EP - 068

AI: If You Don't Get It How Will Your Team

With Guest Ryan Staley

Why business leaders need to embrace AI and adapt quickly to remain competitive

The How To Sell More Podcast


May 22, 2024

The rise of AI has happened faster than anything we've ever had in our lifetime. And in a few years, it will be the only tool we've ever created that's smarter than us.

So, as a leader, maximizing its use in your business is on YOU. 

This week, Mark speaks with Ryan Staley about how the key to long-term success starts with business leaders understanding AI deeply enough to implement it within their organizations effectively. 

As the Founder and CEO of Whale Boss, Ryan has been instrumental in guiding technology founders to scale their businesses from $1M to $30M by applying the powerful sales frameworks he developed while working with giants like Google, Amazon Web Services, and Salesforce. 

Known for dramatically cutting the sales learning curve, Ryan has empowered over 800 C-level executives and leaders to achieve rapid growth and operational efficiency. In his conversation with Mark, he shares some ways leaders can strategically leverage AI to transform SaaS sales and marketing, drawing from his extensive experience in driving multi-million dollar growth.

Senior Leadership: Senior executives need to understand the potential and practical uses of emerging technologies like AI to effectively guide their teams and strategize its application.

Strategic Implementation: Leaders should prioritize AI implementations that offer the highest return on investment and focus on automating tasks that free up employee time for higher-value activities.

Forward-Thinking Leadership: Leaders must be proactive in exploring new technologies, understanding their implications, and integrating them into their business models to stay competitive and future-proof their operations.

“Things are happening so fast…if you're doing really well, it's very easy to get left behind on this.” -- Ryan Staley

Links to This Episode

Key Takeaways

  • Practical Application of AI - AI can be seamlessly integrated into existing business processes to enhance productivity and effectiveness.
  • AI as a Revenue Multiplier -By analyzing data and identifying trends quickly, AI tools can significantly improve decision-making processes and lead to more informed strategic moves, potentially doubling revenue without increasing leads.
  • Continuous Learning and Adaptation - Business leaders should stay informed about AI advancements to continually refine their strategies and ensure their teams are leveraging the most effective tools.

Top 3 Reasons to Listen

Leadership Guidance in the AI Era: Learn why leadership teams must understand and utilize AI, not just for operational efficiency but also to maintain a competitive edge in rapidly changing markets.

Navigating Business Complexities with AI: Ryan explains how AI can simplify complex business challenges, particularly in enhancing decision-making processes and uncovering hidden opportunities in data.

Future-Proofing Your Business: Discover how businesses can stay ahead of technological advancements and ensure their strategies remain relevant as new tools like AI evolve.

Follow Ryan Staley on Social

Website: https://ryanstaley.io/

LinkedIn: https://www.linkedin.com/in/ryan-staley/

Check out his podcast: https://ryanstaley.io/podcast/

More About Today's Guest, Ryan Staley

I help Chief Revenue Officers & Sales Professionals Leverage AI to work 10 hours less weekly and 2x performance.

Meet Ryan, a dynamic leader who has masterfully built revenue machines from scratch, leading to multiple successful business exits. With an impressive track record, Ryan has won 18 President's Club awards, generated over $150 million in revenue, and closed thirty-five deals exceeding $500,000 each.

Ryan's expertise extends beyond deal-making; he has interviewed over 200 tech CEOs and has imparted his knowledge of enterprise sales to more than 800 revenue leaders. His strategic acumen enabled him to scale a business from $0 to $30 million in Annual Recurring Revenue (ARR) in just 5.5 years with only four salespeople and no external funding.

Currently, Ryan is dedicated to building the top resource for AI-driven business growth. By following Ryan, you will gain access to real AI use cases that can propel your business forward, integrate AI with domain expertise that works for both start-ups and Fortune 1000 companies, and discover workflow strategies that can dramatically reduce the time it takes to complete tasks.

Ready to leverage AI to transform your business? Ryan posts daily growth tactics and strategies designed to unlock new possibilities.

