How to Align Marketing and Finance and Fix the Plumbing Problem with Alex Brower
Future Finance host Paul Barnhurst and co-host Glenn Hopper are joined by Alex Brower, co-founder and CEO of QFlow.ai, to unpack why forecasting often breaks down even when teams have plenty of data. The discussion focuses on how finance and go-to-market teams can work from shared inputs, improve planning accuracy, and use AI without losing the structure needed for reliable decisions.
Alex Brower is the co-founder and CEO of QFlow.ai, helping finance and go-to-market teams connect data, improve forecast accuracy, and boost analyst productivity up to five times. He previously led finance and marketing at high-growth tech firms like AppTelligent (acquired by VMware) and Cloud Academy (acquired by QA).
In this episode, you will discover:
Fix data plumbing before system consolidation.
Use data dictionaries for consistent definitions.
AI helps, but semantic logic is still needed.
Vibe coding can create hidden costs if untested.
Start with key unanswered questions, then plan solutions.
Alex shares lessons from leading finance, operations, and marketing teams at high-growth companies, including what he learned from living the forecast tension on both sides of the table.
Follow Alex:
Website: https://qflow.ai/
LinkedIn: https://www.linkedin.com/in/alexbrower/
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
Follow Paul:
LinkedIn: https://www.linkedin.com/in/thefpandaguy
Follow QFlow.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
[03:44] – Finance-to-marketing journey & QFlow’s solution
[07:10] – Data plumbing vs. alignment
[11:37] – MCPs, AI, and logic layers
[14:50] – Risks of vibe coding & maintainability
[21:56] – How QFlow handles data, planning, and storytelling
[30:57] – Alignment before strategy & reducing fact-hunting
[35:39] – Where CFOs/FP&A should start modernizing
[44:03] – Closing & sponsor sign-off
Full Show Transcript:
Host: Paul Barnhurst (00:40):
Welcome to the Future Finance Show where we talk about management on ours. Future Finance is brought to you by qFlow.ai, the strategic finance platform solving the toughest part of planning and analysis. B2B revenue align sales, marketing, and finance seamlessly. Speed up decision-making and lock in accountability with qFlow.ai. Welcome to another episode of Future Finance. I'm Paul Barnhurst, your host, and today kind of a clown. I can't get things right as Glenn will confirm, but we're going to try to do a great interview today. So how are you doing, Glenn?
Co-Host: Glenn Hopper (01:37):
I'm doing great. And I was going to say, how is today any different than any other day?
Host: Paul Barnhurst (01:41):
Shots already and we haven't even got 30 seconds in. Well played, Glenn. Not all of us can be famous like you. Big AI guy and all. Speaking of famous people, we got a great guest today, but before I give it away, I'm going to let Glenn go ahead and introduce our illustrious guest.
Co-Host: Glenn Hopper (01:58):
Today we're joined by our good friend, Mr. Alex Brower, co-founder and CEO of Qflow.ai that listeners to this show will be familiar with. QFlow is a company redefining how finance and go - to-market teams plan, measure, and align. Backed by Precursor Ventures and K-5 Tokyo Black, QFlow helps growth-focused companies connect their data, improve forecast accuracy, and increase analyst productivity by up to five times. Before founding QFlow, Alex led finance operations and marketing teams at high-growth tech firms, including AppTelligent, which was acquired by VMware, and Cloud Academy, which was acquired by a QA. Alex is known for bridging strategy, performance, and data, and for his belief that the real power of technology is in aligning people around shared definitions of success. How's that for my radio voice? Pretty good. Alex, how are you doing? I'm
Guest: Alex Brower (02:56):
Good.
Co-Host: Glenn Hopper (02:59):
We're happy to have you on the show. We've been trying to plan this for a while, so we're glad to finally have you on.
Guest: Alex Brower (03:04):
I'm happy to be finally on. I appreciate you guys and the work that you do for the FP&A and finance community more broadly and it's an honour to be here.
Co-Host: Glenn Hopper (03:13):
We talk all the time and you and I have, we've done a little fire chat session before. I'm going to dredge up some of the stuff we've talked about. I'm going to lead off today with, it's probably this first question I asked you when we did our last chat. I love the shape of your journey. So if you could give us kind of a quick look at going back from leading finance to biz ops to marketing and then ultimately to founding QFlow. Was there a consistent threat or what problem were you trying to solve when you started QFlow?
