Treat AI As A Teammate (Not A Toolstack) | Austin Myers

AI is currently being misused as a volume scaler, causing teams to burn out capacity without increasing real conversion. Austin Myers argues that go-to-market teams need to stop treating AI like early-2000s email sequencers and start focusing on high-converting moments of true intent. The default 'more with more' playbook is breaking sales, replacing authentic connection with overwhelming noise. A major reason 38-46% of AI initiatives fail is a lack of organizational readiness. Organizations need to approach AI with a three-pillar foundation: clean data, well-defined processes, and proper onboarding. AI should be treated as a team member on the org chart, requiring the same continuous training, updating, and coaching as human employees. Furthermore, AI shouldn't be used to replace entire roles like an SDR or an AE. Instead, leaders must break roles down into a 'job stack' and automate specific tasks. Freeing up human capacity from repetitive operational work allows teams to focus on building meaningful connections, elevating the buyer experience, and standing out from the sea of sameness.

Discussed in this episode

• How the 'more with more' volume-based scaling approach is breaking modern go-to-market capacity.
• Why superficial metrics like MQLs often reflect bad measurement rather than true buyer intent.
• The three foundational pillars of AI readiness: data hygiene, defined processes, and continuous onboarding.
• Treating AI as an active part of your organizational chart rather than a plug-and-play tool.
• Why AI cannot replace entire job functions but excels at specific tasks within a defined 'job stack.'
• A breakdown of Austin's daily AI tool stack, including Claude, Grock, Gamma, and Attention.
• How to define a clear metric for AI ROI before blindly purchasing new software subscriptions.
• The importance of leaders asking open-ended questions to unlock creativity rather than imposing rigid expectations.

Episode highlights

1. 0:00 — Welcome and guest introduction
2. 3:00 — The real cause of the capacity problem
3. 6:30 — Stop blasting cold emails for volume
4. 9:15 — Identifying fake MQLs and bad signals
5. 12:45 — Why most AI initiatives fail
6. 15:30 — The three pillars of AI readiness
7. 19:00 — Treating AI as a new teammate
8. 23:20 — Why AI SDRs are a massive misconception
9. 26:10 — Austin's personal AI application stack
10. 33:45 — Measuring AI ROI to avoid paradoxes
11. 36:30 — Rapid fire leadership insights

Key takeaways

• Stop using AI to play the volume game; focus on high conversion.
• Treat AI as a teammate by properly onboarding and coaching it.
• AI cannot replace entire roles, only specific tasks within a stack.
• Before buying an AI tool, define the exact metric it will move.
• Clean data and defined processes are required for successful AI implementation.

Transcript

We need to look at the actual conversion, the the signal, the real intent moment and decide, was it really an intent moment? One of the things that we spoke about is the majority of AI tools now fail, uh, upwards of 38% of AI tools and AI initiatives failed. You have to onboard AI, the same way you onboard your people, and you have to train it and you have to update it and give it the current best practices. Welcome back to another episode of The Bridge the Gap Podcast powered by Revenue Reimagined.

Today is a very special episode for a couple of reasons. We'll start with our guest, Austin Myers, who's VP of Sales and Marketing at Sales AI, a go-to-market leader who's spent more than 15 years leading sales, ops, CS, and go-to-market teams through every kind of high-growth chaos imaginable. He's scaled teams across healthcare, SAS, and tech, served as COO and CRO for startups and growth companies, and now helps organizations remove one of their biggest barriers, which is, as we all know, capacity. We're going to talk about what really breaks go-to-market systems, why strategy isn't the culprit, and how AI can finally give sales and ops teams real leverage instead of more noise.

That's number one, and Austin Myers, welcome to the show, but number two, is everyone sees a different face here today. We are up-leveling. We have pushed Baby in the corner. Dale is away, and we have the third co-founder of Revenue Reimagined with us co-hosting the show today.

Another very special guest, Mr. Jake Renny. Thank you both for us spending your Friday afternoon with me. Thanks for having me.

