GTM in the AI Era w/ Andy Shorkey (CRO, Writer)
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There's not a CIO we talk to that is not staring at a massive backlog. A CIO or head of AI are still struggling to create the velocity required to keep up with the demands of the business. Today, there's tremendous conviction that AI is eating the software ecosystem. Well, today, we speak with Andy Shorkey, the CRO of Rider.

And not only is he in his seat navigating all the complexities of bringing native AI products to market, he's also reinventing the way that go-to-market is done internally in a far more efficient way in the post-AI world. I'm Mark Roberge, and this is the Science of Scaling. ♪

All right. So Andy, so excited to see you. Welcome to the show. Yeah, it's great to see you, Mark. Happy to be here. Yeah, we're talking writer today. Why don't you go back to the beginning? Like how did you all find each other? Well, I met May Habib, who's our co-founder and CEO through the- She's been a force of nature. She's so fantastic. I can probably take up the whole hour talking about May Habib, but we did connect through the

the VC community and you know Ryder was at an inflection point last year. May and Wasim who's our other co-founders like hey we have got something here this is platform this is transformational this is a giant market. May and I got hooked up and

We're off to the races. You know, what makes May so special is she's got such gravitas, such go-to-market chops. She is a selling founder. And so obviously very provocative, very compelling, very passionate.

very authentic, but ultimately she can get into ones and zeros and go as deep as anyone needs to around LLMs and any component of the stack. Hey folks, just Mark here. Please recognize this comment. Even if you're not technical and even if your role is not super technical, sales, marketing, HR, finance, whatever,

You got to get down to the basic foundational tech level the best you can. It just means like going in play with the GPTs, going in play with the various apps that are out there and get into like the configuration level, experiment with it. Like carve out an additional 25% of time to see if you can automate something that you do. It's going to take more time the first few reps, right?

But if suddenly you can cut the rep time in the long term by half the time for that use case, in the long term, that's huge. And it's huge for you and your reinvention of yourself and this post-AI era. And I think like May and team were a wonderful representation and perhaps an important input as to why this particular team and company took off.

All right, let's get back to Andy. Wasim and May are an absolute vicious combination of both technical depth, vision, and really building a company to scale the right way. So can you double click into that conversation? How quickly did you know that May was this force of nature? What specifically was about that opening conversation? And kind of the same question for Ryder as a business.

There certainly was a gap in the market because if you look at all the rest of the frontier models out there, they're very consumer centric. They made the decision at an inflection point and obviously they made the right bet around we are going to design a full stack

that's specifically designed for the needs of the enterprise. Can you elaborate on the current ICP? Like how low, how small can you go and how big can you go? And I guess also like sector-wise, is it concentrated in tech or is it like not tech or can you elaborate on this? We do index our ICP on verticals, which is a little bit unique. Now I'll say that, but Rider is a

horizontal platform we service a broad spectrum of folks across many verticals and many uh startups they they they start mid-market and they move up our journey is that you know intuit and uhd were early customers but we do go downstream we have our mid-market uh business that does uh very well um with more velocity i do not discriminate whatsoever

This is another example of really great ICP formation. Okay, so he's talking about like going out to perfect fit businesses. So when you're going to be proactive, when you're going to build an email list, when you're going to cold call or cold outreach, you're just going to focus on the A plus fits.

But he's also saying he's not going to turn anyone away. You know, I think it might be a slight exaggeration. Like I would be, I would advise you against not turning anyone away. Right. Cause like there are some businesses that are customer types that might like pull you down. They're just not a good fit. You didn't build it for them. You don't want that noise in your roadmap for now. But like we have to appreciate an Andy's comment that like,

You have your hypothesis on what a perfect fit customer is and you're proactively outreaching to them. But there are other folks who might be a good fit and they really want to do business with you. They're coming inbound. So there's this engagement dimension to your ICP.

And so he's kind of defining like, this is my perfect green where I'm going to go and proactively sell to. This is my yellow where I'm not quite sure, but if they're engaged and inbound, I'll do it. But then there has to be that red as well. That's like, even if they want to do business with you, we don't want to do that.

because it will take down our business. So just, you know, as you think about ICP formation, think about it within that framework. If you want more details on ICP framework development, then go ahead and grab yourself a copy of it in the description of the episode.

