Using AI for Smarter Decision-Making

Episode Description:

Join us as we explore the intersection of AI and go-to-market strategies with Sophie Buonassisi, VP of Marketing at GTMfund. We discuss AI's impact on B2B, shifting focus from adoption to effective implementation for smarter decision-making. Learn how GTMfund approaches AI investments, balancing disruptive potential with ethical considerations. Sophie offers career insights, highlighting hands-on experience and continuous learning in the AI landscape.

Resources Mentioned:

Kibsi Computer Vision Platform

Mutiny AI Web Conversion Platform

Pocus Revenue Data Platform

Vanta Security and Compliance Platform

Writer Enterprise Generative AI Platform

The Innovation Stack: Building an Unbeatable Business One Crazy Idea at a Time by Jim McKelvey

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Full Transcript:

Andrew Miller:

[0:01] Welcome to another episode of AI Unboxed. Today, I am privileged to be speaking with Sophie Buonassisi.

Sophie is the Vice President of Marketing at GTM Fund.

In addition to GTM Fund, she also runs GTM Now, which is the media extension of the GTM Fund brand.

GTM Now shares insights on go-to-market from working with hundreds of portfolio companies backed by over 350 of the best-in-class executive operators who have been there, done that, at the world's fastest growing SaaS companies.

GTM now is the place for executive operator insight on what's relevant in go-to-market now.

Insight on AI is surfaced from operators, portfolio companies, and through Sophie's direct application of AI in marketing and go-to-market.

Such a pleasure, such a pleasure to have you here on the show, Sophie.

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Sophie Buonassisi:

[0:55] Thank you. Thank you for having me. I'm honored to be here and excited for our conversation today.

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Andrew Miller:

[1:00] Absolutely. And I know we've had some technical difficulties getting here, but it is happening in this modern age. We're actually able to have this conversation.

So glad we've been able to jump through all the hoops of my internet going out, my power turning off, and then all the other things that come with it.

So super, super, super excited to do this.

I know I already gave a brief introduction, but is there anything additional that you'd like to add, maybe about your background or anything that you think the audience would be interested in hearing?

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Sophie Buonassisi:

[1:31] I think you covered that extremely well. There's two brands, so GTM Fund and GTM Now, which I know can sound confusing a little bit, but GTM Now is the media extension of GTM Fund, and GTM Fund is a C to Series A B2B SaaS venture fund.

My background, I came up from From the MarTech space, running go-to-market, and now working in marketing at GTM Fund.

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Andrew Miller:

[1:56] Absolutely. And that's actually where we crossed paths was back whenever you were with Spiralize. We had some overlap there in putting some different campaigns together. And it was a pleasure working with you.

And I'm glad we stayed connected throughout. I think that's been quite a few years.

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Sophie Buonassisi:

[2:11] It's been years. Yeah. Yeah, it's been really cool to see your journey too.

And I love that we can stay connected through the years.

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Andrew Miller:

[2:18] Same here. Same here. I guess jumping into a few of the questions that I know everybody's antsy to like hear about, you know, because you have a really unique perspective.

You have this operator expertise, but then you're also on like the VC side where you're investing in what I think GTM Fund has like 120 plus, you know, portcos, you know, Mutiny, Pocas, Vanta, like all these, you know, great organizations. organizations.

So what, what could you say attracted you, you know, to the intersection of just, you know, of AI and all these other areas that, you know, you're working in?

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Sophie Buonassisi:

[2:56] Definitely innovation is what attracted me to the intersection of AI from both the marketing side and the overall investment and go-to-market side.

I mean, AI is pivotal for innovation, and it's the notion of how do we always improve the way that things are being done and never sticking with the status quo and constantly looking to elevate and level up how things are being done.

That's ultimately what attracts me to AI.

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Andrew Miller:

[3:20] What would you say, or I guess, how would you say AI might be revolutionizing the different B2B spaces that you're in?

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Sophie Buonassisi:

[3:31] Certainly revolutionizing the space. Most notably, what we see at the surface level right now is how AI is incorporated into workflows to increase efficiency, elevate quality of output.

But we're at a really interesting intersection. So I always call it the age of efficiency adoption right now.