Let's not get the guest too too to uh comfortable with this, Adam. I don't know if they're going to want Dale to come back. So, I I'll do my best not to give 100% today. Uh, I mean, I I feel like that's what I do ever no, I'm kidding, I'm kidding.

Well, Austin, hey, welcome to the show, man. We're we're thrilled to have you. You know, I I think to get things kicked off, Adam was talking about the capacity problem. Um, and let's let's jump into that initially with this thought around every like, you you cannot you know, scroll on your on on social, you can't, you know, open up your inbox, you can't sign into LinkedIn without hearing about the discussion of AI and the impact that it's having on sales teams, revenue, operations, reducing costs, as a whole.

And and and and the pressures of AI in organizations is creating conversation around how can we get more for less. And as companies are trying to figure out how to implement efficiencies with AI, I'm an experiment, and and let's not get there yet, but even the initial failures with those experimentations, the idea is still and the expectation is, we need more. We need greater capacity. So the question, I guess I would lean into you first as companies are trying to figure out how to get more of less, and and and starting to try to scale back while not properly implementing AI strategy.

Where do you see some of the greatest go-to-market failures are stemming from capacity and not necessarily bad strategy as a whole? Yeah, so capacity. I've seen this time and time again, and I feel like I'm going to be uh preaching the choir here. Um, especially with Adam and Jake and Dale, and we've we've had these, um, observing how go-to-market teams have done things, um, to date, even today in the age of AI.

The irony is, we talk about capacity as a problem because we keep, we say do more with less, but the default behavior we keep seeing over and over and over is to just do more with more. Um, and so the problem that we're seeing in in the world of AI, it's it's just sometimes it's history repeating itself over and over again. Some of the challenges that even we've wrestled with in our own product, but also out there in the market and talking is, people are treating AI the same way that we treated email sequencers and call power dial, we're we're we keep looking at the volume game, and we over quantify on math. So, the capacity problem as we've looked at it, it's it's less of a math mathematical problem, because the ways of doing things from the early 2000s, and even the '90s, and even 2010s, and literally last year was how do we just make the math work at a larger scale, rather than looking at the moments that actually convert.

It's a the capacity problem is a discipline problem, because we're not going back to the specific moments that are number one, the the just the high converting moments. And number two, the most important moments that we actually need to scale and optimize for. I use parallels like cold email, right? Cold email, we went we evolved into the sequencers like Apollo, and cuz we were we we felt we just fell into that as a result of trying to scale manual email.

And now tools like Smartlead and, uh, you know, we sales off and outreach, everything that they have have all broken every I'm sorry. I I have to. I I have to interrupt. Listen, I I think Manny Medina is an incredible human being.

I think Outreach had its place. I think Sales Loft with Kyle Porter had its place, but I think that from what they were supposed to be to what they turned into and now with some of these other platforms, they have destroyed sales. Go send 50,000 emails. Go send 100,000 emails, and we get a sub 1% response rate, and that's great because you got some people to respond.

It's everything that's wrong with selling. Yes. And that's what's breaking it. And so we're just taking AI and we're just kept scaling the thing, right?

And so, The capacity to just kind of tie up the thought, it's the capacity problem we're dealing with is because we've never kind of gone through and fixed the core. And you know, Kevin Dorcy, Katie says this all the time, like sales killed sales. Um, and that's a a big problem, marketing is killing kind of killing marketing. Um, and so the problem we're dealing with capacity is usually because we are addicted to scale, we're actually not looking at the highest converting moments and then saying, how do we do more of that, and facilitate those moments.

And it comes from discipline, from just not we we love our pipeline created, our deals created, but we forget like, how do you just build meaningful connection, Um, and you don't just go piss off an entire TAM with with just a this one size fits all or even this highly personalized targeted approach, um, that burns and weighs on the capacity. And so what we often find is, man, conversion rates are down. Um, so let's go and just figure out how we fill that with AI. And right now what we we've even gone through, and what we're trying to even kind of teach the world here at sales AI is, you don't need you don't need like this limitless capacity.

