Laura Bloom started as as a contracts manager for an air cargo company. In a dramatic switch she moved to accounting, then finance and FP&A at top companies including Dotmatics, Salesforce Comcast, and is now senior finance manager at Icertis, a Saas platform for AI-powered contract intelligence.
In 2023 she founded Impact FP&A specializing in Go-To-Market (GTM) finance strategy. In this episode she reveals the importance of flexibility in her career and joining the dots in your story: “For example, with contracts management, you wouldn’t think that would translate into finance and accounting. However, as part of that role I was working to build the sales bids for new business. I was also working very closely with accounting to give them revenue expectations for commercial contracts and OPEX expenses for our commercial properties.”
In this episode:
- My passion for bringing order from chaos in finance
- Examples from my FP&A career including calculating the obsolescence reserve for almost a billion dollars worth of inventory on a biannual basis
- Building a new model and updating assumptions (transforming the process from 6 months to 60 days and releasing $5m back to the balance sheet)
- The importance of data minimalism
- Monte Carlo analysis giving you a wider range of possible outcomes
- Tableau as a data visualization tool and digging into commissions
- Importance of flexibility in a finance and FP&A
- Most important FP&A Skill?
- The ladder of abstraction
- The most vital go-to-market finance metrics
Connect with Laura on LinkedIn or email her at contact@impactfpna.com
Glenn Hopper:
Welcome to FP&A Today, I’m your host, Glenn Hopper. Today we have the pleasure of speaking with Laura Bloom, a seasoned finance leader with more than a decade of finance and accounting experience in high growth pre IPO and Fortune 500 companies. Laura is the founder of Impact FP&A and currently serves as senior finance manager at Icertis, where she focuses on go-to-market finance revenue forecasting in sales pipeline evaluation. Throughout her career, Laura has emphasized the importance of clean accessible data and the critical role of automation in finance. She is passionate about leveraging AI to transform finance functions and is an advocate for continuous learning and upskilling in the evolving landscape of finance and technology. Laura, welcome to the show.
Laura Bloom:
Thank you so much, Glenn. I’m very excited to be here.
Glenn Hopper:
Yeah, yeah. I, no, we, we, we tried a couple times here, um, <laugh> with technical difficulties, so <laugh>, but very excited to, uh, to dive in today. Absolutely. And also, you know, in our previous attempts, we got to know each other better, so that was always good. So, <laugh>, that’s true.
Laura Bloom:
I, I love a good conversation.
Glenn Hopper:
Yeah. <laugh>. Well then you’ve, you’ve come to the perfect place then. So I guess, you know, we’ve talked a lot, I know this, but for our audience, <laugh> <laugh>, walk me through your career journey and, and kind of what initially drew you to finance and how you ended up where you are today.
Laura Bloom:
Yeah, absolutely. You know, as I mentioned to you previously, I, I don’t know that I would say I’ve had a career journey so much as I’ve had a career switch back. Um, it, it’s been a very long hike with a lot of sharp turns to kind of find, uh, my place in life. I think, like most young people, I didn’t really know what I wanted to do when I first went into college. Uh, the only thing I was absolutely clear on was that I didn’t want to have any student debt. And so I did go to a local university, and it took me a very long eight years, um, <laugh>, but I was able to come out the other side, uh, with my degree and no debt. So a win for me, I guess. Um, but, you know, one of the things that, uh, allowed that to happen is my role at the time, which was working as a contracts manager for an air cargo company.
And at the time, you know, I was thinking to myself, well, this has actually worked out pretty well. I’ve kind of, you know, fallen into this. Um, I love to argue, I love a good run on sentence. I should probably just formalize this and go ahead and go to law school. But then, you know, the, the 2008 financial crisis happened, and as I’m sure everyone knows, a lot of people became unemployed very quickly, and law firms were one of the hardest hit industries. Uh, during that time period. There were so many young people that I knew who were either laid off or had their start dates deferred, indefinitely, and that really made a very strong impression on me. So, uh, I decided to go ahead and major in accounting as kind of a hedge against future employability, if you’ll, so after, after I graduated, even though I was currently in a manager role, um, I took a very step down, a very large step down, I should say, both in title and salary to kind of pivot into accounting, um, with the idea that, you know, I would take a step back to take a step forward.