Join Ryan and start redefining what's possible in your business journey.

A Transcription of The Talk

Mark Drager: So, Ryan, you are known, of course, for helping CEOs and revenue leaders who run seven- or eight-figure businesses implement dramatic change within just three months. You do this based on principles you've picked up from your own experience and working with massive companies. This is what I want to break down with you today. Because I think, as I've mentioned to you offline, people often fall into silos where they hear the word SaaS, and they think, "Well, I'm not in the SaaS industry," or it might be managed services, consulting, B2C, tech, or whatever it might be, and they think, "Well, that's not for me." What I love about what I do, and what I love about what you do, is that we can take lessons and principles from one vertical and apply them to another to make some of the greatest gains. So, I would love to hear, first of all, what challenges revenue leaders and CEOs are facing today and how it is possible to see such dramatic change in three months because I feel that shouldn't be possible.

Ryan Staley: Yeah, I think there are a couple of ways to look at this. First, depending on the stage of the journey that the company is at, there are predictable problems, just as there are with weather, affecting your car, house, and landscaping based on weather conditions. The same thing applies based on the revenue range that a company is at. There's a unique journey from zero to one, one to three, three to ten, ten to thirty, thirty to one hundred, and so on. Then you factor in layers like ownership structure, whether it's publicly traded, private, PE-backed, or VC-backed. Regardless of what industry or vertical you're in, those are the overlying factors that are going to affect you one way or the other. For example, one of my clients, whom I was talking to today, is at about 10 million in revenue. They are doing amazingly well, providing an automated marketing solution.

One of the things I love about them is their success, but now they are starting to run into problems where things are beginning to break, or they are not able to give enough attention to certain areas, moving from a founder-led, bootstrapped company to the next phase where there needs to be more handholding with bigger customers and segmentation. Sometimes it's just an easy switch like that; they're doing so well in one area that it's causing things to break in another. Other examples are seen with conversion metrics but just with sales pipeline metrics as a whole. If there's a flaw in the conversion metrics and you just fix that, without even adding more leads or anything like that, it could double your revenue. So, that's one side. The other side that I see people struggling with right now, and I've been focusing a lot on this because it's near and dear to my heart, is how to integrate AI into their workforce. Last year, I think 2023 was a year of integrating AI into products in companies, more on the data side, and now it's about creating a scalable process and strategy to integrate this into our people, so we can create a superhuman workforce if you will. Those are the two big areas I see people struggling with right now.

Mark Drager: So let's touch on AI because I've tried to stay out of the conversation. I am so tired of it already, but let's set that aside. Sorry, audience, I'm admitting this to you: I'm so tired of AI. But let's set that aside. Another reason I'm staying out of it is that things are just changing weekly; things are changing so quickly. And I think if you're going to maybe do some R&D with AI, you're going to test it. We use AI every single day. But for me to think about a brand-new process or SOP, and how we're going to roll the AI into it and how it's going to work, and then how it can be predictable so we can then scale it out—by the time I go through those typical steps that I would kind of bake into an organization, things have changed. And then you go through it again and again. Things are changing so quickly. I almost don't know how to—if we must nail it before we scale it—but I almost don't know when it's baked enough to then send it out to the entire team. Is that a challenge that you find people are running into, or am I overthinking this?

Ryan Staley: Well, I think you might be overthinking it. And here's why: I've been in your exact situation and thought the exact same thing. I was creating an MVP for a product last year in June. I ran into that because I tested the product; the trading results were good. Then the model changed and I didn't realize, oh shoot, this... Like, I didn't understand that now there's versioning control you could use to avoid that, which I didn't know at the time; I was a noob in terms of that side of the house, but I've invested thousands of hours in using it day-to-day. What I would say is that there are core principles that stay the same, so it doesn't need to be a long convoluted process.

And I'll give you an example of something really simple that you could do, based on SOPs, actually for SOPs and AI process for creating SOPs. Basically, what you could do is you can record it in Loom. And you know, within Loom, you basically copy the transcription, paste it into the LLM user prompt, and it'll create an SOP description of the entire video you just created. And then you have it done. Like, that's an example of how even if the models change, processes like that are still going to be the same, right? There’s more complex...

