GTM Innovators: Building Smarter with AI – Tim Macchi on Automation, Research & Product Strategy

Posted on by

In this episode of GTM Innovators, we sit down with Tim Macchi, Growth Product Manager at TeamGantt, to explore how AI is reshaping the way we build and optimize products.

From using AI agents for QA testing to accelerating market research and streamlining product analytics, Tim shares his hands-on experience with automation and AI-powered workflows. We dive into how product managers can leverage AI to work smarter, reduce inefficiencies, and unlock new growth opportunities.

Whether you’re a product leader, marketer, or innovator looking to stay ahead of the AI revolution, this conversation is packed with insights you won’t want to miss!

Subscript to GMT Innovators Series on the following platforms:

Transcript:

Kyle James 00:00
Kyle, welcome to another episode of GTM innovators by 3Sixty Insights. I’m your host, Kyle James, and today we’re diving into all sorts of AI stuff from a product management perspective with my guest, Tim Macchi, Tim, welcome to the show. Thanks

Tim Macchi 00:22
for having me. Kyle, looking forward to the conversation. Yeah, absolutely so.

Kyle James 00:27
So Tim is currently the the growth Product Manager at TeamGantt, where he’s been for gosh, Tim, you’ve been there almost a decade now before spending time at Dynatrace, smart bear and HubSpot, among other places. And you and I’ve known each other forever, from our time back at HubSpot, gosh, almost 15 years ago. Now, time flies does. We’re getting old, buddy, and you’re a huge tinker. You built some small e commerce businesses on the side, and you’re doing a whole bunch of building of AI agents right now that I personally find super fascinating, and I can’t wait to kind of dive into some of dive into some of the stuff you’re playing there and learning and figuring out. Tim, welcome to the show. Any, any more of your background that you want to fill in that I might have missed, or you want to kind of tell the audience here?

Tim Macchi 01:13
No, I think you did a good job. Yeah, it kind of covers, covers the whole the whole gamut, the whole span. So,

Kyle James 01:20
awesome, awesome. So I know, like, I’ll just go ahead and start here. Something that you and I have been talking about on the side is kind of some of the stuff that you’re doing from kind of the growth product manager side, about how you’re thinking about tying AI into some of your work. And you were telling me how you’re doing using kind of AI agents and stuff to like do QA testing now that’s fascinating. Talk, talk, tell us, tell us a little bit more about that.

Tim Macchi 01:45
Yeah, so it’s not really, not really being used yet, because it was really just tinkering around with the the new open AI operator, just trying to think of, you know, things that it could do right now. And really, really came up with the idea, because I didn’t want to be putting any of my passwords into it just yet. So I was like, what else can I do with this right now to just see how, how well it works? And one of the ideas I had is, you know, as a product manager that’s focused on on growth and data onboarding is a major focus. So I was like, alright, well, let’s see what this thing can do if we have it go through team Gantt onboarding. And I kind of already knew some of the things that you know needed to be tweaked and needed to be fixed, but I wanted to see what the AI said. So I just kind of gave it the instructions to go to Team Gant, sign up for a new account, sign up to putting my own passwords in and and then go through the onboarding and go through it as a user and report back on on the user experience, and any areas that you know seem like they might need some improvement. And, yeah, I was blown away for like, a early release product. It really is still early release, you know, it’s, there’s a lot of things that I think that are going to improve with it, but it pretty much inhaled it, man, like it, it, it hit all the points on the onboarding that that we, we’ve been talking about, that we want to fix. So that was pretty impressive.

Kyle James 03:13
So let’s go, let’s, let’s double click on this, right? Because what we’ve heard a lot of people say in the AI space is we’ve hit a place where, what is the right word I’m looking for, like, artificial. We can start generating artificial data, or, or what’s the word, not artificial, but second hand data from the engines are now putting out for themselves. Kind of synthetic data, synthetic data. There you go. Like, so basically, what you’re talking about is you’re doing, instead of paying for, you know, to watch people try to QA and user test your product, you’re able to use AI to, like, generate that synthetic data for you. Is that kind of a good way to think about this?