Guest: Alex Brower (03:44):
Hopefully my answer doesn't vary too much from the last time I answered. I don't think it will. I came up with PE background operators at a startup in Chicago. I was on the finance team and when I left I was running the finance team and this was a startup that went from 200K EBITDA to 30 million EBITDA in under a year. So operationally it was challenging, but I was focused on building the models. We owned the forecast. We lived the pain of board prep. A couple years later, I moved out to San Francisco with the hopes of doing it again. And in this case, I joined a tech company. It was a self-service meets enterprise motion. I had RevOps reporting to me. We had to throw analysts at hygiene issues every cycle to make things like the ARR schedule tie out. It was a tax we paid each month.
(04:38):
And after that company sold to VMware, an investor asked me to run marketing at one of his port codes. Part of what I had done previously was restructuring of a marketing team and it was fun. So I said yes. And I then experienced, I wouldn't call it the forecast fight, the forecast tension from the other chair, right? Same data, different numbers, hard work aligning people. That's really the arc. Every company I've been at was building the same Frankenstein across a data warehouse, Salesforce or HubSpot exports, ERP pull, billing reconciliation, and a lot of spreadsheets. So we built QFlow to change how teams approach fixing the data, planning the growth, running the business and telling the story.
Co-Host: Glenn Hopper (05:24):
Isn't it amazing? The more businesses you talk to, it's like everybody within a certain size of business, they're all dealing with the exact same issue. I can see the desire to productize connecting all that and taking the time out of... Well, it's what everybody's trying to solve with AI with probabilistic results right now. And so that makes complete sense.
Host: Paul Barnhurst (05:45):
Glenn, I was going to say, you said it much nicer than I did. I was going to say the same level of dysfunction between all the
Co-Host: Glenn Hopper (05:52):
Companies. Dysfunction is opportunity, right?
Host: Paul Barnhurst (05:55):
It's all one side of the coin you decide to look at, right? Thank you for that kind of first answer in your journey. That friction between marketing and finance is an interesting one. I've had some guests on and talking about that. And now having run my own business, and I imagine even Glenn some, I have a very different perspective. I would work differently with marketing. So I can see how having that help led to what you're doing because being on both sides of that table gives you a very different perspective than just being on the finance side or just the marketing side.
Co-Host: Glenn Hopper (06:27):
You know, Paul, my first job out of business school was in marketing and not finance. So I've always had a soft spot for marketing as well.
Host: Paul Barnhurst (06:35):
Well, we have three people that know a little bit about marketing. We're almost enough to be dangerous.
Guest: Alex Brower (06:40):
Dangerous. Yeah.
Host: Paul Barnhurst (06:42):
Refinance people, I should say.That's a scary thought. All right. You've mentioned several times you kind of have saying where you said forecasting isn't broken, but the plumbing is. So I'd love to first kind of dive into that. What do you mean by that? And what does good data plumbing look like? Obviously you're trying to connect the CRM, the ERP, you got your billing system. There's never just one source of truth. There's multiple different tools out there. So maybe start by talking a little bit about that.
Guest: Alex Brower (07:10):
There is a tendency for sure to just get all the data into one place. And I have thoughts on that, but I think the headline would be just because you've gotten all of your data into on place doesn't mean you've gotten teams on the same page. Typically, we think about hygiene issues and plumbing issues in a couple different categories. They're the categories of things that you can addres and fix and then there's the stuff that you have to live with and manage. And so examples of stuff that we typically see getting in the way between an organisation and a top-notch quarterly or rolling 36 month forecast are things that obviously impact the top line bookings forecast, close dates in the past, garbage data entered by sales reps who are gaming overly aggressive CRM validation requirements. Then we see stuff that finance has contributed to. And this usually is because they can't trust the data out of the CRM, but they maybe lack the willpower to do something about it.