What you need is just focus and obsession around your buyers and your prospects, and your audience, and then scale the conversations that matter there. Scale the moments that facilitate a good conversation, Um, in a high converting moment, rather than just trying to find it and capture demand. You have to be able to manufacture demand and that's the capacity problem like, we need to be talking about is, the the moments that matter, not just pure operational, you know, hours and minutes in the day. Right.

So what you're saying, Austin, is which is funny because it seems counterintuitive to what we come across most uh, you know, leaders are out there doing is smashing the more button doesn't work, is what you're saying. Right? If if right? Like A plus B equals C, people are thinking, well, if I do more of A and I do more of B, then I'll get more of C.

Um, but as we try to get, I guess, let's hold off on the the moments that matter conversation first cuz I think that's spot on, but I what I want to know is, how can then teams, how can teams identify the warning signs of overloading their their teams, right? Like sales, CS, ops, they're drowning, right? In in the more with more problem, how do what are the warning signs? Yeah.

Um, so I think it comes to, um, we'll call them the the the sub-level metrics. Things that are under the surface. I'll give you a practical example. Um, cuz this is something that we've really wrestled through recently.

Um, MQLs. Um, we need more MQLs, right? The classic, this is classic, right? Like everyone has this problem.

We need Jake and I have very strong thoughts about MQLs. We talk about it weekly. Yeah, we love them. The lead suck.

You know, yeah, sales, leads are terrible. Yeah, smart marketing, we're just missing more the the we we just need to work harder. Um, and and what we found was it was actually an intent problem. Half of the leads that we're dealing with, and calling MQLs, it was a bad measurement.

Um, and and as we took a step back, half of them, they were just low intent. They were taking an action that we failed to recognize as they actually wanted to talk to us. And when we started digging in, the ones that were low quality, and they still would book sometimes. They would even go into the pipeline, and we were converting them at a 1% rate.

So what was the default behavior? Hit the phones. Call them. Reach out.

Text. Email. Noise, noise, noise, noise, noise, noise, noise. And we had to just just pull back and say, we don't need more, we don't need to work harder.

And we need to look at the actual conversion, the the signal, the real intent moment, and decide, was it really an intent moment? Um, we found ourselves even asking some of the prospects, hey, when you did this, were you in the mindset because we realized even some of the stuff we were measuring was broken. And so, I I think when it comes to, it can be people that are in pipeline, this can be post-sales pipeline, this can be, you know, just top of the funnel. We have to actually tie the right behaviors to intent and signal moments, because right now we've we've kind of taken the playbook of the past, and just said, you know, a form fill, or a download, or clicked or visited a page, that was intent.

But that doesn't always translate to intent. And so, we have to be willing as go-to-market leaders to let go of the assumptions and the playbooks that we knew. I even struggle with this. I I've known these things for the last 15 years.

They worked. But they're actually not working anymore. And we have to be able to let go of what we know and get back into founder mode, and just ask, talk to customers, to prospects, to buyers. That's what we've been missing.

Um, and the go-to-market world is getting back to founder mode. So I I agree with you, but I think, and Jake and I talk about this all the time. There are so many marketing teams and marketing leaders now that are so focused on, well, I provided MQLs. And shit, dude, like I I we're up from 476 to 864 MQLs.

And your sales team just might not be able to close them. And I think that narrative is so ass backwards. Um, somewhere, some way, someone came up with this concept of an MQL and made a lot of marketers a lot of money, um, because that's how they were metric and that's how they were compensated. Yet us sales folks still have to close deals to get paid, but I digress, we got MQLs.

Um, so we're all good. I I think as we look at the modern way to build pipeline, um, in the modern way to grow revenue, we're we're starting to see that shift and I think the most progressive CEOs, the founder mode CEOs, if you will, um, are starting to focus on what matters and I think this is where AI, you know, helps a little bit. But most AI tools, and it's funny, so Jake is back in Utah, but he's was in Miami with us this week. We were leading an AI event for one of our um, banking partners.

