Ep 62: Hidden Pitfalls of Productivity Hacks & The Future of Work
Watch the YouTube video version above or listen to the podcast below!
Episode Summary
Dave Dougherty and Alex Pokorny open by asking whether we’re too enamored with the upside of productivity hacks while ignoring hidden costs. Alex frames automation as a “stack” you end up maintaining—dashboards, reports, GPTs—each one creating recurring admin, training, and break-fix loops that boomerang back to the builder (support requests, permissions, upkeep). The time you “saved” now has a monthly maintenance tax.
Dave offers a candid case study: trying to automate podcast production with Make.com—updating calls-to-action across 50+ back episodes, wrangling YouTube descriptions, and bumping into character/rate limits and storage ceilings. After considering the costs of building custom workflows versus buying tools, he chose a purpose-built social scheduler. Lesson: don’t recreate an entire SaaS product for parity.
Pivoting to skill-building, Alex argues kids (and adults) benefit more from understanding how data flows and systems fit together than from grinding syntax—especially with modern code assistants. Conceptual literacy lets you troubleshoot, deploy, and make use of generated code in the real world (domains, hosting, assets, deployment pipelines).
On AI at work, they unpack the overwhelm: new power, but real setup friction. They champion hitting the “first dip”—the sobering moment after initial excitement when you discover the true effort, integration complexity, and ongoing care-and-feeding required. That dip helps you decide whether to buy, build, or bail.
Zooming out, Dave flags IP and legal pitfalls when agencies or vendors use AI—if you need clean copyright or exclusivity, agreements must state what processes can or can’t be AI-generated. Alex adds that “AI as strategy” isn’t a moat—everyone has access. Moats come from brand, distribution, data, trust, or niche execution.
They examine side hustles and platform risk, contrasting fleeting, commodity “AI gigs” with tangible, scoped niches—like “Mike’s weekend construction”—that solve small jobs big firms can’t price efficiently.
Switching gears, they discuss four-day workweek experiments and why output beats hours, with caveats: consultative roles and heavy cross-team scheduling complicate compressed weeks. Good management, fewer meetings, and clear priorities matter more than rigid hour-counting. Dave shares how he sets aggressive annual targets, manages expectations, and shields time by asking how meetings map to outcomes.
Finally, they explore organizational reality: goals whipsawed by reorgs and shifting priorities (the “garbage can model”), plus Dave’s “lava lamp” metaphor for personality clusters rising and falling. The macro market, they note, can overshadow even the best execution—so be pragmatic, keep learning, and don’t worship dashboards without context. They close by urging listeners to seek real experience over anecdotes and to avoid the to-do list trap.
Ep 62: Hidden Pitfalls of Productivity Hacks & The Future of Work Podcast and Video Transcript
[Disclaimer: This transcription was written by AI using a tool called Descript, and has not been edited for content.]
Dave Dougherty: All right. Welcome to the latest episode of Enterprising Minds. You have Alex and Dave here with you this week. Hope you're all doing wonderfully. Alex and I, we're going to talk about the hidden dangers of productivity hacks and everything else that people seem to be obsessed about. Are we too focused on the upside?
Probably. So now that we have the TLDR it's a downer episode.
Alex Pokorny: Sorry, people.
Dave Dougherty: Yeah, no, it's, I think this is a good one to talk about because there's been a lot of stuff that's come out this week. What, as I've been perusing LinkedIn and the various publications I follow and the Alex them.
A topic that you pitched, before we started recording is definitely something I am living right now. Why don't you queue us up for the conversation.
Alex Pokorny: Sure. So, we were talking a little bit in the last couple episodes. We talked a bit about automation and other episodes. We talked about AI's usage and the efficiency gains, right?
There's always a hidden element behind it, and I think we've been fairly unbiased and relatively fair in our kind of persistent kind of prompting of this takes work. This isn't, it's not a freebie,
The Hidden Costs of Automation
Alex Pokorny: Automation. Even we get to some of like more basic stuff of like just, live dashboards, reporting automation, just some little things like that.
There's a lot of effort behind it, and the more that you start to do it, you start building up this stack, and it's a MarTech stack in a way where you are training people, you're sharing it, you're maintaining it. So now you have a stack of administrative, duties that you've created for yourself.