Tim Macchi 03:55
Yeah, yeah. I think, you know, it’s, it’s definitely a proxy in general, like, I think a good, a good kind of general rule that I’ve found is that AI seems to be really good at kind of emulating market research data, customer data, Uh, feedback data. So like another area that I’ve been exploring, using AI for is kind of just synthesizing market research data. So one of the big obviously, a lot of people might be familiar with jobs to be done, and there’s a flavor of jobs to be done called outcome driven innovation, or ODI, and even the ODI people have been playing around with this, because what they’re what they’re finding is there’s, you know, there’s steps in the ODI process where you have to kind of do a lot of, do a lot of phone calls, do a lot of interviews, and find out, you know, drill down and find out what the unmet needs of user. Are, and the whole, the whole point of that process is to de risk innovation, right? So make sure you’re not putting your ladder on the wrong wall. You’re not building the wrong thing. Um, and it’s really awesome process. I think the ODI version of jobs to be done in particular is, like, really fascinating, but it’s very intense. It’s very involved. There’s a lot of steps. It’s very expensive to do. So it’s really interesting to see how it’s evolving, and how good AI is at acting as a proxy for some of that. So if you want to do a lightweight version of it, obviously, if you want to do like the best version of it, you know, hire, hire the ODI guys or or, you know, take the course. Learn how to do it yourself. But if you’re, if you’re, if you’re looking just for, you know, some, some useful version of that that AI can do a lot of the steps really well. So, you know, doing the initial kind of collection of what the unmet needs might be of a particular persona I’ve found, like having done both the full process and then seeing how a like, what AI comes up with. It’s, it’s mind blowing. How close it can get.

Kyle James 06:11
Well, I’m sure, right? Because personas are like fictitious versions of a of a synthetic, real person. Anyway, right? Like, it’s a soccer mom that likes to do these activities, which is kind of an artificial creation anyway, so it makes sense that they’d be able to, kind of like, lump that bucket together in that cohort. So if I’m pros and con in this out right? Like, the Pro is obviously super fast iterations, cheap. You mentioned that, kind of like, because you could do this fast and keep the cost down. But like, the fear that I just comes to my, where my, you know, my, my antennas are kind of going off, my spider sense is, like, Do you worry that we’re not getting some of the authenticity of it right? Like, because it’s, it’s replicating a human, and I know I just basically Pro that already. But like, because you’re not actually getting some of that. Are you missing some of those, maybe stereotypes or or devil in the details that we might not find out because we’re kind of like, yeah, pulling that, yeah,

Tim Macchi 07:12
I That’s a good point, like I don’t see, and maybe this will change in the future. I’m not, I’m not super, I’m not as super convinced that it will, but like that AI is going to completely replace the human aspect of things. And I think it’s important, too, to realize the shortfall of the technology, and, you know, use it in the right way. So like, one of the things that I kind of preach is AI shouldn’t be used as a replacement for your process. It should be used to augment your process, and that’s when you get the best results. So whether you’re like trying to, like, create blog posts or, you know, whatever you’re trying to do, rather than looking to have ai do a one shot replacement for you, and then you can just go, like, you know, go do something, go to the beach or something. It’s just really not there yet. And I think a lot of people can get into trouble if they’re, you know, or they’re just, the results are not going to be great if they’re looking to use AI that way. But if you, if you, if you use AI, and this goes for the tool builders too. Like, I’m seeing a lot of AI tools be built, and everyone’s kind of going for that one button click generation, you know, like, we’ve all seen those tools where it’s like, you know, build a website, just to give us a brief prompt, and we’ll, we’ll build it for you, right? I think people ultimately, it’s, like, kind of a GET. We’re kind of in the gimmicky phase of the technology still. And I think the people that are going to win, an example that I like to use is like real loom. They’re a technology for helping you build websites with with AI. And the approach they take, I think, is a much better approach, where the human is is really in the loop the whole time. And the way they do it is, rather than taking the one click generation approach, they break down the human workflow. Like, what does a good website designer do? What are all the steps that they take to build a good website? Yeah, and they take you through each of those steps, and AI augments every step of the way. So the first thing you do is it helps you generate a site map so you put in a prompt for the website you want to build. Want to build. It helps you build a site map. You then talk with it, modify it, you know, to improve it if it’s not what you want. And then the next step is it builds out the pages on the on the on the site map, into into wire frames, you know. And then you go through that, and you you augment the wire frames, the copy and all that stuff. So it’s kind of taking you through the workflow process, and the AI is kind of augmenting you as you go. And I think those are the workflows and the tools that are going to win out after kind of, like the hype cycle and the kind of the novelty phase interesting wears off.

