Good morning, everyone. It's great to be here. Yes.
I mean, welcome back to Sydney. It's great being here.
Yeah, it's almost like San Francisco, but better.
Some similarities. We'll take better, for sure. Um, hey, it's a fascinating time for everyone to be in business, for you to be a CEO. So, I'm really looking forward to spending a bit of time getting an understanding of what's going on in your head and how you're going about dealing with what everybody else here is facing into.
It is a very fascinating time indeed. We were talking backstage about how this is all about change and managing change, and you've done that significantly throughout your career. So, I'm looking forward to this conversation as well. It is all about change.
Let's kick off with the big picture. All right. Because we do live in, I don't know, call them whatever you want, unpredictable, crazy times. The 2020s, for sure, is the craziest decade I've experienced over many. Um, AI now being the accelerant and the thing everybody is focused on is a significant shift. So, how do you see things in the state of the tech industry and more broadly?
I know. If you say that this is crazy, I will take it because I joined HubSpot January 2020. A little bit of didn't know what was going to happen, but a lot has happened. And I would say that, you know, beyond the COVID years and dealing with all of that, 2022 and specifically November 2022, when ChatGPT came, there was a big shift. And you know, you and I have seen this. It's a platform shift. It feels overwhelming. It feels like things are changing every single day. I do have a couple of slides I want to share, you know, just kind of like, you know, walk us through where we are.
This is what happens during a platform shift. And that's what we're going through in the industry. Every 15 to 20 years, we go through a platform shift. And when we go through a platform shift, a new technology emerges. People start building, many times over building. And then there is a question of who controls that technology. And then there's a question of who wins and who loses in that new technology wave. It's as simple as that. We've now seen it through PC. We've seen it through internet. We've seen this through mobile. We've seen this through social. And the same pattern follows, right? So, if you take internet, when internet emerged was completely new as a technology, and you saw level of overbuilding and building, which is kind of happening right now, right? Back then it was, you know, routers, WANs, LANs. And I know you remember this. Um, there was a point in the early 2000s where 97% of the fiber that had been built was not used. It was called dark fiber. That, you know, was a level of overbuilding that happened. And then the new technology emerged. Google became one of the gatekeepers of search and new business models like DoorDash, like Uber couldn't have emerged without the combination of internet and mobile. And new winners and losers emerged.
I think the same thing is happening with generative AI. Obviously, it's a new moment, and there's a lot of building and I would say overbuilding that is happening in terms of the data center capacity. And that is going to lead us to a place where there will be new winners and there will be some losers who are not moving quick enough. I think that that's the same pattern, Steve, but my observation is two things. One is that it's happening much faster than any of the previous cycles. What we saw happen over 8 years, 10 years is now happening over a 3 to 5 year period. And everybody's got a megaphone. And everybody is on a podcast. Everybody is on, you know, X. Everybody is on LinkedIn. Everybody's got an opinion. And so, it feels even more chaotic to navigate through what we've seen as a platform shift.
Yeah, absolutely. And it's there for us all to see and use, too. Yep. And once you immerse yourself in it, you can see the, in a sense, the scaling up of the way you're going to interact with these new tools, let alone how your organization is going to respond.
Absolutely. Absolutely. Yep.
So, just talking about people in the room and how they're responding, how do you see the response so far by Australian customers? And how should they be thinking about their investment in AI and the outcomes they should want for it?
I first of all love the way you framed it, which is what should they be doing for the outcomes? Because it is about, you know, technology driving outcomes, not technology for the sake of technology. But let me ask you this, like I can see most of the room. Um, how many of you use AI every single day? Okay. How many of your organizations have seen significant growth and outcomes because of AI? Okay. Okay. So, about like, you know, half. Uh, I would say everybody is using AI, whether it is an LLM or some kind of tools. And there's a slightly lesser number or percent that has seen organizational transformation because of AI. That is the state of adoption, and that is the state of transformation right now globally, right? And you asked specifically about Australian customers, but I think globally what we're seeing is that everybody is using AI. Everybody is, you know, got at least a couple of LLMs, a combination of some tools in their daily usage. But we're all trying to go from what I call individual AI productivity to institutional AI productivity.
And that means individual AI productivity is like, you know, you getting up in the morning and you're writing an email, and now you have a better thought partner, maybe some, you know, LLM that actually understands you, your style, and can give you a better email. And then there is institutional AI productivity, which actually requires a lot more context. Because, you know, the change between individual and institutional is you need to get the growth context of the whole organization. Your best campaign manager, how do they pick the best campaign? Your best sales rep, how do they think about the best deal to work or how to save a deal that is not moving forward? Or your best support person and what should they care about? That requires a growth context. And so, I think that's the stage that we're in.