By creating that report that everybody loves, by creating that GPT that's been shared around. Everybody, someone has a question goes to you, someone has, wants access to it. It's back to you again, right? It breaks back to you again. So, you're building up this like artificial stack.
And I don't think it's fully recognized, at least. One element of this that I've mentioned is the understanding of how much maintenance costs all of these things creates. Like each one that you've done great, but now there's. A maintenance cost on top of that. So that's a monthly cost of time and you kind of have to factor that in.
It may have saved you a bunch of time from creating it manually, but now you are maintaining a new dashboard, a new report, a new thing, Dave, you've seen some of the, also the pain of the build as well. Yeah. You've done some automation work. You want to reference or mention that?
Challenges in Podcast Production Automation
Dave Dougherty: Yeah, so I think I brought this up in one of the last episodes where I was playing around with Make, and I was looking at looking at the podcast sort of production side of things.
cause as we've grown and as we've gotten more listeners and subscribers, we added the something simple like we added the. Email newsletter. And so that call to action needed to go into the description of every episode. When we do this, we're doing an audio version and the video version for YouTube.
And so that meant going back into the podcast player or platform and updating the. The things there and then going into every single YouTube description and uploading it there. Now there are ways, if you're using, make.com to do this I just saw somebody's workflow on how to do this exact thing, but like most things in life, I saw that after I had done this, of course.
So, it, it was a lot to go through 57 episodes, I think, at the time we were at, and to go in and go find this, go find that, go find this. But then also just updating the templates that I use for the calls to action, right? Because each episode has the description for the episode and then the calls to action and ours.
Episode links and tagging of my other channels on YouTube and tagging you guys when necessary. So, there's a lot of optional extra stuff that goes into each thing. So then as I'm looking at even just the template stuff, I'm not trying to do, any sort of fancy thing. cause this is still a new way of thinking for me.
This is my way of like jumping into the coder dev side of stuff. Without being one. So, I just pulled up a template on make.com and there was one for getting descriptions and whatever else, but then I realized, oh, I have to add a new column to the Google sheet that I'm using. So now I have to go through all of the descriptions and upload those.
And I had been piecing out elements of the description, like the chapters. And the description and the hashtags and the, everything else. Whereas if I wanted to fully automate it, all of those things need to be brought into a single cell for description. So, is it worth it for me to then go through and recapture every single description as is?
No. And I know some of you on the more techie side are thinking you just create a make.com thing to scrape any of the content off of the, YouTube to then get a new sheet. Yeah. I tried that. It was only pulling a certain character limit because I wasn't about to pay x number of dollars a month for full stuff.
Also, the rate limiting on these platforms is interesting to me because it's, your free version. Gets you like kilobytes. It's okay, so I'm going to have to, or megabytes, I should say. I'm only going to be able to do any sort of text-based content. I'm not going to be able to download a video, upload a video upload a video to multiple places, right?
Because each one of the videos on these things are 10, 20, 30 gigabytes. So now I'm in like the upper tier for each of those things for the automations plus the automations of, the text-based content and the admin and the whatever else. And I said, okay, because it's text. Let's look at social media.
And so, I started looking at it and I'm like, okay, but I could also just pay $20 a month for a social media platform that just automatically does this for me. And all I have to do is drag and drop. Is it worth it for me to build the functionality of a social media tool that already exists for basically the same amount of money?
No. No, it doesn't. I'm just going to get the social media platform. I'm going to, put it in and be done with it. Now that being said, I'm fully happy to say I'm new to this. I am learning. I think there are some things there. Honestly, I don't really find a lot of joy in a lot of this automation stuff.
So, this is where, some of the ai promises to fall flat because you still need to be able to set up the thing that you know you want to do. So, it still means you need to have that kind of developer friend that you can go to set things up for you so that it just works, right? Thoughts, comments, feedback. I know you're similar to me where we want to at least test some things and try some things out. Sure. What have you worked on or what are you thinking? Yeah,
Alex Pokorny: I was thinking there's a, an old book. There's nearly as behind me, so I was going to look at it.
Algorithms of the intelligent web. This book has got to be 15 years old now. At least it was on. Eye-opening book for me because I started to realize all the API connection sort of websites that are out there where you're scraping all the rental properties off of Craigslist and you're putting it into a nicer looking website and you're having people sign up for this apartment finder website and you're charging these apartments, money for them to show the phone number or contact or have ads or be listed first in your rankings.