Mark Drager: To me, it sounds like an AI assistant prompt like that. Maybe I was overlooking that because it is so obvious that you leverage AI, you know, for—I mean, we throw all kinds of questions at AI that we don’t know what questions to ask Google, or what questions to find or how to research it, or even the terminology to use. We used to spend a lot of time on, say, keyword research; we’d have to jump through all these hoops to try and figure out what word a target audience would use through audience research and keyword research and hashtag research, and we’d do all this stuff. And then now, we can just go to AI very quickly, like just continually use it as a sounding board to try things, test things, and figure things out. But, so maybe correct me if I’m wrong, is not everyone? Aren’t most people already doing this?

Ryan Staley: I mean, there are basics like that, but there are 10 levels deeper you could go on it, right? And I think like maybe for you, the listener right now who’s looking at data on the marketing side and say you’re that or a leader? If you use a solution like ChatGPT for teams, where there's commercial data protection, or Microsoft Copilot, I think like, one of the things you could do is like, you can literally take a screenshot of data that you see from your outputs, have it analyzed, find hidden trends and opportunities, and then create a plan all within a few minutes. So, like, there are areas like that, that I think a lot of people are missing, or using just really basic tactical things, versus integrating it in everything that they do, tracking that, and then innovating on top of it. And that's where there's a lot of opportunity.

Mark Drager: Okay, that is super helpful. So let's take a step back. So you're sitting down with either someone in charge of revenue—we're talking, you know, CRO or CMO, CEO—we're talking about people in business development and marketing. And they're sitting across from you and they're saying, "I want to be able to unlock this growth that people are promising, that if I go to your website, you're promising. I want to unlock this growth." Obviously, the first step is like, well, what's the constraint? What's the problem? What are the potential solutions? What's the lowest-hanging fruit? How do we resource it? How do we go about it? Let's, it's a very simple plan. But beyond those—set aside—that the table stakes of putting together any kind of strategy or plan, if you're looking forward to Q3, of this year and Q4 of this year, and with everything that's changing, what are you typically recommending?

Ryan Staley: That's a great question. What I'm finding is most leaders don't even know how to use it themselves. And they don't know what's possible. So if you have those, I don’t know, first principle gaps and understanding it, then you can deploy it to a team successfully, and in a coordinated manner. So it's funny, there's a company I'm working with, they’re about 250 million in revenue. And the same thing, like the executives don't know how to use it. There's another company that I'm working with, bots are working with, and they're built from the ground up as an AI company, but they don't know how to use it in their day-to-day work. So what I would say is start with the leadership team, and understand what’s possible. And then I take it a little bit different approach from what you're talking about in terms of like diagnosing, like where to focus, and it's really simple. And I heard Kibana, the CMO of HubSpot, talk about this as a great example, where it basically takes a two-by-two matrix and looks at, you know, on the different axis, one is like outcome-related, like what's the core KPI outcome that they want? And then the other on the x-axis is productivity, right? Like how much, you know, on that scale, can we get it going up and to the right, you know, the top right quadrant, where basically we’re freeing up time for a large number of people in our organization while hitting that KPI and that's usually the focus area.

However, I'm sure you know, there are like with marketing and sales. There are core predictable problems that happen like pipelines. Conversion metrics aren’t where they need to be. It’s just stack ranking them and prioritizing them and then doing that with it, you know what I mean? So that’s what I would say, let’s see, oh, I just talked to specifically for marketing, one of the things he brought up, and this is how companies are starting to look at this. So if you’re working for an organization, be aware of this, especially if they’re PE-backed, if they’re looking at either, how can I get two to 3x? Out of my employees? Or how can I get the same work with fewer employees more profitably? So this is the strategy that ownership groups, and investors are starting to take. So buyer beware, if you’re working for a company like that, that’s what they’re talking about, and maybe not telling you.