I will say that specifically calling out some Australian customers. I know many of them are in the room, but there is good adoption and movement towards this. You know, Hungry Hungry is one of the customers. I hope you're there in the room. Um, very interesting in how they have completely reimagined marketing with Loop, which is the playbook that we have been talking about for a while, as well as, you know, customer agent. They're resolving tickets using that. Um, Cannibuild is another Australian customer. Again, leveraging AI in almost every aspect of their go-to-market and going beyond the experimentation stage to scaling with it. And my own favorite is Builderness, New Zealand-based company and beautiful country. And they're actually in the hospitality as well as, you know, helping folks kind of figure out how to spend time in New Zealand. And they are also adopting AI in almost every part of the go-to-market. And I think that we're in that process of translating real productivity gains into go-to-market. And that's a, you know, it's a fascinating time to be here.
It's interesting when you talk about those organizations. I would pretty well guess, without knowing many of them, that they're well led. And what I mean by that is the job we all have as leaders to get the institutional benefits of AI is a leadership challenge. It's about cross-functionality. It's about being explicit from the top down as to what it means to be successful and what actions are expected across the organization. So, I think that's the challenge, because it is much easier to adopt individually than collectively.
Um, actually, we should go on to something else I think is really important here, which is just some of the best use cases that you see that are driving results. And how should organizations that are beginning the journey, how should they sort of tackle it?
The answer is what are your company's goals? What are your team's goals? Because while AI is a new platform shift, and of course, you know, it is something that everybody wants to have a roadmap. It has to start with your company, your goals, what you actually want to reimagine, and not the other way around. So, I think almost every customer or prospect conversation that we have, we start with what are you trying to drive? Where have you seen the biggest challenges in terms of how you want to grow? Let's then identify the use cases of what that looks like. So, it really has to start with you identifying the biggest challenges in the way, like, you know, it's new ways to solve old problems. That's what AI is all about. But having said that, we are now 3 and 1/2 years into AI. And there are a set of very specific use cases that can help you unlock growth. And now I wanted to share and I'm going to like flip to what we see as AI customer journey. And again, you wouldn't just start with the entire journey. You'd actually pick one or two areas that makes the most sense to your business and what you want to solve.
And let's start with AIO. I would also plug Asia Frost who is, you know, speaking today. I would really plug her whole session because she started experimenting with AIO two and a half years ago for HubSpot. And what we found is that there is a completely new way in which you need to show up in the answers that LLMs are providing, right? We've made it a science to show up in Google searches and blue links and how to show up in blue links actually came up. But then now almost 60% of Google searches do not end in a click, which means you have to find diversified channels and AIO and showing up in LLM is one and that's a place that, you know, we really encourage people to think about and, you know, Asia will talk a lot about that.
Um, we're also finding, you know, talking about like old problems that we've had, it's pretty difficult to understand who your ideal customer profile is and how to reach them at scale. And so a lot of our customers who are trying to kind of like expand their growth, they start with the data agent and prospecting agent. And in super simple terms, data agent will enrich all the companies that you have within your CRM and the contacts that you have within your CRM and it will get the latest information so that you have an ideal customer profile matched TAM so that you can expand there. And then what we are finding is prospecting. Steve, I don't know when you started in sales. I started in sales, the difficulty was the first time I got I had like 500 accounts and it was pretty hard to cover 500 accounts. You're going to go and look at like 10 case and 10 Qs and actually find information about every single account. Now you can do it with AI. That's the thing that AI has made super easy and so we find that prospecting agent you can actually identify the right intent signals and then from there you are able to send personalized email based on your specific company and your specific, you know, problem that you're trying to solve and that means better open rates. And so I think like in the sales arena, we are beginning to see that.
And then, you know, again talking about old problems with new solutions, you know, in sales the hardest thing was trying to take notes during a call, entering that information into a CRM and then doing a follow-up. You just didn't have enough hours in the day to be able to do that. Now, what you can do is just have a bot listen to your conversation and tell you what the action items are, even give you an email to send and that makes it much, much better for reps to focus their time and energy. And so I think those are really the top use cases that we're beginning to see from a sales perspective.
And then the last thing I'll finish off with is customer agent which is support. In fact, like I think one of the first use cases which found product market fit is the customer agent where you can resolve questions using your knowledge base, your history of how you've responded to your customers and we now have over 10,000 customers at 70% resolution rate, um, you know, resolving tickets and therefore scaling using AI. So some of the handful of use cases, but my guidance to any one of our customers and prospects is basically start with your problems and what you are trying to solve where you have challenges and then pick an area and then go deep in that area and once you scale that and once you get confidence in that, then you can pick other use cases.