Where really all you're doing is you're scraping Craigslist and throwing it on a nicer Google map and a nicer looking, setup. Add some filters based upon, the different things that already Craigslist has. There's, that kind of opened my mind and it helped me understand some of the options, the realistic options that are out there, not that the output won't take a ton of work.
I'm sure it does. It's that, that it's an option in the first place. And I think that's a really key piece with AI is trying to understand what are the potential outcomes that you can get,
right?
And then keeping those in mind how, what's the best way to go after it. And what you found through your kind of long discovery process is that there are tools already out there that.
Skip a bunch of steps, but the tools that you were familiar with took a bunch of steps and then eventually you came to the education point of realizing, hey, isn't there something else? And then now you know, very specifically the problem you're trying to solve. And now you can very easily find that other thing that's already been pre-made, either purchase that or build that or go after that. Which aligns a lot with a lot of the different podcasts I've been listening to of different of these different AI companies. Claude, a couple different people from Open ai, including Sam Altman, a few others, they have kids. And one of the most common questions the hosts I always ask them is, would you still teach your kids?
Programming and coding. And the common answer to all of them is no. And these folks are all developers by trade or kind of background, at least. Maybe their new role is product or something like that, but they had a background at least of development. And the response to this was, no. But I want them to understand computer science.
And they all had the same response. And it's so funny how just they all say it in slightly different ways, but it’s basically everybody wants to make sure that their kids still understand how data can flow, how websites get built, in theory, in essence, basically how they actually are built. Not that you actually need to know of CSS, you don't.
Especially with GPT five, a lot of different coding platforms that are out just now they're fantastic and they can really build a lot. So, you don't really need to know all those details, but you still have to be able to troubleshoot them to some degree. You have to have a conceptual understanding of what they do.
You have to have some kind of understanding of how to implement them. Let's say you get the CSS, that's fantastic. What do you do with that? You have a file, right? Great. You need a domain name; you have to have a host. You have to have, start building out the website. You need all these different files; you need to have all these different things set up.
How do you do that? Go through the process of buying a domain, build a website, give it a shot. Those things, right? You'll, you can scale up in every single direction that's out there, but you still have to understand those basic things and keep that element moving. So, I think.
The Future of Work and AI's Impact
Alex Pokorny: With any of the automation of MarTech kind of, and AI builds the, everyone feels overwhelmed by ai.
I think that's a pretty fair blanket statement. It's moving so fast. There is so much that could be possible. Will I lose my job? Will I lose elements of my job? Are there elements of my job that can totally change? Or can this be super beneficial to me? Can I do brand new things I've never done before?
All of that is related to. Understanding conceptually what the potential outputs could be and then having enough of that understanding so that you know that learning curve, which shoots up really fast. At the beginning, you're like, oh man, everything's really easy. And then you try it, you have that first dip.
And I think that's the most important piece to get to with any of this stuff is getting to that first dip. You have to get to that low point because then you actually have some understanding of, oh, I can do all these things, but it's going to take a whole bunch of work to implement it. It's going to take a bunch of troubleshooting; it's going to take a whole bunch of whatever to try to get this going on.
I learned one piece, and it turns out there's 20. I thought it was just one. Once you get to that dip, I think that's a key. Either stopping point or key point to at least try to always attain with any of these things is actually is the low point. It's not the high point, not the, oh my gosh, I can do cool things.
Splash page of any, SAS product they have, they always have fantastic homepages and all the, all the options you can do with these things. It's amazing. That's not helpful. The key piece after that is what's it going to take? And I think that low points really important. So, what you went through is a very important journey of understanding.
Yeah, make can do cool stuff, but right. Try to implement one, and now you start to understand, oh, it takes a bunch of effort and there's cost and actually the usage, the way I'm using it is not going to really work. And maybe that's a different company or a different product or something like that. Like that.
That's such a, yeah. That's such an important discovery point with every project, every new tech.
Dave Dougherty: Yeah. Cause it's, is AI going to take my job and maybe elements of it. I think everybody's, yeah. Trying to really find that. And there was a Gartner study that came out recently that basically found that.
Employment in the 22 to 25-year-old sort of college graduate is down 13% since 2022. Yeah. So that's brutal because I remember being in being a graduate into the great recession with arts degrees and just being like, I know I can outperform a ton. I need to I just need somebody to gimme a chance.