(08:24):
You usually see this in the form of an ARR waterfall that's like this massive file that gets updated every month. And that's because the customer list coming out of the CRM doesn't match the customer list in the billing system, which has drifted from the customer list in the ERP. And once we start addressing those, we see that there are missing transactions in the CRM or in the RP or hopefully not in the billing system, but it's happened. Probably biggest thing that companies can fix, and this is perhaps not new news, is around the data dictionary. So these are data dictionaries that may not exist. They may have been updated a long time ago. And when you lack a data dictionary across teams, every team's true number disagrees with every other team's true number because no one agreed on what a dollar of ARR means or what actual a qualified opportunity looks like or how we think about customer hierarchy.
(09:29):
So those are three categories of hygiene issues that I think are immediately addressable.
(09:38):
The fourth is a category of issues that you have to live with. So many of our customers have grown through acquisition and they have no intention of consolidating their ERPs or their CRMs. Maybe they bought a B2B2C company, they have a couple B2B acquisitions that run vastly different go - to-market motions. So they're running in four different CRMs. And in that case, a data warehouse actually locates that complexity. It doesn't reconcile it. So that's what I mean by getting the data in one place, doesn't mean you've gotten people on the same page. These companies live with sort of a birectional pull. They still need the fine-tuned models, the predictions, the business logic per entity, per segment within that entity, but they also owe the board one aggregate forecast. So it's attention to manage and not a problem to solve.
Host: Paul Barnhurst (10:38):
As I listened to you say all that, I was like, has he been sitting in the companies I worked for? Yeah, I could still remember the conversation with senior leadership. We were trying to find what a booking was as we moved toward a SaaS model. We had the CEO, the CFO, the CRO all on the phone and I was kind of leading that because my job was helping us make the transition. And so it was an interesting conversation or when you said missing billings, I remember I had to go through and dig through all our billing stuff. We had someone, we were giving a $10,000 discount for two years that was only supposed to be a one-month discount and nobody turned it off. Just all kinds of stuff where you're just like, "Really?" Because there was zero connection. I mean, it was all broken. So I can really relate to that.
(11:29):
What do you say of this idea now of, well, as long as you can hook it up to an MCP connector, AI can help you figure it all out. What would you say to that?
Guest: Alex Brower (11:37):
Yeah, I think that what would I say? I would say MCPs are powerful. They're a great transport layer, but they're not a semantic layer. So we see MCPs being used sort of again on two different ends of the spectrum. On one hand, like the one-offs. So I'll pick on the HubSpot OpenAI plugin great for querying the status of an open deal, pretty good at updating a record, but it's not going to give you a nuanced view of your win rate by segment or tell you why your renewal motion is slipping or thoughtfully assemble a low, medium and high growth plan for potential investors. So that's sort of like on the one-off side. On the other hand, it's full on vibe coding. A CFO asks marketing for conversion analysis, someone's got Claude desktop or cloud code hooked up, both powerful tools and someone vibe codes like a complex attribution conversion analysis in Node or Python.
(12:57):
It works for the next QBR, but it breaks the next time a new definition or a new activity type is introduced and it's difficult to maintain because that marketing operations person doesn't actually know Python or known. So I'd say right now the MCP on its own coupled with a frontier model does not get you to the durable analytical layer that you need. Some of these things we're talking about like segmented win rate, multi-entity waterfall, or even variance commentary that holds up at the board level. So I think that's the gap. MCP is the pipe, you still need the water treatment plant, you need the semantic alignment, the business model reconciliation and that has to happen before any insight is generated.
Host: Paul Barnhurst (13:46):
Now we keep hearing that semantic layer. I was going to say one thing and then I'll let you go, Glen. I'd love to get both your thoughts on this. So as I listen, we hear a tonne about vibe coding and I've been thinking a lot about this and it feels a little bit to me like the Excel wizard or the VBA person, but it's available to everybody. What always happened when somebody did a bunch of stuff in VBA or they did complex Excel models and they turned it over to the next person? What happened 80% of the time? The next person scrapped it and started over. Are we just duplicating that now that it's much easier, there's lower friction with vibe coding? Not to say there aren't some great solutions and there aren't some great Excel files to get transferred and some good work that can be done, but it feels a little bit like we're repeating it just with new technology.
(14:31):
Am I off on that? I'd love your guys' thoughts. Then I'll let you go, Glenn. I'm just kind of curious, Alex, your take on that because that's kind of how I feel and I like it and I think people will use it, but I worry we're going to see a similar type of kind of sprawl and problems that we've seen with the expert in Excel or the VBA just in a different way.