So I did work first time as a public accountant and then as a senior accountant and a Fortune 500 company. And it will say from a finance perspective, it was good experience. Um, it’s, it’s good to have a background in accounting. I do think it makes you a better business partner. You have a solid understanding of gap, uh, how accruals work, the general ledger, and how those flow into the financial statements. And that’s something I’ve often seen my peers in finance really struggle with. Also, I would say accounting was really kind of the gateway for me into automation and implementation projects, which has been a, a pretty big passion of mine throughout my career. So altogether, it was a good decision, but accounting simply was not my passion <laugh> in any way, shape, or form. So for the second time in my career, I decided to step down again, to pivot into finance.
And that really was kind of a watershed moment for me. Um, you know, I think, you know, you’re in the right place when you have just an absolutely unlimited amount of enthusiasm for a subject. I was always pushing to learn more learn, um, and it, that enthusiasm has never abated. And so eventually I did go back and get my MBA and I specialized in finance that just kind of, you know, get that extra insight into the field. I think at this time, you know, I’ve been in finance for over 10 years. Um, about five of them I spent in telecom before moving over into the SaaS space. And I spent about an equivalent amount of time in Fortune 500 companies before moving over into private equity. You know, just from, from my experience, I love being in the tech space. I, I learned the hard way over the last few years.
It’s very high risk. Um, when times are great, they’re really great, but, you know, when you’re in an economic downturn, it can be very difficult indeed. But it is an incredibly exciting place to work in. Um, you know, you’re often at the forefront of a lot of cutting edge technology, which is, you know, absolutely exciting and, and not really, you’re not really gonna find that anywhere else. So I’d say, looking back, the only thing I wish I’d really done differently was moving to a smaller company first. There’s a lot to be said for working for a larger company. You know, you have a higher level of employment stability. You, you know, if you’re someone that doesn’t like to work with, you know, well, I would say if you’re someone that doesn’t like to deal with ambiguity, uh, larger companies typically have more mature processes. So, um, you know, you, you kind of know what you’re gonna get from day to day. Um, but there’s really not, I would say, a lot of room for personal growth and innovation, and I’m the type of person that really likes to bring order outta the chaos and see, you know, the impact that I make within an organization. So for those people who are considering a future career in finance, I would say, please do consider a smaller company or, you know, maybe a mid-size company, your scope of responsibility will be much broader, and therefore your growth trajectory will be a lot steeper.
Glenn Hopper:
Yeah, and as you were saying that, I was thinking about, um, you mentioned impact on the organization, and it is, having spent the bulk of my career in the startup, or at least, you know, smaller company phase, it is a, a great way to learn a lot. Mm-Hmm. <affirmative>, because you’re, you know, it’s, you’re, the lane that you’re in is much wider because at a big company, they’ve got people staffed for everything <laugh>. Um, but at, um, uh, you know, at a small company, you, you may have to, I’ve been CFO slash IT guy slash you know, <laugh> every slash insurance guy slash everything. Yes,
Laura Bloom:
Exactly.
Glenn Hopper:
All right. So, you know, and speaking of having that impact, can you describe a significant project or initiative you led and, and kind of the impact it had on the organization?
Laura Bloom:
<laugh>, I love that question. <laugh>, as a finance business partner, I’ve actually worked on a lot of implementation projects over my career, but there is one in particular that definitely stands out. And that occurred early on in my career when I was working for a Fortune 500 company. One of my roles was to calculate the obsolescence reserve for almost a billion dollars worth of inventory on a biannual basis. And it was a, it was a big responsibility with high visibility made incredibly difficult by the fact that they had the oldest inventory system I have ever seen. We are talking pixelated text and dos commands, no joke. Um, the whole process from start to finish was about six months in length. And one of the major reasons why is because there was literally no way to export the data from the inventory system into Excel. So I would, you know, the first time I did it, I had to print out hundreds of pages of, of paper and flip through each and every one, find a, a given in, uh, piece of inventory, look for the ending inventory, look for the, the average cost, and then manually type that into a spreadsheet.