Yeah. Yeah. And luckily that ended up happening eventually, but it was like a five-year journey, right? Just with the market and everything else. The higher level, maybe mid-career people who actually know how a lot of processes work already. They're staying then in the jobs that are there and are becoming AI assisted.
I. But yeah, you're still going to need the MarTech stack. You're still going to need, whatever else. And then every time I look at the LinkedIn the posts from every, people around us, the experts. And yeah. I'm guilty of it too. We're running a podcast and promoting it and, whatever else.
I feel like a lot of these people who are like, oh, I'm using this to create like my own agency of pure AI bots. I was like that's cool. If you want to outsource trust and relationships and everything else, you know that's there. But then also that is something that is uniquely solopreneur.
Because the amount of data that you're giving up and the amount of IP that you're giving up by automating everything is massive. For those of us in larger organizations or even mid-size organizations, who have, maybe one or two lawyers, employed, you're going to have to have conversations.
cause man if you need the intellectual property of it or you need the copyright of any of the content you create, you need to have done. The content yourself and you need to have in your agency agreements or vendor agreements that you will not use AI for these particular things because we need to hold copyright, so I think it's cool how far certain people are taking it. For me, it's like avantgarde jazz, where it's okay, that's a necessary part of the journey. You need people on the edges of stuff. But let's not forget that the vast majority of people. Just want to listen to a little bit of feel-good music when they're dropping their kids off at school and starting their day.
That's 90% of how people are leveraging stuff. Yeah. Yeah. I'll get off my soapbox.
Alex Pokorny: Yeah. There's another piece there is building a moat and if AI is your solution to everything. Guess what? Everyone has access to it. That's not really much of a barrier for anybody else to enter that single market.
Your remote is non-existent or very small. The moment someone starts to try just as hard as you did, they get it too. So that's not a, that's not a strong position to be in. It's not a long-term position either.
Side Hustles and AI Limitations
Alex Pokorny: I was thinking about that with a lot of the side hustle things that people do is.
It, there's two elements to it as well, of not just the amount of time and effort you're putting into it right now to build it up and create it, and time and energy and thought. You're thinking about it probably in your evenings. You're thinking about it during the day. There's effort, a lot of effort gets put towards any.
Substantial project. There's also a limitation to it as well. Let's say you build up your Etsy store fantastic. And Etsy goes down and gets bought by somebody else. There goes that, like there, there's a timeline to all these projects too, of I'm going to create an album. Cool. Once you create the album, you've done it.
Now you're going to sell it, but are you going to sell it for the next 20 years? No. There's a period of time in which you're going to push it and pitch it, and then the market's going to change, and things will change. You're going to probably have to go do it again. So there, there's continual elements to a lot of these projects too, that I think had quickly forgotten.
And if you're creating an AI only, which I've seen too many of those where it's, I’m all over Fiverr, and except it's actually just a whole bunch of Zapier kind of prompts and open ai, and it's responding immediately to people and giving them SEO advice or something like that. And I'm collecting my five bucks every time someone signs up for my service.
Okay. You're not going to build a brand off of that though,
Dave Dougherty: right? It's almost like the, do you remember the really old carnival? Arcade thing where it was like the puppet that would tell you your future and you put in two bucks and say some sort of generic thing after starting up. That's what I feel like that is, is this sort.
Yeah. Gimme the two boxes. I'll collect it and you walk away feeling okay for 30 seconds and you'll forget it.
Alex Pokorny: Yeah.
Dave Dougherty: Yep.
Alex Pokorny: There it goes. Yeah. Yeah, you're not really building something long term. You're not also creating enough of a retainer-based model or anything else that's actually going to provide consistent income.
And the moment that you know, 50 other people have the same thought and you're now going to have 50 competitors pushing you down and the results on Fiverr. There's just not a whole lot of length to it.
Dave Dougherty: I saw something driving around you speaking of side hustles and it got me thinking about some of the conversations you and I have had off mic.
And some on too, where it was this guy, it was a beat-up pickup truck and it just said Mike's weekend construction. Yeah. Talk about side hustle and that is really well positioned. I'm only working Saturday, Sunday, I'm only being the contractor for projects that can be done Saturday and Sunday.
Dave Dougherty: Exactly.
Alex Pokorny: How long does this take? Four weekends?