You know, there's companies across the board that are, you know, trying to leverage capability and to accelerate their AI transformation. As I look at some of the breakout, you know, the few breakout native AI companies where they seem like they're creating real value, they're growing very healthy revenue and customer bases. And they seem to have established a strong moat from the foundational models as well as the current incumbents. And there's a pattern I see around

proven success with enterprise implementations and bringing the value to life in the enterprise, which is no easy task for a native AI and zero point with IT, et cetera. So once you get into those bigger companies, upper mid-market, and eventually the Goldman Sachs and the name brands you have, and you actually can bring them to life with success, word gets around.

And if someone, if like a writer 2.0 copycat with like, you know, the same store and half the price, uh,

they don't care bank number two doesn't care they're just like they made it happen at goldman just go with them is there truth to that listen we're very fortunate we've worked very hard to drive a ton of value and success for our customers right and so you know our customers do scream from the rooftops around you know the experience that we're delivering around our customer success and our go-to-market teams i think we've done a great job we go after mission critical

workloads and use cases. And we are not selling a lick of software unless we are defending the ROI and the value that's being delivered. We help customers frame that business case, both kind of initially and then ongoing. But we do tend to gravitate toward mission critical use cases.

It's not to say there's in many organizations, you know, the project backlog of use cases that are coming from the business. You know, there's a river of nickels that do add up. You know, the agentic storm has arrived in full force. In many ways, it's really helped executives and companies see the unlock much easier than

than what we did with the early productivity tools with copilots and chat GPT. Now, I think with the agentic around how much orchestration,

Executives and certainly organizations can see how massive the unlock is around like fundamentally transforming the way work is going to be delivered. In many cases now, executives are much more pronounced on, we are an AI-first organization. We are going to be very aggressive of embedding AI into our organization and we're going to transform.

And so that alleviates a little bit of the burden on like, you know, locking down an ROI for every single use case because organizationally they're adopting this, you know, this massive movement. Biggest question I get every day. How do you build the next unicorn?

How do you build predictable, scalable revenue growth? Luckily, the folks at HubSpot have put together the Science of Scaling Database, real playbooks from companies that have gotten to the scale of like $40 billion in market cap. The playbooks cover key decisions like hiring

hiring, compensation, go-to-market strategy, ICP development, even stuff on AI in sales. All from the folks behind the fastest growing companies in tech. It's not fluffy. It's super tactical. You know my content. You know my brand. It's stuff that you're going to pick up, download, and put to use within an hour. So head to the description, click on the link, and download your free copy to start scaling your business today.

So I'm sure like as you, as the conversations progressed, you probably started to identify some hypotheses on where you could add some value for the next value, you know, next level stage. And then you came in, you have the whole first 90 days. Can you unpack that context when you came in and what some of those opportunities were? Selling primarily to line of business, you know, marketing with some of our early offerings around style guide and the functionality that was, um,

you know, consumed by UX teams or marketing teams. Well, it would quickly shift in terms of, you know, our platform capability was certainly going to be much more pronounced across the enterprise as we continue to drive more feature and function within the product. When you say style guide, can you give like a simple use case of what like a common use case was at that time? And then like, as you were looking forward, what are the use cases that you were trying to

enable with the platform shift? Yeah, I think maybe the early days of folks who were trying to drive a lot of consistency with their brand voice and compliance and those solutions were able to help either, you know, folks that were generating a lot of content. And so, you know, that quickly evolved into our

knowledge graph, which obviously is is essentially the connective tissue that allows us to connect into the enterprise data for an organization. You get into things like, you know, much more broader set of use cases around content supply chain, you know, for the marketing folks, market commentary, you know, creating a lot of of market commentary use cases in around financial services.

If we look at, you know, more of the B2B tech where you and I come from around driving product releases, all the POV and pipeline generation kits, you know, we have...

All of the account-based applications that folks use to drive just a ton of AE productivity, for instance. So there's a whole set of use cases that are being driven and go to market, but also cross-functionally around the UX teams, the marketing teams, certainly legal and compliance around that.

really streamlining a lot of the reviews they're doing around content review, brand compliance. That would certainly apply heavily in the CPG and retail world as well. Pivoting away from just being line of business. So that could be CRO, it could be COO, it could be CMO.

The CIO and heads of AI were becoming much more prevalent executive sponsor because of how much demand was coming in and they had to certainly get their arms around all the available options and tooling out there. And so as we pivot around running a more complex motion, because ultimately in our world, we have two executive sponsors.

the line of business owns the outcome and they own all the benefits that everyone is getting in terms of transforming work. But ultimately the, the CIO and heads of AI have, you know, have a big role in terms of how they're evaluating and standardizing on the

on different platforms and tools within the enterprise. So it's not the wild, wild west. So we had to certainly evolve our playbook around go-to-market, around engaging directly with the CIOs and the heads of AIs and those technical stakeholders, as well as continuing to run our play of driving those outcomes for the line of business.