And it's about overall adoption. Are you using AI, yes or no, in your processes?

I think what we'll see us transition to is what I call at least, and I'm sure there's different terms for it, but the age of efficiency effectiveness. effectiveness.

So moving from adoption to effectiveness, where the adoption of AI just becomes baseline table stakes, frankly.

And we become much more intentional about our adoption and usage.

So we shift from, are you using AI to how are you using AI?

And what's the impact of it? So some companies are doing this, and predominantly we're seeing it in terms of AI companies.

Most of of them are PLG, for example, Ryder, you know, POCUS.

We've got a lot of great companies that are really measuring their efficiency metrics, time to goal, you know, supporting all these goals by business knowledge integrated with AI, their accurate outputs.

So how accurate are the actual efficiency outputs, AI productivity gains, time to market.

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[4:59] These are all efficiency indicators, and these measurements are what enable us to move to an age of efficiency effectiveness as opposed to just overall adoption.

I was actually on a podcast recently, like one of our podcasts at GTM Now, the GTM podcast with Andy Jalls.

If you haven't talked to him, he'd be a good person to connect with, actually. He's an AI CMO.

And he was referencing Gartner's framework because they've got a framework around AI maturity where there's three stages, quick wins as number one, number two, differentiated use case, and number three, transformational initiatives.

And we're very much so in the early, early days, which is more greatly around quick wins.

So what are those things from an efficiency perspective that how can we write emails quicker? How can we create images for content more quickly? Fill out forms, right?

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[5:53] But I think what we'll see again as we transition and progress is leaning into the deeper value associated with it.

So rather than simply summarizing the data, we are bringing the overall context at the right time with actionable insights.

So, for example, if a sales rep is on a call and they get a summary, AI, you know, joins the call recording and transmits the notes directly to CRM, incredibly, incredibly helpful for efficiency.

But that next stage is how do we get all of the context around that account, maybe even outside context too, and actually amalgamate all of that context to then surface insights that we can then make more informed decisions from, as opposed to simply the efficiency of it.

So less about just overall productivity. Again, that'll become table stakes.

More about kind of really winning and how we'd be better.

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Andrew Miller:

[6:53] Yeah, yeah, absolutely. Absolutely. And I know you're, you're like at the edge of all of this happening because you're working with so many different, you know, operators and also coming from like that VC lens.

How quickly do you think, you know, that adoption to efficiency transition could happen?

You know, because things move really fast and you have early adopters, right? So they're moving quicker.

There's people that have been in the AI space for 10, 15 years. And it was before it became came kind of like a prosumer concept out there that everybody can like integrate into it it's like oh chat gbt i can jump into the api and all this but that's still early adopters are getting in there but this is becoming more like mass used and there's that adoption phase that's happening and so that starts spreading out beyond the early adopters how how long do you think it'll take for it to hit some sort of like critical mass on okay this is fully adopted they're understanding how to to use it, like you said, like it, but, and move into more of that holistic efficiency, aspect and shifting their minds from saying, yeah, this is just our core process.

This is where we're trying to go to.

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Sophie Buonassisi:

[8:01] That's a really interesting question. It's hard to necessarily pinpoint a timeline, but I would say if we had this conversation at the end of this year, I think it would look entirely different.

I think that that transition will occur far more swiftly than we anticipate, but the depth of its capabilities will also be way, way, way more deep.

So maybe we should regroup. We should.

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Andrew Miller:

[8:26] We should. I have like a recap, you know, in December.

And like next year, bring on some of the favorite guests and we'll go through what has changed in this past year in AI, which I think would be really, really interesting. I love that.

You've already gone through a bunch of different processes where people are improving using AI, like traditional process of increasing your email, sending your AI notes into a CRM like HubSpot, you know, some of these core things there.

But is there any maybe particular process or, you know, framework that pops into your mind that you said, OK, this is something that you've been impressed with, that it's uprooted a traditional process by using AI?

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Sophie Buonassisi:

[9:13] It's a usual answer, but repurposing content.

I've been really impressed by because it's something that you can feel the direct effect on as a marketer your every day.