Kyle James 09:57
So let me, let me pick like, what I think. I heard you said, and you tell me if this is kind of accurate or not, is you really have to be you have to know what you want when you’re kind of going in and using these tools, and you have to be very explicit and detailed in that entire prompt to get a good result. Is that, is that kind of like a good way to think about that? Yeah, I

Tim Macchi 10:19
think right now that’s where we’re at, like, yeah, I will say, though, the other thing that I like about AI is, you, you don’t necessarily have to come into it with that domain expertise, but you have to at least be curious and want to learn it first before you try. And AI is really good at that as well. So sometimes, for things that, like, for instance, I’ve built multiple applications, like mobile applications, web applications, with AI, I came into it, not knowing anything about those spaces, but one of the first things I did is I spent a lot of time with the, you know, with chat, GPT, with Claude, just asking it questions, trying to figure out, like, get, kind of get my bearings, so I understood, like, how these things work, before I started trying to work with it to build those things so you can still kind of, like, do things that you might not have domain expertise. If you have domain expertise, you can jump right into these tools and have them augment your workflow and get you know, double, triple, quadruple your productivity. But if there’s a new area that you’ve interested you’ve always been itching to try to, like, learn more about AI can really help you there, too. And then, you know, get you to that next step of once you know a little bit more, you can use AI to help you through that. So

Kyle James 11:30
let’s, let’s, like, try to give example that, because, like, what I’m hearing you say is, like, from a go to market perspective, if you’re trying to, like, do the initial research on stuff, this is super powerful, because you could say hey, or who are the buyers of this kind of product? You know, play the pros and cons, or devils advocate. Out of this is a good go to market product. What’s the TAM? What is the pivot? Like, you could iterate through all that, like, in hours instead of months, years? Is that? Is that kind of,

Tim Macchi 11:57
yeah, yeah. I think, I think you can, and you just, you know, you just gotta, you gotta decide how critical the fidelity of the data is, right? So if you really need to ensure that the fidelity did is good, you’d want to back up those findings with some actual, like, feet on the ground market research. But you can get really far and get pretty, pretty good fidelity right now, with AI, it’s

Kyle James 12:23
interesting, and I think that, yeah, I think a lot we just probably need to spend more time, like, doing that research. And it does. I’ve noticed you could easily, like, put a finger on a best guess, or, like, show me your math process about how you came up with this estimate, right whether it’s, you know, we’re on a podcast. It’s like, alright, what is the, what is, what is a good podcast, total audience, you know, like, how fast can you expect to grow things? You know, what are the different ways to measure analytics and stuff? And that’s just some of the stuff I’ve been playing around with recently. But it really is good for doing maybe that research that is not your standard stuff. Yeah, yeah. I

Tim Macchi 13:01
definitely, I definitely think so. I think that if, if people are kind of not, really haven’t integrated AI into their workflow yet, I would just encourage them to experiment with different models, because they’re all good at different things, right? Like certain ones are better at, you know, providing references. And, you know, there’s different strengths and weaknesses that each one has, and the only way you can, I mean, you can, you can look it up, but I think it also helps to just play around with them and see what the output is like. You know, there’s a lot of tools out there. You don’t necessarily have to sign up for all the different platforms. There’s a lot of tools out there that allow you to kind of, kind of experiment with different different ones. So there’s, you know, some some tools that allow you to chat with a lot of different models and kind of just compare the output. I mean, Agent AI is one of those. They they definitely allow you to do that. So, yeah, I would just encourage people to, like, get out there, experiment with the different, the different models for whatever they’re trying to do, and kind of figure out which ones are are better for, you know, writing blog posts, or which ones are better for doing market research, for, you know, go to market research, so they can really start to understand, kind of get, get those tools in their tool belt? Well, alright, let’s

Kyle James 14:21
double click into that, because I think that’s interesting. I think a lot of people are probably at that barrier, right? Like, because you have Claude out there and you’ve got perplexity, and you’ve got chat GPT, and you’ve got Gemini, and that’s just four right? Like, how are you making your decision about what to do with each platform, right? And, like, what are some of the things that you’re doing, you know, at Team Gant, in your roles, kind of with these and like, like, how do you draw the line, right? Like, or do you kind of use a little bit of all of them? Or do you have special preferences? Or do you think some are better at some things and not like, like, What is your thought process there now, are you, are you breaking that out?

