.530
than that. Dave, could you tell us a little bit
00:01:37.530 --> 00:01:39.870
about who you are and what you do? Thank you
00:01:39.870 --> 00:01:43.090
so much for having me, Daniel. I'm Dave. I'm
00:01:43.090 --> 00:01:45.629
Dave. I'm a father. I'm a software executive.
00:01:45.810 --> 00:01:48.909
I'm an entrepreneur, an adventurer. I really
00:01:48.909 --> 00:01:51.930
am drawn to figuring out You know, taking steps
00:01:51.930 --> 00:01:54.090
into the unknown, which is why I keep going back.
00:01:54.109 --> 00:01:56.750
You know, we'll get a we'll get a company landed
00:01:56.750 --> 00:01:59.109
and, you know, good, bad or ugly. We'll get it,
00:01:59.109 --> 00:02:00.689
bring it in for a landing. And I've done this
00:02:00.689 --> 00:02:03.189
seven times. And then I go back to the beginning,
00:02:03.310 --> 00:02:06.409
like and then you figure it out or go, you know,
00:02:06.409 --> 00:02:08.849
go into the unknown in some way or push into
00:02:08.849 --> 00:02:11.569
that. That's who I am dispositionally. And I
00:02:11.569 --> 00:02:14.590
think it keeps you, you know, just stay curious.
00:02:14.650 --> 00:02:17.610
You can stay young, stay engaged. That's that's
00:02:17.610 --> 00:02:20.099
what I try to do. Where are you now? And then
00:02:20.099 --> 00:02:21.860
how did you get there? Could you just give us
00:02:21.860 --> 00:02:25.759
a version of that journey? Totally, yeah. Just
00:02:25.759 --> 00:02:28.460
like time -lapse. I want to start with, you know,
00:02:28.500 --> 00:02:30.919
I was born the oldest of eight kids. We'll skip
00:02:30.919 --> 00:02:33.900
forward to, you know, professional life. I've
00:02:33.900 --> 00:02:35.560
probably done it more than eight times or seven
00:02:35.560 --> 00:02:37.800
times, honestly, if you count all the kind of,
00:02:37.819 --> 00:02:41.080
you know, failure to launches. But, you know,
00:02:41.080 --> 00:02:44.319
four companies we built as an operator and got
00:02:44.319 --> 00:02:46.740
them sold. One was ProfitLogic, which sold to
00:02:46.740 --> 00:02:50.699
Oracle. One was Inside Sales, which sold to Aurea,
00:02:50.800 --> 00:02:53.080
which sounds similar. One was Zenprint, which
00:02:53.080 --> 00:02:58.360
sold to private investors. And one was Marks,
00:02:58.360 --> 00:03:02.180
which sold to Amazon. And then as a kind of active
00:03:02.180 --> 00:03:06.379
investor advisor, three other ones. Now I'm at
00:03:06.379 --> 00:03:09.460
Winning by Design. I run product and I'm executive
00:03:09.460 --> 00:03:12.219
chair. And Winning by Design is a chance to kind
00:03:12.219 --> 00:03:16.039
of click up a few clicks in altitude. observe
00:03:16.039 --> 00:03:18.680
the success patterns across the industry. Because
00:03:18.680 --> 00:03:20.400
when you're in a business, you know, you're heads
00:03:20.400 --> 00:03:22.800
down, you're trying to pay attention, but really
00:03:22.800 --> 00:03:25.319
you're working hard, heads down. Now I've been
00:03:25.319 --> 00:03:27.219
able to spend the last four years kind of a click
00:03:27.219 --> 00:03:30.539
up, got a book published, got contributed to
00:03:30.539 --> 00:03:34.159
some really cool framework developments at Winning
00:03:34.159 --> 00:03:35.800
by Design, working with some of the smartest
00:03:35.800 --> 00:03:39.219
people in the industry. And so that's where I
00:03:39.219 --> 00:03:43.219
am right now. Very cool. And have you held different
00:03:43.219 --> 00:03:46.840
roles across? that journey? And what have those
00:03:46.840 --> 00:03:49.560
roles looked like? It's a great question. It's
00:03:49.560 --> 00:03:51.319
interesting because there's certain things that
00:03:51.319 --> 00:03:53.259
you're good at, right? Like dispossessionally
00:03:53.259 --> 00:03:55.500
good at. There's certain things that where if
00:03:55.500 --> 00:03:57.740
the job is that Daniel is your person, like,
00:03:57.819 --> 00:04:01.659
you know, superpower, that's who you need. And
00:04:01.659 --> 00:04:03.719
you know it. And the people who are closest to
00:04:03.719 --> 00:04:05.659
you know it. It's true about me. It's true about
00:04:05.659 --> 00:04:09.280
you. And then you get into a company and stuff
00:04:09.280 --> 00:04:12.180
happens, you know, and so there's a battlefield
00:04:12.180 --> 00:04:15.639
promotion or a battle. field scope expansion
00:04:15.639 --> 00:04:18.860
or a last man standing kind of like assignment.
00:04:19.259 --> 00:04:22.439
So the stuff I'm really good at are product and
00:04:22.439 --> 00:04:24.720
strategy and positioning. Like I'm really good
00:04:24.720 --> 00:04:26.660
at that and I'm good at representing it externally,
00:04:26.980 --> 00:04:30.339
which makes it natural for me to be, you know,
00:04:30.339 --> 00:04:33.120
an evangelist or to lead partners or channels
00:04:33.120 --> 00:04:35.600
or analyst relations. I've done all those roles.
00:04:35.980 --> 00:04:38.600
Also, I have, I've also run customer success
00:04:38.600 --> 00:04:41.860
and I've also been a CEO. Let's see, what am
00:04:41.860 --> 00:04:44.860
I missing? So I've run marketing, product, product
00:04:44.860 --> 00:04:48.899
marketing, customer success. I've never run finance.
00:04:49.040 --> 00:04:52.139
I have run business development and I've carried
00:04:52.139 --> 00:04:55.579
a quota. So there you go. More things you've
00:04:55.579 --> 00:04:58.160
done than you haven't. Yeah. And that's what,
00:04:58.199 --> 00:05:01.000
so being a CEO at this point does feel natural
00:05:01.000 --> 00:05:03.160
to me because I've been in many of those seats.
00:05:03.240 --> 00:05:05.259
It's almost like, you know, if you're going to
00:05:05.259 --> 00:05:08.110
become a general contractor and you. learned
00:05:08.110 --> 00:05:10.250
multiple trades you can just do a better job
00:05:10.250 --> 00:05:12.410
you know if you learned plumbing and framing
00:05:12.410 --> 00:05:14.689
and finished carpentry and painting it's like
00:05:14.689 --> 00:05:17.189
okay maybe you didn't do electrical maybe you
00:05:17.189 --> 00:05:19.629
didn't do excavation or concrete but you've done
00:05:19.629 --> 00:05:21.810
a lot of those jobs and now you can be a good
00:05:21.810 --> 00:05:25.069
kind of overseer of a project i feel that way
00:05:25.069 --> 00:05:30.629
as when i'm in a ceo seat very cool and there's
00:05:30.629 --> 00:05:32.490
a question on everybody's minds with a podcast
00:05:32.490 --> 00:05:37.600
like this which is dave are you an ai I'd be
00:05:37.600 --> 00:05:40.459
much smarter. I'd be much wittier if I were.
00:05:40.899 --> 00:05:43.139
I'd be better looking too. I've seen some of
00:05:43.139 --> 00:05:46.360
those AI kind of brush ups. No, I'm very, very
00:05:46.360 --> 00:05:49.339
human. You and I shared stories about kids and
00:05:49.339 --> 00:05:52.540
families and, you know, and that comes with all
00:05:52.540 --> 00:05:55.779
kinds of bumps and bruises and apologies and
00:05:55.779 --> 00:05:59.540
do -overs. That's who I am. I appreciate that.