Mark Drager: It's so interesting, especially when talking about a medium-sized organization. Because with my own agency back in 2016-17, we went from being a specialized agency to a more full-service one. We moved from being a specialized agency to being more performance-based. I didn't account for the complexity that I was introducing into my company when I decided to do that. I talked to Rand Fishkin, the founder of Moz, about this, actually, because he mentioned that focus is a superpower. I've been working really hard over the last few years in my business to help simplify things, simply because it feels overwhelming and impossible to stay on top of everything. And if we think about all the different tech stacks, the different communication channels, the different advertising platforms, the nuances within each platform, and the complexity, I am very confident and bullish that as things continue, we're moving more and more into a specialized world, where you can continue to niche down further and further because there are the channels there. And there's the required expertise and specialization.

But a few weeks ago, I was thinking at a high level, I'll go high level, and I'm like, "Oh, I've got to learn some of this stuff." I attended a workshop on the changes in email automation and marketing. I heard someone speak for two hours, and I thought I could spend the next six months of my life going all in on this, and I would probably know as much as this person does. But as soon as I took my eye off the ball, the industry would change, everything would change, and I would lose track of what was happening. And I share this story because I realized, you know, I'm a big believer in 'who not how,' right—find the 'who,' don't try to figure out the 'how.' And it hit me just that morning, I was like, "Oh man, I am not going to spend a single second trying to learn this; I need to find out who." And so as you're describing the changes that are happening within organizations and how AI is out there, we have to start with leadership. I don't even know how an organization that may feel a few years out of date might feel like things are constantly changing. How do you implement this without bringing someone into your organization who is going to be responsible for knowing it, figuring it out, and staying on top of it? I mean, are we seeing AI heads, and their leads, come out of IT departments or consultants who are focusing specifically on AI to help implement this in businesses? Because I have not come across this yet.

Ryan Staley: Yeah, so there's the role of the chief AI officer, which is starting to become more prevalent. And so that's like a cross-functional role. They have to understand tech enough to be dangerous, but at the same time, they have to be able to communicate and understand business issues. And so it's almost like a combination of a tactical person with a business liaison person with a strategic thinker, which is hard to find. I mean, that's almost like the CEO of a business, right, to have that kind of thinking. And so I think you almost have to split that role into two and have one that's more oriented on, like your company and your systems that are...

Mark Drager: A business analyst or something.

Ryan Staley: Yeah, more oriented, more heavily focused on that side of the house with the product, right? And then I think you almost need a separate one specifically for people. And it's funny, I haven't really talked about this before, but it's something I've been thinking a lot about. Because if you're trying to integrate that into people, it's way different than integrating it into a product or a tech product. And so it's like, I think you've got to split those roles if you're really being honest with yourself about who you need in your organization. So that's kind of the way that I would look at it. And the second is what I'm really helping companies set up. And it's funny because the range of expertise I've seen is mind-blowing, you know, like, I've had people that are running really big companies that have no idea what to do with this. And it's not their fault, because things are happening so fast. If you're doing really well, it's very easy to get left behind on this.

Mark Drager: But we've seen this before, like when programmatic became a thing. Suddenly, you had to have specialized media buyers, alright, and then media continued to change. As I was saying, the complexity of my business, I didn't account for how much you have to understand about a very specific channel to be able to squeeze every drop out of it. So, if we think about how it was introduced into companies, then marketing, and then social departments, now there are all kinds of different titles for all kinds of different people because businesses have just become so complex and competitive. So, I have to imagine if we were to approach AI from operations and efficiency, from a process point of view, it's simply a new tool. But we go about it the same way we go about any kind of business improvement now.

