How Finance Teams Can Overcome AI Fear and Build Real Use Cases That Work with Josh Schauer
In this episode of Future Finance, Paul Barnhurst and Glenn Hopper are joined by Josh Schauer to explore how AI is being adopted in finance and why many teams are still hesitant. They discuss the gap between understanding AI’s importance and actually using it, along with the challenges around data quality, risk, and expectations of accuracy.
Josh Schauer is the CFO at insightsoftware, where he leads financial strategy and operations. He previously served as SVP of Finance from 2020 to 2024 and led Financial Planning and Consolidation at Longview before its acquisition by insightsoftware. Josh has extensive experience in FP&A, consolidation, and finance transformation, and holds a Bachelor’s degree in Accounting from the University of Wisconsin–La Crosse.
In this episode, you will discover:
Why are finance teams slow to adopt AI despite knowing its importance
Why AI should be treated as a teammate, not a replacement
How poor data and broken processes limit AI’s value
Practical use cases for AI in FP&A, including variance analysis
How scenario planning can be done faster using AI
Josh emphasizes that AI is not a shortcut. It works best when built on strong data, clear processes, and a thoughtful approach. While the pressure to adopt AI is real, the focus should be on using it in a practical way that improves decision-making and productivity without compromising accuracy.
Follow Josh:
LinkedIn: https://www.linkedin.com/in/josh-schauer-51b18b115/
Website: https://insightsoftware.com/
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
Follow Paul:
LinkedIn - https://www.linkedin.com/in/thefpandaguy
FollowQFlow.AI:
Website - https://bit.ly/4i1Ekjg
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.
Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.
In Today’s Episode:
[00:00] – Trailer
[01:35] – Josh Schauer Background
[03:02] – AI in Finance & Accounting for Dummies
[05:57] – AI as a Teammate, Not a Threat
[07:09] – AI as a Thought Partner
[10:01] – Why Finance Teams Struggle with AI
[11:51] – Evolution of FP&A
[14:30] – RevOps & Finance Alignment
[16:41] – AI Use Cases in Finance
[19:20] – Agentic AI & Future Direction
[22:31] – Process, Data & AI Limitations
[28:52] – AI Questions & Closing
Full Show Transcript:
Co-Host: Glenn Hopper (01:13):
Welcome to another exciting episode of Future Finance. I am Glenn Hopper, and with me, as always, is Constable Paul Barnhurst, the FP&A guy. I'm trying to give you a different title every time we refer.
Guest: Josh Schauer (01:24):
Constable, I've noticed you've been going with different titles. I used Hops last week. I'm going to call you the AI Master this week.
Co-Host: Glenn Hopper (01:30):
We have another AI finance guest. It's amazing how this keeps happening.
Host: Paul Barnhurst (01:35):
It is. We had with us this week, Josh Schauer. Josh, welcome to the show.
Guest: Josh Schauer (01:38):
Thank you. Appreciate it. Thanks for having me.
Host: Paul Barnhurst (01:40):
Yeah, we're excited to have you. So let me give just a little bit of background about Josh, and we'll jump into the questions. We're thrilled to have him. So Josh is a seasoned finance professional with extensive, significant experience in financial planning, consolidation, and analysis. Currently, he is serving as the CFO at Insight Software. He's been the CFO since January of 2025. Previously, he held the role of SVP of finance from 2020 to 2024. Prior to that, he led the financial planning and consolidation at Longview, which was acquired by Insight Software in 2020. He holds a bachelor's degree in accounting from the University of Wisconsin Lacrosse. So again, welcome, Josh.
Guest: Josh Schauer (02:23):
Yes, welcome. Thank you. Thank you for having me.
Host: Paul Barnhurst (02:24):
Really excited to have you as we were chatting beforehand, just so the audience knows. I mentioned I work quite a bit with Insight Software, so excited to get to chat with you. I've probably met with 30 people across the team over the years, and so it's fun to get to talk with a finance person as much as I love the marketing team. So excited for a different thing.
Guest: Josh Schauer (02:43):
Yeah. Yeah. Saving all the fun conversations.