Guest: Alex Brower (14:50):
Yeah, I think it's an interesting analogy. I think that it's a very powerful tool if you are looking at it, if you're actually reading what it's doing, if you're testing it, basically it's a very powerful tool for someone who probably already could write the code and I think it's a cool tool for people who can, but I do think that there's another aspect to this that we're chatting about before the show. The vibe coding can get expensive fast and if you're vibe coding basically a mini application for every aspect of financial analysis or go to market analysis, you're going to end up like some of the companies that I spoke to earlier this week, one company we talked to now has a new policy where it's not opt out. You have to ask for permission to get access to the corporate anthropic account because of pricing and cost concerns.
(16:01):
Another company was literally the whole company was out of OpenAI tokens for the month. So if you're running agents or if you're relying on AI to fix a vibe coded app and halfway through the month you're out of tokens, that's a problem. So for sure, I think the team leaderboards on who's using the most tokens, that's probably been over for a few months, but that's I think really over now and people are starting to think about costs. And if you can do something with deterministic code as opposed to reinventing it every time using an LLM, you're going to be much better off. So that are my thoughts. I think it's a good analogy. I appreciate
Host: Paul Barnhurst (16:49):
That. I was just kind of curious. And sorry, Glenn, I know I took your question, so I'll let you have back control, but every so often I like to do what you do, just one more.
Co-Host: Glenn Hopper (16:58):
Yeah, just talk over everyone. "Oh wait, we have a guest today." I think from my standpoint, what you said about if somebody's writing VBA, they at least know what they're doing. So even if they hand it off to someone else and they're not using it, it's like, "Well, this is traceable. If you understood VBA, you could make it work." If you're vibe coding an app and it's a magic black box that's writing code for you and you don't understand the first thing about coding and you're handing and then you leave or I mean, I think what's the Mark Twain quote, "The reports of my death have been greatly exaggerated." I think that whole SaaS apocalypse is... Yeah, I get it. It would be cool if we could just all vibe code our own ERPs, but come on. I mean, I'm a finance guy by day and I'm building my own SaaS platform at night.
(17:58):
That's not to say there's not a place for it, but people, if you don't know a semantic layer from a layer cake, how are you going to structure and architect all this? So to me, vibe coding certainly has its place, but I think when people...
(18:18):
It's interesting, Alex, because in a lot of ways we're solving very similar problems, except I'm doing it every client is a snowflake and it's all bespoke and you're doing it the smart way where you have the product that applies across the digital universe. But the whole idea that someone who is a CPA and has spent 25 years as a controller up to CFO that they're suddenly going to be able to build apps that aren't leaking their company data, that they can explain to an auditor, sure, the code sits out there, but a lot of times the code doesn't even sit out there. If they're building artefacts and Claude and sharing artefacts, it's like, well, you just made that a public link so somebody could stumble across. I mean, there's so many ways that this could be a problem. So I think maybe one day down the road, but it's a discredit to me.
(19:15):
I mean, if you say, "Oh, we don't need developers anymore, we can all develop our own apps." That's a discredit to what true engineers are doing and what they're building and you don't even know it's the unknown unknowns. You don't even know the right questions to ask, the things to check, how to set up OAuth or whatever it is that you're doing, you can have AI do it, but how are you going to check that to even make sure that it's doing the math right? Anyway, that's a long, long run.
Host: Paul Barnhurst (19:42):
Yeah, we could spend all time on... Sorry, I didn't mean to derail us. It's just it feels like there's going to become a moment of truth where we're going to recognise there's a lot of stuff that we probably went too far on, but there'll be a lot of great stuff that comes out of it as well. And so it's just interesting. I think you have two camps. Don't do it at all almost like these people are, "Oh yeah, it should be developers. You're stupid. Nothing's going to work." And others of, "I can vibe code everything."
(20:09):
So anyway, sorry, I'll let us get back on track here. Ever feel like your go - to market teams and finance 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. Meet qflow.ai, the strategic finance platform purpose built to solve the toughest part of planning and analysis B2B revenue. QFO quickly integrates key data from your go - to-market stack and accounting platform then handles all the data prep and normalisation under the hood. It automatically assembles your go - to-market stats, makes segmented scenario planning a breeze and closes the planning loop, create airtight alignment, improve decision latency, and ensure accountability across the team.