It was horrendous. Um, and then added to that, uh, the assumptions in the model were several years old because of the complexity of the database. Um, you know, various metrics in that model, like product lifecycle and average scrap value all had to be calculated by a consulting firm. And the company didn’t have the money to bring them in year over year, over year. So quite a few challenges. Um, there was definitely a lack of transparency in the process, and definitely a lack of trust in the output <laugh>. And after the first time I went through that process, I went to our VP of finance for the Americas, and I was like, look, this isn’t insane. Why are we doing it this way? And, you know, her response was, look, you know, we don’t have the budget to replace this inventory system, but, you know, if you’d like to lead a project to figure out how we can make this more efficient, you know, that would be fantastic.
So I was like, Hmm, okay, <laugh>. So I took my hundreds of pieces of paper, and I went to our product management department. I was like, there’s a lot of numbers on here, you know, help me dial in on what these mean and which are important to this process. And then once, you know, we kind of redacted all of that data down to something more manageable. I took that to our business intelligence team and said, look, you know, we really need to find a way to automate this process. Um, let’s work together and, and do this. And I wasn’t sure if, if we could accomplish it, but fortunately, there was already kind of a backend built out so that the data could be, um, taken from the inventory system and exported into, uh, Oracle for the financial statements. So it wasn’t as simple as just pivoting on that.
Um, we had to build and go through a lot of iterations on the report to make sure that everything was calculating as as intended, but the effort was definitely worth it. Once we had that report in production, we were able to build a new model and update all those assumptions. And that process went from six months to about 60 days. And also, you know, after I was able to really spend time in the data and not just be a data jockey, I realized we were massively over reserved. So after I presented those results to our senior leadership, and then also, you know, vetted it through our, uh, public accountants, we were able ultimately to release about $5 million back to the balance sheet, which even for a Fortune 500 company is a very large amount of money. So, you know, I would say digital transformation stories like that are not particularly common, but I really like trotting out this example because it, it, it truly underscores what can happen when companies just accept the status quo and don’t seek, um, better ways of doing business. You know, I’m a strong believer that businesses need champions of change in order to thrive. And I feel that no department is better poised to do that than finance and helping the company make innovation a priority.
Glenn Hopper:
Yeah, and you’re, you’re preaching to the choir here on that. And I will say, you know, maybe not incredibly common. I do think there’s, there’s kind of two mentalities, um, in one, I’ll call the, the old school way of thinking about what we do as finance and accounting people. But then there’s the new approach, which is much more that business partnering and where can we add value? And I will say, out of all the FP&A folks I talked to from, you know, sort of mid-level management all the way up to CFO, um, that the ones who make the biggest impact are the ones who lean into technology and automation. Yeah. So I think you’re, you know, you’re right on the, the right track there. And you know, I, in all of our talking before the show, you and I share a passion for automation and data and analytics, and I know kind of what I’m seeing, but, um, I, I suspect you’re, you’re saying very similar <laugh>, uh, similar things and seeing where we’re, we’re finding success, but with that kind of mentality, how are you leveraging data and analytics to, to drive decision making, business strategy, and to do that in a way that we’re showing, you know, that we’re expanding our value from FP&A to the rest of the company?
Laura Bloom:
I wanna preface it by saying that, you know, I do come from a purely finance and accounting background. I’m not an expert in, uh, business intelligence and programming. So I do have a bit of a spicy or perhaps controversial take on this subject. You know, when I look out across the business landscape, and I see this all the time, companies are absolutely drowning in data, and the volume is increasing exponentially. I think it’s not surprising because a lot of products, right, are marketed towards data mining and visualization. And you really see kind of the result of that, where companies have essentially internalized this idea that more data will always lead to better decision making and better company performance. And I think actually the, the opposite is the case <laugh>. And again, you know, you can see that, you can see an echo of that mindset in like these massively over-engineered reports and dashboards that look like the cockpit of an airplane.