00:05:59.600 --> 00:06:03.079
Never had an AI apologize to me genuinely. I
00:06:03.079 --> 00:06:07.199
don't think it has to, but it never does. With
00:06:07.199 --> 00:06:11.779
that journey, now that you're seeing the industry
00:06:11.779 --> 00:06:14.899
that you're working across in a new way, you
00:06:14.899 --> 00:06:18.459
called it a click up. What is the industry that
00:06:18.459 --> 00:06:22.300
you are seeing at that big level? Yeah, now we're
00:06:22.300 --> 00:06:25.139
getting into it. So Daniel, when I first started,
00:06:25.319 --> 00:06:27.379
the first of all those startups that I mentioned
00:06:27.379 --> 00:06:30.980
was very much a sales led company. Our average
00:06:30.980 --> 00:06:34.420
ACV annual contract value was $1 .2 million.
00:06:35.389 --> 00:06:37.649
We were hosting these very large machine learning
00:06:37.649 --> 00:06:41.069
models in colo facilities, which eventually became
00:06:41.069 --> 00:06:44.189
known as the cloud in a model that eventually
00:06:44.189 --> 00:06:46.629
became known as SAS. This was in the, in like
00:06:46.629 --> 00:06:50.069
2000, I think SAS, this term SAS was coined like
00:06:50.069 --> 00:06:54.790
a year later, $1 .2 million. Then the next I
00:06:54.790 --> 00:06:56.730
left, we sold that company to Oracle. I left,
00:06:56.730 --> 00:06:59.290
had a global VP role there for almost five years.
00:06:59.410 --> 00:07:02.129
I left, go back to the beginning and do a startup
00:07:02.129 --> 00:07:05.980
called Fundly. And the. The median, I'm not going
00:07:05.980 --> 00:07:07.480
to say average because the average was a little
00:07:07.480 --> 00:07:09.680
higher than this, but the median ACV at Fundly
00:07:09.680 --> 00:07:15.079
was $1 .2 thousand. That is a completely different
00:07:15.079 --> 00:07:17.899
business. So I had to learn, I had to learn like
00:07:17.899 --> 00:07:19.839
how do you, how do you set up self -service?
00:07:19.860 --> 00:07:22.180
How do you get your unit economics to work when
00:07:22.180 --> 00:07:25.579
you've only got $1 .2 thousand of kind of total
00:07:25.579 --> 00:07:29.600
possible margin? And if you have any decent margin,
00:07:29.720 --> 00:07:31.620
it's going to be a fraction of that to work with
00:07:31.620 --> 00:07:33.600
on cost of sales and cost of marketing. Okay,
00:07:33.680 --> 00:07:36.139
got it. So we figured that out. We ended up getting
00:07:36.139 --> 00:07:39.560
that company sold. And now, Daniel, where we
00:07:39.560 --> 00:07:42.040
are now, so fast forward, we sold that company
00:07:42.040 --> 00:07:45.439
in 2010, I think. Now fast forward to what's
00:07:45.439 --> 00:07:48.060
happening now. Everything's changing again. Like
00:07:48.060 --> 00:07:52.839
AI is making itself useful in purchasing. I just
00:07:52.839 --> 00:07:57.519
read that over 50 % of B2B purchases. Start with
00:07:57.519 --> 00:08:01.839
an LLM inquiry. Okay, that's new. That's new.
00:08:02.939 --> 00:08:06.439
That's new. Like we haven't done like SEO type
00:08:06.439 --> 00:08:09.420
optimization on LLM. We don't exactly know how
00:08:09.420 --> 00:08:11.680
to trace the footsteps of how things get into
00:08:11.680 --> 00:08:13.339
LLM. We don't know how to make our companies
00:08:13.339 --> 00:08:16.180
visible on LLM. And yet, that's a place where
00:08:16.180 --> 00:08:18.420
a buyer can have a very, very rich conversation
00:08:18.420 --> 00:08:20.600
about my product, about my competitor's product,
00:08:20.680 --> 00:08:22.879
about my price, about their price, about my reviews,
00:08:22.920 --> 00:08:25.660
about their reviews. So we have to figure this
00:08:25.660 --> 00:08:27.970
out. And we have to meet our buyers where we
00:08:27.970 --> 00:08:30.629
are. So I think what we're seeing right now is
00:08:30.629 --> 00:08:34.850
some AI native companies who kind of grew up
00:08:34.850 --> 00:08:37.950
just assuming that everything would be AI -able
00:08:37.950 --> 00:08:39.730
and they built their whole go -to -market in
00:08:39.730 --> 00:08:42.210
a no -touch way. You know, if you think about
00:08:42.210 --> 00:08:48.370
OpenAI or Anthropic or Mistral, you don't talk
00:08:48.370 --> 00:08:50.149
to a human to buy that product. You don't talk
00:08:50.149 --> 00:08:52.250
to a human to learn how to use that product.
00:08:52.309 --> 00:08:54.470
You don't talk to a human to upgrade to the enterprise
00:08:54.470 --> 00:08:57.080
or Teams version of that product. No, it's very
00:08:57.080 --> 00:08:59.419
much feels like a PLG, a product -led growth
00:08:59.419 --> 00:09:02.159
journey, but inside of like a consumption -based
00:09:02.159 --> 00:09:05.759
kind of model and built by people who just assume
00:09:05.759 --> 00:09:08.059
that AI can bridge all the gaps. So they have
00:09:08.059 --> 00:09:11.159
real -time data feeding the machine, helping
00:09:11.159 --> 00:09:13.820
it. And those are foundation models. Then you
00:09:13.820 --> 00:09:15.740
talk about, you know, what everyone likes to
00:09:15.740 --> 00:09:17.820
talk about, Lovable and Replit and these companies
00:09:17.820 --> 00:09:21.120
that are setting crazy growth targets. But Daniel,
00:09:21.259 --> 00:09:23.610
that's like the new world we live in. If you
00:09:23.610 --> 00:09:26.450
wanted to meet today's B2B buyer who behaves
00:09:26.450 --> 00:09:29.830
like I just said, and by the way, 70 % of the
00:09:29.830 --> 00:09:31.570
B2B buyers, this is a different statistic. I
00:09:31.570 --> 00:09:34.230
don't know how they crosshatch. 70 % of B2B buyers
00:09:34.230 --> 00:09:37.710
are either millennials or Gen Z. Okay. All right.
00:09:37.750 --> 00:09:40.509
I'm not. So if I'm building a go -to -market
00:09:40.509 --> 00:09:44.730
motion for B2B and I build it a way that makes
00:09:44.730 --> 00:09:48.169
me comfortable as a Gen Xer, I'm doing it wrong.