Tim Macchi 14:58
Yeah. So I think it’s just constantly experimenting. So I personally, I spend a lot of my own personal budget on AI tools. I think my AI, I think that my monthly spend on AI right now is, like, probably over $400 a month. Oh, wow, yeah. So, I mean, I use one of the ones that I’m using the most right now is, is GPT Pro, because the deep research is just awesome, like it’s really good. And the reason it’s really good, especially for stuff like market research, is that it combines searching with chain of thought, and so it’s able to search through multiple sources and then and then do chain of thought reasoning, which basically is just kind of like the LLM kind of just checking its own work and making sure that it’s achieving the goal that you gave it. You have to wait a little bit longer, but I just like when I, you know when I need something for work, or even just in my personal life, I’m a pretty curious person, so I’ll, I’ll just, I’ll just give it to the deep research, and then, you know, go do, go on with my work, and then come back to it a few minutes later to check, yeah. So, like, I think that Claude is the best for for code, still, surprisingly, like, sonnet is still the best. And it’s kind of crazy because they haven’t really updated it, um,

Kyle James 16:25
and you have, like, no experience or background coding, right? And you’re using this thing to, like, build application and code for you, right? Yeah.

Tim Macchi 16:31
I mean, I’d say I was probably like me and you were probably on similar level, like, I can, I can read and understand CSS, HTML and JavaScript, but if you gave me, give me a blank page, and I can’t, like, I can’t start building page, but, but, yeah, so I’m able to pretty much build anything I’ve tried, like, any ideas that come to my head, and, you know, I’ve tried all the different tools right now. I prefer cursor for coding, but if you’re not super experienced, there’s tools like lovable, and this is great too, in terms of go to market, right? So, like, you know, one of the big things that’s still big with, obviously, SEO, is changing. Everyone’s talking about, you know, LLM optimization instead of SEO optimization. And it’s kind of like we’re a little bit in the fog of war right now with it. I think, you know, people have ideas, but they’re not super flushed out. But one of the things that still seems to be working pretty well is, you know, the the old HubSpot trick with marketing greater like building, building these kind of micro apps as marketing vehicles, right? So this is kind of important, even if, even if you’re like a marketer, or somebody that’s trying to go to market, you maybe you don’t, maybe you don’t, maybe you have a developer, but they’re really focused on your core product. You can build these types. I mean, this is, this is it can. You can really build complex products with AI encoding. But these kind of simple, micro marketing apps are really later

Kyle James 17:57
and little apps that feel, feel a little, yeah, you can,

Tim Macchi 18:01
you can output one of those in a couple hours. And if, if you don’t want to use cursor, because cursor is a little bit more like, you gotta know a little bit about what you’re doing. You gotta know a little bit. And I’ve only gotten there just because I’ve been the more, the more you use it, the more you kind of, it’s almost like, just through osmosis, like, become a little bit of a developer. But there’s tools like lovable and bolt dot new and what’s the other one? I can’t remember right now, replet, the replica agent, okay, that are really good for for people that don’t really know anything about COVID, they just know what they want to build, and they can just type, you know, type in a prompt, and it brings them through kind of the process, and then they can iterate from there. So that’s another, I think, area where AI can really help with, specifically with like marketing and go to market.

Kyle James 18:49
So, so that’s super interesting, right? Like, from a product marketer, product management perspective, too, right? Like, instead of writing like a traditional product requirements doc, you’re almost able to, like, prototype up for an engineer some of the stuff that maybe you want them to, like, really flesh out and stand up architecture for build. Like, yeah, it seems like what you’re saying is like, and so my product is, like, spinning to like, this is how you turbo charge and empower product people to go to new levels, just for some of these things that we couldn’t do before because we weren’t full time engineers.