You’ve probably seen a ton of those yourself in your career. Um, they’re really nice to look at, but the reality is that the majority of data on there, um, has no economic value, and therefore it’s taking valuable resources away from more critical areas of the business. Um, for me, the question is not, you know, how do you leverage data and analytics, um, but how do you leverage it effectively? And the way to do that really, um, is to cut through the noise and only focus on those data points that help move the needle forward in your company. I kind of, I kind of have, you know, a name for it in my mind, data minimalism. And actually, now that I say that out loud, there’s probably someone somewhere who’s coined that, and I’ll get a cease and desist letter <laugh> after this podcast. But, um, you know, it’s, it’s really kind of a to, in my mind, an operating efficiency mindset that has to be driven from the top down as part of a company’s overall data strategy.
And the goal is really to have companies dial in and become subject matter experts in a core set of leading indicators and metrics that are critical to their business. And, and the way to really do that is to differentiate between what is a data need versus a data want. What <laugh> a data need, for example, would be, you know, your, your cash burn rate, your cash forecast, your revenue churn. Um, if you don’t have a handle on those, you’re probably gonna go outta business very quickly. Versus a data want, which is, you know, things like marketing channel, ROI, SPIFF efficacy and so forth. Um, those data points are all, you know, good to know and helpful in running the business, but you, you know, if you don’t know them right away, um, your company is not gonna be in trouble. So I would say, you know, when you are looking to leverage data and analytics, make sure they are meaningful, measurable, and actionable, um, and of immediate concern to your company, and that, look, I realize saying that is easy in theory and, and probably difficult in practice.
You know, one of the things that I, I typically do with clients is use something of a roadmap to help build out concrete steps, um, and timelines for the development of that core metrics. It’s like once you have looked across the business and decided these are absolutely the metrics we need to have, um, you know, here, here are the steps we need to take to get there. Um, here are check-ins, you know, and through that process, you’re also able to identify what the roadblocks are and then, you know, adjust your roadmap accordingly. And you really just wanna keep iterating on that until you are a team, whether it’s finance, accounting, your business intelligence team are able to reproduce those metrics x quickly and efficiently. You want your process to be as transparent as possible, and you have full trust in the data. And then, and only then, once you have that ready with your core metrics, can you start double clicking down into that data.
And I think probably the last thing I would say on that is, um, you know, I think it’s important for people to remember that the data needs of the business are not static. They’re dynamic, and they do change over time. So I always recommend that people establish quarterly, or at least biannual, um, report reviews to have just kind of informal discussion about what reports are and metrics are being tracked, and whether they’re still relevant and necessary to the business. I think a lot of businesses suffer from data bloat, um, because leaders especially have a really hard time of letting go of, you know, kind of like their passion projects or their passion metrics, but they have to, they have to learn to let what’s not relevant, go. Um, otherwise you’re just holding your team back from, um, forward.
Glenn Hopper:
It’s funny, as you say that, um, I understand, you know, the point about what data you’re tracking and measuring, but as a, as a data guy, so I, I look at data in a couple of ways, sort of, there’s the raw data. This could be structured unstructured, it could be the, the Twitter fire hose that could be Internet of things, data, it’s all, all this digital information or, you know, digital noise that’s out there. But as a data guy, I’m thinking, collect that, I don’t know what I’m gonna do with it, collect it. But the difference is, I’ll say, is the difference between data and information, if it’s just harnessing, just grabbing the data and putting it somewhere and then figuring out if there’s a use for it later, that’s, that’s one thing. But if it’s actually requiring resources to transform the data in some way, and the worst case is if you have a human who actually is having to do something with together a report that nobody’s using, get rid of that. But, so to your point on define the, the key metrics you’re gonna track and then track those and don’t have a dashboard that has a billion charts and graphs that you don’t even, it’s like driving down the interstate and you see all the billboards you don’t know, you know, you just start tuning ’em all out because it’s too much information. So you have the, the few key metrics, but then I know the data nerd behind me is like, but go ahead and collect that other stuff if we might need <laugh>.