00:09:48.710 --> 00:09:51.919
Like I got to meet Gen Z where she is. And I
00:09:51.919 --> 00:09:53.820
got to build a machine that makes her comfortable,
00:09:53.960 --> 00:09:55.740
that makes it easy for her to do business with
00:09:55.740 --> 00:09:58.620
me. So we're seeing these revolutions. They just
00:09:58.620 --> 00:10:02.000
happen, right? Like they happen kind of generation
00:10:02.000 --> 00:10:04.220
to generation. We're in the middle one. We got
00:10:04.220 --> 00:10:07.059
to put on our curiosity hat, channel our best
00:10:07.059 --> 00:10:10.120
problem solving, and not assume that the playbooks
00:10:10.120 --> 00:10:12.639
that got us here are going to get us there. Yeah,
00:10:12.639 --> 00:10:15.700
there's a lot to dig in there. But I'd like to
00:10:15.700 --> 00:10:18.600
ask you, with what you're seeing, you're seeing
00:10:18.600 --> 00:10:21.659
it through, I think, two different lenses. And
00:10:21.659 --> 00:10:23.919
one of those is you're currently working for
00:10:23.919 --> 00:10:26.820
winning by design. Yeah. How does winning by
00:10:26.820 --> 00:10:29.720
design get involved with, with all of those growth
00:10:29.720 --> 00:10:32.759
motions? Yeah. I'm super lucky at winning by
00:10:32.759 --> 00:10:35.559
design for the vantage. So we work with growth
00:10:35.559 --> 00:10:39.259
stage companies, typically 50 million to 2 billion
00:10:39.259 --> 00:10:43.779
in revenue. And in many cases, not AI native
00:10:43.779 --> 00:10:47.179
companies. I mean, in basically all cases, we're
00:10:47.179 --> 00:10:50.470
not working with a single. 50 million plus AI
00:10:50.470 --> 00:10:52.649
native company. We're working with some startups
00:10:52.649 --> 00:10:55.370
within a company that are building AI native.
00:10:55.470 --> 00:10:57.889
But what we do is we work with those companies
00:10:57.889 --> 00:11:00.090
who have tremendous enterprise value. They've
00:11:00.090 --> 00:11:02.990
got rule of 40, rule of 50, rule of 60. They're
00:11:02.990 --> 00:11:05.070
sitting pretty right. They've got high growth,
00:11:05.149 --> 00:11:08.450
high profitability inside a PE portfolio. And
00:11:08.450 --> 00:11:10.629
yet they see the same thing that you and I are
00:11:10.629 --> 00:11:12.669
talking about right now. They see the industry
00:11:12.669 --> 00:11:15.070
evolving in a different way and they know that
00:11:15.070 --> 00:11:18.299
they have to stay relevant. We use kind of our
00:11:18.299 --> 00:11:20.779
bird's eye view to help those types of companies
00:11:20.779 --> 00:11:23.700
build growth infrastructure, growth processes,
00:11:23.860 --> 00:11:26.340
growth architecture that allows them to keep
00:11:26.340 --> 00:11:28.320
doing what they're doing well, but also then
00:11:28.320 --> 00:11:31.600
evolve to their next stage of growth and to their
00:11:31.600 --> 00:11:34.200
next stage of buyer. So the way that looks is
00:11:34.200 --> 00:11:36.679
very basic things like putting in a data model
00:11:36.679 --> 00:11:39.879
or putting in an operating model so that we have
00:11:39.879 --> 00:11:43.120
visibility and consistency and so that we're
00:11:43.120 --> 00:11:46.039
AI ready. And then very kind of tactical things
00:11:46.039 --> 00:11:50.019
like figuring out which tasks within a customer
00:11:50.019 --> 00:11:52.940
journey should be performed by a human and which
00:11:52.940 --> 00:11:55.360
ones should be performed by a robot and how the
00:11:55.360 --> 00:11:57.980
handoffs happen from human to robot to human
00:11:57.980 --> 00:12:01.179
and where all that kind of information lives
00:12:01.179 --> 00:12:04.159
in an operating model. The very, very cool frameworks
00:12:04.159 --> 00:12:06.539
that we're using to just help those companies.
00:12:07.320 --> 00:12:11.279
Basically, you don't have to run faster than
00:12:11.279 --> 00:12:14.049
the bear. You just have to run faster. than your
00:12:14.049 --> 00:12:17.110
hiking buddy, right? So every one of these companies
00:12:17.110 --> 00:12:20.370
is under the gun and they have to keep up with
00:12:20.370 --> 00:12:22.590
the times and that this is a super exciting place
00:12:22.590 --> 00:12:25.210
to be. And before I dig into that even more,
00:12:25.370 --> 00:12:28.590
the other angle is you told me about a book that
00:12:28.590 --> 00:12:32.230
you've published. Yeah. What is that about? Rock
00:12:32.230 --> 00:12:34.750
on. Well, it kind of is about this journey, Daniel.
00:12:34.830 --> 00:12:37.070
It's called Freemium. I'm going to see if I can
00:12:37.070 --> 00:12:39.070
put it into the frame of the camera without messing
00:12:39.070 --> 00:12:42.250
us up. It's called, there we go, Freemium. Beautiful
00:12:42.250 --> 00:12:44.519
cover. We published it earlier this year with
00:12:44.519 --> 00:12:47.039
Stanford. And it is about this journey, this
00:12:47.039 --> 00:12:49.600
self -service journey. It's a long -term evolution,
00:12:49.740 --> 00:12:53.080
in my opinion. It goes all the way back to shareware,
00:12:53.179 --> 00:12:56.419
free software downloads. Then we had Atlassian.
00:12:56.440 --> 00:13:00.659
Then we had Dropbox and DocuSign and Twilio,
00:13:00.779 --> 00:13:05.200
defining kind of Gen 1 PLG. We got that all pushed
00:13:05.200 --> 00:13:07.820
out. We started becoming very used to software
00:13:07.820 --> 00:13:09.779
just being part of our lives on our phones, pushed
00:13:09.779 --> 00:13:13.570
out to mobile. very easy kind of emerge of kind
00:13:13.570 --> 00:13:15.429
of consumer and enterprise software. So in our
00:13:15.429 --> 00:13:17.409
work life, everything became, you know, easy
00:13:17.409 --> 00:13:20.690
to discover, easy to use. Document what that
00:13:20.690 --> 00:13:23.210
is, how we got here, where we are today, but
00:13:23.210 --> 00:13:25.590
then also how would you build it? Like, let's
00:13:25.590 --> 00:13:27.610
say you're one of the 99 % of companies who didn't
00:13:27.610 --> 00:13:29.710
start that way. Then what do you do? Like, how,
00:13:29.789 --> 00:13:31.549
you know, what, how would you, how would you
00:13:31.549 --> 00:13:33.470
retrofit it in? So I talked to several companies
00:13:33.470 --> 00:13:35.750
who already were north of a hundred million and
00:13:35.750 --> 00:13:38.179
then retrofit. some of these self -service motions
00:13:38.179 --> 00:13:40.179
into their company one of those companies was
00:13:40.179 --> 00:13:42.659
hubspot one of those companies was mongodb one
00:13:42.659 --> 00:13:44.840
of those companies was unity and then i'm and
00:13:44.840 --> 00:13:46.919
then we take a look at all right so then where
00:13:46.919 --> 00:13:48.419
is it going from here what does the internet
00:13:48.419 --> 00:13:50.600
of things do what is what is software embedded
00:13:50.600 --> 00:13:53.059
into physical things do what does that unlock
00:13:53.059 --> 00:13:55.139
in terms of self -service and then the and then
00:13:55.139 --> 00:13:57.980
the kind of the you know 50 million dollar question
00:13:57.980 --> 00:14:00.100
probably more than that 50 billion dollar question
00:14:00.100 --> 00:14:03.100
500 billion dollar question What does AI unlock?
00:14:03.379 --> 00:14:05.779
Like how much self -service does AI unlock and
00:14:05.779 --> 00:14:07.779
where does that take us? So that's what freemium
00:14:07.779 --> 00:14:10.059
does. Where have we been? Where are we now? Where
00:14:10.059 --> 00:14:12.700
are we going? And how do you implement that in
00:14:12.700 --> 00:14:15.120
your company? One of the things you mentioned
00:14:15.120 --> 00:14:19.000
was being involved with sales processes and trying
00:14:19.000 --> 00:14:21.620
to bring AI into that sales process. Where does
00:14:21.620 --> 00:14:24.539
it fit? Where does it hand off to a human? And
00:14:24.539 --> 00:14:27.600
where does a human fit? Could you tell us more
00:14:27.600 --> 00:14:30.000
about the experiments you've done and what you've
00:14:30.000 --> 00:14:32.419
seen there? Yeah. You want to get real tactical?