Tim Macchi 19:25
Yeah, I’ve started actually submitting code to TeamGantt, like, in the last couple weeks, because, you know, our developers are like any other company, right? It’s like, I don’t know if you’ve noticed this, but you might hire a developer to just focus on marketing, right? Or you might hire a developer to just focus on on growth or onboarding, but inevitably, what happens is they end up getting cannibalized, because there’s just always a hunger for more engineers in the core product, right? So one of the things I’ve started doing is just, you know, I I. Asked and I and I got and I did some pairing with our developer to understand our process and make sure that, you know, as I’m building things in the app, that I’m doing them with the right process. And that’s super important. But the things that are now accessible to a product manager, that’s somewhat technical, right? Is you can, you can update your onboarding. You don’t have to, you know, you don’t have to ask for a developer to help and that. So like, the areas that I’m focused on right now in terms of submitting code are the areas that I’m responsible for from a product management point of view. So like growth, which includes, like onboarding and integrations. So one of the things I did was, you know, integrations are very hard. I think you know, any, any company that has to build integrations for their app, and have has tried to do it, will will attest to the pain, and it’s also something that, from a developer’s point of view, is, is somewhat like, you have to learn some of these new platforms that that might it’s not necessarily like a transferable skill, right? So, like you work on this, you might have to learn, like some something for JIRA that you’re not going to really use again, other than just to maintain and update that integration. So just through AI and, like, kind of asking it questions. I found these platforms. One of them is, is nango dot dev that makes it really easy to build integrations. And, you know, as a product manager, I can do most of the work, you know, and then and then and then. All we really need is, you know, a little bit of front end help, to help connect the front end, and now we can, you know, 5x our integration output. So, yeah, you’re totally right. Like,

Kyle James 21:33
so it’s doing a lot of that solution, architecting and field mapping and stuff for you in a way that it’s just handing off the engineer to, like, all right, build your workbooks, yeah.

Tim Macchi 21:43
And it kind of fills that gap, you know, the gap between product manager and engineering sometimes is, like, we don’t know how hard this is to build, you know. And so you can get a lot more intelligent about, you know, your specs and what you’re what you’re kind of putting on the developers plate by understanding a little bit more about what’s happening under the hood and what it takes to get kind of some of that work done. So I, I think, I think, honestly, like, that’s the future of Product Management personally. Like, I know there’s a couple other product thought leaders on LinkedIn that have had a similar hot take, and I’m totally in agreement that with AI, there’s going to be a little bit more of a of a blending of the edges in the product management role, and you’re going to be, I think it’s not going to happen tomorrow, but I think you are going to be expected to know a little more technically or be a little more technically capable. Yeah, it’s

Kyle James 22:37
probably not even coding. I mean, you can have the tools code for you, but is, understand what you want to get coded and why? Like, yeah, yeah. That makes a total that makes a bunch of sense. What else? What? What else are you? What else? How else are you? Kind of, like optimizing and like bringing in stuff to, you know, optimizing, bringing this AI co pilot for product role, like, besides, I mean, clearly, we’ve talked about research, we’ve talked about prototyping, we’ve talked about we’ve talked about what about analytics? How are you like? We’ve talked a lot about the qualitative, I guess, qualitative. What about the quantitative, the numbers and data stuff? Are you using any of that to think about how you aggregate up whatever you’re measuring, or what you’re able to pull and look for new insights that maybe you might not think for directly. Yeah, so