Dave Dougherty: You mean eight days? No.
Alex Pokorny: Four
Dave Dougherty: weekends. Four weekends. Let's make sure this is, scoped correctly here. Yeah, but I was like, man alive.
Finding a Niche Market
Dave Dougherty: You talk about niching down like, nope. I'm saying no to five out of the seven days. Exactly.
Alex Pokorny: Smart though.
Also, because there's actually so a bottom tier, bottom dollar market there. There's a significant gap there where basically any company who has a number of employees, trucks, equipment, rentals, everything else, can't quote a hundred dollars job. It's just not worth their time to even send somebody out there, cause they're going to lose it by the time they've, done all the overhead of just doing, processing the job.
They're going to lose it. So, there's a pretty big gap there, actually. So, I could totally see someone like that being like, hey, I've got, just a small job. Something like very small. And then. That being a perfect fit, where actually you find that niche, you find that hole in new market, and then there's something there.
There's something available there. It's like I've had
Dave Dougherty: these framed pictures and album cover things that I've been meaning to. Hang up since we moved into this house like six years ago, they're still not up at this point. I'm willing to pay somebody to just come in and make sure they're level, get off my list and just get it done.
Yeah. Because I'm clearly not doing it.
So yeah. It's just and I just hate that stuff. That's just not what I'm good at. That's not what I like doing. Yeah.
The Four-Day Work Week Experiment
Dave Dougherty: Speaking of things that are annoying your other topic that you pitched for today's conversation is the four-day work week versus three-day work week. What is the point and ultimate productivity, I think is what you said, right?
Alex Pokorny: Yeah. At what point, yeah. Talked over you, but at what point does the benefits fail to meet productivity? Yeah. There's a. There was a study done recently, I think a hundred something organizations, over a thousand individuals. They did a six-month test of the four-day work week trying to understand both employee happiness based on survey results as well as productivity.
And it was 90% of the organizations after six months decided to keep it. Basically, they sell value in both directions of people. Were pretty happy with it, plus productivity was still there and the long story short on a 40-hour work week and a five day a week work agreement basically is, it's based on manufacturing shifts, right?
And a good, easy way to basically split up the 24 hours of a day. And how many shifts do you have while you do basically split it, and you have a 40-hour shift or one shift? Which is why we do office work this way, which is actually kind of ridiculous. It doesn't really make a whole lot of sense.
Dave Dougherty: Right.
Alex Pokorny: I think it works to a point. It depends on the complexity and the organization of an organization. So, if you're highly organized, punching tickets basically, and there's not a big deadline on them, it could be done this week or it could be done next week. Will part work really well.
If you're constantly trying to meet with people who are all over the place. You're really going to struggle to book a meeting with a whole bunch of people if they're only working that many hours, right? Yeah. So, if you have a job that's more consultative, discussion-based politics, internal office politics, kind of junk, that's going to be a tough one.
So, I think there's got to be, there, there's a point there of maybe it's meeting reduction, maybe it's better priorities and better goals at work, better management. And then with that. You start to slim down the number of hours that people, you know, if there's any expectation, which I think is disappointing.
As a manager myself of a few different teams. Now at this point, I'm always disappointed by the idea of hours. I care about outputs. I don't care about the hours, right? If the hours are ridiculous, they're past 40. That's worth the conversation of trying to figure out, how do you prioritize work better?
How do you scope it down better? How do you basically set expectations better? Because it shouldn't be outside of 40. Think about it from your salary standpoint, honestly, cut it to an hour. That's how much you're making.
Dave Dougherty: Right.
Alex Pokorny: And the moment you start working more hours than that, you're diluting your pay.
You're no longer getting that hourly amount, you're now getting less, right? So don't do that. Keep it tight, right? And if you had good management and good practices, you probably aren't going to be exceeding 40 ever. Really. You're going to do it realistically, and you're going to scope it appropriately.
Dave Dougherty: Like a lot of our conversations, even on texts, I have to shout out the fact that
Alex Pokorny: if is doing a
Dave Dougherty: lot of heavy lifting there.
Alex Pokorny: Oh my gosh. Yeah. I can't say that's been most organizations I've been at where you, at least I've been able to typically have freedom, especially as a manager of a specialty.