00:14:32.860 --> 00:14:37.059
Oh, I'd love to. Yeah. Okay. So we have seen
00:14:37.059 --> 00:14:39.659
a lot of experiments. I've run, I launched personally,
00:14:39.919 --> 00:14:43.320
Winning by Design launched five agents last year.
00:14:43.360 --> 00:14:44.860
I was personally involved with four of them.
00:14:45.080 --> 00:14:47.259
And we, and a lot of our clients are launching
00:14:47.259 --> 00:14:49.679
agents to either an assistive agent to help a
00:14:49.679 --> 00:14:54.200
human do her job better or, or like an agentic
00:14:54.200 --> 00:14:56.299
kind of AI approach where they actually take
00:14:56.299 --> 00:14:58.750
over a piece of the customer journey. have a
00:14:58.750 --> 00:15:01.049
conversation with a customer, like front lines.
00:15:01.529 --> 00:15:03.009
And then what we're eventually going to get to
00:15:03.009 --> 00:15:04.929
is orchestrative, where we're actually kind of
00:15:04.929 --> 00:15:07.450
orchestrating workflows. I haven't done any of
00:15:07.450 --> 00:15:09.129
that yet. We're working with one client who's,
00:15:09.149 --> 00:15:10.850
I haven't done any of that at Winning by Design.
00:15:10.909 --> 00:15:12.250
We're working with one client that's doing it.
00:15:12.629 --> 00:15:14.769
But here's what you need. You have to have a
00:15:14.769 --> 00:15:17.090
point of view. Like you have to understand a
00:15:17.090 --> 00:15:18.950
theory of the case in order to do this. We've
00:15:18.950 --> 00:15:22.350
seen, you saw the MIT study where, you know,
00:15:22.350 --> 00:15:25.789
90 % of these projects are not showing ROI. That's
00:15:25.789 --> 00:15:28.529
because you launched it. as a point solution
00:15:28.529 --> 00:15:31.970
to a problem that's not well -defined inside
00:15:31.970 --> 00:15:35.129
of a system that isn't architected. That's fine.
00:15:35.269 --> 00:15:38.710
Guess what? Getting 10 % success, I think, was
00:15:38.710 --> 00:15:41.370
it 85 -15 or 90 -10? I can't remember. But let's
00:15:41.370 --> 00:15:44.909
say it was 85 -15. So getting 15 % success in
00:15:44.909 --> 00:15:47.370
those environments, pretty good. That means like
00:15:47.370 --> 00:15:50.850
15 % of the time, you know, even a blind monkey
00:15:50.850 --> 00:15:53.809
can hit the bullseye. But what we need to do
00:15:53.809 --> 00:15:56.309
is have a theory of the case. So what we want
00:15:56.309 --> 00:15:59.820
to do... is establish two things. One is a data
00:15:59.820 --> 00:16:02.740
model. So we want to understand, I'll go from
00:16:02.740 --> 00:16:07.000
left to right, how a prospect makes it from awareness
00:16:07.000 --> 00:16:12.240
to education to selection to commit. And if you're
00:16:12.240 --> 00:16:15.220
in PLG, you're going to actually not even ask
00:16:15.220 --> 00:16:19.500
for a commit until after acquisition, activation,
00:16:19.840 --> 00:16:22.879
and first impact, and maybe even habit development.
00:16:23.000 --> 00:16:25.250
Then I'm going to come ask for a contract. But
00:16:25.250 --> 00:16:27.129
then I want to know after I've got them in there,
00:16:27.169 --> 00:16:30.730
what's my ongoing engagement and what's my expansion
00:16:30.730 --> 00:16:33.590
and what's my, what's my retention and what's
00:16:33.590 --> 00:16:36.269
my expansion. If I can document all those stages,
00:16:36.330 --> 00:16:37.730
we talk about, at Winning by Design, we talk
00:16:37.730 --> 00:16:40.370
about a bow tie. So it's a traditional sales
00:16:40.370 --> 00:16:42.409
and marketing funnel, wide at the top, narrow
00:16:42.409 --> 00:16:45.629
at the bottom. Turn that thing on its side. The
00:16:45.629 --> 00:16:48.409
narrowest part is the, is where, you know, the
00:16:48.409 --> 00:16:50.909
customer first makes her commitment and then
00:16:50.909 --> 00:16:54.860
it starts to widen out through usage. and renewal
00:16:54.860 --> 00:16:57.120
and expansion. And the right -hand side of the
00:16:57.120 --> 00:16:59.100
bow tie is where all the compounding happens.
00:16:59.379 --> 00:17:01.580
That's the whole customer kind of magic. That's
00:17:01.580 --> 00:17:03.179
where all the impact gets delivered. That's where
00:17:03.179 --> 00:17:06.579
all the recurring revenue is. But let's say I've
00:17:06.579 --> 00:17:09.079
structured that and I've defined each of those
00:17:09.079 --> 00:17:12.220
stages and I can watch cohorts of customers come
00:17:12.220 --> 00:17:14.980
through and either succeed or fail. Amazing.
00:17:15.180 --> 00:17:18.460
Turns out it's very hard to do that. CRM wasn't
00:17:18.460 --> 00:17:20.720
originally built to track the right -hand side
00:17:20.720 --> 00:17:23.599
of the bow tie. Turns out... Because of recurring
00:17:23.599 --> 00:17:26.579
revenue, there's a lot of recursiveness in a
00:17:26.579 --> 00:17:29.259
go -to -market motion, like these renewals and
00:17:29.259 --> 00:17:32.500
these expansions. And if you want to get really,
00:17:32.579 --> 00:17:36.319
really honest about it, these models don't compound
00:17:36.319 --> 00:17:38.619
until you have advocacy going from the right
00:17:38.619 --> 00:17:40.619
-hand side back over to the left -hand side,
00:17:40.720 --> 00:17:42.779
meaning your existing customers are so happy
00:17:42.779 --> 00:17:46.160
that they're helping pull in new customers, not
00:17:46.160 --> 00:17:48.539
in a cold start way, like a cold outbound way.
00:17:48.680 --> 00:17:50.400
And I want to track that too. That's called a
00:17:50.400 --> 00:17:52.309
growth loop. I want to track all of that. So
00:17:52.309 --> 00:17:54.130
now I have it all kind of modeled out. Cool.
00:17:54.210 --> 00:17:56.829
I've got a theory of the case. I know my conversion
00:17:56.829 --> 00:17:59.349
rates that need to happen from left to right.
00:17:59.490 --> 00:18:03.069
I know how that's going to result in stacking
00:18:03.069 --> 00:18:06.869
ARR, annual recurring revenue. Now within that,
00:18:06.950 --> 00:18:09.690
I need to figure out what happens within each
00:18:09.690 --> 00:18:11.450
stage. Like what is the customer experience?
00:18:11.650 --> 00:18:14.509
How does she progress from awareness to education,
00:18:14.730 --> 00:18:18.069
from education to trial, from trial to conversion?
00:18:19.020 --> 00:18:21.480
And I'm going to build kind of the steps in there.
00:18:21.559 --> 00:18:24.099
And some of those steps, as we said before, might
00:18:24.099 --> 00:18:26.940
be executed by a human and some might be executed
00:18:26.940 --> 00:18:29.579
by a robot. By a robot, I mean like an AI agent.