Laura Bloom:
Well, the collection is one thing, you know, I’m really talking about targeted use. You know, to your point, uh, companies suffer from an embar an embarrassment of riches in terms of data, and it can be a huge distraction from, you know, what’s critical to the business. Um, and so I’m not arguing against collecting the data or digging in, um, to other areas. But what I am saying is that, you know, when you, almost everyone I’m sure on that’s ever been on your podcast, or really anyone who’s ever worked in, in an organization has had one or several reports that they’ve had to generate, right? That nobody looks at. Um, but they’re doing it just because, you know, the leaders leadership or maybe a manager somewhere still wants it to be done. Um, and you know, as I said, you’re, you’re really pulling resources away from other areas that need greater attention. So I’m a very strong advocate for that data minimalism in terms of how, have all the data you want, but make sure that you, um, are laser focused first on, you know, what is moving your business forward. And then, you know, once you have that process established and people aren’t, you know, juggling across six different systems to cobble, you know, uh, a report together, then you can start double clicking it.
Glenn Hopper:
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I think about our, the, the jobs we do in fp and a where you’re, you know, you have your stuff, you know, you have to do every month. This is sort of just the, the routine work that you do from the first management reports that come out after the books are closed to, you know, whatever pipeline, whatever, whatever type of reporting you’re doing. And then, uh, you maybe you have this project work that’s out there that you have to do as well, and then there’s all these ad hoc requests and somebody wants a report because somebody mentioned it in a meeting, and you think about how distracted you can get from the core information, or if a part of your job is every day. I mean, and these are the kind of things I, I am just passionate about. We have to automate this as much as possible, but there’s reports that we put together that come out of fp and a that I, you know, is anyone still looking at this?
I don’t know. But you try to take it away and there’s gonna be one person out there saying, well, I kind of need that. But the truth is, if you watched, you know, if you could put a read receipt on and see when they opened the, the document or the report they were getting, exactly, how much are you actually looking at, and why does it matter? So if you’re, you know, and since we don’t have unlimited resources, um, how about we track the things that actually are levers that we can do, so that, that impact something else, rather than just giving information for the sake of information, it’s, that’s not very helpful.
Laura Bloom:
Precisely. And, you know, I, technically speaking, the business intelligence and data teams are supposed to be the gatekeepers of the data. You know, they’re the ones that are supposed to, um, make sure that the data sets are clean and ready for use to cross the organization. But, and, and Nick, please no, um, <laugh>, no, no shade here. Um, but I think one of the problems with that is that while a lot of times data teams have exceptional technical expertise, they often lack the business acumen, um, to ask the right questions or really to identify red flags in the data. And also, you know, I, I don’t think I’ve ever seen any data team that hasn’t been absolutely overwhelmed with work. So it’s really hard to get time to be, you know, an effective business partner with them. So, you know, when you, when you see the net result of that, what happens is that FP&A oftentimes becomes the source of truth.
I think I read somewhere, and I believe this wholeheartedly, that 75% of FP&A role today is data collection. You know, and if you think about it, you know, finance sits at the Nexus point of so many different departments. And so you’re pulling information in from all these places, cleaning up, transforming it. Um, you have all these recurring reports for month end, quarter end, year end, that need to get out on top of all the ad hoc reporting request. So when you put all that together, it’s really hard, I think, for modern finance teams to pause and be thoughtful about the data that they are floating out into the organization. You know, uh, how does this help, uh, the company move forward? You know, what are the strategic implications to the business?
There are a lot of amazing analytical tools these days, Cube, mosaic, planful datarails to help kind of move that process forward. And while I do think that lifts up the potential of FP&A to be more of a strategic partner, I do, I do think oftentimes finance teams these days lack the time and ability to bring that vision to fruition.
Glenn Hopper:
Yeah. And that’s actually, and I, we can, we can talk about AI and machine learning and, and generative AI in particular, uh, in a minute, but I, you know, as, as you mentioned, the tools and the whole idea with these tools is to, you know, make it easier for us to convert this data into information and into metrics that matter. What’s been your experience with using these tools and, uh, you know, bi tools, technologies and all that are, uh, are there aspects that you, like, something you wish you could see out there that you haven’t? I mean, what, what level, you know, be frank, every, everybody, we always default back to Excel, but beyond that, like, what kind of, what kind of tools and technologies do you like out there?