Tim Macchi 23:25
that’s like an area that I’m I’m trying to focus more on right now, really, in my job, we’ve really used amplitude for analytics, but now with AI, you know, it opens up, I know, a little bit of SQL. But it opens up even for a smaller company like TeamGantt, we’re small in terms of employees, right? It opens up like the ability for a product manager to be a data scientist. So one of the things that we’re trying to do right now is, you know, you know, spin up a meta base, or spin up some, some sort of access to to our database. So we can start doing some some data pipeline analysis and some deeper analysis of things that can be hard to do right now when you have all these data silos. So not an area that I have done a ton of work on, but it’s an area that we’re kind of, we’re kind of looking at right now. I think, like, one area that I’ve thought a lot about in terms of product is, I think a lot of things are broken right now, specifically with how we how we deal with gather user feedback, yeah, so like the Current so the there’s a lot of problems, right? So, like, the current landscape is you don’t really gather much data on people in terms of, like, qualitative data. And by the way, with AI, it’s a lot easier to turn qualitative data into quantitative data. Yes, um, it’s really good at that, but there’s. Is, we’re only really getting data on users after they, like, come into the application for the most part, right? And then they might use the tool, and we’re trying, and then we try to do things like put a feedback button or put a, you know, feedback link somewhere in the app, and they click that, and maybe they’ll get to a, you know, like a canny or something like that, and and, you know, submit their feedback, or be asked to, like, Look search for their feedback, to see if it already exists, and then upvote it. And like, we’re asking the user to do a lot of work, and the quality of the data that we’re getting is not as good as it could be, and it puts a lot of work on us too. So we’re asking the user to do a lot of work, and then we’re asking the product managers to do a lot of work in terms of, like, merging that feedback, kind of reviewing it, doing all that stuff that can be a lot of work, a lot

Kyle James 25:48
of work, and it’s all manual, yeah, yeah. And it takes a lot of time, takes a

Tim Macchi 25:53
lot of time, and then inevitably, you’re still left with questions, right? So like, I think one of the things that we’re going to see is AI is going to do a lot better job of gathering that data, normalizing it, and doing it in a way that’s easier for the user and easier for the product manager. So you know, a lot of the a lot of the detail digging can be done by the AI to get to the root of what the user really wants. And then the merging can be done by the AI. And then, you know, really, all you need is the product manager going spot check and kind of, you know, run an analysis on, you know, what people are looking for when they’re looking to build a new product. And then, you know, reach out to those users that all kind of had similar cohorts. But I don’t think, like, I think the whole model of like, having this public website where you have all your feature requests open for every all your competitors to see, and you’re asking users to upvote, it was a cool trend, but I think that’s going to go away. And I’m actually, like, in my free time, working on building something that, like, I want to use for TeamGantt, you know, so I’m working on something. So why do you think it’s

Kyle James 27:01
going away? Because it’s you’re sharing that competitive intelligence, or a lot of reasons, your dirty laundry and warts to your competition. I think it was the

Tim Macchi 27:10
best way to do things before AI, but it’s one of those things that, like, I think has to be completely rethump A rethought in the in the age of AI, because, because of those reasons, because I think it requires a lot of work on the user. We’re not getting as much feedback as we could get, it’s not as good as we could get, and it’s a lot of work on the user and a lot of work on the pm to gain insight from it. And then I think the icing on the cake is that, like, yeah, you are kind of just exposed, exposing everything to your competitors. Like, if I wanted to compete with a with a product today, one of the first things I would do would be go to go to their candy board, or go to their, you know, their product feedback board, and see, like, what are the highest requests for users that they just haven’t gotten to in a really long time? Yeah. And then solve that, build a solution that sells that for cheap, yeah. Yeah. So I think, like that’s a huge innovation. The other part of that is we’re not, we’re not really get gathering data on users that might not make it to our products. So everyone’s got, if you’re if you have a product company, if you have a software product company, you have a website, you have people coming to the website. Some of those people might sign up for a trial or sign up for an account, whatever it is, right? But you have probably a much larger share, usually, of people that just don’t do that right? And so I think AI is going to be used in a similar way to find out what are those people looking for and why? Why aren’t we fulfilling what they need? So I think it’s about kind of like conversational feedback is going to be the as a general trend across all these things that we’ve discussed, for product and for marketing, conversational feedback, I think is going to be huge. And I don’t see anyone doing it yet, but I think there’s going to be people doing it soon, and maybe there are, I just haven’t found them,

Kyle James 28:57
and let’s be honest, like that is something AI is already really good at right. Like, pull transcripts, summarize, you know, give me feedback, tell me the action points, and you can go listen to hundreds of sales calls you might have had in the last week or month. And, like, what are the trends? What are the things that people are complaining about a lot? Let’s fix those. Yep, that’s really easy to do now, as long as you have the pipes and the infrastructure set up to collect all that and you’re recording all of that, it’s not hard, Yep,

Tim Macchi 29:27
yeah. Like another perfect dovetail off of that Kyle is like, I think you have all of this really valuable data locked up in these conversations that salespeople are having with your customers, that support people are having with your customers, right? What if you could aggregate all of all of those transcripts and use it to generate social posts, use it to generate blog posts for your brand? I mean, that’s all possible now. And I think, like one of the huge general trend that I hear a lot of people saying, and it’s totally true, is like. So we just haven’t caught up to what AI is capable of. We don’t need AGI like, we’re still trying to wrap our heads around like, what’s possible now. Yeah, with, with, with where AI currently is today, and, I mean, obviously it’s accelerating every day.