And one of the things that we spoke about is the majority of AI tools now fail. Uh, upwards of 38% of AI tools and AI initiatives failed. One study was as high as 46%. And I think that's because they are built for, we'll call it between 3 to 5% of users, which are like the power users.

So when you're looking at AI and how AI could help with capacity, how do you address this so that we're not using AI tools that only the best of the best could use. Whether that be an N8N, whether that be just again, any of the complicated tools that's like, oh, it's I'm going to send you this 47 node N8N workflow and you're going to do great. I mean, sure if you're a coder. Yes.

Um, you know, I think there are a lot of tools in the market and a lot of platforms that come out that are going low-code, right? You know, I scrolled in my LinkedIn feed and I just got fed up seeing, you know, everyone was showcasing their 500 million, uh, step N8N workflow. It was all the range. Um, you know, all the cool kids were doing it and we all got comment workflow and I'll give you my secret for free.

Yes. If I saw one more of those, I was ready to just, you know, delete my LinkedIn, but it was but I think what we found was, um, we had to take take a step back. Um, this is about, you know, four months ago, and realizing most people don't actually know how to prepare for AI. Um, so much so that we even kind of developed an AI readiness assessment for people to just figure out like, we're an AI company, right?

As Sales AI, and just and but we had to be honest with ourselves and say, our customers even ready? Um, and so there are foundational things. Elephant in the room, we talk about it a lot, but it's data at the core. I mean, garbage in, garbage out.

And AI is it's really good at coming up with context. It's really bad at giving you the right context. So, an example, there are holes in your data, it's going to fill the hole, but probably with the wrong thing. Um, and so, you know, we're shocked when it's hallucinating all of a sudden, but we're acting like our data wasn't completely broken to begin with.

And so we find, people need to go back to the core and address the data problem. That's one pillar that is just straightforward. The second is a a process problem. Um, and I say a process problem, most people actually didn't have a defined process for how things should be done.

Um, and so, you know, the actual system that needs to happen, um, and sequence of events, people lacked process. And then we went into, you know, I I the MIT study that, you know, we turn off our brains the more we interact with Chat GPT. Go-to-market did the same thing. So we'd turn off the brains and say, AI can just run it, um, even though I don't have a process.

So, uh, and then the third pillar is onboarding. Um, we onboard humans and, you know, the three of us, we've we've we've brought on people in our teams, and we obsess, right? We listen to the calls, we read the emails, we coach every day. But then AI, we just decided we're not going to do that.

Um, and it should just plug and play, and I'll spot check it a couple times. The three disciplines are data. You got to have really good clean data and and understand how you manage the data. Number two, you need to have very clearly articulated defined processes.

Um, with the right. And third, you have to onboard AI the same way you onboard your people. And you have to train it and you have to update it and give it the current best practices. And people are forgetting that, you know, it's we need to treat AI as part of your org chart.

So how do you onboard, how do you train, how do you upskill? Those three things we found have been missing, even in customers that failed on our product, and other go-to-market leaders I've talked to that have failed in their implementations, it was usually one of the three, or all three, one of those things was missing and they completely failed and botched they just botched the implementation because they weren't ready. People buy from people. That's why companies who invest in meaningful connections win.

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Is the companies that are winning, they they were ready. They've prepared. They've been AI forward. The ones that are falling behind, it's because they never took the proper steps.

And those three things are the steps go-to-market teams in every company need to take now to make sure they can incorporate AI and be part of the three top 3 to 5% power users. It's when you talk about AI being part of the org chart, I think this was one of my biggest takeaways from the event we did this week. Um, and I'm glad to hear that we weren't the only people who were talking about it, because we did not prep you, you were not at said event. Um, but we spent, god, Jake, what?