00:18:29.640 --> 00:18:32.940
Okay, great. Or it could be a human who's getting
00:18:32.940 --> 00:18:36.000
assistance from a robot. Okay, all good. I got
00:18:36.000 --> 00:18:38.099
to define those things. I got to get them kind
00:18:38.099 --> 00:18:41.559
of documented. And then we're going to talk about
00:18:41.559 --> 00:18:43.700
an operating model. So the bow tie is the data
00:18:43.700 --> 00:18:46.640
model. The plays inside of it are just, you know,
00:18:46.680 --> 00:18:48.789
that's just like process definition. But the
00:18:48.789 --> 00:18:52.190
operating model is how am I translating information
00:18:52.190 --> 00:18:55.150
from one silo to the next silo? Like what's the
00:18:55.150 --> 00:18:58.390
common framework that allows me to follow a through
00:18:58.390 --> 00:19:00.190
line all the way across, even through multiple
00:19:00.190 --> 00:19:02.970
handoffs, even through human to agent and agent
00:19:02.970 --> 00:19:05.309
to human handoffs. And that's what I'm going
00:19:05.309 --> 00:19:07.089
to call an operating model. You can think about
00:19:07.089 --> 00:19:10.150
in technology, a codec and encoding and decoding
00:19:10.150 --> 00:19:12.430
algorithm. That's kind of like how video is going
00:19:12.430 --> 00:19:14.970
over the internet right now between you and me.
00:19:15.029 --> 00:19:17.539
It goes from. You know, an optical image, it
00:19:17.539 --> 00:19:19.920
gets coded into something that's very compact.
00:19:19.980 --> 00:19:22.640
It gets translated into bits and bytes, goes
00:19:22.640 --> 00:19:24.960
over the, you know, to a satellite. And we're
00:19:24.960 --> 00:19:27.660
all, we almost feel like real, and it gets decoded
00:19:27.660 --> 00:19:30.099
on your side. It's, it's like magic. Well, what
00:19:30.099 --> 00:19:33.039
a similar amount of rich information is contained
00:19:33.039 --> 00:19:35.980
in customer interactions, like conversations,
00:19:36.259 --> 00:19:39.779
but I can't take the entire conversation out
00:19:39.779 --> 00:19:41.819
of my brain and stick it into my colleague's
00:19:41.819 --> 00:19:43.680
brain so that she understands how to onboard
00:19:43.680 --> 00:19:45.869
a customer. I can't do that. It's impossible.
00:19:46.089 --> 00:19:49.190
So what I need to do is I need to encode it into
00:19:49.190 --> 00:19:53.230
a minimum viable set of information that's very
00:19:53.230 --> 00:19:56.990
important that can then get handed off. So that's,
00:19:56.990 --> 00:20:00.009
we use, when we design, we use a operating model
00:20:00.009 --> 00:20:03.210
called SPICED, situation, pain, impact, critical
00:20:03.210 --> 00:20:06.430
event, and decision. And situation, pain, and
00:20:06.430 --> 00:20:09.369
impact are always, always important. It doesn't
00:20:09.369 --> 00:20:12.359
matter. If you're considering a purchase, you're
00:20:12.359 --> 00:20:14.599
purchasing, you're in the middle of onboarding,
00:20:14.599 --> 00:20:16.759
you're renewing or you're expanding, awareness
00:20:16.759 --> 00:20:20.000
of the situation and the pain of the customer
00:20:20.000 --> 00:20:21.660
and then the impact that the solution can make
00:20:21.660 --> 00:20:24.119
is always important. So we take all those interactions,
00:20:24.279 --> 00:20:26.200
whether it's an interaction with a product, interaction
00:20:26.200 --> 00:20:29.279
with a human, or interaction with an AI, we reduce
00:20:29.279 --> 00:20:32.680
it into that very compact kind of delivery mechanism,
00:20:32.819 --> 00:20:35.519
and then we use that to hand off from agent to
00:20:35.519 --> 00:20:39.059
human. So now you can imagine that. famous onboarding
00:20:39.059 --> 00:20:41.000
call that you or handoff call that you always
00:20:41.000 --> 00:20:44.160
want to make happen between sales and cs that
00:20:44.160 --> 00:20:46.579
you know that basically sales always no shows
00:20:46.579 --> 00:20:49.259
for and then cs is always like left holding the
00:20:49.259 --> 00:20:51.259
bag we don't have to do that anymore we can actually
00:20:51.259 --> 00:20:53.519
codify this like we can we have an agent named
00:20:53.519 --> 00:20:55.460
celeste at when anybody's and it listens to every
00:20:55.460 --> 00:20:59.079
single sales call and maps them back to both
00:20:59.079 --> 00:21:01.980
contacts and opportunities and then and so when
00:21:01.980 --> 00:21:04.220
that opportunity closes all that information
00:21:04.220 --> 00:21:07.269
can is automatically available. I can literally
00:21:07.269 --> 00:21:09.390
go on Slack and talk with Celeste and say, hey,
00:21:09.390 --> 00:21:11.569
catch me up on this account. And she knows everything
00:21:11.569 --> 00:21:14.029
about that account. So that's what I mean by
00:21:14.029 --> 00:21:15.789
an operating model. But just those two things,
00:21:15.869 --> 00:21:18.230
a data model and an operating model can really
00:21:18.230 --> 00:21:21.930
set you up for an AI forward go to market. Wow.
00:21:21.990 --> 00:21:25.769
Yeah. That's a lot to set up before you're equipped
00:21:25.769 --> 00:21:29.849
to even apply AI to the situation. Well, it is.
00:21:30.839 --> 00:21:32.859
It is. That's a great call out. So does that
00:21:32.859 --> 00:21:34.599
mean we don't run experiments in the meantime?
00:21:34.859 --> 00:21:36.059
No, I think you should be running experiments.
00:21:36.400 --> 00:21:39.539
Like figure out, we launched a bunch of agents
00:21:39.539 --> 00:21:41.460
before we had anything like that plugged into
00:21:41.460 --> 00:21:43.700
them a bunch. I told you we launched five, but
00:21:43.700 --> 00:21:45.640
like one of those agents is Jack. You can see
00:21:45.640 --> 00:21:47.019
it on the Winning by Design website. You can
00:21:47.019 --> 00:21:48.460
have a conversation with Jack. When we first
00:21:48.460 --> 00:21:50.460
launched Jack, he couldn't even push information
00:21:50.460 --> 00:21:54.299
into the CRM. Okay. That's horrible. But then
00:21:54.299 --> 00:21:56.539
he could. And then he couldn't do research on
00:21:56.539 --> 00:21:58.660
you while you were talking to him, but then he
00:21:58.660 --> 00:22:00.180
could because we connected him up with Clay.
00:22:00.599 --> 00:22:02.259
And then he couldn't, and then we didn't have
00:22:02.259 --> 00:22:04.539
it kind of going and despised in the way that
00:22:04.539 --> 00:22:07.259
we wanted it to, but then it could. So I say,
00:22:07.279 --> 00:22:09.420
start where you are and learn along the way,
00:22:09.480 --> 00:22:10.859
but just know what you're shooting for. Have
00:22:10.859 --> 00:22:12.380
a theory of the case, know what you're shooting
00:22:12.380 --> 00:22:16.640
for. Yeah, man, that's a lot to take in at once,
00:22:16.700 --> 00:22:19.900
but I want to keep going deeper. I'll listen
00:22:19.900 --> 00:22:22.480
to this again myself and relearn from it as well.
00:22:22.859 --> 00:22:25.700
You mentioned that some of the AI experiments
00:22:25.700 --> 00:22:29.440
that you've either seen firsthand or at customers.