Kyle James 30:17
So what do you do with that? Because I guess I’ve heard kind of a couple different schools of thought, right? Like, one, it’s going to leap forward again, and so maybe it doesn’t make sense to fully commit where it is now. Or two, more of like, we need to get our infrastructure in place to be collecting all this data and synthesizing it, because the outputs and things that we can do with the future are only going to be better the more data that we data that we’ve collected. Like, how are you thinking about that, and what are you doing on kind of that, that respect? Yeah,

Tim Macchi 30:47
I’m totally I know what you’re talking about. Like, there’s two camps of people, right? There’s the people that are like, and I’ve

Kyle James 30:52
heard this, and there’s a whole other camp that, like, we don’t want anything to do with it. We’re trying to keep our distance. And that’s that, that seems like a death sentence long term, but, but I get while they whether, while they’re like that?

Tim Macchi 31:03
Yeah, it’s just like every, every innovation curve, right? Like you have your early adopters, your early majority, your laggards, right? And I think there’s, it’s no different here. I think it’s a little there is a little bit different of an element here that we haven’t really seen with other innovations, though, where you have a group of people that they might normally be people that would be willing to try new things, and you you tapped into this, but they’re like, well, everything’s moving so fast, I don’t want to dive in until, like, it settles, you know, because, like, if I start learning now, then, you know, and I can’t keep up with it in a week, that’ll be more advanced than what I’m doing now will be kind of obsolete. And I don’t think that that’s the right approach. Personally, I think that the best thing you can be doing right now is just diving in and learning as much as you can about AI as it stands today, and how it can help you in whatever you’re doing, whatever your role is, because there is some regardless of what your role is or what you’re doing or what your seniority level is, there is something that it can it can accelerate your your workflow, it can accelerate your productivity. And so I would say, why not, right? Like, if there’s benefit in it now, why not? And it will also help you, kind of innately stay up to date with the innovation if you’re in it. You know what I mean, like, if you’re waiting, first of all, I don’t, I don’t think it’s going to slow down anytime soon, but if you’re waiting, I think you’re going to kind of, yeah, I think you’re going to kind of get left behind, honestly. I think, you know, obviously, there’s a huge discussion about AI is going to replace a lot of people’s jobs, and I think it will, it will shrink, you know, it will shrink the job pool for some roles, but for the foreseeable future, I think the people that are going to be winning those jobs are the people that are the most skilled with The with the AI tools that they have simply because they’re going to be more productive. So if you have, if you have exponential more, exponentially more productivity coming out of a role, you just don’t need as many of those people in the role. Yeah, and, you know, that’s a whole nother discussion. I i just think, like, you know, it’s unfortunate. There’s other there’s, you know, other opportunities for people.

Kyle James 33:25
But history, I kind of look at history and think, you know what happened in the industrial revolution, everybody started working in factories. We saw all these farming jobs go away. They were replacing factories. What happened in the Mad mid age when, you know, they had every single person in a company had a secretary those days right then we got tools and automation, and half those jobs went away, and we found other more productive, valuable things for people to do. And you just have to feel like that will happen again. It might take some time to work out, but like that has to come. You know, who knows what form that’ll be, and maybe that’ll be a lot more artisan, creative, custom things where, sure, you can have ai do something cookie cutter, but if you want something really pristine, you know, nice, you still have human artisans come and do it. And they could put more time into it ways they wouldn’t. I don’t know, like