Host: Paul Barnhurst (02:46):
All right. So want to start with the first question, because I know Insights Software recently published a small book with Wiley, AI in Finance and Accounting for Dummies. I'd love to know a little bit of the backstory. How did that all come about and what was kind of the purpose behind that?
Guest: Josh Schauer (03:02):
Yeah, no, appreciate the question. I think what led to the book was that there is a lack of confidence in AI that we kept seeing across finance functions. So I think the majority of finance professionals would say and understand that AI is essential, but I think a minority would feel ready to use it or know how to use it. And so I think there's a reality that hesitation to adopt AI can have real business consequences. And I think you can put your business at a competitive disadvantage. So we created the book, AI and Finance and Accounting for Dummies to address just that, which is how do you make AI practical approachable and something that finance teams can start using with confidence and aren't intimidated by our skids?
Host: Paul Barnhurst (03:45):
Got it. And how's the response been so far?
Guest: Josh Schauer (03:48):
It's been good. It's been great. I mean, it's obviously a well-known brand, and so there's value in just having that brand recognition out there. But the response has been good because again, it is a problem that I think a lot of finance teams are trying to solve. Everyone knows, "Hey, we need to do it. We're being told we need to do it, " but they don't know where to start. And so obviously that's what the book is intended to solve for.
Host: Paul Barnhurst (04:08):
Yeah, no, definitely. Everybody knows they have to do it and they're being mandated, but it's really hard. It's like when the computer first came out, start using it. Never used the computer before. How do I figure this out? What do I need to learn? What should I be doing? I think there's probably a lot of similarities or, hey, marketing or product team, go put us on the internet now. It's the internet for the early adopters. So I think there's probably some similarities.
Co-Host: Glenn Hopper (04:34):
I actually did download a copy of the book and went through it. And Paul, shameless self-promotion here. I too have a book coming out from Wiley Finance later this year. The AI-ready CFO is mine. It's available for pre-order now if any of our listeners want to pick that up. So always interested in reading and learning more about this area that I talk about and work in every day. But I loved, I guess if I had to identify a core thesis of the book is that treat AI like it's a virtual finance teammate. Let it take the repetitive work while the humans keep the judgement . And that's the exact same message that I'm always trying to get across too. It's like that jagged frontier with AI, you see it does some things amazingly well, and then you ask it to do another thing and it can't find a certain clause and a contract one day for whatever reason.
(05:26):
So I think for that reason, AI has been kind of a hard sell in accounting and finance where there's no grey areas to work in. The trial balance has to balance. The numbers that the CFO signs off on have to be correct and they can't be hallucinated. As I went through and looked at all that, I know you have a 10-step implementation guide and all that, but is there, and hopefully I didn't just steal your thunder on this question, but would you say there's a key takeaway from the book that finance and accounting professionals should remember out of it?
Guest: Josh Schauer (05:57):
I think from my perspective, I think the key takeaway is that finance teams should view AI as a teammate, not as a threat to their job. And that is part of the misperception or misconception is that AI is here to take the job of financial professionals and we're nowhere near that. I think the reality is you need to take time to understand what it can do, how it can improve decisions, spot anomalies, and ultimately make your work more effective.That's what AI can do to assist you. I think the book does a great job of walking you through practical examples of that and a clear roadmap so that you can intentionally put AI to work in your finance organisation. But that's the fundamental takeaway is that it's a friend, not a foe, and that you really need to take time to get to know it and learn it and understand how it can help you.
(06:41):
And that it's at worst right now, it's a fantastic assistant to being able to make you much more productive at what you do. You need to find ways to understand how to implement that into your daily tasks.
Co-Host: Glenn Hopper (06:53):
Is there a particular function or area where you've kind of just ... And I think we've all had these moments though, and there may be many of them where you've kind of just been blown away where AI does something that you thought, "Oh, it's going to be years before we can do this, " or is there an area where you're finding it more useful even than you thought it would be?
Guest: Josh Schauer (07:09):
Yeah. Look, I will say just like LLMs and their ability to learn who you are, it grows and becomes quite fascinating with respects to, you start to use it just as kind of a generic thought partner and very quickly it starts to interpret answers based on how you want to hear the answers because it learns all of the different questions you ask. And it has become quite fascinating. You can relate to this, but the CFO position is a very lonely role just in general. You kind of get to a point where you don't have a lot of people to go to to ask questions to and so on and so forth. And it really is a very impressive thought partner in that regard, like just again, ChatGPT, Claude, your tools like that, that can make you feel more confident in your decisions. And they can also provide different perspectives and things to look out for and so on and so forth.
(07:56):
But I think what's been most fascinating about it is how quickly it really learns who you are and who your organisation is so that I can tailor its answers to being more productive for your use case. So I'm
Host: Paul Barnhurst (08:06):
Waiting for the CFO's digital system brought to you by Josh and Insight Software. I'm just kidding. No, it really is amazing how you can use it as, I'll call it a sparring partner, a mentor, somebody to bounce ideas off, all those type of things like having a play devil's advocate. It's usually really good at that or helping you build a plan and then think about the issues in the plan. It can go back and say, "Well, I did this, but I probably could have done these six other things." And you're like, "Exactly right." It can pull from an endless number of possibilities, and so it at least gets you thinking if nothing else, for sure.
Guest: Josh Schauer (08:44):
That's exactly it. And they're getting better by that hour, day, week for sure.
Host: Paul Barnhurst (08:49):
Yeah, no, I mean, I think in the last month, and I imagine you've been there, I know I was blown away when Claude came out and you saw how well it modelled compared to the prior versions. You're like, "Okay, before that, I wouldn't have had someone building a three state. You could get there, but I wouldn't recommend doing it. " Now I'm like, if you know what you're doing and you're willing to put in the time on the prompting, yeah, it's going to be 90, 95% accurate. I wouldn't have said that three months ago in Excel, six months ago. So it's amazing to watch.
Guest: Josh Schauer (09:16):
It is. That is inherently a problem though, because we expect 100%. And yes, I mean, when you get down to it with respects to agentic capabilities and so on and so forth, that is where a lot of the reluctance comes in place for financial professionals.
Host: Paul Barnhurst (09:28):
A hundred percent agree. I mean, right, especially helping people understand, I think you'll appreciate this. I was talking to somebody today and he was like, "Yeah, we were cleaning up our board deck and trying to automate all of it. " It's like, Claude help, but actually our data was in a bad place. PowerQuery helped a lot more to automate things. It was just that reminder of just because AI can do it or it can help you doesn't mean it's the right tool. Yeah, I'm sure you've seen a lot of that, but I'm curious, why do you think finance professionals are struggling as much as they are to figure out how to implement it? Where do you think that main challenge lies for them?
Guest: Josh Schauer (10:01):
Yeah. Look, I think without trying to be too stereotypical, I think just generally speaking, finance professionals are typically kind of risk averse and this is a risky area. It's a grey area kind of to your point earlier about black and white, this is a world of grey. I think furthermore, AI really depends on data. It starts with data. And I think the reality is, is that the overwhelming majority of companies have a data foundation that in some way, shape or form is imperfect. And so in that case, even if there's a willingness to try it, you're not going to get obviously the full capacity and power of what it can do. So it's going to limit the value of it. I think of finance professionals as being very analytically rigorous, but organizationally conservative, if that makes sense. And AI, it's almost like a real-time experiment.
(10:46):
It just doesn't come natural for people that are designed to optimise accuracy and control in black and white. So I think that's the reality of why it's a little bit slow to be adopted. And then obviously with respects to the data and the requirements, there's an expectation of 100% accuracy, like I alluded to, and there's just not quite that track record or level of proven performance yet within the tools for the most part.
Host: Paul Barnhurst (11:08):
I think you nailed it there. One, we are tend to be risk adverse too, right? Audit and all that. You don't want to be like, "Well, we used AI and it was 94% accurate." I mean, our financials are close for the quarter.
Co-Host: Glenn Hopper (11:23):
It's hard to have an audit trail when you say the answer came from a magic black box.
Guest: Josh Schauer (11:29):
That's right. Yeah. I'd like to see the audit opinion. Yeah.
Host: Paul Barnhurst (11:33):
So you've worked in the finance and FP&A space for most of your career for quite a while now between Longview and Insight Software. As you see AI, you see technology growing and rapidly changing, what has you most excited for the future of kind of FP&A and software in the office of the CFO?
Guest: Josh Schauer (11:51):
Look, I think that the FP&A function as a whole has evolved a lot in the last five years and then AI has changed it even more. At least the requirement has become that it needs to be far, far more proactive than it was at one point in time. It used to be thought of like a reporting function and hey, get the results, you'd roll them up, you'd report. But the impetus and focus behind it being a real differentiator within the organisation and a competitive differentiator with respect to the strategic components of what the FP&A organisations have to bring at this point in time. And it really starts with a strong, inquisitive, proactive FP&A team. So the function as a whole is growing with respect to its value in organisations. And so naturally, adding on the AI layer and playing in that space with respect to your products is a really exciting space to be.
(12:40):
I think that it's only going to continue growing in its value within organisations. I think finance functions, I think revenue operations functions, I think that there's a clear differentiator in companies with very strong performing functions in those two areas and that that is only going to continue to differentiate itself.
Host: Paul Barnhurst (12:57):
I'm curious when you talk about the strength of the functions, how do you see kind of RevOps and FP&A working in best in class? What's kind of your take of how that should be structured? I hear some people, "Hey, some of your RevOps should sit in finance. Some people have the marketing. They obviously have to work together." But how do you try to manage that to make sure you're getting the best out of both sides and they're working together? Because if they're not, we've all seen it. The company struggles.Ever feel like you go to market teams and finance and speak different languages? This misalignment is a breeding ground for failure in pairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. MeetQFlow.ai, the strategic finance platform purpose-built to solve the toughest part of planning and analysis of B2B revenue. Q flow quickly integrates key data from your go-to-market stack and accounting platform, then handles all the data prep and normalization. Under the hood, it automatically assembles your go-to-market stats, makes segmented scenario planning a breeze, and closes the planning loop. Create air-tight alignment, improve decision latency, and ensure accountability across the team.
Guest: Josh Schauer (14:30):
Yeah, that's exactly right. I mean, working together is the big ... I mean, collaboration, communication is so, so, so critical. I think the finance function, again, it has this perception of just being like the numbers. We close the books and we just focus, but really it's become so much more upstream. We are very much involved in pipeline, pipeline generation and pipeline flow through and win rates and all those things that really are very much hand in hand with what the revenue operations team does. We may be a little bit more focused on following it all the way through and the ultimate financial implications and ramifications of that data set, but the two functions have to be completely married throughout the process. It starts with a good annual operating plan or planning process where, again, the finance team is intimately involved all the way up into the product strategy and subsequently the go- to-market strategies.
(15:16):
So communication and collaboration is really, really critical. I like for RevOps teams to really own everything, all the way from a lead all the way up, all the way through the invoice and the renewal, because that's the customer journey. And so you're far less likely to have gaps in the process and an unfortunate customer experience if you have people who are thinking holistically all the way from when we first reached out and contacted you to now you've renewed for the 18th year in a row.
Co-Host: Glenn Hopper (15:44):
And now you have the capability to build an army of bots to further connect and enhance that team. And I asked this question as someone who has spent my career in finance, an embarrassingly bad bookkeeping situation at the end of 2025, and for which I got a Claude cohort to help me out and get all my delayed entries and my commingled cards and typical things that you do in a small business sorted out. And people ask me this all the time, so I'm going to pose the same question to you. You guys have literally written the book on AI and finance and there's always the question, well, there's sort of the ivory tower of academia and book writing, and then there's what you're actually doing. So I'd love to hear, what can you tell us about how it's being used and kind of across the finance function or maybe even more broadly across the company?
Guest: Josh Schauer (16:41):
Yeah, look, there are a lot of use cases internally for what I would say are like very practical things, right? So you've got generating commentary that may accompany some sort of numerical charter image on a slide. You've got Excel formula help, right? It's like the best Excel assistant, and there's really no reason why everyone isn't an Excel wiz to the end degree, which by the way, is a differentiator in the finance world. You've got slide deck creation that's now automated. You've got those very basic things. And then we mentioned this earlier, but just using AI as a general thought partner is obviously really important. We've got cloud licences for every employee in the company and there's an expectation and there are performance measures and it's part of the EPMR process, et cetera, that they're utilising it. I think from a more analytical perspective, which is generally kind of the root and spirit of what that question is trying to get at, we do a lot of automated variance analysis.
(17:27):
So think about month end comparisons from the actuals to what the forecast was. And then when you've got it at the right data level, you can even output the why and the justification, have your commentary and so on and so forth. So things like that obviously carve a lot of time, a lot of manual effort out of the closed process. And then scenario planning is something we've started to get really, really deep with respects to how AI can help us there. Things that even like when you think about scenario planning, there are like controllable factors oftentimes, and there are uncontrollable factors oftentimes like market economics and those kinds of things. And it does a very good job at very quickly if you're like, "Hey, I just want to be 90% accurate on this. " You can throw those things together in 30 minutes and they used to take two weeks.
(18:08):
And so there's just so many ways to really accelerate what can be pretty critical decisions that your organisation wants to make very quick.
Co-Host: Glenn Hopper (18:15):
The crazy thing right now is, you and I are at the cutting edge of this, and Paul too, I'm not excluding you here, Paul.
Host: Paul Barnhurst (18:23):
Away. I just talked, Glen, I'm not like you.
Co-Host: Glenn Hopper (18:27):
But even in the last three months, I mean, we've been talking about agents and everybody's been calling everything they do in agent and kind of mislabeling things as agents that were really just some kind of customised chatbot or whatever. But looking at Claude cowork or even abilities in ChatGPT 5.4 right now, I mean, we're finally starting to see true agentic capabilities. And especially on the coding side where you'll hear from OpenAI or Anthropic that they've got their coding agents will go out for nine hours and work on something. And it's crazy to say this when we're only what, two and a half years into generative AI being widely available and now we've finally got functioning agents. I wonder, how is Insight Software looking at, and I don't know if you have any Agentic tools in your software right now, but how are you thinking about AI within going forward and what it might take over or replace or enhance?
Guest: Josh Schauer (19:20):
Yeah, look, it's a great question. I think in some of the functions like engineering and support, where you've got Devon Replit, you've got all these different tools forethought, et cetera, et cetera. There are much more clear agentic use cases at this point in time. I think it's coming for finance. It's coming for FP&A, right? But to the root about what we talked about, which is that there's this hesitancy and reluctance around the specific data that comes out of this office, it's going to be a little bit late to the party. Yeah, I think like many companies right now incorporating AI into our products, it's a major, in fact, the number one priority for insight software, making sure that your biggest and best competitor, making sure you don't get passed by and so on and so forth. I would add, we are very thoughtful in our approach to provide what I would call our real world value to finance and accounting teams.
(20:04):
There's a lot of people who, and it's fine, right? You just want to be the first one there like, "Hey, we're just going to show it. We want to be the first one there." And then the more you dive in, you're like, "Oh, this isn't agentic and it really doesn't do a whole lot and it definitely doesn't do more than Cloud could do, " that kind of a thing. When it comes to Agentic AI, what I would say is we're pretty big advocates for the human in the loop approach, especially in the finance industry. I'm certainly personally an advocate for that, at least at this point in time, given the potential hallucinations and so on and so forth. And so that's really where we're at. I would say specifically within our product set, our just perform product, you mentioned it earlier, having spoken with them before the acquisition, we just launched a just perform account reconciliation product in January that has some adjective capabilities with respects to being able to do variance analyses and account reconciliations and things that right now humans have to do.
(20:51):
And then we've got to just perform budgeting and planning tool as well that has agentic capabilities that can automate routine tasks, it can improve accuracy and so on and so forth. But to your point, it's relatively new. I think it was like 5% of companies had some sort of real agentic capabilities within their finance department. It's going to grow, it's going to be 30 or 40% this year and 70 and 80% next year, like it's growing quite exponentially, but that's where we sit today. It's a focal point of ours. It's something we talk about every day.
Co-Host: Glenn Hopper (21:19):
It's exciting to see what's going on out there. But at the same time, you don't want to be on the bleeding edge. And if you're the software provider, if people are experimenting and doing it on their own and ChatGPT messes up, you can just blame OpenAI or whatever. But if you're shipping software, I know it's still on the early edge, but at the same time, there's got to be pressure like, "These tools are awesome. What are you putting in there? How soon can we expect it? " And it's just that jagged frontier again.
Guest: Josh Schauer (21:44):
That's exactly what it is. And it's quite an interesting environment right now because there's so much of hurry up, hurry up, hurry up. And then yet you feel this responsibility to also slow down to some extent and make sure that what you're outputting is a responsible output. And so it's like everyone's running around and you want to make sure that you're doing it in a very targeted way. I mean, even with internal implementation and adoption, one thing that I'm a big advocate for is that you need to understand the process that you're installing AI into. And I think in many cases, we don't slow down to kind of process map, okay, how is this? Because if there are fundamental problems with the process or fundamental problems with the data, then AI just isn't going to be nearly the value. It could almost be a danger at times, but not nearly the value that we want it to be.
(22:31):
So it's not hurry up and wait, but it is definitely like hurry up and then maybe pause for a second and make sure that what we're outputting is responsible.
Co-Host: Glenn Hopper (22:39):
That's such a key point that you just hit on because I always tell clients, you can't automate chaos. So if they have an expectation that you're going to come in and just sprinkle some AI on their broken processes, their broken data, when they have no data governance in place and they don't have that foundation, that's why you see all these stats about all these projects failing. I don't think it's a failure of the technology. It's misunderstanding of the capabilities and where to apply it and not ever building that foundation. So yeah, 100%. I think you're spot on there.You've got to document the process first so that you can know what to automate and then when to put AI in. 100%
Host: Paul Barnhurst (23:15):
Agree with you, Glenn. We've all heard the ... Sometimes you hear the term the three Ps, people, process, platform. You could almost put a fourth in there, not a P, but your data. If you don't have the right people, if you don't really know your processes, have a data foundation, then don't try to upgrade your technology to AI and throw it at everything because you wouldn't throw the technology at ... Well, you shouldn't throw it at a bad process. You wouldn't get good results. Why would you expect AI to all of a sudden magically give you good results? Well, we hear that a lot. I mean, I have a friend that works in the software space selling finance software and he had someone come to them and he's like, "Well, you guys need to fix this. You really need to implement this. " He was trying to tell them data stuff.
(23:57):
They're like, "Well, no, we just need AI. Our CFO said if we didn't have it in a year, we were all fired." Okay, well, I can sell you something with AI, but that doesn't mean you're going to get good results. And so that kind of pressure from the board and from above sometimes can be a real problem in my mind.
Guest: Josh Schauer (24:14):
I would agree. It's a very real pressure, by the way. I would venture to bet that the majority of finance functions are getting pressure from someone or somewhere to make sure that they're utilising AI. And again, that's the reason for the book is because it's very difficult to understand what a practical way of utilising it is.
Host: Paul Barnhurst (24:31):
I agree with you. I'm still trying to figure it out myself. I feel like I'm still using AI a lot of times, like it's 2024 and I keep going, "I need to do this and that. And I need a week to figure this out. " It's not natural to most of us. Glenn, you've been doing this stuff for a decade, so you're a little further along than most of us, but at least for me. All right, so we're going to move into our fun little section here. Let me lay out how it works. What we did is we took your bio, we took the six questions, we gave it LinkedIn in the website and said, "Come up with 25 kind of personal questions we could ask for a podcast." We usually say have a little fun. It breaks them into sections. And Glenn and I each have a question we ask.
(25:09):
We each take a different approach. So here's my approach. You got one of two options. We can go with no human in the loop and the random number generator can pick a number between one and 25, or we can keep a human in the loop and you can pick a number between one and 25 and we'll see what question you get.
Guest: Josh Schauer (25:25):
Let's go human in the loop since I just spoke to it that that's-
Host: Paul Barnhurst (25:28):
I figured when I said it that way, you'd pick human in the loop.
Guest: Josh Schauer (25:31):
Yeah. Let's do number three.
Host: Paul Barnhurst (25:34):
Number three. Okay. This is in the section called The Dummies Book and AI Hook. All right. I don't know why they named it that, but that's the section. So let's see. Number three, if AI and finance was a superhero, what would be its secret weakness or kryptonite?
Guest: Josh Schauer (25:51):
Oh, fascinating. Look, it would have to be something about the foundational data. I mean, that's what it would have to be. I'd have to think what data would be kind of the equivalent of or synonymous with respects to superheroes, but it would be kind of the underlying core, if that makes sense. It may be the immune system or something of that nature, right? I mean, it's really before you can even get to the actions that the superhero can take to go save people and do incredible things, if the superhero is sick or has some sort of foundational weakness that could take it down from the inside, then it's not going to be of value.
Host: Paul Barnhurst (26:29):
So data is like the immune system of a superhero. If you don't have good data, the immune system is compromised.
Co-Host: Glenn Hopper (26:37):
Okay. So my approach is these questions this week were created by Gemini, and I always will pick another model and say, "What's the funniest, best question out of this? " And I ran it while I ran my query while Paul was asking you, "This has never happened before." It came up with the same question. So I asked it, that's never happened before. So I guess- Two years, that's pretty good. It says it's great visual humour potential, egryptonite equals bad data or Excel errors. So you were on point with Grock anyway. I don't know what that means, but-
Host: Paul Barnhurst (27:14):
Oh, is Gronk the one going for your questions today? All right, so let's ask it for another one.
Co-Host: Glenn Hopper (27:18):
Yep. Okay, here we go. I like this one better, actually. With Agentic AI on the rise, if you could hire an AI agent to take over one part of your personal life, like grocery shopping or choosing what to watch on Netflix, what would you delegate first?
Guest: Josh Schauer (27:34):
Out of those two, it would definitely what to watch on Netflix. I spend the majority of time scrolling before I actually picture, and I love grocery shopping. Let's see, let me find an alternative chore that I'm not a big fan of. Yeah, I would say just making the bed after I get ... Generally, I want to get up and get going, and I don't like pausing for a second to just make the bed. So having something there that you could kind of snap your fingers and have it be done for you would be a great start to the day and it would sound a great tone.
Co-Host: Glenn Hopper (28:01):
Love it. Love it. Yeah.
Host: Paul Barnhurst (28:02):
Right. So Glenn, we have our first customer if we make a bedmaking AI machine.
Co-Host: Glenn Hopper (28:07):
I mean, that's pretty high on my priority list as well.
Host: Paul Barnhurst (28:11):
If you could make it affordable, I think you would have a big audience for that. There are plenty of people that would-
Guest: Josh Schauer (28:17):
There'll be a lot of robots in homes eventually. I think it's a matter of time.
Host: Paul Barnhurst (28:21):
I agree. I mean, they're already out there. They're just not affordable. We don't have ... Or they're probably not quite there yet, but they're good at some basic tasks.
Co-Host: Glenn Hopper (28:29):
Yeah. The Chinese ones that did all the dancing or whatever at the Chinese New Year, if you've seen that, if you needed a dancing robot, I think we're there.
Host: Paul Barnhurst (28:36):
Alrighty. Well, thank you so much for joining us today, Josh. Really enjoyed chatting with you and having you on the show. Thanks for humouring us with the AI section, superhero, bed making. We've covered all kinds of fun things today. So thanks for coming on and carving out a few minutes for us.
Guest: Josh Schauer (28:52):
Yeah, thank you for having me. I enjoyed my time and I appreciate the opportunity.
Host: Paul Barnhurst (28:56):
Thanks for listening to the Future Finance Show. And thanks to our sponsor, QFlow.ai. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice, and may your robot overlords be with you.