Probably 15, 20 minutes talking about AI being part of the org chart. Um, so I, I There are people who are having people on their teams managing what they're calling like AI employees or AI agents, but as as an employee, I mean, this is a trend that we're starting to see more and more. Yep. And you know, Clay kind of coined the term go-to-market engineer, right?

Like they they created an entire category. Um, but there are a lot of that that kind of idea, right? Is you know, go-to-market engineer is the fun new thing, and whatever your stance is, right? For anyone listening here, you might love it, you might hate it.

It's like pineapple on pizza. Um, but the idea is Do you like pineapple on your pizza? You know, I I I I I kind of, it depends on who I'm talking to and how much, you know, if you ask my in-laws, yes. If I'm by myself, no.

My heart's broken. I like the concept though, like of the three pillars, right, Austin? Like I think Jake's like, Adam, shut up. No, I mean, look, like it depends on the on the joint, for for real.

Pizza. What's gonna like, depends on the pizza place, man. It some can pull it off. Most can't.

Definitely not a domino's. Right. Um, go ahead, go ahead. No, no, no, and you're and you're you're pushing like, whatever people want to call that role, right?

It's really, I would define the go-to-market engineer role that that Clay has kind of create that category. That's really what the SDR role should have evolved into, right? And somehow we've called it both. We found the skill set.

You're really well prepared if you're already in that mode, you're you have the foundations to start layering in AI, cuz you're already thinking in terms of data, process, training, and then you're validating and you're kind of treating it like a growth lab. Um, the same thing for for organizations. So if you're like stuck in the past, and you're trying to think about, can it replace what this job does, you're probably already behind the eightball. But how do you layer it in sequentially to all the different jobs that somebody does, that's where the magic is.

And that's, you know, that that's where companies need to start going to. And we we think about replacing just the head count entirely, but we need to think about replacing the specific like jobs and actions that that occur in the day. you're touching on an interesting point here, you like what what with with history like prevail and Clay is a perfect example. We often see when when new technology comes in or new new new process comes in, and specifically around technology, often times we start to create the roles around the process, right?

We we hire for what we believe is the right process or function. And and yet it it seems to be quite opposite. As we evolve and it just becomes part of what we do, we start to then ingrain that process in the role. And it becomes kind of a flip.

But right now, like with with CRM we saw that and and and and then it's just become overall a portion of of the revenue operations function. We saw that with Clay, all of a sudden there were these Clay architects coming out of never everyone just felt like, oh, we should be hiring a role to to solve for the process and instead of like making it ingrained in what the function is for the role that is there today. And now like now we're seeing this, you know, with AI, nobody really knows like where it should be owned and it just seems to be a misconception. I'm curious from your perspective in in what you you guys see, what are other common big misconception?

Like, what's one major common misconception you all see about implementing AI and go-to-market as a whole across organizations? The biggest misconception is AI can replace an entire job function. Yeah. So, the the term started to use is the job stack, right?

Like if you think about, you know, we say full stack developers, right? There's full stack SDRs, full stack AEs, full stack marketers. There's there're different layers of the tools that they use and the jobs that they perform. So as an SDR, as an example, AI SDRs don't work.

They don't, right? You go into the market and you ask and everyone's like, no, it doesn't work. Cuz the whole job cannot be replaced by AI. AI's not there yet.

Sure, we're on the brink of AGI. No, we're every time we see it, it feels like it's a bigger colossal failure because we're assuming it can just do all the things that a human is trained and knows to do and reason and do all the tasks. What AI is really good at, is when we narrow it down to one process, one job, and you can start layering those in. So in a specific example is, a follow-up no-show, great.

If you have a defined process, defined way of working, AI can replace that. But it's not not going to replace the entire SDR or the entire AE, but it can start taking those little just those slivers of processes, and eventually, you're you're powering up and leveling up the humans, but that's the biggest misconception is, this job is AI. We need to start thinking in terms of breaking down the tasks and what what we're trying to do, processes and actions can be replaced by AI when they're defined correctly. So where Oh, no, I'm just like, I like I like you summed it up well.

Yeah. Austin, where where are you as a leader? I'm curious because the majority of folks who listen to the show, um, founders, VP level, C level. Where are you using AI personally?

As a sales and marketing leader, where are you getting the most bang for your buck with AI? And then I'd love to know, to double click on that, what AI are you using? Absolutely. Um, across I mean, many different tools.

Um, in my day-to-day, I mean, I've been nerding out over Atlass, uh, the browser. I mean, I've automated four different things in my morning routine. Um, the way I think and reason, and dashboards, and data I look at and pull data from this thing and start writing. Um, I've automated a full hour of my morning.

Um, just things to do in browser, um, actions I take. And it's really cool. TLDR, you can have it take control and do things in the background. So I have one monitor up that it's performing actions here, and I have a different monitor where I'm I'm doing other other work, while it's just living in the background.

Um, the the second thing that for me as a leader, I'm going to give a shout out here, attention. Um, I live and die by attention, it gives its You and Austin both. 100%. And the reason is we've, in the past, the term, uh, we all know this one, you can't scale yourself.

If I had a dollar for every time a CEO told me that, well now we can. I can give my team the very kind of Austin GPT, what's the coaching I would receive on these things? It's open source. And so they know, here's how I can improve.

I don't have to sit down and go through. And then the times that I do coach and go through that, it's even higher value, cuz now we're meeting in context, we know the gaps, we know how to upskill and see if training is translating to better performance. Um, you know, another another thing I I live and die by is like Chat GPT. Um, you know, obviously I use it every day in um, and in workflows and automating even some of the the tools I use every day.

So, a lot of unstructured data that I can move back and forth into Do you toggle between some of the models? Like do you will you go from GBT to to Cloud or Grock or or like have you stuck to GBT and and I'm curious how you go back and forth and perhaps even like which your model of choice? Claude can actually do PowerPoint as of this morning and it actually does it well. Claude, um, I love Claude for um, for uh, slide creation and just natural language.

I feel Claude kind of writes better, um, and it can match my tone. Um, a lot better in how I write. Chat GPT, I think helps kind of synthesize and reason a lot better. Um, with what I've tried to do.

Um, Grock, I love for research. Um, cuz it Grock's kind of does its own thing. It doesn't really fully depend on the Google, uh, you know, search API. It actually searches more than the first 10 things.

And so I use Grock heavily for research. And then Gemini, because we use the G Suite, use Gemini a ton. So I I think each tool for its own the job to be done. Um, I'll use each one for the the the biggest power lift.

Um, but then the biggest tool, you know, is our own with sales AI. Um, you know, for AI calls and um, and uh, and just conversations, um, like just contextual conversations with prospects, and with customers, and, um, facilitating the journey, um, through natural language. Those are those are the biggest biggest ones I use. I mean, every day as a leader and kind of lean into.

Um, and it's not just time saved, it's revenue driven, but I I free myself up so much more to to actually do the work than look for the work I need to do. So it helps surface all this stuff to the top, and then I can start start 9:00 AM pushing on things rather than looking through dashboards and searching for the work that I need to start doing. So, I shouldn't have to work to find my work. That's one piece of feedback I I heard from a user one one point in time.

Yeah. I like that. You shouldn't have to work to find your work. Yes.

And that's what AI definitely does so well for me. But how about you guys? What am what am I not using? You know, what are what are you guys what are you guys finding the most value or what's a nugget that you found that you're like, if if every founder just knew this thing, it would like be a game changer.

I'll give I'll give one first just because it's top of mind up on my screen. Um, my favorite tool at the moment is Gamma. Um, I think that when it comes to creating impactful slide presentations, especially with their new updates, um, I'll say it here because I would say it in the meeting. I I'm going to be in California in a week for an executive committee meeting for one of our clients.

Um, and I literally took all of my notes and about the stuff, put it into Gamma and created my 25 point presentation. Do I need to tweak it? Yes, but is it 90% there that I would ship it and be comfortable with it? Hell yeah.

I think they've done an incredible job of that product for $20 a month. I love it. And for those and I agree. I love Gamma.

For those of us that are not the creative brains, you know, I I tried like, we use Canva and marketing and all that stuff. And my product marketer was like, oh, lord, like, here, let me, you got the the body of this is right. It just look let me just let me make this It looks like shit. Jake, what's up, man?

What else do you love? Um, you know, I'm gonna go with it's um, so the the funnest one by far now for creative processing is the combination of open art, um, to create characters. Um, and then taking those characters and kind of scenes, and using VO3, um, I VO3 a little bit better than Sora, um, to create videos with Yeah. Yeah, we actually will use it for ad creatives and, um, and uh, and just things that are, you know, video and not using it to spoof that, hey, this is humans.

Um, this is AI, but it's it's like the fun creative element and, um, you know, instead of just using stock footage, we can actually go to the level of creativity we want for for certain video, and, um, event, uh, like promotion, and all that fun stuff. So that has been a real big. I've done video at all. Okay.

You if if you haven't, would would 10 out of 10 recommend. Open art for character generation, and you can get character consistency. VO3 using um, uh, it's flow is what Google gives you to use to play on top of VO3. And you can just roll through.

It's it's the simplest tech stack, and it's a little bit of a learning curve, but once you learn, it unlocks a whole level of creativity. Um, I used to be big on Midjourney. Sometimes you can do Midjourney. Uh, I don't use it quite as much.

I like like a a character, but you can start in Midjourney, create your scene, and then use VO to actually bring it to life. So, that one's been really huge for like, video just video and things that look different. Um, especially for LinkedIn content, um, uh, long form YouTube, just it's a different way and you it unlocks just looking different because, um, you know, I we both know Jared Robin and there's there's one thing that, you know, the conversations we have every day is like, how do we just get away from the sea of sameness? And and and instead of using AI to look like everything else and scale that, how do we actually use it to look very different?

And just delightfully different. So, that's what we use. Um, learning out on those platforms. It's funny though, you like you kind of in in the process of this conversation, you you kind of bring it up it I think a worthy issue or a worthy topic, like it's you know, all these subscriptions, it's actually expensive, right?

Like it it adds up. If you're going to be paying whatever, $20, $30 a month for for one, and you're going to be paying the same for Claude, you're going to be paying for Gamma, then you're going to be paying for VO. Like, this adds up and I I don't even know if every like we're realizing how much we're paying even out of pocket for for like these playgrounds that we have and and all to produce what? Like is there actual like a return on some of this playing that we're doing?

And the funny thing is like, I think you can actually draw a parallel to what is happening in go-to-market right now. Like we're we're like we're seeing there's a lot of excitement with the playground of AI. And what people like the the belief of the art of possible of what we can do. And so there's a lot of experimentation to then get what?

You know, like our our partner Dale likes to talk about this concept of like the like the productivity paradox. Like because we're so involved in in these tools and testing and playing it feels productive because we're busy and we're active, are are we getting somewhere? Like and and and I guess what would be one piece of advice for you to to those listeners who are feeling that pain right now saying, shit, like we've tried a lot of cool ideas. Very few have really proven an ROI.

What what would you give there? So, it it comes back to jobs to be done. Um, what I've heard a lot and observed in the market is like, there are a bunch of edicts and there's all this like pressure of like, we have to be using AI. Guys, put AI into this process.

And so in this rush, and like we're really curious, we've amongst amidst all the scrutiny, the way that we buy every other platform, right? We go back and we're like, here's the way I buy SAS, I'm looking for this thing and this thing and I have criteria and I have a committee and an approval process and an ROI justification. But then with AI, we're just like, yeah. Let's just roll the dice, right?