00:22:29.980 --> 00:22:33.140
Some of them work and some of them don't. What's
00:22:33.140 --> 00:22:34.880
the differentiating factors that you've seen?
00:22:35.500 --> 00:22:38.779
Yeah. Well, I mean, if we're in figure it out
00:22:38.779 --> 00:22:42.000
mode, then we're going to expect some stuff not
00:22:42.000 --> 00:22:45.279
to work. So like in the PLG world that, you know,
00:22:45.279 --> 00:22:47.700
that I had to figure out when I went, you know,
00:22:47.700 --> 00:22:49.859
and launched that company called Fundly that
00:22:49.859 --> 00:22:54.099
had a $1 .2 thousand median ACV. If we're in
00:22:54.099 --> 00:22:57.640
figure it out mode, we were by definition launching
00:22:57.640 --> 00:23:00.799
experiments almost weekly. Well, in PLG, you
00:23:00.799 --> 00:23:04.079
are launching multiple experiments weekly, and
00:23:04.079 --> 00:23:06.819
you're retiring experiments weekly too that might
00:23:06.819 --> 00:23:08.420
have launched a week ago, two weeks ago, three
00:23:08.420 --> 00:23:10.960
weeks ago. And every time you retire an experiment,
00:23:11.019 --> 00:23:13.180
it's either with a successful outcome or a not
00:23:13.180 --> 00:23:15.579
successful outcome. If it was successful, you
00:23:15.579 --> 00:23:18.099
take whatever the MVP version of that experiment
00:23:18.099 --> 00:23:21.480
was, and you hard code it into the experience
00:23:21.480 --> 00:23:23.339
so that it's going to be part of the machine
00:23:23.339 --> 00:23:25.339
going forward. If it was unsuccessful, you just
00:23:25.339 --> 00:23:27.319
deprecate it, like get rid of it. So it's like
00:23:27.319 --> 00:23:29.960
an A -B test, you know. A worked, B didn't, keep
00:23:29.960 --> 00:23:32.380
A, kill B. And we're doing that relentlessly,
00:23:32.420 --> 00:23:35.180
like week after week. So now we're in a mode
00:23:35.180 --> 00:23:37.579
where you say, like, what are the distinguishing
00:23:37.579 --> 00:23:39.519
characteristics of a successful or unsuccessful
00:23:39.519 --> 00:23:42.359
AI experiment? One thing that's not successful
00:23:42.359 --> 00:23:45.900
is don't start. I can guarantee you how much
00:23:45.900 --> 00:23:48.359
success will come out of that. But I can also
00:23:48.359 --> 00:23:50.240
guarantee you that if you're aggressive, that
00:23:50.240 --> 00:23:52.000
some of your stuff will work and some of your
00:23:52.000 --> 00:23:54.720
stuff won't. The problem is when we talk about
00:23:54.720 --> 00:23:58.410
experiments, if you don't have measurement, How
00:23:58.410 --> 00:24:01.210
will you know? How will you know? Is it just
00:24:01.210 --> 00:24:03.950
gut feel like that feels pretty good? You need
00:24:03.950 --> 00:24:06.769
to understand where that experiment is supposed
00:24:06.769 --> 00:24:09.589
to make a lift in your customer journey. Is it
00:24:09.589 --> 00:24:11.789
supposed to make a rep more effective? Is it
00:24:11.789 --> 00:24:14.029
supposed to close a deal faster? Is it supposed
00:24:14.029 --> 00:24:16.269
to speed up qualification? What's it supposed
00:24:16.269 --> 00:24:18.670
to do? Okay, cool. Now let me set a baseline.
00:24:19.589 --> 00:24:21.930
Now let me launch that experiment. And now let
00:24:21.930 --> 00:24:23.970
me see if it delivers better results or not.
00:24:24.210 --> 00:24:27.150
And then relentlessly, if it doesn't, I'm either
00:24:27.150 --> 00:24:28.650
going to tune it a little more and see if I can
00:24:28.650 --> 00:24:30.289
get it to do it or I'm just going to kill it.
00:24:30.529 --> 00:24:33.230
I think the number one way to fail is to not
00:24:33.230 --> 00:24:35.589
try. The number two way to fail is to not measure.
00:24:36.089 --> 00:24:40.210
And after that, every single failure is actually
00:24:40.210 --> 00:24:43.230
positive. Like if I launch something with a theory
00:24:43.230 --> 00:24:45.529
of the case and it didn't hit what I thought,
00:24:45.670 --> 00:24:47.730
cool, I'll never do that again. That's going
00:24:47.730 --> 00:24:51.650
in column B and I'm going to keep trying things
00:24:51.650 --> 00:24:55.029
until I get things to move the needle. And you
00:24:55.029 --> 00:24:58.720
mentioned, That some companies are, I think you
00:24:58.720 --> 00:25:02.799
used the term AI native. And you said to me before
00:25:02.799 --> 00:25:07.180
that those growth rates can be significantly
00:25:07.180 --> 00:25:11.160
larger than, I guess, the older SaaS model of
00:25:11.160 --> 00:25:15.640
product -led growth. Why is that? Is that what
00:25:15.640 --> 00:25:18.299
you're seeing? Yeah, I've reported elsewhere.
00:25:19.019 --> 00:25:21.900
that ever since 2001, if you go back and look
00:25:21.900 --> 00:25:24.180
at the fastest ever growing software companies
00:25:24.180 --> 00:25:28.380
throughout ages of history, you know, the, and
00:25:28.380 --> 00:25:29.839
let me just, let me not say software, let me
00:25:29.839 --> 00:25:31.720
say SaaS. So we're not going back to ancient
00:25:31.720 --> 00:25:34.299
history. We're not going to mainframes. Salesforce
00:25:34.299 --> 00:25:37.019
was the fastest ever growing software company.
00:25:37.180 --> 00:25:39.460
And then it was VMware. Amazing. They were really
00:25:39.460 --> 00:25:41.859
good. And then it was Atlassian. And then it
00:25:41.859 --> 00:25:44.599
was, I can't, I think it might've been Dropbox
00:25:44.599 --> 00:25:46.839
and then Box and then Slack or something like
00:25:46.839 --> 00:25:50.289
that. So kind of every era has a fastest ever.
00:25:50.349 --> 00:25:53.190
They set new high watermarks. From Atlassian
00:25:53.190 --> 00:25:57.490
forward, 2001 forward, every single fastest ever
00:25:57.490 --> 00:26:00.730
-growing software company has been PLG. Then
00:26:00.730 --> 00:26:04.029
you say, yeah, but that's pre -AI. What about
00:26:04.029 --> 00:26:07.069
AI? And then you look at the fastest ever -growing
00:26:07.069 --> 00:26:11.410
AI companies. That's OpenAI, Mistral, Replit,
00:26:11.430 --> 00:26:14.750
Lovable. These are crazy, you know, get to 100
00:26:14.750 --> 00:26:19.579
million in 10 quarters. six quarters, four quarters
00:26:19.579 --> 00:26:21.940
in the case of lovable, like crazy, like one
00:26:21.940 --> 00:26:24.180
to a hundred million, what? And then you look
00:26:24.180 --> 00:26:26.240
at what is the, what's the kind of underlying
00:26:26.240 --> 00:26:29.960
mechanism that they're using? It is also like
00:26:29.960 --> 00:26:32.259
it's self -service, meaning you don't talk to
00:26:32.259 --> 00:26:34.440
a human to start an open, a chat GPT account.