And that is a massive efficiency gain to repurposing content, for example, taking long form content, enabling the repurposing into all these different kind of shorter form contents.

AI can take us, you know, 70%, if not more of the way there, oftentimes 100, depending on what it is.

And that gain rather than starting at zero, at being able to start at 70% has a massive effect on efficiency.

So personally, that's one that I've really felt the benefit of, but it's in, I mean, everything.

If we go back to our roots of actually when we met actually, website optimization. So if we think about.

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[10:10] How are we actually surfacing the most relevant information to our prospects?

Big part of that is the website from organic, from paid searches.

And we want to make it relevant. So part of that involves personalization.

And there's two parts of that that we've seen a really big impact from AI.

Number one is the different variants that are possible. Previously, if we wanted to actually break out our audience into different cohorts, we would have to really manually come up with all the different variations, which is extremely time consuming.

Now, AI enables us to actually do that with ease and incredible ease, actually, and it's baked into products.

So, for example, say you're using Mutiny, which is what I had used previously before they were even before I joined GTM Fund, another portfolio company that used it previously in website optimization.

And you can actually test different content suggestions from AI for different audiences ahead of time.

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[11:13] And then the other is the actual process of optimization.

So rather than manually checking all of these different audiences and cohorts, AI and machine learning facilitates us to have a continuous loop of testing.

And that has really changed, I mean, the game at which we can process optimization and really kind of spin that flywheel for multiple different cohorts on an ongoing basis as opposed to having kind of disparate processes happening.

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Andrew Miller:

[11:47] Yeah, absolutely. I mean, it's, it's almost like bringing that on the personalization side.

It's almost like bringing that, that promise that has always been out there when any marketing software has been sold.

It's we can truly do one-to-one marketing, you know, and you've, you've heard that from Salesforce, from HubSpot, from way, way back when they were launching and like every other product that jumps out there is like, no, we truly know all the insights and we're going to give you that personalization and all that.

But then when you jump into it, it's like, okay, yeah, we can maybe eventually get there, but it's, it's a lot of manual work and getting everything to talk to each other and all the different systems requires, you know, these specialists, these, you know, marketing ops people and data scientists sometimes to like pull, pull in everything with this.

And it's not perfect, but it's getting a lot quicker.

You know, it's really understanding that digital body language that's always been like promised out there and then plugging it into these disparate systems and giving you that output that it says, okay, this is what they're doing.

Here are some recommendations.

And there's some language that you can use on the one-to-one basis.

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[12:54] So it's really exciting. I know there's still bugs and we're still working on it, but it's getting so much closer to being perfect. Perfect.

So exciting, exciting applications.

I think from the investment standpoint, because you have that lens that not a lot of people have, you know, from GTM Funds perspective, could you maybe describe a fascinating AI application that maybe your team recently invested in or been maybe looking at?

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Sophie Buonassisi:

[13:27] Definitely. Yeah, we've got a couple or a good group of kind of core AI forward products.

I think the really interesting thing is now it's becoming even more difficult to differentiate what is an AI investment and what is a just SaaS investment, because AI is naturally just embedded and integrated in all products.

If it's not, there's a problem.

But there are still some very, very forward focused AI companies.

Even when we invested in them, a lot of them were not positioned as AI forward and have surfaced over time.

So companies like Rider, for example, I mentioned Mutiny already, Simplify. There's ones that are less in the go-to-market kind of tech stack or traditional tech stack like Weeviate that provides really important AI infrastructure.

Kibse, those are all really interesting ones. And Kibzee, for example, you can really increase and expedite the efficiency of your operations by pairing AI and computer vision superpowers.

So that's really focused on computer vision using existing cameras. Okay. And so they're.

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Andrew Miller:

[14:39] Yeah.

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Sophie Buonassisi:

[14:40] They're trying to transform every video camera using AI-powered insights. sites.

So it's taking a traditional product that you have and changing the actual scope of output of it, which is really interesting because we're much more kind of accustomed to SAF's products that we don't have the physical infrastructure for, whereas this is actually leaning in in warehouses and kind of supply chain use cases.

How do we up-level the camera output? Yeah.

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Andrew Miller:

[15:10] Yeah, it's really interesting. I guess...