Yeah, it's super common, like go-to-market leadership scenario here. We're working through a pivot. And when people say pivot, lots of times they think it's a bad thing. They think it's like something didn't work and they have to change who they're targeting or the product. We're pivoting, we're pivoting. Like it was kind of like a joke meme for a while over the last decade. But I don't, it's different than that. And I like to, I think it's broader than that. It's not always bad.

I like to go back to a classic work of lean startup, Eric Reese, a beautiful work in the entrepreneur ecosystem. It's now like 20, 25 years old. He has four different types of pivots. Zoom in, zoom out, customer segment, and customer need. Okay. And so what Andy's navigating here is the zoom out pivot. So what that means is you, it's very common. It's a successful indicator.

Like you entered the market and you had one kind of constrained use case that went wild. And now there's a whole bunch of other use cases you could build around it. So we're executing a zoom out pivot. Now the zoom in pivot is different where it's like we built too much and this one thing works. Now let's just focus there.

The customer segment pivot means we build something and we thought it was for this customer, but it turns out it's for this other customer. So we have to change segments. And the customer need pivot is we're working on the right customer, but we don't have the right product. We have to build something different. So those last two, those can be like a little harder to navigate and mean something like you miss something. That's fine. You got to pivot. But in this case, it's positive.

The other thing that's coming out with the zoom out pivot that's really common and is often a pothole when you try to pull it off is an appreciation of the complexities of the decision making unit. In this case, Ryder was selling the style guide UX team type use case, simple use case, probably cheaper product, probably could go into like the head of branding and close a deal.

Now they're doing this like zoom out pivot and they're selling all these other use cases that Andy's talking about. They're selling sales use cases, finance use cases, HR use cases, and they're running this expansion pivot. And now all of a sudden the CIO is showing up.

And so you have to have a very different sales playbook. You have to have a very different type of salesperson that can deal with these different personas. So we have to adapt our playbook and our salesperson to this more complex DMU in this zoom out pivot. That's why you bring in a leader like Andy. And that's what he's unpacking here for us. Let's get back to him.

Those relationships with the line of business and IT vary dramatically depending on what organization you're entering. But when you're representing a disruptive enterprise platform, ultimately, we've got to make sure that we're running a consistent play around a broader set of stakeholders.

What are the two extremes you see? You mentioned like they could vary a bunch in terms of how like the CIO functions, IT functions. Give us a couple extremes so we can feel that out. Extreme would be we are in line of business with...

In many of these large enterprises, CML has tons of budget and power on their Martech stack. And we would enter the building with strong sponsorship there. You fast forward the tape, ownership of the AI platforms shifting to a certain degree, and they're trying to centralize into the CIO org.

I've seen front and center that there's absolute disdain between the CMO and CIO in terms of the battle over those platforms. There is heavy tension because I think historically, in some cases, the IT org is looked at a slow or a bottleneck or getting in the way of driving those accelerated outcomes that the CMO, for

has, um, has benefited from over the last couple of years. In other cases, you know, what we would advocate is ultimately you are not going to accelerate AI transformation unless you have the business and IT working and, and acknowledging the facts that, that this level of collaboration in today's day and age around AI has got to be tight. So, um,

How do you engage the CIO in the process? I'm sure that was something that you really had to bring to the playbook and the team. Everything from the opening awareness stage, do you only market to the line of business person and then bring in the CIO as they move into the decision and evaluation stage? Yeah.

Do you also market to the CIO at that awareness stage? And I guess another question is like, when you do bring the CIO in, how do you do the, what discovery do you do? Like, how do you do that discovery so you know which variation you have?

without creating too much animosity or anything within the account. It doesn't really matter the entry point. I would say there's a higher percentage now that our initial conversations are with a senior level IT exec, CIO, or head of AI. For the large enterprises, they're trying to build this

they're trying to build it. And so our value pop obviously resonates very well because as a end to end full stack platform, we would advocate that we have abstracted all of the technical complexity that you need and you simply should just be building on rider because we can deliver

10x, 20x the number of agents and transformation at scale than what their internal IT build teams can have. Ultimately, a CIO or head of AI are still struggling to create the velocity required to keep up with the demands of the business. There's not a CIO we talk to that is not staring at a massive backlog. I can help you crush that backlog

Backlog, because we're all around speed, all around time to value, and all around delivering those outcomes at scale. We're delivering 50, 100 agents over the course of the year where internal IT teams are dripping out a handful of apps that may or may not be fully adopted. How does this impact the optimal sales hiring profile? Both looking at from the view of,

the first couple at writer and like the challenges that were there and those reps and how they were successful and maybe it evolved. And also in your experience across so many great companies, you had a perspective of like what a great sales hire looked like. Is it the same for you as it was 10 years ago?