Ryan Staley: It is a tool. I think the reason why it's different, my opinion, right, is because it has happened faster than anything we've ever had in our lifetime. And it's going to get to the point in a few years, where it's the only tool we've ever created that's smarter than us. And like, that's hard for a lot of people to wrap their heads around. I mean, even to the stomach. For me, there's like my AI origin story, if you will. I'm like, I gotta change everything that I'm doing. What you were saying about looking at and understanding how people work... One of the things that I did was, when it first came out, I had a founder on my podcast called the Scale-Up Show—the million-dollar company is a guy named Chris Savage from Wistia. And he's like, "Hey, Ryan, you heard of DALL-E before?" And I'm like, "No, I haven't," because this was in like, October of '22. Tried it out with my daughter, Homer, like, "Oh, this is kind of cool." It was like v1, right? So it kind of sucked. Then I used it, but then I got on the early list for ChatGPT. And then the first thing I did when it came out is like, "Is this going to replace this or not?" So I'm like, "Alright, I'm going to test it on something that I know to be true, that I spent like 10,000 hours, right? I use the 10,000-hour rule—10,000 hours that I did, in customer meetings and understand how they're compensated, and evaluated. How do they emotionally feel?" The thing that scared the living bejesus out of me is literally after two minutes, and two or three questions, I got like 90% of the way there. Right. And so I'm like, "Okay, this is why it's different, you know." And that was a general-use tool that wasn't even specific; that was even asking questions...

Mark Drager: 2022 was that—is that when this happened?

Ryan Staley: Yeah, it was like, right when ChatGPT came out. And like, I think it was like November of '22, late October...

Mark Drager: You know what scared the shit out of me in 2016, I think it was 2015 or '16, someone released a product called The Grid. Okay, that was the idea. And then they released Grid 2. And I don't know if it ever went anywhere. But the idea was that it was a no-code website builder, that was 100% AI-driven. All you would do is upload, some photos, some colors, some simple font, and some messaging prompts. And it would build out the entire website, automatically. Based on conversion paths, and kind of endpoints, you set it continually split test in real time, different messaging, different layouts, different everything. And it hit me where I was, like, you know, I'm running a creative production agency, marketing agency, well, we get paid to make stuff for people. And I'm looking at that happen. And I'm looking at Facebook announcing that you know, you can just upload assets, and it'll make its own ads for you, you can go to Google with the responsive display.

And you know, you upload 15 different combinations, and it'll pick the best combination. And I'm looking at my team of people, my profit center people, and I'm looking at them and I'm going, "I feel like we're only a few years away from all of you not having jobs anymore." And I said to them, like, "Listen, like you got to be careful, because right now your entire livelihood, our entire livelihood is based on making things for people. And I think there'll come a time in the near future, where machines will do this much better than us, you know, if the data is there, and if the direction is there, and if the prompts are there." And so that's really what sort of changed our entire journey. But I'm not afraid of it anymore, only because I think people who are behind outdated, conservative, safe approaches and stuck in the past will do very poorly over the next few years with the transition. But if you're continually looking to see how can I add value today? And how can I drive results today with the tools that are available to me today, then I think you'll be okay. Because everyone else will be freaking out as the world burns around them. And you'll just be focused on driving value, driving results, and figuring out what works right now. That's mine. I haven't ever shared that with anyone. But that's what I'm putting my faith trust and optimism in.

Ryan Staley: Yeah, I think that's Spider-Man, it's like, you can't get too stuck in the future or you'll lose the present, right? And so I think there's a healthy balance of like, focusing on you know, the value creation in real time. And then stacked on top of that, like everything that's coming at you. But focus like you said, focus—the superpower of focusing on how that relates to your specific core value creation and then kind of look at it through that lens because if you try and ingest every freaking tool of 15,000 tools and steps and models and everything like it's just too much for anybody to handle.

Mark Drager: So how much of this do you think we should just ignore? That's what I keep wondering. Yeah, like in the future, part of me is like, if our time and our creative energy are finite resources that I only have so much of today and only so much tomorrow, part of me just wants to put my head in the sand and then figure out kind of when everybody has thought about everything, figure out the winning team, and just jump on.