Where I can sign up for work as I feel appropriate versus, being told to do, part of a larger org where you're, there's preset expectations. Yep. I get to set the expectations, so that's very helpful. But in the organizations where I haven't had that choice.
Yeah. Like it just doesn't happen,
Dave Dougherty: Yeah.
Setting and Achieving Aggressive Goals
Dave Dougherty: I think for me, the. The big things that I try to do, so I chunk out the year based on quarter one is setting expectations. This is what I think I'm going to be able to do for this year. Here are my targets, here's how they break out, sort of quarter by quarter, month over month, depending on how.
Specific people want to get. Personally, I don't like getting overly specific, but definitely having here is, here's the main thing and a couple of milestones to shoot for to make it more manageable. Then Q2, Q3 is. Working hard to make sure that you're hitting those milestones and communicating out to people to, Hey, we're on track.
I've run into this hiccup, but I think we'll still be able to, do it may be like a week later. Even just having those reoccurring conversations because. Already, we haven't even hit like true fall yet. I'm getting feedback from people like, hey, you have the most aggressive goals. Are you sure you're going to be able to hit those?
And I'm like yeah, if you just look at the dashboard, I look really bad. But when you look at it, I said I would hit 10% this year. Other people said they were hit 2% this year, so I'm five x-ing. So just let me get to work. And. Yeah. It's funny the reliance on the dashboards and the conversations with leadership, because there is a little bit of context there.
If I'm trying to outperform by quite a bit, there's going to be a lot more conversations I need to talk about, to make sure that everybody's okay when they look at the dashboard and they see it's red or green or whatever color combination you choose to institute for, yours
Your organization. But to your point, those milestones are the outcomes. So, if I'm on track and I'm getting enough work done each week, each, month, who cares if that takes me? 50 hours, 30 hours, 40 hours. That's on me to maintain. Yeah, and one of the things that I did this year in particular to make sure that I could get those aggressive goals is anytime anybody would throw on a meeting, I would say, okay, how is you realize I'm trying to do X, Y, Z?
How does this meeting help me get there? And if it's not, if it's something else that needs to either go into somebody else or we can put it in a backlog, and I'll get to you when I'm. Done working on the priorities. Luckily for me, that has worked. I have had enough supportive people around me that appreciate the communication.
They appreciate the aggressive goals. So, it's been working, but it also, I know a lot of people who would be very uncomfortable with. Being the tall poppy and having to communicate, too much. Yeah. Yeah. How does that ring with what you were thinking? Because I know I go off sideways sometimes.
Oh, it does.
Alex Pokorny: I mean that it does get down to good management and consistent role definition, consistent outputs and stuff like that. Past orgs. I've always struggled with that because we would set goals in the first month and then by like month two things had changed. And by month three it's unrecognizable from what month was.
And then about six months into it, we got reorged yet again. And now our job duties and responsibilities have completely shifted yet again. Which that is not. An element of myself or even my, direct surrounding team. It's always a larger org kind of thing that's going on,
Dave Dougherty: right?
Alex Pokorny: But that again, is based upon the organization's leadership and how they're doing that. You do it well, you have good information flow, everybody's on the same page and has a realistic understanding of pragmatic view of the business and the industry and the market. And from top down. You have a very general goal.
That's the company strategy. You have a more specific goal, which is maybe your department division. You have a much more refined goal that gets down to your day-to-day tasks, which that's your immediate team and manager, and that's how that's supposed to work, right? Companies supposed to have a general direction, and then below that, it's supposed to be interpreted for what's realistic and what you're going to actually influence.
Not just be like, hey, by the way, we should be manufacturing more and be like it's not my job. But what can I do to help that situation? Maybe it's demand gen, maybe it's service and support side of it. There's probably an element that still gets translated down and that's good company organization.
Dave Dougherty: Right?
Alex Pokorny: I was going to bring it up today and I think I may have brought it up a few times in the past.
The Garbage Can Model of Organizations
Alex Pokorny: The garbage can model. It's a fun way. Remind me read. The garbage can model was basically in understanding that most organizations are pretty dysfunctional. And the idea of which I was, this is a theory back from I think the early seventies, and it was an ongoing issue of what was going on when they were at a college and they were trying to find a new dean and they were doing these interviews, but not everybody could make every interview from the interview panel.
So, then they had a random scoring system, but then that didn't work, and then the timing didn't work out. So eventually it was like the person who was head of the search just became the dean because. They just gave up.