Co-Host: Glenn Hopper (21:19):
Well, and then I think the transition from that is we started talking, I think, before we went off about that data layer and how important that is. And one of the things, I guess the QFlow promises is that you're going to help companies prep the data, plan the growth, run the business and tell the story. And that's a whole workflow that has to start with the data. But maybe if you could tie those together of how those stages work together and practise. And if you've got a real example of how that would work, that would be great to sort of visualise it.
Guest: Alex Brower (21:56):
Prep the data is always part of it and sometimes it's fix the data. Everyone gets the data dictionary out of the box when they sign up for QFlow. There's a light configuration layer that is AI quarterbacked and that automatically builds several dozen data sets that are deterministic and ready for analysis. So everyone has that. Some people that fix the data. We talked about some of those examples earlier. I can't give my board a good revenue number or a forecast because I've got the four different CRMs and the company names don't match and we've kind of hacked it together. So that falls in the fix the data and there we have a series of tools that do entity matching and transaction matching and reconciliation on an ongoing basis. So sometimes there's a push to clean up the skeletons in the closet or clean out the skeletons in the closet.
(23:02):
And for those companies, that's like the hair on fire problem. We talked about this earlier. The forecast isn't broken, but the plumbing is they know roughly how to forecast their business. Maybe it's seasonal, maybe it's based on number of subscribers or number of cameras under management or something, but they've got a data problem. And so often we'll work with them to get their data into a good place. And then we're off to the races in terms of planning the growth and running the business. We take, I think, a unique approach to the growth planning. It's one workspace where both finance and go - to-market leaders share the same inputs. These are the finance grade conversion stats, timing stats that you would need to properly forecast out revenue, but it is also the unstructured sales data, the call recordings that salespeople are having. So we integrate all of that in to get a true multifaceted look at pipeline and unknown pipeline.
(24:14):
So that's where those marketing statistics come in. And we assemble all those for you into scenarios. You can create as many as you'd like. It's a 36-month rolling view.
(24:26):
The company I was describing that has the four CRMs can manage all of these scenarios at a per entity level, a per segment level, and have them all roll up into one unified number that they call to the board. The run the business, we've got dedicated agents that run daily doing things like evaluating why companies are winning and losing deals. That's a surprisingly popular product. Turns out that sales reps like to choose just the first loss reason in the dropdown. And when they win, it's always because of excellent relationship that they've created with the client. When you kill back the layers of the onion and you actually look at the call recordings and you map those to a canonical list of reasons that you wish you had set up and someone had filled out thoughtfully, you get very different answers. So we have agents that do things like that, deal evaluation, a budget versus actual go - to-market stat monitoring and it's built on a blend of deterministic BI and LLM reasoning, the why for the narrative.
(25:44):
And the final piece is telling the story. This is where we do expose an MCP. It is through a QFlow desktop app, which allows you to run recipes which are in our product. Recipe is a series of deterministic steps that are auditable, that have drill downs, that have timestamps, that have history tracking in terms of who touched what, when, and why. And those serve up the biggest use case here is automating portions of the board deck. So you can drag your existing board deck into QFlow, it will analyse it, and it will create recipes to create essentially the deterministic basis for each one of those slides. And then you can take the results of that and do with it what you wish in Cloud Desktop or Codex in terms of formatting. So that's really the telling the story piece and it's all the same dataset.
(26:53):
It's not for products
Host: Paul Barnhurst (26:55):
Data, which makes sense.
Co-Host: Glenn Hopper (26:58):
Interesting to hear Qflow has clearly evolved as AI capabilities have evolved too, and you're bringing them in. And I think that's maybe another way. If you ask a CFO how many people, if they're using AI, they're going to say, "Heck yeah, we're using AI." But for a lot of people, using AI means just dragging stuff into a chatbot, doing an analysis in a one-off situation. It's kind of like the vibe coding thing. If everybody's building their own apps or having their own analysis or whatever, but when you can bring it closer to the engine and you have it all in one system where everybody's using the same AI and to your point, which a lot of people may not be thinking about, when you build that recipe, there's deterministic steps. So that's repeatable, it's explainable, auditors get it, everybody gets it.