One of the normal questions that I have after an interesting application has been brought up is, what were the challenges, do you think?

I know you didn't work directly on it. You're investing in it, but when you do the due diligence, I'm sure they bring up some of the challenges.

And also, you have this background of go-to-market, so there's a whole positioning thing around this product.

What are some of the major challenges that you think pop up into your mind with bringing that to market and getting true adoption and expansion?

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Sophie Buonassisi:

[15:46] We are extremely, extremely fortunate to have this incredible network of fantastic operators, so about over 350 operators. And that is part of our due diligence process.

So we're able to actually vet through the go-to-market operators, the landscape, and understand that holistically from other perspectives other than ourselves, too. So that's super helpful.

But what is most challenging are examples like Kibse, right, where it's actually a supply chain, for example.

You know, they're taking these cameras, security cameras, and creating real-time dashboards with them. We have less operators baked into our network that are experts in that area.

So it's really finding connections to people that have expertise in that area.

Of course, we've got, you know, our own diligence. We've got a very comprehensive seven-step process overall.

But we definitely, as a step, look to the network to understand the space.

And that's definitely a competitive advantage, I would say, that we have with the operator.

Kind of space baked in. But the challenge always comes with when it's less rev tech, if you will, finding and identifying the right people and ensuring that we have that comprehensive understanding of the landscape.

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Andrew Miller:

[16:57] Absolutely. No, I love that. That goes to kind of like the power of community.

Whenever you have a large network like that of individuals who are in this space, I mean, they touch all different areas.

They have to know somebody. You know, you, you just overlap, uh, with so many different markets out there that you run into somebody that, oh, that was interesting at one point.

And so just tapping into your current network, they have like that, that 10 X effect out there that, oh, maybe we have somebody else that could be useful here. And it's coming from a trusted source.

So I love that. That's how it's kind of built out. Um, I don't mean to jump on Kibse so much.

We could go to somebody else, but it does bring up, and it's the question I always ask, is the ethical considerations around AI.

And this, Kibse's using video, it's using visuals, and it's scanning people and security cameras.

So that just becomes, that makes me think, and I'm sure a lot of people are thinking like, oh, big brother, what's going on out there?

There, from just an ethical consideration and incorporating AI into it, what do you think are some things that either they're thinking about?

And maybe if you want to move beyond Kibzee as well and other things, just generally in AI, what are some of those ethical things that should be kept top of mind?

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Sophie Buonassisi:

[18:20] Examples like Kibzee are definitely where the ethical considerations become a little bit more complicated, I would say, than perhaps our usual go-to-market conversations. conversations because you're dealing with people physically at a location.

So paramount, absolutely. It's certainly a conversation that occurs.

And I think the important thing is for it to always be at the forefront.

And it's we may not have an exact answer because the technology is transforming at just such a rapid pace that we don't necessarily have every single ethical answer of how something thing will be addressed in the future, but there's a plan and a discussion around it and we're adapting with it.

So as long as that is in place and as long as we have the fluidity to adapt with it and ensure that we are always keeping ethics paramount and top of mind, which all of our founders do, then we feel comfortable and it's always an ongoing discussion.

[19:19] On more of the the overall broad go-to-market side, I mean, one that I think is particularly fascinating is just overall biases in data sets.

So human biases that are already rooted there. I mean, you could say recruiting is, of course, a big one.

We just hired a VP of talent, so recruiting is top of mind.

But when we have these kind of AI-driven recruitment tools, how are they really ensuring that candidates benefits.

Are vetted properly or vetted fairly because oftentimes the data that we're actually trading that AI on may have human biases rooted in it.

So I think that is one of the most interesting kind of use cases is understanding how to actually parse out or kind of remove the human bias from data sets.