Or does this introduce some new heavily weighted attributes you got to sniff out? This will typically be the most complex selling motion that our seller will see. And I come from a fairly complex platform selling background, but I'm not shy around that because the nature of we do sell both to lines of business as well as to IT. We're very use case oriented. We're very vertical oriented.

It's transformational. It's disruptive. You have to lead the customer. We're not displacing old tech. The customers need help. And as a market leader, we have a responsibility to lead them.

I share with you that just because, you know, when we index on our profile at our stage and speed, you know, versatility, you have to be able to inspire. You've got to be able to evangelize. We're going to continue to get have more of a technical selling motion given where the market is heading. But we also...

will remain grounded in terms of the outcomes and the value that we're delivering for the business. Everyone demos at Rider. We are very product forward. Right. We've got a bunch of new hires here in New York and, you know, we get them dipped into the product like week one. Nice. Because everyone demos the product and it just brings the story to light in terms of, you know, the ease of use and the power of the platform. And so we've got,

But our solution maps based on the verticals we support, so we can be very prescriptive. And so there's a whole library of use cases and certainly demos that we have. I'm getting so much conviction on this barrier to entry, this moat that's evolving in the native AI startup area for the breakouts. I'm still trying to frame it.

It's really just about the ability to bring this value, this unique value created by AI and AI agents to life in a very large, complicated enterprise. Ideally, even like a non-technical sector where it's even harder to bring that type of technical innovation and make it work and mobilize it within these companies. These companies have regimented processes, tight security teams, ruthless IT teams, ruthless legal teams.

and a bunch of legacy employees that are somewhat resistant to change. That is pushing a boulder up a mountain when you're trying to get AI adoption in there. Now, at the same time, the CEO is probably talking to the street being like, we're an AI first company. We're not going to get disrupted. We're going to be a leader. This is going to allow us to capture market share and they need to make it happen.

And that's the trend I'm seeing. Evaluating barriers to entry and moats is always critical in choosing a winner, whether you're an investor or a joiner looking for your next career move. And as I talked with Andy about, like it's pretty unique in this native AI arena because not only do you have the traditional situation of like, how do we fend off the legacy incumbents who can just build our product into their platform?

But we also have to fend off the foundational models, as I'm showing here in this image, is you've got to look at this from both lenses. Because there's already a growing graveyard of native AI startups that raised over $100 million and they're now at zero. And for probably more than half, it was because the foundational models integrated up.

This is a new risk. And we have to make sure that we properly analyze the longer term mo against both of these pressures. Traditionally, I've found that founders jumped to the enterprise too soon. They thought that winning the big brand would cause everyone else to come and it didn't. They underestimated how long it would take to close a million dollar deal with golden sacks and it would kill them from a cashflow standpoint before they made it happen. I constantly pushed entrepreneurs to,

to go after that smaller company. And to some degree, that's still true because you can't walk into Goldman Sachs as the first customer and have a bunch of bugs. So to some degree, that's true. But eventually you go to the enterprise, maybe you go there sooner and that becomes the answer. I'm just seeing that pattern.

and it's coming to life very strongly again here with Ryder. Let's get back with Andy. For some enterprise sellers, that is a little bit of a shift that folks are used to dragging their solution engineer along for every call. That's not the case. How has AI changed the go-to-market motion there? How are you all adopting AI to reinvent that? I think back to our folks

demo the product very hands-on. They create a lot of apps organically. Our enablement team leverages our platform to ideate and to do needs analysis, how to create the right enablement materials for certain teams. From an SDR perspective, we've got

We've got closed loss agents. We've got, you name it. I would say we have really moved the needle in terms of what I view as always like, it goes beyond a productivity. It's really go to market productivity around of really giving time back and having an AE really be efficient with their time around whether it's developing an account plan, a point of view.