00:26:34.779 --> 00:26:37.119
You don't start, talk to a human to upgrade your
00:26:37.119 --> 00:26:39.099
chat GPT account. You don't talk to a human to
00:26:39.099 --> 00:26:41.460
share your chat GPT account, your, your projects
00:26:41.460 --> 00:26:44.059
with, with colleagues. Like that's all just built
00:26:44.059 --> 00:26:47.880
in. But here's, but here's the truth. They wouldn't
00:26:47.880 --> 00:26:50.140
be able to harvest, and they have a ton of tailwinds
00:26:50.140 --> 00:26:53.559
because of the kind of AI nature of their products.
00:26:53.839 --> 00:26:55.940
They have a ton of tailwinds. They would not
00:26:55.940 --> 00:26:57.819
be able to harvest them if they were trying to
00:26:57.819 --> 00:27:00.720
harvest it the way that VMware or Salesforce
00:27:00.720 --> 00:27:03.859
did. If they were dependent on hiring, onboarding,
00:27:03.880 --> 00:27:06.380
training, managing, and coaching reps in order
00:27:06.380 --> 00:27:09.539
to turn inbound demand into paying contracts
00:27:09.539 --> 00:27:12.079
and expand them, they'd go way slower by definition.
00:27:12.079 --> 00:27:14.619
It just takes longer to identify a good rep.
00:27:15.069 --> 00:27:17.609
Get her hired and onboarded, then get her trained
00:27:17.609 --> 00:27:19.809
and managed and coached up to kind of productivity.
00:27:19.890 --> 00:27:23.250
That takes time. But it doesn't take a machine
00:27:23.250 --> 00:27:26.049
time as long as you can program it well. So AI
00:27:26.049 --> 00:27:28.589
natives is a very long windup. Sorry, Dan. But
00:27:28.589 --> 00:27:32.809
AI natives never even considered that other path.
00:27:32.950 --> 00:27:36.190
They just built thinking every single interaction
00:27:36.190 --> 00:27:38.289
is a piece of data that can feed back into the
00:27:38.289 --> 00:27:41.390
machine and help me tune it. Every single engagement
00:27:41.390 --> 00:27:43.920
with the customer. is a piece of her journey
00:27:43.920 --> 00:27:46.119
it fits here in the journey i'm going to track
00:27:46.119 --> 00:27:48.380
her over time i'm going to run a customer data
00:27:48.380 --> 00:27:51.220
platform not a crm i'm going to run real time
00:27:51.220 --> 00:27:53.539
not you know one month or one quarter delayed
00:27:53.539 --> 00:27:56.859
data i'm going to update my my processes as i
00:27:56.859 --> 00:27:59.480
go and so ai native even though they're not doing
00:27:59.480 --> 00:28:02.279
anything necessarily new it's kind of plg -ish
00:28:02.279 --> 00:28:04.720
they they just built from the ground up that
00:28:04.720 --> 00:28:08.720
way and now if you can imagine Using AI to analyze
00:28:08.720 --> 00:28:11.259
that data and make real -time changes and adjustments
00:28:11.259 --> 00:28:14.420
and launch real -time experiments just allows
00:28:14.420 --> 00:28:17.720
them to go even faster. So it sounds like the
00:28:17.720 --> 00:28:20.640
effect is real. Like it's really changing the
00:28:20.640 --> 00:28:23.099
way that these companies are able to scale. It
00:28:23.099 --> 00:28:25.359
is. Now, we don't know the end of the story.
00:28:25.839 --> 00:28:29.299
So here, we'll pick on Lovable. They're everyone's
00:28:29.299 --> 00:28:31.920
favorite success story. Four quarters to 100
00:28:31.920 --> 00:28:34.259
million. Guess what? Guess how many birthdays
00:28:34.259 --> 00:28:37.099
you've had? within the first four quarters? Zero.
00:28:37.900 --> 00:28:42.019
So do we really know what their renewal rate
00:28:42.019 --> 00:28:44.460
is going to be? Do we really know what their
00:28:44.460 --> 00:28:46.740
ongoing ARR is going to be? Do we really know
00:28:46.740 --> 00:28:48.700
that it's going to keep compounding at that crazy
00:28:48.700 --> 00:28:51.420
rate? Are they going to hit some headwinds once
00:28:51.420 --> 00:28:53.599
they get out of the white hot center of their
00:28:53.599 --> 00:28:56.099
ICP? The answer is, of course, they're going
00:28:56.099 --> 00:28:58.319
to hit some headwinds and they're going to have
00:28:58.319 --> 00:29:00.740
to adjust. They have a smart team. They'll figure
00:29:00.740 --> 00:29:03.259
it out. But I don't think this is the entire
00:29:03.259 --> 00:29:06.410
story. They have a lot of journey left to go
00:29:06.410 --> 00:29:08.369
and they've got a lot to figure out. I think
00:29:08.369 --> 00:29:09.990
first principles are going to be their friends
00:29:09.990 --> 00:29:12.470
as they figure that out going forward. So can
00:29:12.470 --> 00:29:15.930
this unlock exponential growth? Yes. Will it
00:29:15.930 --> 00:29:19.829
follow a different set of laws of physics than
00:29:19.829 --> 00:29:22.109
the rest of the recurring revenue world? I don't
00:29:22.109 --> 00:29:24.990
think so. I don't think so. Like we're playing
00:29:24.990 --> 00:29:26.930
within a laws of physics and we're trying to
00:29:26.930 --> 00:29:28.650
optimize within it. Now we have a new set of
00:29:28.650 --> 00:29:30.589
tools. That's great. But we're still playing
00:29:30.589 --> 00:29:33.849
within like the same universe of laws of physics.
00:29:34.069 --> 00:29:37.289
Yeah. Now that's a good longer -term perspective.
00:29:37.670 --> 00:29:41.130
With AI especially, everybody seems to be a little
00:29:41.130 --> 00:29:43.690
fixated on the near term, and it's hard to remember
00:29:43.690 --> 00:29:46.809
that this is the beginning. We haven't seen much
00:29:46.809 --> 00:29:50.809
yet. So exciting though, right? I love it. I
00:29:50.809 --> 00:29:56.009
just can't even imagine watching the AI train
00:29:56.009 --> 00:29:59.670
kind of come by my station and deciding to stay
00:29:59.670 --> 00:30:03.829
on the bench. I mean, the only thing worse than
00:30:03.829 --> 00:30:06.170
missing a train is missing a fast train. And
00:30:06.170 --> 00:30:08.670
the AI train is going really fast. Like, I want
00:30:08.670 --> 00:30:13.650
to get on that thing. Yeah, absolutely. It reminds
00:30:13.650 --> 00:30:16.890
me that, well, one of the things you said, I
00:30:16.890 --> 00:30:21.250
think, being able to see each one of these new
00:30:21.250 --> 00:30:25.230
revolutions and even being able to see the invention
00:30:25.230 --> 00:30:29.630
of SaaS as a term. product -like growth as emotion
00:30:29.630 --> 00:30:33.289
that people actually understand. And it feels
00:30:33.289 --> 00:30:38.769
like we have a lot to build. Yeah. And you asked
00:30:38.769 --> 00:30:40.430
in the beginning, like, who am I? And I said,
00:30:40.450 --> 00:30:42.730
I'm dispositionally an adventurer and, you know,
00:30:42.730 --> 00:30:45.250
curious mindset. Not everyone's built that way.
00:30:45.250 --> 00:30:47.730
I totally get it, Daniel. That gives me energy.
00:30:47.930 --> 00:30:50.250
Like, I like stepping into the unknown. I like
00:30:50.250 --> 00:30:54.069
a reasonable chance of failure. For me, it keeps
00:30:54.069 --> 00:30:56.680
me on my toes. But that's not true for everyone.