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Andrew Miller:

[20:12] Yeah. You're speaking about one of my favorite topics in the world, you know, coming from behavioral economics as my master's uh getting into those biases and the the different like heuristics that people associate with like simple anchoring and like different things that are like placed in there uh such such a huge topic and we all know that code is written by people and i think i discussed this in another podcast episode and we do our best to not insert our own personal biases and that's why we have additional coders and everything that are brought into there to help look over quality assurance uh you know tweak the code but it still finds its way through there and you have to like continue to optimize and work on it especially when you train ai to start acting a certain way and then it like doubles down we've all seen earlier versions of chat you know gbt maybe like three uh where you start asking certain questions and training in a a certain way and it just goes on a weird journey that originally you wouldn't have expected is like uh i don't think that was the intentional you know code base behind this and and it still turns out that way so there's a lot of i think barriers or like safeguards that we need to put in place and what we'll learn as we continue to experiment with with these um you know these tools.

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[21:39] Not to go too much back on Kibse, but I'm sure that, I spoke with Hilary Coover a few weeks ago, and she's very heavily influenced in national security space and working with government contractors.

And with that, she did talk about different examples of video AI and crunching big data used by the military and other things like that.

I think that's a good starting point.

And I'm sure Kipsey's done their due diligence and they've looked into this, but that's a good model to look after. and of course, politically, you can argue yes or no about that.

But in general, that's probably one of the best models that we have there is they've been using data like this.

They do have national security protection and other things around there, but that at least gives us a glimpse into maybe some of the regulations and rules that have been put out there and how that can be applied into like the personal sector so that it doesn't fall into too many red zones.

But anybody who jumps into that space, it's amazing. It's super interesting.

And I'm sure they've spent their time doing that due diligence in there because you don't bring something to market like that without understanding all those comprehensive variables in there. So that's just really, really cool.

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Sophie Buonassisi:

[22:56] Definitely. I think it's these kind of use cases that, Impact, just non-go-to-market sectors, supply chain, construction, all fascinating use cases.

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Andrew Miller:

[23:11] Yeah, I think too many times we're in our lens.

I know I'm an example of that. Too many times I'm in just B2B tech sass, you know?

That's it. it. That's my mindset and the tooling and all the systems and processes around there that it's like, like you said, it's not a real, it's not in the real world, you know, it's these things that are all digital and we live in this digital world.

So having use cases that are actually in the day-to-day, you know, in real life is fascinating and something that I definitely want to dig into further because the applications there are so, so interesting.

Okay. So what would you say is is maybe a groundbreaking yet underutilized AI tech that you've come across?

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Sophie Buonassisi:

[23:59] Specific to go-to-market and personal use case.

I think there's incredible, incredible technology spanning across go-to-market right now.

New technology, but also AI being injected in existing products.

And that's a big part of the journey is, you know, don't under count what you already have as they're constantly introducing new technology and new different ways of incorporating AI.

So that's been one of my favorite use cases of existing products.

A new product, at least on the marketing side that I've been jumping into recently is called Simplified.

It's for video editing, but it's actually quite robust. So it extends far beyond there.

It's for overall content. I have, I will say, scratched the surface of the product in terms of adoption utilization, but planning to dive even further because it is quite robust.

So right now what we're using it for is video repurposing.

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[25:00] It's kind of like a Descript style, but with a few other abilities baked into it.

So you can actually do specific text grabs for videos, but you can also have AI introduce it. You can have your brand kits, of course, things like that.

But then it extends beyond video to AI blog writing, AI presentation makers, even social posting.

So everything is connected in one platform.

That's a very marketing specific example. I would say that, again, we are in the current stage of furthering our adoption of.

But overall, I would say there's a ton of just interesting overall applications in go-to-market and AI. eyes, so we'll definitely have to keep our eyes peeled for even more, though I will just reinforce that.

There's a lot of new AI introductions to existing products, too.

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Andrew Miller:

[25:56] Oh, for sure. For sure. I think, what was it? Like Process Street, I had Vinay on the show a week or two ago.

And one of the things was they didn't have AI built in originally.

You know, like the way that they built their product was not, I mean, it was process optimization, but it didn't have any kind of AI functionality built in.

But whenever it started getting more mass adopted, they looked at it, and then they They up-leveled a lot of the processes that they were able to build by having AI in there, basically build out briefs and other different kind of like scripts and, you know, just to power up the particular process.

One of the unique things, so that's to your point of like, it's being built into current products and just like taking them up to the next level.