(27:54):
And obviously everybody in SaaS is trying to do this right now, but solving for that is what is going to keep people on your platform and make them trust the AI. Even if you're using the same model underneath, you've got the guardrails in because engineers are actually thinking about the layout there. And I think that's a significant thing to watch. And it's been pretty fascinating to me, whereas it's kind of like the same, maybe it's in the same vein, but where if you're worried about AI taking your job, I focus more on how am I going to be better at my job with AI? And then with SaaS, it's, well, could AI do this? I don't know, maybe if you had the right guardrails in place, but what if the people that made your favourite SaaS product actually just incorporated it in and you didn't have to worry about it and you got those better tools out of it.
(28:45):
And it sounds like that's the path you guys are on too evolving with the technology as it's coming out there.
Guest: Alex Brower (28:52):
Yeah, for sure. I mean, AI was in our domain name because we were AI and ML first from a statistics perspective and a model perspective. The large language stuff, we're clearly evolving as everyone is as that gets better. But I think the two advantages are the reliability and repeatability of using something like our recipes feature where you can see what it's doing and what's resulting from that run number one. And number two, back to the comment earlier about expenses for companies that are focused on budgeting for AI and specifically who are averse to tokens, you can bring your own token to QFlow, that's great. It's great for our margins, go for it, but you can also just use one of the models that we have built in and that roll Reliable pricing is attractive to certain CFOs right now.
Host: Paul Barnhurst (30:03):
Totally makes sense. I think everybody's trying to get a handle on the pricing. It's kind of like we're dealing with AI sprawl, just like we all dealt with SaaS sprawl. Something new, great comes out, you all run to use it and gets adopted widely and all of a sudden you're like, "Wait, we're spending how much? What's the benefit we're getting? We really did. That was a million dollars last month. Are we crazy?" And you start to put discipline in place. It seems to be a reoccurring theme. But I want to shift gears a little bit. Something you've often said that I agree with is the real purpose of technology is around aligning people. So how does that shape Q flow and how you do things on a day-to-day basis? Especially as we all know, sales and finance, there's often a lot of friction, disagreement about forecast that kind of come at the data from different perspectives.
(30:56):
So talk a little bit about that.
Guest: Alex Brower (30:57):
You were talking earlier about getting everyone into a room and getting everyone aligned from the CRO to the CFO on what a booking was. And I think a great FP&A professional is the one orchestrating meetings like that and pulling people into the room. Probably there's a class of FP&A professionals who would just say, "Nope, this is the definition of bookings and I'm going to filter the dataset down this way and I'm going to continue with my predictions off the side of my desk or in the closet." Predictions in the closet are worthless really. And forecasts matter when they justify operational change or future operational changes. And operational change requires having a team that agrees on what is true. So I think alignment is the prerequisite, not necessarily the byproduct. And most leaders get this intellectually, but day-to-day they're stuck hunting the facts because of bad data and they're not strategizing and that's the tax.
(32:11):
So what we're building flips the ratio with one source of truth with evidence attached to analyses, even if they're AI powered, the same number in the QVR, the board deck and the variance analysis. So I think it's about fact hunting time collapsing and strategy time expanding. And in our product where the agents stop, humans start not humans on the key flow side, but the workflows are built in there. So agents handle the reconciliation, the hygiene, daily variants, the deal risk surfacing scenario assembly, and then they're surfaced to humans so that they can handle judgement calls, evaluating genuine risk, the strategic bets and ultimately the numbers that they share with their stakeholders. So the fact hunting theme is one that whether I'm talking to a chief revenue officer or a chief financial officer is a common thing and whether it's facts hunting on a forecast call with the sales reps about what's actually happening with the deal or we're fact hunting between CFO and FP&A who's trying to get a handle on pipeline conversion and 365 day sales cycles and what's real and what's not.
(33:39):
And so I think it's about getting to a place where we can make decisions, not arguing over who's right or who's cut of the data is right.
Host: Paul Barnhurst (33:49):
As I always like to say, if you're in a meeting and I don't recognise that number, how did you calculate it, you're pretty much done. You're not going to get anything good done the rest of the meeting if that's brought up early. That's what I always find because it just delves into a conversation of data instead of what we're trying to accomplish.