00:30:56.759 --> 00:30:59.000
It doesn't give everyone energy. It can actually,
00:30:59.339 --> 00:31:02.259
for some personalities, you know, freeze me in
00:31:02.259 --> 00:31:04.940
my tracks. You know, I'm not saying everybody
00:31:04.940 --> 00:31:07.539
should be putting themselves in harm's way every
00:31:07.539 --> 00:31:11.180
day like that. I'm just saying if it's your predisposition,
00:31:11.319 --> 00:31:14.319
great. If you're the one who's going to keep
00:31:14.319 --> 00:31:16.900
everything honest and safe and understandable,
00:31:17.079 --> 00:31:20.140
then great. Partner with somebody who likes to
00:31:20.140 --> 00:31:22.480
go into the unknown. You'll be the one that keeps
00:31:22.480 --> 00:31:24.119
them grounded. They'll be the one that keeps
00:31:24.119 --> 00:31:27.000
pushing us forward. That's very often kind of
00:31:27.000 --> 00:31:30.319
a product and engineering partnership or a marketing
00:31:30.319 --> 00:31:33.900
and sales partnership. It's great. We need both.
00:31:34.099 --> 00:31:37.359
We absolutely need both. For me and my sanity,
00:31:37.519 --> 00:31:40.400
I want to be pushing into the unknown. And then
00:31:40.400 --> 00:31:43.160
if somebody's listening here who says, ooh, I
00:31:43.160 --> 00:31:44.740
like that, but I don't want to be that person,
00:31:44.799 --> 00:31:47.400
cool, I'll take the arrows and I'll come back
00:31:47.400 --> 00:31:49.380
to camp and you'll help me figure out, you know.
00:31:49.740 --> 00:31:53.259
how not to get shot next time. I like that. Is
00:31:53.259 --> 00:31:55.019
there anything that I haven't asked you that
00:31:55.019 --> 00:31:57.339
I shouldn't? It's been interesting to just think
00:31:57.339 --> 00:31:59.420
about the parallels between what's happening
00:31:59.420 --> 00:32:02.140
right now in AI and what's happened in the past.
00:32:02.400 --> 00:32:04.640
Yeah, here's something, Daniel. Like, let's say
00:32:04.640 --> 00:32:07.359
I'm listening to this and I'm freaked out because
00:32:07.359 --> 00:32:12.119
I got a job and now I'm thinking very, very existentially
00:32:12.119 --> 00:32:15.400
about me and my livelihood and the people I take
00:32:15.400 --> 00:32:17.890
care of in my life. and whether or not I have
00:32:17.890 --> 00:32:19.789
a job going forward, because I think AI is coming
00:32:19.789 --> 00:32:22.529
for my job. I think that's a very fair feeling.
00:32:23.210 --> 00:32:27.069
And I also think it is fair, a fair assessment
00:32:27.069 --> 00:32:30.970
that AI cannot replace you as a human. You asked
00:32:30.970 --> 00:32:32.769
at the beginning of the call, am I an AI? I'm
00:32:32.769 --> 00:32:35.349
not. I'm a human. You're a human. Every one of
00:32:35.349 --> 00:32:36.829
our listeners, well, that's probably not true.
00:32:36.869 --> 00:32:38.630
Half of our listeners are AI, but you guys shut
00:32:38.630 --> 00:32:40.710
it off right now. And for our human listeners,
00:32:40.950 --> 00:32:42.950
you know, we're humans. We can do things that
00:32:42.950 --> 00:32:45.339
are uniquely human. AI is not going to take our
00:32:45.339 --> 00:32:47.720
jobs as long as we're not trying to hold on to
00:32:47.720 --> 00:32:50.119
a job that AI should have. Like if I'm trying
00:32:50.119 --> 00:32:52.799
to hold on to an order processing job and that's
00:32:52.799 --> 00:32:54.759
how I define myself, that's where I get all of
00:32:54.759 --> 00:32:57.339
my self -worth. Yeah, AI is going to take that
00:32:57.339 --> 00:33:03.000
job. But if I am a human and I show up as my
00:33:03.000 --> 00:33:05.500
best human self and I'm interacting with other
00:33:05.500 --> 00:33:07.660
humans and I'm helping them manage fear, uncertainty,
00:33:07.759 --> 00:33:10.180
and doubt, I'm helping manage kind of stakeholder
00:33:10.180 --> 00:33:12.660
alignment, I'm helping manage strategy, I'm helping
00:33:12.660 --> 00:33:16.710
build. confidence. I'm helping chart kind of
00:33:16.710 --> 00:33:19.109
a course forward. I'm helping make connections
00:33:19.109 --> 00:33:23.109
between humans. AIs can't do those things. Just
00:33:23.109 --> 00:33:26.170
step into the upper kind of half of the realm
00:33:26.170 --> 00:33:28.829
of your job, lean into that and figure out how
00:33:28.829 --> 00:33:31.210
to outsource the rest to AI and you won't be
00:33:31.210 --> 00:33:34.930
worried anymore. Like you'll be automating everything
00:33:34.930 --> 00:33:36.730
that's predictable. Yes, AI is going to take
00:33:36.730 --> 00:33:39.990
that and it's going to give you a chance to humanize
00:33:39.990 --> 00:33:42.960
all the exceptional stuff. The stuff that only
00:33:42.960 --> 00:33:46.319
you could do. That's where we want to live. I
00:33:46.319 --> 00:33:49.079
like that call. Thank you, Dave. And is there
00:33:49.079 --> 00:33:51.839
anything that you'd like to say before we sign
00:33:51.839 --> 00:33:55.519
off? Thank you. Well, I'd love to see you online.
00:33:55.680 --> 00:33:57.119
I'd love to see the listeners online if you like
00:33:57.119 --> 00:33:59.579
this conversation. Winning by Design has amazing
00:33:59.579 --> 00:34:03.519
resources. One of them is free. I'll flash one
00:34:03.519 --> 00:34:05.880
more thing on the screen. It's the Growth Journal.
00:34:06.019 --> 00:34:08.800
It's a quarterly journal. It's like the HBR for
00:34:08.800 --> 00:34:11.340
growth executives. The digital version is absolutely
00:34:11.340 --> 00:34:13.340
free. You can also get the print version if you
00:34:13.340 --> 00:34:15.599
want, but that costs a little bit of money. I
00:34:15.599 --> 00:34:17.960
go to winningbydesign .com and sign up for that
00:34:17.960 --> 00:34:20.079
immediately. If you want to find me, I've got
00:34:20.079 --> 00:34:22.539
a sub stack and I'm on LinkedIn. Love to see
00:34:22.539 --> 00:34:24.360
you out there, especially all those courageous
00:34:24.360 --> 00:34:28.820
adventurers. I started this podcast because of
00:34:28.820 --> 00:34:31.940
an image that came to me in a dream. Back when
00:34:31.940 --> 00:34:34.079
cities had walls, everyone passed through the
00:34:34.079 --> 00:34:37.019
gate. Talk to enough people there and you'd understand
00:34:37.019 --> 00:34:40.760
the city. I wanted to stand at the gate and understand
00:34:40.760 --> 00:34:43.619
not just how AI was changing, but how AI was
00:34:43.619 --> 00:34:46.420
changing us. If you know someone building or
00:34:46.420 --> 00:34:49.039
leading with AI, or if this episode was useful,
00:34:49.320 --> 00:34:52.599
an introduction, a share, or a quick review on
00:34:52.599 --> 00:34:54.940
your podcast app all help keep these conversations
00:34:54.940 --> 00:34:58.559
going and help others find them. You can reach
00:34:58.559 --> 00:35:01.599
me on LinkedIn at danielmanary or by email at
00:35:01.599 --> 00:35:04.940
daniel at manary dot house. That's daniel at
00:35:04.940 --> 00:35:10.159
m -a -n -a -r -y dot Thanks for listening.