It was like the individuals involved eventually wrote a paper on this and created what they call the garbage can model. And there's various versions of this as well. And most organizations basically hit this because it's a very complex, messy, some people are narcissistic, some people are egotistical, some people are, lackadaisical. It doesn't matter what how many kinds of personalities you have in a room, but you're probably going to have a mix of all of them at some point over the course of the year as people's personalities, change by the minute from what's going on in the environment. You always have this messy model, basically.
Good leadership that has good information that it's working off of, which means honest communication going both directions, complete information. There's a lot of, as you said, is that are going on. Those create strong organizations, and I think with those strong organizations, it's lot easier to then identify goals, progress towards them, stick with them, that kind of thing, like that.
That's entirely possible, the messier an organization is. The more who knows what's going on and the hot project that you worked on a month ago might be forgotten. Which is so disappointing. Like you spend so much time on something and it's nope. Not anymore. Yeah.
Dave Dougherty: That's why like that's makes it tough.
Yeah. No, that's a. I like that model idea. Although, when you said seventies, my, my mind immediately thought of lava lamps because my experience in big corporations especially conglomerates, is that like a lava lamp, you will have multiple blobs that come together. Yep. And those tend to be personalities of a similar kind.
Yes. And that'll be one division that'll be the biggest one that has, the most aggressive behavior. Then you got these little ones that kind of circle around and capture whatever they can. And then you get the medium ones that, are nice to look at, they don't really get in the way of too much.
Everybody's just circling in whatever. Coming up with this metaphor live, so failing in a little way, but I don't know. That's the way my brain works. I thought of that. Yeah, you do place my experience with that, right.
Alex Pokorny: Yeah. You get ones that rise to the top, you get ones that cool down and start to fall, you do that cycle over and over again and it's random, it's randomized on the way up. So, continuing your metaphor there,
Dave Dougherty: although I do, I've been thinking about this a lot. There was there was a leader in one of the previous businesses I was in who constantly talked about how no matter what you do, you cannot. Outperform the market, right?
Or the influence of the market. Okay. So, if the market's growing 3%, you can grow four or 5%. And that's wonderful. But ultimately. The facade that we all work towards, and especially you see this on LinkedIn, is that we have more control over it than we, actually do. Yes. My gosh.
Yes.
Alex Pokorny: Yep.
Dave Dougherty: Had this conversation, the macro environment will slam you like a cartoon hammer in the head. Yes. Yeah, and you're just going to have to make do with what you got. Especially coming into the back half of 2025. I think that is going to be an interesting metaphor to keeping in my back of our heads.
All right.
Final Thoughts and Takeaways
Dave Dougherty: How to end on a happy note, Alex, because that went south real quick.
Alex Pokorny: It's a lot of education and a lot of, yeah. A lot of information from the beginning of this episode to the end. We've talked about that, of. Learning and educating yourself as much as possible on what's going on around you. Which includes AI and tech and all the rest of that kind of stuff. But understanding also from a pragmatic view, there's limits to it all.
Same thing with the market and your role and your organization. There's a lot of kind of hoorah, happiness and positivity, but at the same time, you have to be pragmatic and learn and push for learning. And I think that's. That's a big misconception of any organization and they constantly fall into this and I think that's just humanity in general because we work off of it, is we love anecdotes and it's a lot harder to get real life experience and the more that you can get real life experience versus someone's random anecdote of, things are going great.
How great is great. Oh, things were going great a month ago this month's not so great. And actually, compared to our industry, we're doing terrible like that. That's a whole lot of different information than what you just heard from the first anecdote, right? So, you have to get into it, and you have to try it and push on it, and then just be an open learner who's not afraid to ask this stupid question, not afraid to try.
Dave Dougherty: No, that was a really good summary. Subscribe, share. And, come by for the next thing. We do have the pathways newsletter, which has been awesome and a lot of fun to see the response to that. And you, we will do deeper dives into, maybe off of this episode we'll do a deeper dive into the CAN model.
I don't know. That sounds intriguing. So yeah, thanks for hanging out. I hope. Your next two weeks are enjoyable, and you take some time to educate and try some things out and have some time for yourself. Don't get caught in the to-do list trap. That is a mantra that I think we should all adapt.
So have a wonderful weekend, and Alex, we'll see you on the next.