Tim Macchi 34:16
a lot of a lot of barriers are lower, right? So, like, the average person now can do things that they couldn’t have done before, without, without a lot of without a lot of education or time. So I think that’s going to just continue to accelerate. So like, a good example, I keep focusing on it because it’s like, what I’m thinking about a lot is like building building applications or or AI agents, right? So it used to be the case, right, that you you would have to raise money, you would have to have a lot of experience, you’d either have to be a developer or recruit a developer. But all of that now is like, I think what’s going to happen is that’s still going to be the case. There’s still going to be venture money for. You know, for software and for technology, but it’s going to go up the ladder in terms of its technical, you know, it’s, it’s how technical it is, how difficult it is to execute. It’s going to open doors for us to do things that are more difficult. And that’s where that money is going to go. But for the for the rest of the tools that were previously, you know, in that category, they’re becoming open to everybody. So I think we’re going to see kind of this, like, revolution of bespoke software happen, where these niches are going to open up that maybe weren’t economical. It just didn’t make sense for someone to go and try to raise money for it, because the, you know, the total addressable market wasn’t big enough, or, like, all these other things, and like, now all that math has changed. And so my point is, is that, like, there’s opportunities that are shrinking, but there’s new opportunities that are opening as well. And so that’s another reason to be kind of, you know, paying attention and to be learning about this stuff, is like, you’re able to spot a lot of these things a lot sooner and maybe end up doing something that’s more aligned with your passions, you know, and empowered by AI than than you would have done otherwise. So I like, I’m not a, I’m not like a, I’m not like a negative person when it comes to AI, I’m, you know, I think I’m like everyone else where I’m like, there’s kind of a a certain percentage that’s like, super excited and energized by it. There’s also a portion that’s also a little bit scared. I think that’s normal, but on the whole, it’s more on, like, the energized and excited part,

Kyle James 36:27
yeah, yeah, I tend to agree with that. Like, there’s definitely some scary stuff that could come but, you know, it’s, it’s, you got to look at the half glass full, half empty, and, like, weigh it out. Well, I want to be mindful of your time, but I do want to ask kind of one final question In closing, because you kind of teased on it. Like, how do you see AI changing product in the future? Like, how do you see this evolving and, like, what a future products do? Like, I don’t know, open ended, run with it. What like?

Tim Macchi 36:59
I think, I think we’re going to see less people running into running their head into the walls, building things that nobody wants. I think that we’re going to see more people building useful things for people. And I think we’re going to see more useful products in niches that previously, like I said, might not have been made economic sense to go after. I think we’re going to have kind of, like, a, you know, an explosion of really useful products, and I think that people are going to be more empowered, you know, part, part of going to market, it really comes down to building the right thing. Like, you know, that that job is going to be a lot harder if you’re not building the right thing, and so, you know, making sure that you’re starting off with market research, you know, enhanced with AI, and then also using AI to help you brainstorm your go to market strategy. I predict that product development, and product developers in particular, our product managers have the necessary tool set right now to build amazing things, so I think it’s an exciting time.

Kyle James 38:14
Love it awesome. Well, Tim, thanks so much for joining us today. This is incredibly insightful, and, you know, really has, hopefully as many people out there spinning and their wheels circled about interesting things they could do, and now they can, like, just get out there and start playing with it. So let me ask you, like, how could people find you? How can they help you? You know, what are your lifelines online, and what are you up to? And how could people be of service to you out there?

Tim Macchi 38:40
Yeah, yeah. I think, you know, I wasn’t actually prepared for this, but we can put it in the show notes. You could follow me on Twitter, I don’t know the handle off top of my head, LinkedIn, and, you know, I will plug my employer team, Gantt, if you guys are looking for project management software, it’s Gant based, so it’s really good for, you know, more complex type projects that have a lot of moving parts, maybe, like the asanas and the Mondays just don’t seem to scratch that itch. I’d say, go check out team Gant. It’s great, great project management software. And, yeah, I’m working on all sorts of things, you know, on on the side, and I’ll be posting those things on LinkedIn and Twitter for really focused on, you know, for product people and just for tinkerers and AI tools. So

Kyle James 39:25
love it. Love it. Awesome. Well, everybody you know, if you enjoyed this episode, please like subscribe, give us five star review. If you don’t want to give it a five star review, don’t give it a review. And if you’ve got a guest or a topic suggestion you know, feel free to email me at research, at 3Sixty Insights.com Once again, everybody, thank you for tuning in, and we’ll be back next week with another go to market innovators. And until then, keep growing. Everybody. You.

Share your comments: