Dave Sackett is Co-Founder and CFO of ecommerce company, AIOne, and former CFO of semiconductor company, ULVAC Technologies. He is a vocal advocate for blockchain and AI and finance, bringing a blend of tech savvy, operational rigor, and a focus on “servant leadership” – that is putting the needs of the employees first and helping people develop and perform as highly as possible. He’s a frequent keynote speaker, a Forbes contributor, and an active member of Financial Executives International.
In this episode:
- Cost accountant to CFO and AI leader
- How I practice servant leadership mindset as a CFO
- Being an introvert CFO leader and overcoming the challenges
- Costly failures of traditional forecasting in FP&A
- Where FP&A Software is going to be in 5 years
- Finance AI use cases
- FP&A as custodians of financial truth in AI age
Connect with Dave Sackett on LinkedIn: https://www.linkedin.com/in/davesackett/
Full Transcript
Glenn Hopper:
Today. Welcome to fp NA today, I’m your host, Glenn Hopper. Today we’re joined by Dave Sackett, a forward-thinking finance executive and co-founder of ai one where he is pioneering AI powered e-commerce with deep experience. As A-C-F-O-E-R-P system architect and a vocal advocate for blockchain and AI and finance, Dave brings a unique blend of tech savvy, operational rigor, and a genuine servant leadership mindset. He’s a frequent keynote speaker, a Forbes contributor, and an active member of Financial Executives International. In this episode, we explore how modern CFOs can embrace ai, rethink forecasting, and build finance teams ready for tomorrow. Dave, welcome to the show.
Dave Sackett:
Hey, thanks Glenn. Thanks for the introduction
Glenn Hopper:
It’s been a while since our last webinar together, and, uh, for those who haven’t heard you speak and aren’t familiar, could you maybe give us the short version of your journey, how you got started in finance and, and what, what you’re doing today?
Dave Sackett:
Sure, absolutely. So my background is cost accounting. So decided to be a cost accountant to let people know where are you making money, where are you not making money, and from there kind of rose up, uh, to the CFO level, working 20 years for a Japanese owned semiconductor company. About 2016, I got really interested in AI and be started talking about AI started, you know, telling people about what’s coming. And happy to say that, yeah, that forecast of where AI was going is coming true, and it’s quite amazing today. So I do a lot of talks on ai, I write for Forbes, um, really trying to promote it finance years.
Glenn Hopper:
And you’ve described your leadership style as grounded in a growth mindset and servant leadership. How has that influenced your transition from kind of individual contributor to CFO and from CFO to tech entrepreneur at this point?
Dave Sackett:
So, yeah, one of my strategies has been to adopt servant leadership, meaning I give my team resources that I’m not, I’m, my bosses have always been the traditional do what I say, my idea is the best. And I wanted to not be that person. I wanted to listen to team members. I wanted ideas not just to come from me, but from my team, and really empower them to have success so that they had good job satisfaction and they could have their own wins to have their careers advance. You know, I’m already at the top at CFO, I don’t plan on going to CEO, you know, my job now is to manage the team and make sure they perform in finance so that, uh, the whole department and responsibilities works well, have empathy for your teammates and for the people that you manage. I was definitely a contributor that was just a sole contributor.
Dave Sackett:
You know, I started out digging into the numbers, coming up with solutions, automating finance. Like that was really, my brand was just being kind of a superstar in what I did. And then from there, got moved into management. And so my shift went from I’m gonna make, solve all the problems to now I’m gonna let my team train my team to solve the problems. So that was my kind of shift. So people new to management, um, really understand that you’re probably managing sole contributors, that now they have to work as a team together and they have to solve their own issues and solve their own problems. So what skills do they need to become independent, still working as a team, but really to find solutions on their own and not to be afraid to ask questions or even cross department lines and really, um, reach out to anyone in the company that helps solve the issue.
Glenn Hopper:
Yeah. You, you and I both talked previously about being introverts, um, and you know, now you’re out there speaking at conferences, you’re writing for Forbes, you’re doing webinars, you’re really out there as a thought leader. And I’m wondering, as an introvert, how do you make that shift? Do you have to psych yourself up for it? And, and what’s, what’s kind of your approach to this as an introvert?
Dave Sackett:
Okay. Yeah, that’s an excellent question. I used to be terrified of public speaking. Um, in school, I remember counting who was gonna be up next so that hopefully I didn’t have to talk that day. And just absolutely afraid of what people were gonna think of me based on my opinions, what I said, how I said it. If I stumbled on my words, um, if I used the filler words, like terrified and in my mind, so fear and excitement are very close if you study brain technology. So it’s really having that fear and turning it into excitement. And then in my mind, I’m someone that’s trying to share my information and my knowledge, so I’m trying to help people. So in my mind, I’m here talking not to, you know, do anything else, but just help people in the audience understand my experience and where I think things are, you know, information I should share.
Dave Sackett:
So it’s, um, definitely from fear to excitement when it comes to public speaking or writing or putting my thoughts and ideas out there. And it’s, yeah, when I do a presentation, it’s things that I thought were super cool, you know what I mean? So everything, when I present it, it’s like, whoa, Dave thought this was interesting enough to share. Like, that’s all I do when I try to promote and do a webinar or my ideas. Um, and it is passion. It’s not, I don’t have to do it. I do it because I want to help other people and want them to see how cool things are and, and make it part of their lives too. And if it helps them, you know, if I only help one person outta all this, then it’s worth it.
Glenn Hopper:
So one of your recent webinars that I saw, you talked about the, the costly failures of traditional forecasting, and I’m, I think that that’s a very timely webinar right now. And I’m wondering from that, and and from your experience, what’s the biggest kind of mindset shift that fp and a teams need to make to modernize their approach? And what is an AI augmented forecast actually look like in practice?
Dave Sackett:
Yep. So I would think, you know, come at it from your end product, you know, what are you gonna deliver in terms of forecasting? How is that gonna, can you back up that forecast? What are the assumptions that go into that forecast? You know, traditionally you’re using internal drivers like your backlog, new product releases, um, you know, things very traditional to fp and a to do a forecast. But my prediction is that you’re gonna bring in outside influences and outside drivers, external to the business and blend the two so that you’ve got a dynamic model that predicts the future and how well you train your model and, you know, add different things into it. You’re gonna get better forecasting results and hopefully, you know, that’s gonna protect your company by having a good forecast.
Glenn Hopper:
Yeah. And it feels like we’re at this moment of economic uncertainty. Again, it’s, it’s not unlike the global financial crisis, but with different pressures this time. And I think back to all the different scenarios we were trying to model back then and the tools we had. So this is oh 7, 0 8, 0 9, uh, very different world technology wise than what we’re dealing with today. So based on the tools that are out there now, do you think AI enables a, like a, a better approach to scenario analysis than what we had back then?
Dave Sackett:
Uh, yes. So I’ll even, I, I guess I can bring in AI at this point because <laugh>, using AI in forecasting, you can do a lot of what if analysis and just change a few parameters, press a button, bang, now I’ve got a new forecast, and that doesn’t disappear. Now you have it. And if you’re training your model and training your bot to, to be accurate by doing back testing and, you know, including all the factors reading your ERP data, right? Um, you know, you can use that as instead of number crunching and having a junior FP and a person just put everything together, you’re having AI do that automatically. And you know, what’s driving that forecast? What were the assumptions at the time? What was the, you know, the backlog? What was the new product release schedule, the timing of sales, you know, all of that can be documented and kept by AI so that now the team isn’t just putting numbers together, they’re guiding the models and they’re actually influencing how AI is gonna be useful as a tool.
Glenn Hopper:
So let’s talk practical use, um, right now. I mean, if you’re a a giant enterprise out there and you’ve got a data science team, you’re probably doing some really cool stuff in the company. But for the rest of the world, midcap SMBs, uh, using generative AI typically means taking data outta your workflow, uploading a CSV and the chat GPT working with it, and it can, it can do amazing stuff, but it’s a little bit clunky and, and, uh, you know, we know things are changing fast, but what do you see kind of current best practices and, um, where maybe fp and a software goes in the next three to five years, say,
Dave Sackett:
From my prediction point of view, and, uh, where I think we’re going is that, you know, every fp and a software is gonna have an AI component, uh, whether you realize it or not, it’s gonna be built in and people won’t have to become data scientists in finance. They’ll just have to be able to use the software, uh, as a tool, just as you would use a calculator 30 years ago. Now it’s gonna be, hey, your skill is gonna be using AI within software. So I do agree most companies in, you know, probably on the planet are SMBs, um, even though the big companies get all the press, but most people are in smaller companies that can leverage ai. It’s just not, it won’t be through an SAP design to have it built in and have, you know, in your menus to choose and to run. And it’ll be very much far low key and just, you know, company to company. It’s like, where do they need the help with ai? And that’s where they’ll adopt.
Glenn Hopper:
Yeah, I always say that, uh, you know, people in finance don’t need to become data scientists, but we do need to understand the fundamentals of data science so we can ask better questions. And the example I always give is, anyone who’s in management and has been for a while, you’ve seen financial statements. You can read them, you understand, this is what goes on the balance sheet. This is income statement, this is cashflow statement. Um, but maybe there’s subtleties that you don’t get. Like you don’t, you maybe looking at operating income, net income ebitda, not being a domain expert there. That could be an area where you, you need to have that domain expertise. And similarly, it used to be if you were gonna be a data scientist, you would have to learn programming, and you would have to, um, so you could understand the fundamentals, but the barrier to entry was, oh, and by the way, you have to learn how to code in Python.
Glenn Hopper:
So that kept a lot of people from being data scientists, but now your, your naturally spoken language becomes sort of the new programming language. So you can access data science tools without having to write Python, but if you have an understanding of those, like this is what machine learning does, it’s, it, it does, uh, regression or prediction and, um, you know, classification and clustering, and these are the, the types of questions to ask. And this is a core what a correlation matrix is, and this is how we assess how well our model’s predicting and, and all that. So if you have the basic understanding, you know the right questions to ask. And I really feel like whether it’s finance or, um, or whatever department you’re in, having that data science skill is gonna help you sort of keep up with the, the future of the technology. And I’m wondering from your perspective, what skills should FB and a teams be looking at, you know, right now to sort of prepare for that AI driven finance function that’s coming?
Dave Sackett:
Yeah, so you do bring a point where you need to know how to use ai. That’s gonna be, you don’t need to be a data scientist, but you do really need to, you know, understand where is it strong and where is it weak? You know, where are your skills as the human gonna be important in an AI driven finance department? Like, you still need to be accountable for the numbers. You need to know how AI arrives at a certain value, you know, is how can you, you know, prove out a, what AI did was correct. You know, you don’t wanna blindly accept the AI’s output. And at the same time, AI shouldn’t blindly accept a human’s input. So I think there’s gonna be that symbiosis in the future where anything, an AI does a human checks anything a hu you know, a human does AI checks, and it’s gonna be that kind of internal control check and balance within a department so that you know, everyone’s on the same page and you’ve got backup in terms of, uh, what the numbers really mean and what’s really driving your business results.
Glenn Hopper:
Yeah, and you said something really insightful there, that, that not only should humans validate what AI produces, kind of that human in the loop that we talk about a all the time, but that AI should also validate what humans produce. And I think that it’s not a framing. I, I’ve I’ve heard before, and I think it’s so smart, and I think it’s, uh, recently I, um, I, I think the timing’s interesting too, because they’re, uh, you know, these models learn from, from human feedback. And so like chat GPT and, and Gemini and, and all and, uh, anthropic all out there, they’ll give you responses and ask you to rate the response, and they’d give side by side responses. And I guess, um, Chad CPT was giving responses and the humans were picking ones that were very, um, uh, friendly to them and, and maybe have a little bit of that sycophantic.
Glenn Hopper:
And it was maybe on these responses, it was subtle, but, uh, it’s the, the language model telling you, the chat bot telling you, oh, yeah, that’s, that’s brilliant. You’re, you’re right. And people were clicking those with thumbs up a lot more than just the regular sort of neutral responses. So when chat GPT rolled out a new model a couple weeks ago, they really, they overweighted these sort of sycophantic responses. So it got to be, uh, you know, we talk about bias in, in AI sometimes, but these models were so tuned to be like telling the user each user that they’re great, that it wasn’t doing that, it wasn’t checking them. It would, you could tell it the worst business idea in the world, and it would come back and say, oh, that’s great, you’re brilliant and all that. But that was an error in training, and it was so bad that, uh, OpenAI actually had to roll that model back and, and go make the corrections, uh, to it. So I think that that’s a very interesting concept, the idea that we’re checking the model’s work and that the model’s checking ours, and it also shows something that we have to solve for right now. So maybe, maybe talk about from your perspective, um, a little bit more of that, that symbiosis between the, the model and the, the human in the loop.
Dave Sackett:
Yeah, no, I agree. And it’s, yeah, and it’s the dopamine reaction that, you know, people want to see the little notifications and the thumbs up and like, that’s a human thing that we’re, we’re doing. But do you want AI doing that? It’s more, you know, I’d much rather have an objective. Here’s what the data says. Um, you know, from an the human point of view, I’ll be able to take that into a narrative in terms of, you know, should we do this? Should we not do that? What actions do we take based on this objective data?
Glenn Hopper:
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Dave Sackett:
You know, I mean, from a personal point of view, you know, people are definitely using chat, GPT, you know, copilot all the tools that are out there to help write business documents, make an email sound better. People are kind of dipping their toes in the water from a, an a business point of view. I’m doing more of, uh, like robotic process automation that was popular years ago, but even now, now it’s, you know, a cloud app. It’s able to, you know, interact with my department. And so they’re using AI in that, uh, that way now, at least at my company in my experience. So I think there’s many different directions to kind of get your feet wet, uh, in terms of using free tools, having, you know, corporate tools to really kind of future proof yourself, like you said earlier, and just be aware of what it can do and what it can’t do. And if you can find solutions that are non-AI, I’m all for it. But if you do find something where AI is definitely the best tool for the job, let’s take a look.
Glenn Hopper:
Yeah, and, and you know, RPA was for years, that was sort of the state of the art. That was when you talked about actual practical use cases. What was being done in finance RPA was it, and now, um, we’re seeing that that’s, uh, you know, generative AI is really taking a place there. And even the, the, um, RPA companies, UiPath, ethanol, um, are, uh, leaning into agents and, um, to using generative AI in RPA. And I think about this, um, they’re not there now. And I know there’s been a lot of talk about agents, but like, when I see operator from open ai, it’s pretty cool. It’s a little buggy, but it’s more of a sign of things to come. So when, but when you watch operator work or computer use from an anthropic or any of the other tools out there, it reminds me of RPA, you see the mouse moving around, interacting with the machine the way way a human would.
Glenn Hopper:
And that made me think of RPA, but the idea is contextually with generative ai, you wouldn’t have to, um, have that level of, uh, um, of training that you have to do for RPA that it just contextually picks it up. So you kind of see the future there. And it’s gonna be interesting to see how, um, how people, when they imagine if you have your own RPA for bank reconciliations or any kind of basic job that you’re doing, that you’re able to turn it over to a bot. I really see that happening. So for, for CFOs who want to introduce generative AI into their teams, but maybe they’re nervous about privacy data, mis misuse, like how do you recommend these finance leaders get started? What guardrails and and governance policies would, would you recommend?
Dave Sackett:
Yeah, I mean, from a cybersecurity point of view, you know, is it locked down? Are is a bot going out to the internet where they’re not supposed to be, like, really have internal control in place, just like they were another finance employee, making sure that what they’re pulling is the correct data, they’re linking to the right sources, you’re getting the right information. Uh, humans in finance, like you said it before, we have to have, be the, you know, proponents of truth in the data. So people come to us to see what’s real. So we can’t get that wrong. And we can’t just say, well, AI did it. We have to know what did AI do? Where did they get the data? Um, not really a black box where you kind of throw your hands up, but really understand where it’s coming, how it’s being influenced, and how you’re getting the information the way you’re getting it. Um, and really keeping that as, you know, we’re custodians of truth. That doesn’t change with ai, that’s still, I have to make sure the numbers are correct and whether I’m using a calculator, ai, whatever the tool is, you know, at the end of the day, the human is responsible to make sure that you’re accurate and that it’s you’re doing your best with the data you have.
Glenn Hopper:
Yeah. And another, uh, similarity that you and I have, and I think it probably influenced both the way that both of us think about, um, the role of, of CFO, but you were both A CFO and an ERP system administrator,
Dave Sackett:
Right?
Glenn Hopper:
And I think, you know, maybe at, in the past that was rare, but there is so much more crossover today between the CFO’s role and then our, how important our, our finance tech stack has become to that. So, but it makes you, if you are a, a CIS admin and you’re really leaning that much into the technology side, I, I, I would have to imagine that that dual role would change your perspective on everything from data, data integrity to the kind of the role of ERPs in, in modern finance.
Dave Sackett:
Yep. The ERP keeps all data, and to me, as a finance person, I use all data. You know, even though I see the output, I wanna make sure transactions are correct in operations, I wanna make sure that the numbers are correct, that, you know, there’s backup for things, that things are going to the right accounts. So, you know, taking it from really just focused on finance, but now focus it on the entire company, and are we keeping good data? And especially with AI and automation, you don’t wanna start that with bad data. So you wanna make sure everyone’s on the same page of, you know, whatever that is put into ERP, that’s the truth. And to say, these transactions happen outside the system, like no, the, the system is there to keep the transactions in. So it’s kind of teaching the company to really be data mindful and to leverage not just the, the ERP, but understanding what you do has an impact on the business. And in the front of finance point of view, what you do, I can translate into the bottom line into a a p and L forecast, but really it’s making sure that it’s all done on the right information.
Glenn Hopper:
Yeah. And you know, I, there’s talk about, uh, moving towards the unbundling of the ERP between all these different SaaS tools that you could sort of put ’em together, um, with APIs and have them talk to each other, and you could get the functionality of an ERP, but coming from many different sources. And then if you factor that in with Satya Nadella said, um, uh, a couple months ago about the entire SaaS industry could be at risk from generative ai. Because if you think about SaaS tools, they’re wrappers or interfaces that go over these big databases, and what if through generative ai, you don’t need these big complicated UX tools that you can just chat with the data or build agen workflows or have agents going in and doing the work, that whole SaaS layer goes away, and it’s just about the data that’s in the database and, and how we interact with it. So I just wonder where does the ERP in your mind fit in in the future going forward?
Dave Sackett:
Yep. No, I think, uh, ERP becomes your, you know, the database of truth, I would say. And, and you have to link in all your other databases and all your other tools to really compliment each other to have, um, you know, a source of the true data. So linking ERP to different tools and to push and pull information, I think that’s all the future. So it won’t be, traditionally everything’s done in ERP, now you’re gonna have cloud versions, you’re gonna have, you know, this software, that software that you’ll pull and push information from to get a better picture of what’s happening in your company.
Glenn Hopper:
Yeah. So let’s, let’s switch gears for a a moment. I do wanna talk about AI one. Yeah. Uh, because I, you know, I know as a CFO where your focus is, but I’m wondering what inspired you to build, uh, this AI powered, it’s an e-commerce platform, right? So what inspired you to build that from the, the ground up?
Dave Sackett:
Yeah, no, thanks for asking about it. Yeah. It’s a startup company based on, you know, tracking information and using tools, uh, AI tools to really drive the company. So we wanted to be AI kind of first, and you almost like an experiment. We don’t need to just throw bodies at a company. Let’s use AI tools to really drive a company and make that a foundation of how we go forward. So it’s intentionally choosing that, you know, we’re gonna use AI to do marketing, to do, um, tracking of sales, to do payments of referrals. Like we’re go, we want to use the AI tools in a company from the ground up. You know, we don’t wanna go traditional and switch over to ai. We wanna say, okay, this is new. This is something we’re just making out of the thin air. Let’s intentionally go into the, the business as we’re gonna be an AI driven business.
Dave Sackett:
And this was, you know, years ago before kind of all the hype happened and now everyone wants to be an AI company. So I’m trying to kind of just shift it to, you know, we are trying to provide a service to match up buyers and sellers, and also have a, a concept called co-sell where you and I could sell fp and a software, and if we just do a referral and influence a sale, you know, my technology recognizes that and rewards people who not only close a sale, but influence it. And so the concept was kind of started based on, today everyone goes to Amazon or Walmart, these giant companies to get whatever you need in the future. We predict people are gonna buy from their friends and what their friends think is cool. So by doing that, the seller is kind of leveraging that network of friends to now make sales. So it’s, the idea is that the seller wins because he’s selling the buyer wins, they get something that they’re looking for, and then whoever influences or closes that sale get commissioned from the seller. And all that’s known ahead of time in networks so that my company is, uh, gets rewarded when we build networks of buyers, sellers, and salespeople.
Glenn Hopper:
And now you’ve worked in both large enterprises and now in, in the startup space. And I’m wondering, um, looking at it from both sides, how do each of these environments approach AI adoption? And maybe what <laugh>, what could each learn learn from each other?
Dave Sackett:
Yeah, no, that’s a good question. Um, yeah, we’re like, right now we’re bootstrapped, so we’re trying to do everything without spending money, which for a startup is very difficult, but it’s, uh, we have gotten nothing to lose. So the idea is that, you know, when the, the, the customers come in, we get paid. That allows us to now go in this direction and take on this tool. If we get more business, then now that’ll gen, that’ll give us budget to do something else. Whereas my traditional job, I’ve got an annual budget, I know how much I can spend, you know, it comes outta the business. I’m not personally affected by it with the startup. Yeah, I’m putting in the money. Like I’m the one, I’m using that customer cash to now fund building networks and marketing. So do I put it towards salaries? Do I put it towards marketing?
Dave Sackett:
Do I put it towards whatever? You know, I’m very much in sync with cash flow and how that really runs the business versus my traditional, I’m, you know, I’ve got a sales department, I have all these different departments all working together with the startup. It’s all me and my partner. So it’s, it gets boiled down to you wear many hats and you’re trying to just keep the, you know, at this point, I’m just trying to keep it going. You know, nothing that I, that I don’t get a salary from it. I don’t, you know, make money off it yet, but I’m just trying to build something cool that will eventually make money once I have enough networks and have enough people getting value from my system. I’ve got no intentions. Just add people. If I can use AI, and okay, now, you know, instead of a lawyer or an a legal internal legal team, I’m using AI and say, okay, give me a contract that protects us from X, Y, and Z and make it double indemnity. And, you know, things that I’ve known from my CFO times, simplify it for the startup. So that that becomes, you know, okay, that’s my contract, that’s my NDA, that’s gonna be, you know, that’s a heavy use of AI for us. Um, and it seems to work, knock on wood, but, uh, <laugh> every, you know, that’s how we’re building things. So if we need, if know things to be done, if we’re gonna promote, if we’re gonna do contract review, if we’re gonna, you know, whatever it is, you know, leveraging AI as the resource is our plan.
Glenn Hopper:
Yeah. As finance and accounting people, we’re risk averse. That’s part of our, our job and our nature. You know, we’re, we’ve definitely hit the peak of the AI hype hype cycle. Um, but I’m wondering for finance leaders who feel like they’re behind, how could they, how could they catch up? But without being overwhelmed by all the, all the noise, the hype and the technical complexity, and just the different benchmarks and everything that’s going on around it right now. ’cause I know you and I watch this stuff every day, but for someone new to it, that, that’s a lot.
Dave Sackett:
Yeah, no, I would suggest really researching, you know, agentic AI or using AI agents, uh, which is kind of the, where we are today and where the future’s going. And it’s understanding the capability, you know, what can AI do well and where can it not do well? If you’re ju, if you’re a CFO and you’re just trying to get understanding of what AI can and can’t do, I think that’s a good spot to start. Um, it’s evolved into that, but now you’re looking at, these are the functions that I can rely on. I can have agents verify the warehouse management system that something shipped. I can have agents, you know, organize, uh, what’s happening in the company and to actually influence policy. You know, if this, if I look at a credit customer and their overdue, you know, the AI agent can put them on hold and, you know, depending on what the policy is, you can make that a very strict process or a very open process. And it’s really digging in at that level, not at the technical AI side, but really the applications. How is it gonna help me in the future? You know, how can I look at AI as a tool that’s gonna just make what I do more efficient, faster with a good audit trail if you’re, if you’re in finance.
Glenn Hopper:
Yeah. And you, you mentioned ag agentic ai, where this is like autonomous AI agents take on roles just like they’re another junior analyst. And I’m wondering, you know, looking ahead, this is get out your crystal ball. Um, so what’s a, what’s a bold prediction you’d make about kind of where the role of AI and finance goes in, say, five years? And maybe what should these fp and a teams start, start doing to prepare for that today?
Dave Sackett:
Yep. No, so I’ll say, yeah, machine learning is the foundation for all the cool stuff that we have today. So from five years from now, what’s not that popular is having a finance assistant or a junior finance person that’s ai. I think that’s gonna be someone that’s gonna be something that you hire almost, that you’re gonna have this financial assistant that’s AI driven, and you’re gonna give them tasks to do that they execute just like they would a human. And it’s gonna be, this is the, the AI side. They’re gonna connect to the data. They’re gonna do kind of the slogging and the, the time crunching part of the, the finance, whereas the other people will be more, uh, analytic and, you know, presenting, you know, things that are not just the number crunching, the digging, the flux analysis creation. It’ll be, uh, an AI agent or financial chat bot that’ll be popular in five years.
Glenn Hopper:
All right. So we always, uh, we always wrap up with two questions. The first one, we get all kinds of interesting. It’s, it’s always fascinating to me to see what we get. We’ve had, I’ve learned that people are, uh, huge opera fans, that they run ultra marathons, all the stuff we learn about ’em. Uh, yeah. Now that I’ve set the bar high with that, yeah, definitely with that. Um, what’s something that, um, not many people know about you? Something we couldn’t find just on your, your LinkedIn profile?
Dave Sackett:
Okay, well, yeah. Even by saying what other people have done, I’m a karaoke superstar in Japan,
Glenn Hopper:
Japanese karaoke. That’s awesome. That right.
Dave Sackett:
It’s in a very safe environment. They have rooms, they have the lyrics on the screen, and you get rated based on how well your English is. So I always get very high marks in Japan relative to my friends who are trying to, to get the English just right.
Glenn Hopper:
All right. All right. Um, and finally everyone’s favorite question, and I’m, I’m always hesitant to ask CFOs this because we use Excel differently, <laugh> maybe than we did when we were really building models, but, um, we all came up using Excel. So, um, what is your favorite Excel function? And y
Dave Sackett:
Ooh, uh, x lookup. I was an index and match guy. I was v lookup, H lookups. Um, but now X lookup saves me time. So if I wanna tell you, if you, you know, does Dave know Excel well? He knows X lookup. Okay. He’s in our club. Um, that’s kinda my favorite, but there’s, I, I do love Excel and I’ve used it to get promoted essentially and rise through the ranks based on what I could do in Excel. So I have a, I’m very loyal to that program and I, you know, use it all the time, but, you know, X lookup kind of makes me look like a superstar, being able to link data and pull it in and it’s just a simple formula. So,
Glenn Hopper:
Ah, yeah, I love that. And it, and it speaks to how mastering Excel just becomes a superpower in finance. And I had similar path early in my career, and I, you know, I always said I was promoted because I was good at Excel and PowerPoint didn’t even need the MBA, just have the basic Microsoft skills. Um, but what I didn’t realize at the time was that being able to take that raw data, analyze it, and then visualize it in a way that kind of tells, that tells the story, well, that is what moves you up. Agree. That’s kind of what we do in, in fp and a. So yeah, those, I mean the ability to use those tools, and now I have the same approach with, with AI though, it’s like, that’s great. Excel got us this far, now where are we gonna go with the new technology? And it’s not time to be bearing our head in the sand and, uh, building our, our, our moat around Excel or whatever. So Dave, this was a, a fantastic conversation. I guess, uh, for listeners who want to follow your work, learn more about AI one, what’s, what’s the best way for them to connect with you?
Dave Sackett:
Sure, yeah. I’m big on LinkedIn. I answer every message whether you’re trying to sell me something or you’re asking me a question. Yeah. The website is AI one.vc and it kind of shows our idea and it’s, we’re really kind looking, it’s, it’s not an idea now, it’s a future idea that we’re building in the stages now. So to me, it’s interesting, it’s, we show our philosophy, we show how we wanna give back to people and allow everyone in the circle of the network to earn. So the idea is that everyone wins in this concept.
Glenn Hopper:
Alright, Dave, well thank you again. Thank you so much for coming on.
Dave Sackett:
Yeah, thanks Glen. Yeah, it’s really fun. I liked, uh, being a guest.