Ryan Staley: Yeah, here's what I would say, man: if you look at the large language models, like ChatGPT, CLIP, and Microsoft's Copilot, or Anthropic's model, as long as you pick one of those, you're going to be fine, right? Because if you learn how to use one of them, it translates to the others. There used to be big differences; they're also starting to catch up with each other a little bit. Now, I know ChatGPT is going to have Sora, which is released, which is text video generation, which I think is going to be pretty game-changing. We will see what happens, right? You never know from the demo video what actually translates. So I think as long as you learn one of them and understand the core concepts, and then focus it on like you're saying, what's the value I could create now with this tool, and then keep iterating on top of it, you're going to be okay, right? That's the focus as long as it's attached to tangible outcomes and business values, and I think you'll be good.

Mark Drager: So I like to end my conversations this way. And I would love to hear from you what your number one tip or strategy would be to help those of us listening sell more. But since we've been talking about AI, and you're so into this right now, I would love it if you could tailor it around that.

Ryan Staley: Yeah, well, we have a mix of the two. I think we have them have a baby together, right? Sell more with them together. This will integrate with marketing and sales. And this is one of the simplest things that I think you could do. So if you're looking at customer data, this is a multi-step process, but I'm getting very tactical, so you can implement it. Step one is to sign up for a ChatGPT Teams account. It's basically you have two people on it, or you can do a Microsoft Copilot account, I think ChatGPT Teams is probably the best, it'll cost you $50 a month, right? Just do that even though you and one other user, that's step one. Step two, export the data. Now that you have the security layer wrapped around it, export the data that you have from your client list, right? In terms of your revenue and your deals, won and everything like that, enrich it through ZoomInfo or Apollo or something like that. So you have a full dataset, then you can leverage that file in CSV format, and basically say, okay, identify the top 20% of my client base, that accounts for 80% of my revenue, right, or just the top 20% of clients, and create a separate ICP, specifically for that segment, and then do a different one for the bottom 80%. You're going to find really weird things. But that allowed me to double our deal size every year without doing anything else, besides focusing on the right targets. So that's a really simple thing that you could do. And you could do that in like 15 minutes after you've exported the data might take maybe a half hour, but that can literally change your entire year if you do that.

Mark Drager: That is such amazing advice. I'm working through the book right now, "How I Raised Myself from Failing to Success in Selling," which is, I don't know when the book was written, maybe in the '50s. I don't know, the guy was a baseball player in 1910. And he joined a Dale Carnegie course. So this is an old book. But it's funny because in chapter two, where he's talking about how he was able to like, I think, increase his conversion rate 30 times over. It literally came from what you're suggesting way back then, which was like, track everything, hold the data, look at the data, and then figure out where you should be spending your time. And how you should customize your approach, depending if it's the top 20% or lower people. So I love that we just had a conversation around AI and tech and what's happening this year, and yet, what you're suggesting has not changed in 100 years.

Ryan Staley: It's just a faster way to do it. I used to do it manually, and it would take six hours, or eight hours, and because of that, a lot of companies don't do it. Now, you can do it in a half-hour, right? That's where this becomes beautiful, I think you know.

Mark Drager: I love it. I love it. Ryan, thank you so much, man. I really appreciate your time.

Ryan Staley: Yeah, this was a lot of fun, man. I appreciate you having me on.

Resources & Go Deeper

"AI and the Workforce: How Gen AI Can Help Employees Flourish" by Knowledge at Wharton

Description: This article explores the positive impacts of generative AI on the workforce. It discusses how AI can handle repetitive tasks, thus boosting employee morale and productivity by allowing workers to focus on more complex and creative tasks. The research highlights the importance of clear communication from employers about the role of AI to mitigate fears of job displacement.

AI and the Workforce: How Gen AI Can Help Employees Flourish - Knowledge at Wharton (upenn.edu)

"A Generative AI Reset: Rewiring to Turn Potential into Value in 2024" by McKinsey

Description: This article discusses the evolving landscape of generative AI and its impact on businesses in 2024. It highlights the importance of upskilling employees, forming centralized teams for AI governance, and setting up scalable technology architectures to fully leverage AI’s capabilities. The article stresses the need for comprehensive training and community-building among AI practitioners.

A generative AI reset: Rewiring to turn potential into value in 2024 | McKinsey