Guest: Alex Brower (34:10):
That's right.
Co-Host: Glenn Hopper (34:11):
God, you're talking about aligning people and I think my old military roots came out and I was like, the way you align people is tell them, "This is the way that I'm doing it. I'm the captain." So it's funny the soft skills.
(34:30):
The soft skills and all this are tough and I've really just been sort of fixated on, I'm not going to go too much further down the road of what we started with generative AI and how that's changing SaaS today. But I do think right now there's so much noise between you need to do AI and this is the tech stack you need to have and this is the data warehouse model, here's your semantic layer. There's so much going on. And I think again, going back to my domain expertise comments earlier, it's hard if you didn't come up as a technologist. I think we all have to be to whatever extent in our roles now. But for a CFO or an FP&A leader that's trying right now to modernise their planning stack in the midst of all this noise, from your point of view, Alex, where do they start?
(35:28):
What do they need to look at with their tech stack and what should they stop doing first before they make any technology decisions?
Guest: Alex Brower (35:39):
I think that I would focus more on the questions that your team can't answer in numbers as opposed to evaluating just like the tech stack. Clearly I have opinions about different vendors and models, but I would focus on the one or two questions that your team can't answer in numbers and to solve those first. So some recent examples that come to mind I mentioned deeply understanding of what's going into long sales cycles and partnering to fix it or understanding why 3% of marketing leads convert when you think that number should be closer to 30. So I would separate these from longer plays and I would stop the scavenger hunts for answers. People should have one place to go to ask the question and get the answer and not piece it together from CRM, BI, finance files, Slack threads and MCPs that hook into each one of those things.
(36:56):
I would also probably stop adding dashboards. Every new Looker dashboard is yet one more thing that someone can change a filter on and disagree with. One more place where people have to check before they trust the number. And I would also stop treating manual reconciliation as routine. I mean, the number of companies that are well endowed with funding and lots of revenue who are still doing bunch of manual reconciliation as opposed to having humans investigate flagged issues continues to surprise me. I answer your question sort of from a different angle there, Glen, not really like the tech stack angle. Yeah,
Co-Host: Glenn Hopper (37:54):
And we're aligned on that. And for me, you could always tell when the right salesperson got to a CFO and sold them on an ERP or whatever before even knowing the requirements. So I think the mistake is there's some shiny, new, fancy, whether it's a SaaS product or AI itself, something out there that you think, "Oh, we're just going to go sprinkle some AI on this or we're just going to go plug in this one magic piece of software and it's going to solve all of our problems." I think you nailed it in the first part of that answer of, how about let's come up with requirements first.What are we solving for? And then search for software around that rather than trying to jam the square peg into a round hole.
Guest: Alex Brower (38:40):
Yeah. And I think that then it's also about setting timelines, honestly. There are certain things which are absolutely quick wins that AI can help with. And then there are other things where like replacing that 29 megabyte ARR waterfall spreadsheet where someone from finance has literally typed out the contract details for the last four years into columns that drive the waterfall. That might not be today's Tuesday, I'm not going to promise that's going to be fixed by Thursday. We can use AI to help us in that project, but there's some underlying data and data structures that need to be fixed and need to be transitioned somewhere else. So requirements and realistic timeline depending on what those requirements are.
Host: Paul Barnhurst (39:36):
We're going to move into the fund where we ask a couple personal questions developed by AI. I bought this fund, got to use Fable F before it got taken away from us. We all got banned so to speak. So I'm going to read what I said because I thought it was kind of funny at the beginning and then I'm going to let you pick a number. So Glenn and I both take a different quote. We both get one question. You get two options with me, Alex. You can pick a number yourself between one and 25, or you can take a human out of the loop and I let the random number generator pick a number between one and 25. So I'm going to let you choose that and then I'm going to read this first little part because I think you'll find it interesting. So which one do you want?
(40:17):
Random number generator or are you going to pick yourself, Alex?
Guest: Alex Brower (40:20):
There's probably, without knowing the random number generator, there's probably an argument that it's not truly random. So I'm just going to say 25. All
Host: Paul Barnhurst (40:30):
Right. So we'll get there in a second. Before I do that, here's what it said. So it said, I gave it the prompt and it goes excavated candidate background to enrich interview questions, found some good material to work with Alex has a BA in international relations from the College of Wooster, studied international relations and history in Paris, is based in San Francisco, spent some time at UC Berkeley. And here's the part that I thought was interesting that I think you'll laugh at. And his Twitter bio includes Fear is the mindkiller, a dune reference, which is a gift for this kind of question list. And then it goes on to say, "Here are 25 fun, personal and slightly quirky questions." All right, here we go, number 25. It says lightning round. So I'm going to ask you four questions and you have to pick one or the other.
(41:22):
Excel or Google Sheets?
Guest: Alex Brower (41:23):
Excel.
Host: Paul Barnhurst (41:25):
In person or remote?
Guest: Alex Brower (41:27):
It's hybrid, but in person, I guess then.
Host: Paul Barnhurst (41:29):
All right. Tabs or spaces?
Guest: Alex Brower (41:32):
Tabs or spaces?
Host: Paul Barnhurst (41:34):
Your guess is as good as mine.
Guest: Alex Brower (41:37):
I guess I'll say tabs.
Host: Paul Barnhurst (41:38):
A pineapple on pizza, yes or no?
Guest: Alex Brower (41:41):
No.
Host: Paul Barnhurst (41:42):
All right. Yo passed. That was number 25.
Co-Host: Glenn Hopper (41:45):
With tabs or spaces, is that something like in a new line? I don't know. I'm trying to think of when you would tab do a... I don't know. Yeah, that's kind
Host: Paul Barnhurst (41:53):
Of what I'm thinking. Is it with typing for tap? Do you space all the way through or you just hit tap? I was struggling a little with that one. That's why that one was actually listed first, but I decided to put it toward the end because I'm like, the other ones make sense. We'll switch the order a little. So I did do a little human in the loop there.
Guest: Alex Brower (42:08):
That's good.
Co-Host: Glenn Hopper (42:09):
I just realised what I did. So my approach, I just copy and paste whatever model comes up with and ask it what's the best question, but ended up a leading question on my point, but I think I'm going to stick by my rule. So I always figure AI wrote the questions. I ask it which one's the best one to ask, but this one based on what Paul read at the beginning and then at the end, a few notes, question one is probably the strongest. So with all that as prompting, of course it picks question number one. So I'll go with that and say question number one, your Twitter bio says fear is the mind killer. Are you a hardcore Dune fan as a hardcore Dune fan? I'm asking this. And do you have a hill you'll die on about the books versus the movie?
Guest: Alex Brower (42:52):
I don't have a Hill I'll die on about the books and movies. I actually enjoyed the movies, but obviously read the books long before. In my wedding vows with my wife, there are quotes from Dune.
Co-Host: Glenn Hopper (43:04):
Nice.
Host: Paul Barnhurst (43:05):
We got some good material out of that one. Well done, Glen. You want to share any of those lines that you used?
Guest: Alex Brower (43:10):
I could probably search for it in Google Docs.
Host: Paul Barnhurst (43:13):
We won't need you to do that.
Co-Host: Glenn Hopper (43:14):
I think my comment would be Denny Velnuv killed it. I'm super excited about the third instalment coming. David Lynch, interesting guy. Boy, that was a botched movie. That was his version. And I've still seen it a dozen times, I'm sure, but it's nice to see that because for years it just seemed like Dune was an unfilmable book and I think breaking it into free movies.
Guest: Alex Brower (43:37):
They had the
Co-Host: Glenn Hopper (43:37):
Version looked like
Guest: Alex Brower (43:38):
Sting, like a TV.
Co-Host: Glenn Hopper (43:40):
Yeah, that was the one that David Lynch did. It was just weird.
Host: Paul Barnhurst (43:44):
In all seriousness, thank you so much for joining us. Alex is our guest recognised Alex does run QFlow. He's our sponsor, so we're incredibly grateful for his support on the show. We think he has a great product and really enjoyed the interview with you. So thank you so much for joining us, Alex.
Guest: Alex Brower (43:59):
Thanks for having me. I appreciate you guys.
Co-Host: Glenn Hopper (44:01):
Thanks, Alex. Always fun.
Host: Paul Barnhurst (44:03):
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.