A really interesting thing, and I wonder if you've seen this in other organizations, this goes to kind of the adoption side and ethical side. but we've seen in a lot of enterprise organizations, they have to go through like InfoSec.

They want to make sure that you have like SOC 2 compliance.

They want to make sure all this stuff, like their data is truly private.

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[27:03] The cool thing with Process Street, and I'm not selling Process Street here at all, but a cool thing about it was they have a function where you can just turn off the AI ability.

Now it's not like scraping your personal data anyways, but for those organizations that say, okay, yeah, we're going to have to take this.

To our CIO, we're going to have to go through all this, you know, compliance and everything.

If you just turn it off, they skip that whole thing and they're able to get adopted a lot quicker.

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[27:30] I haven't seen that anywhere else. Maybe, maybe there are other companies, maybe you've seen it.

I haven't worked with, you know, 150 plus companies, but I don't know.

Have you seen any kind of like barriers to entry because of that aspect of, is our data truly private?

You know, know, you have to go through all these regulations, just in conversations with your operators.

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Sophie Buonassisi:

[27:55] It's typically, I mean, it's always a conversation. It's always brought up.

But oftentimes, it's not anything that is going to create absolute red tape.

And that's typically because companies are so proactive of trying to approach it in the best manner possible and really trying to ensure that they are complying with all of the different regulations. So I'd say it is because companies are doing a good job of being proactive about it.

Of course, there's always ways we can improve, but we haven't really heard or seen a lot of just blanket red tape or running up against a brick wall situations.

More about, you know, this is going to take an extra step of diligence or an extra step of redlining and so forth.

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Andrew Miller:

[28:36] Okay. Yeah, makes sense. I guess back on the groundbreaking AI technology example, where do you think AI might have fallen short in maybe looking over your different AI experiences? Yeah.

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Sophie Buonassisi:

[28:53] AI has tremendous opportunity to aid in overall decision-making.

We touched on this briefly beforehand with the example of a sales call, right? But right now, our utilization is greatly focused on efficiency.

Even if we pull the kids example, it's how do you surface a dashboard, right?

How do you make better data-driven informed decisions? So we're surfacing these kind of data points, but that next step, and I don't I hesitate to say we've fallen short because I think we'll get there.

We just aren't quite there yet. I think we'll be quite eager to get there and fascinated when we do is how is AI actually surfacing the overall context so that we as humans can more easily and more quickly make those overall decisions as opposed to more of just overall workflow?

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Andrew Miller:

[29:47] Yeah, yeah. No, that makes a lot of sense. It's kind of removing that analysis paralysis that a lot of us marketers like to get into.

We just start going down that rabbit hole and we don't have the comprehensive picture because we can't analyze data, like you said, at the same speed or through all those different sources as an AI could, especially when it's programmed properly.

So it brings that to light and it gives you that clearer picture that you're like, okay, if I do want to double click on something and get more granular, I can, but this still is giving me a better picture than just I'm going down this one data set and spending days or weeks or months and I never made a decision and I still don't actually know the answer. So that makes a lot of sense. I like that.

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Sophie Buonassisi:

[30:33] Exactly. And I think we'll continue to see the consolidation of data points, like you said, in one place so that it is easier for AI to surface those kind of insights.

Like one of our portfolio companies, Pocus, for example, is really focused on doing this for sales.

And that was the whole premise when they got started. So one of their founders, like their founder and CEO, Alexa, she'd come off of building something where she saw this kind of shortfall and shortcoming around data accessibility.

So that was really the goal and objective of the company when it came to inception is how do we make data more accessible so that we can make those better decisions.

Now, when it was formed, AI wasn't necessarily the underpinning part of it, but now it's really transformed and having all the data points in one place enables the technology to help surface these kind of insights.

So we're seeing this happen across technologies.

We're seeing, especially PLG companies, because they've got so much different data points across product usage, everything.

They've got this kind of data. They have to be data-driven.

So I think those will be the early adopters of taking it to that next level.

But we are starting to see that in the products themselves.

And I'm excited to see the continuation of it.

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Andrew Miller:

[31:49] Definitely, definitely. And I'm a big fan of POCUS.

I think they coined a really unique term that I hadn't heard before I came across them.