Let's do like a lightning round of each mini step of the sales process. And then you could tell me if like, ah, it's still kind of the same or no, like we do a totally different with AI and you can give me like a 30 second, like how you do it. So like, let's just start at the beginning. Like, I think like maybe once a year, someone in like RevOps does an analysis on like, if they do it right, they look at the segments that have the highest LTV and they say, okay, this is the ICP. And like, they may even go so far as like saying,

We have capacity to call a thousand new accounts this quarter. These are the thousand accounts to call. Are you all using AI? We layer in a lot of our own capability in terms of the applications or the workflows that we're doing to enhance further. We're taking advantage of the stack and AI that's built into those around AI.

smoke reports and we've got a lot of activity and there's signal out there that's warm. There is a quickly evolving best practice on how to use just general AI, foundational models, specialty apps, et cetera, to prepare for these meetings. I would say the simplest way I see it described is just do the meeting with the agent. If I go into a pitch

And I'm going to be like, okay, why'd you take this meeting? I'm going to like start with some really open-ended questions. And then I'm going to like explore the organizational needs. And then I might explore their personal needs and their role. And then I might explore what other tools they're looking at and how they think about that. I can do that with the AI. Like, let's just say I was pitching Andy at Ryder. I could go in to the agent and just say, okay, tell me about Andy.

And they're like, tell me about Ryder. Like really open-ended. And I can like, I can get in real deep, real quick. And then it's like, is there any, let's say I was selling them like a, you know, finance package. Like have they, you know, what are they doing on the finance side? I can't even ask him like, how would they evaluate my company? And I can't even ask him like, how would they evaluate my competitor?

And how would you sell my product against the competitor? And how would I sell the competitor against me? I mean, the list goes on. But I think just coming back to like, what are you going to do in the meeting? And just do it now. And you are like so much more prepared than you ever could be in a pre-AI era.

So give that a shot. And so I think I would give us high marks in terms of how we're prioritizing going after ICP, where there's activity, where there's signal around website, certainly tying it to LinkedIn around those types of personas that typically gravitate toward us in certain roles as well. And so like, let's go to like, okay, got the first meeting, I'm prepping for the meeting.

I think we all have a picture of what that looks like traditionally. If you do it well, like you went online, you looked at their LinkedIn, what are they into? You looked at the team, you got yourself prepared.

how is that different that would all be just through an an agent with us so the rep would sit down and say hey i'm going in to have a meeting with dick's sporting goods and essentially the app would already have everything stubbed out in terms of the point of view with the customer the orientation towards ai what are the five areas that are mapped toward

brighter capabilities. And so it essentially would create a solution map. It would create the point of view. It would also create the script or the questions for the executives, depending on their role and responsibility. And so it essentially would, it would give you the first call

It's very cool. Let's go to like toward the end of the process. So maybe, I don't know, you probably have to do a lot of POCs, I imagine. Or is there any other later stage like step where like now we've been in there two months and AI still applies to like change the way we used to do it? I don't know.

You know, I would say maybe up front on NDA process, you know, we do a lot of NDAs. We have, you know, we've got an agent that basically NDA comes in, like our legal team rarely will touch an NDA because it'll run through the app and it will flag anything that is nonstandard, but for the most part,

you know, our tolerance and governance around like that NDA template, reps will just turn and burn. Legal in terms of our contract reviews as well. You know, we drive a lot of velocity and efficiency folks that come here and go like, I can't believe how quick we turn these contracts in our legal process. A lot of it is, you know, we get a jumpstart in terms of, you know, being able to quickly assess contracts.

some of the key standard terms and it just allows a lot more velocity for, you know, for our legal team, which is relatively small, but, you know, highly, highly efficient. That's a very cool list, Andy. I'm really excited that, you know, main team found you and congratulations on the run so far and continued good luck. We're hiring across all functions, Mark. So keep, keep those referrals, keep those referrals coming. All right.

Love it, Andy. Thanks for spending time with us today. Yeah, super. All right, that does it for today, folks. Our episode was written and produced by my favorite producer, Matthew Brown. Editing comes from Patrick Edwards. The science of scaling is a proud part of HubSpot Media. And if you like what you heard today, make sure you follow or subscribe to us wherever you're a fan of the show. And if you're a founder ready to scale, check out my VC firm, Stage 2 Capital.

We are backed by over 800 CROs, CMOs, CCOs from the best companies in tech, and we're ready to help with your scaling journey. Okay, see you on the next episode.

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