And so the buyer behavior I've seen with AI platforms like that, are just scoop it up, try it and then there's no end in sight to measure like, why did we what were we even trying to do with this? And so it just was an attempt to play with it. And so, I think just laddering all the way back, we need to be honest with ourselves before we do that. Cuz I've even had to wrestle with that here lately.

I'm like, what am I trying to do? I need to just pause before I hit the button the pay now button. If I know what I'm trying to do and what I'm trying to solve, I need to just define some sort of metric. Maybe not ROI on the front end, but if the metric I need to measure and move.

Um, and if I know what metric I'm trying to to move with that, then I can layer back and say, do I have when it comes to AI readiness, do I have a process? Do I need to think about a process? Those do I have, can I give it the right data depending on the type of tool? And then can I train it in and train it on an ongoing basis?

And so the three pillars again, that's the discipline we have to have when we're buying AI tooling. Um, otherwise, it just turns into kind of figuring out as we go and, you know, we can't McGuyver the whole thing and shoot from the hip with every single tool, especially when you're missing those three pillars. You're doomed to fail, no matter your creativity. But that's why every initiative is failing candidly.

That's right. Well, we in my at least 70% are failing is what we're seeing, right? Up there on the Fair enough. It's high enough.

All right, we are we are coming up on time, um, which means we need to shift over to some rapid fire questions. So Austin, here here's the rules. No one follows them, but I'm obliged to give them per contractual terms. I'm just gonna give them to you.

Um, 10 words or less, um, to each question. Otherwise, um, every every word you go over is $100 I take out of Jake's distributions when I do them next week. I'm doing a thousand for sure. All right, okay.

All right, what's one GTM role you think is undervalued right now? Product marketing. I like it. And the context proper customer journeys.

I like that too. Yep. Perfect. All right, I'm going to skip over the next one because I think we touched on it enough.

I'm going to go to this other one. What's what's one bad leadership habit you have had to unlearn in your career? Ooh, good one. Don't ask questions with expectations from my from my uh team.

I'm going to go past 10 words here, but to ask questions to unlock creativity. Um, it's a principle I just learned here, um, you know, over the last year in in a book, just like the way I was leading was leading with my expectations on the team, and the way I would ask questions, um, and so it stifled creativity. So instead, lying back, ask questions that are open-ended and are purposely aimed at unlocking creativity. And a specific example is, what instead of asking, why did this fail, shift into, what did you learn and how do we grow and do this better next time?

It's been a big learning habit for me. Yeah. I like it. I like that a lot.

What's a system or process every go-to-market org should kill today, right now? You get off this spot, you you're done listening to this podcast on the way in to your office. What do you kill right now? Blasting 100,000 cold emails to cross the entire TAM.

Amen. There are no wrong answers, but that is the only right answer right now. Solid. Okay, um, What's uh what's a framework, a book, or a mentor that has shaped your leadership that just like first that comes to mind?

Play bigger. Play bigger. I like it. Um, it shapes that like, no matter where you are, you can go and define an entire category.

Like, you can create a category, define it, but there are strategy, it doesn't just fall into your lap. Um, that has influenced the the way I think across every org. All right, that was like 50 words, so Jake, I guess that's that's like, that's like, yeah, five grand. All right.

We're cooking. All right, last last one, Austin. What's one one thing go-to-market leaders should stop apologizing for? Being bold.

P I like that. We we get so afraid of like being direct and bold and like ourselves. Yeah. and injecting our personality.

And instead, we revert back to just corporate jargon. We all do it. And we're afraid of, you know, when we get a little bit of backlash, that's okay. Like we have to know our ICP and our personas and the way they communicate.

And we have to be clear with our personality. Um, and speak to them. That's like, we are for the ICP. And I think sometimes we apologize to the entire world for trying to be who we who we are as an org.

Um, and every go-to-market team and then we just regress into vanilla, you know, we gotta be, uh, so we don't need to apologize for being bold and and being for our audience. So be for somebody not for everyone. I love it. Austin Myers, Sales AI, thanks for joining the show, man.

Thank you, guys.