Product-led growth is product-led sales. And so they take a different go-to-market positioning strategy. strategy.

Uh, and I think that really resonates because you're cutting through, you know, you're, you're showing the difference between the two, but also how they support each other.

So it was really, really smart how they've gone and like positioned and owned that, that term.

Um, how do you see, you know, the relationship between AI and human expertise?

And I know you you work with a lot of operators, but evolving over, you know, the next few years or so.

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Sophie Buonassisi:

[32:37] Few years?

Incredibly. Like I said, we should definitely have this conversation at the end of the year.

We will evolve at an incredibly rapid rate. But overall, I would say even nearer term than the next few years.

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[32:52] Because again, it will be much more rapid than we anticipate.

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[32:56] And we'll think of it, as you mentioned, product-led sales, a similar kind of perspective, how we have sales-led, we have sales-assisted PLG or product-led sales, we have full PLG.

Similarly with AI we've got human generated AI assisted and AI generated and they're very complementary and it's this kind of symbiotic relationship where we're able to work and leverage AI as a tool to support our innovation and our growth and so part of that may be that something is fully AI produced or again leaning in on the go-to-market side maybe it's AI assisted So for product-led sales or product-led growth in the product itself, at what stage are we measuring and understanding when somebody in the product needs that touch from the salesperson?

And they're actually complementary to each other. So we've realized they don't actually exist in isolation.

It's a spectrum as opposed to, I know I said buckets, but it is an overall spectrum.

And it's where do people fall in that spectrum?

Same thing with AI. So how much AI utilization do we need to blend with this specific objective?

And that is what I believe we will see our relationship grow towards, is understanding where we may fall for which specific tasks on that spectrum and really leaning into it to leverage AI to the best of our abilities.

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Andrew Miller:

[34:24] Absolutely. What would you say if we take a step back away from AI and we're just talking about, you know, Sophie now, what's a lesson that maybe you've learned in your career that you wish you knew earlier?

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Sophie Buonassisi:

[34:39] That I wish I knew earlier would be to step outside the bounds of what may be perceived as traditional growth or traditional education and get your hands dirty.

I think that's changed a lot and has changed now.

It's never been easier to get your hands dirty and build something and learn by building.

But if I were to kind of backpedal and look at myself early in my career that is what I wish I would have done earlier and I was always very curious for knowledge curious to learn but I was often taking a very traditional approach to doing so so burying my head in books and in articles and understanding the space from a very from a very kind of, perspective. Whereas the minute I started actually diving in, still taking courses, still reading, of course, but pairing that with actual hands-on application is when I really saw my career elevate and kind of take off my learnings.

And, you know, whether that's building, you know, just trying things like building a D2C product just to learn the production process, or I've joined an incubator, things like that, where you're actually learning by application.

Those are the most valuable. And if I were to rewind in my career, I would say, do that earlier.

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Andrew Miller:

[36:02] Yeah, yeah, no, no, that that's perfect, perfect advice.

And I think that's probably why you're you're in like the startup in tech space, because when you start doing things, that's where you see all of these opportunities.

And that's, that's also, it's hard to do everything or try a lot of things when you're at a massive enterprise where they already have their, you know, SOPs built out.

And this is what you do. And you just repeat kind of like doing that. that.

While at a startup, and you know this, you might be the only marketer on the team of like 50 people or something like that.

And it's, everybody looks to you for everything.

I had a really good conversation with Hannah Recker, who's, who's the growth lead at Coefficient. And that's, basically similar to what you said. She's like, the only way I really started learning was hands on, you know, doing it, being in the weeds, uh, and trying to like knock everything out.

And then you learn what you can, can do and what you like, you're not great at doing, but you can eventually get it done even though it takes longer.

Uh, but that's how you grow and you find that area that you're truly passionate about.

So, uh, I think those are are definitely words of wisdom that you shared.

Would you say there's any, for our listeners, any reading recommendations or I know you have podcasts or anything like that that you would recommend people should check out?

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Sophie Buonassisi:

[37:29] AI specific, everything is moving so quickly. So I find I gravitate more towards online sources.

So podcasts, just like this podcast, not to plug it, but it's true.

You're surfacing some really great insights.

Anyone groundbreaking in the AI space, I find companies are actually even publishing really helpful information.

Of course, it's from their lens, right? But really taking an overall education standpoint around AI as an overall industry is becoming much more common.

So anything that you can just follow along with in terms of online, if I'm the best for expediting AI, and then paired with probably understanding, and you could get at this from more traditional, you know, books and so forth.

But again, I've just leaned on online sources specifically for understanding the real inner workings of AI and machine learning.

Because if we peel back the layers to really understand what's happening, that's when you can really understand the different applications in different industries at a whole other depth.

And then if we step outside the bounds of AI specifically, one book I recently read that I found particularly interesting was the Innovation Stack by Jim McKelvey.

He's actually, he's the co-founder of Square.

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[38:46] And it's interesting because he reflects back on his whole journey of building Square.

It's not specific to Square.

So the book's actually overall about innovation, but he kind of pulls in examples from Square, just super interesting and his experience building it and what he was thinking about around innovation.

And the reason I bring it up is because, of course, I mean, Square, different kind of specific niche, but he's talking about innovation in such an overall, general, broad, interesting way, and even pulling examples of past banks, how they innovated, or how with the inception of Square, they even thought about innovating.

They saw this problem, small merchants didn't have access, and they actually entirely got all of these policies changed.

So they really went against the current to innovate and create what is now accessible to small merchants with that purpose and that drive behind it.

So I think from an overall innovation perspective, which AI is massively innovating, everything that we touch, that book is definitely a good read.

So that's the innovation stack.

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Andrew Miller:

[40:03] Innovation stack. Well, I think I've come across it and I've saved it as future reading, but I have not read it. So you just helped bump up that sense of urgency, like, oh, I definitely need to read it.

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Sophie Buonassisi:

[40:16] Definitely, definitely. I will admit, I did listen to it on Audible.

This wasn't actually one of my physical book reads, but it is fantastic on Audible. I highly recommend it.

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Andrew Miller:

[40:25] Awesome, awesome. I do most of my stuff on Audible as well. So that's perfect for me.

Now, I know you're in a unique position.

And so I usually ask people, you know, if they're the creators, you know, what's your moonshot AI project for the future?

I guess if we reposition this from your lens as a marketing operator, but also as having your finger on the pulse of all these operators and, you know, portfolio companies, you know, what do you think is maybe a moonshot project for the future that you as GTM Fund might be interested in?

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Sophie Buonassisi:

[41:05] We are certainly interested in anything that is revolutionizing the way that things are done.

So, for example, Armada is a company and less specific to AI forward, but they're essentially bringing full stack edge computing to these very, very remote areas that previously did not have that access.

And so they're entirely revolutionizing how things are done.

I think we're seeing that in different industries and in different ways.

Again, an example with the video recordings and actual supply chains and other industries that can benefit from that.

That's an example of how something is really being revolutionized or writing for enterprise across all of go to market.

All of these companies now can greatly expedite the way that they're producing content through companies like Writer. So it is not industry or it is not segment specific.

It's more what kind of impact are you looking to have?

How big are you going?

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Andrew Miller:

[42:21] Right, right. I love that. If our listeners want to follow you, what are the best ways to connect?

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Sophie Buonassisi:

[42:29] The best way to find me is on LinkedIn. Feel free to drop me a note. Love to hear from you.

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Andrew Miller:

[42:33] Perfect. And, I'll have those links at the bottom of the podcast so that everybody can follow you.

Very last question that I have. Are there any final words of wisdom that you would want to share with the audience before we close out? out?

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Sophie Buonassisi:

[42:49] From discomfort stems growth. It is the breeding ground for growth.

So if you feel nervous or uncomfortable with AI, whether it's the adoption, the growth, dig in more.

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Andrew Miller:

[43:03] Absolutely. Love it. Love it. Well, Sophie, thank you so much for being on the show today.

I know that I've taken tons of notes and I'm going to include that in the episode recap.

But thank you all for listening and we will catch you next time.

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Sophie Buonassisi:

[43:18] Thanks, everyone. Thanks, Andrew.

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