Laura Bloom:
I love getting in those, um, arguments on LinkedIn where people <laugh> are like, Excel is out the door, you know, business intelligence, people hate Excel. And I’m like, here we are, what, 30, 40 years on? I’m like, you’re, you’re gonna have to try harder if you wanna get it out the door, <laugh>. But, um, you know, from a, from a bi tool standpoint, um, on a, on a technical spectrum, I would say probably the most, uh, advanced software I’ve used is Matlab. Um, for statistical analysis specifically. Um, once upon a time, I did do Monte Carlo simulations, uh, for a scenario analysis, uh, exercise, particularly around sales forecasting. And I think everyone knows, you know, when you’re doing forecasting, a lot of the scenarios like your high, medium, and low are specifically chosen by leadership, you know, with a particular and in mind. And so I like Monte Carlo simulations in the forecasting framework because it gives you a wider range of possible outcomes.
The challenge with that though, is a lot of people, um, feel very uncomfortable with statistics. It’s not like an Excel formula where they can kind of track that process back. So I’ve found, um, limited use, you know, and, uh, li pe not as many people willing to listen to the results of that on a leadership level. Um, however, I will say from a bi standpoint, uh, the tool I have had the most success with and I’ve used the most is Tableau. And I, I absolutely love Tableau, and that is not at all, um, biased by the fact that I used to work for Tableau, uh, when they were owned by Salesforce. It, Tableau is an a, is a fantastic d uh, data visualization tool. It is great for storytelling. Um, and I, you know, I do think it’s also, uh, a great tool for handling large data sets.
Personally. I, I’ve had great, uh, experience using it, um, to dig into commissions. For example, at one point in time, you know, I pulled, uh, different data sets together from Salesforce workmen and, uh, a commission system called IComp. Um, we were having these wide swings in our forecasting model, and through these visualizations, we were able to determine that there was a very, um, big delta in the time to connect between our, uh, SMB business, which was about 60 days versus our enterprise business, which was, um, a just a little bit over a year. So that, you know, we were able to take that back, um, that learning back to the model and refine on it. Um, and then again, you know, with incentive programs, um, using those visualization tools again, you know, uh, at a prior role, um, I was able to show, uh, our senior leaders, we were literally paying $2 out the door for every dollar that we were pulling in.
Um, in terms of revenue, it was just, uh, very much during the beehive. So I think, um, you know, all things considered, I would, I would strongly recommend Tableau to anyone. It does have its limitations, uh, as a data summarization and a aggregation tool that it does leave a lot to be desired. Um, its formula writer can be pretty difficult. Um, I did have to teach myself SQL in order to be able to really effectively, um, wield Tableau as a tool. And also their custom for formatting is absolutely laborious. Lately, I’ve been, since we don’t have access to Tableau now, I’ve been getting more into Power BI. Um, I’m very much late to the game on that tool. Um, but I, I do like the fact that, um, it ties directly to Excel, and so you can automate a lot of the reports that you have in place.
Um, I’m literally going through some learnings on that right now and starting to wield that and weave it into the reports, um, that we have existing to increase the turnaround time. I would say probably those, those three tools, the matlab, Tableau and Power BI are probably the, the main three, um, that I’ve used. There have been a lot of add-ons to our CRM systems, particularly for Salesforce. Uh, at service right now, I’m helping to validate data for, uh, the implementation of CPQ, um, which will definitely help to, uh, drive better insights into our sales funnel. But yeah, that’s, uh, that’s kind of, uh, my use in a nutshell.
Glenn Hopper:
Yeah. And I can fully relate on the Tableau Power BI thing. I, I feel like years ago, like, I dunno, 2017 or, or somewhere, I, I, I had a decision to make, ’cause I was the one that was gonna put it in. It was, are we gonna go with Tableau or Power Bi? Mm-Hmm. <affirmative>, Tableau seemed more immediately accessible and easier to use outta the gate. And so I went down the road of Tableau. Yes. But now here we are the, all these years later, and I’m seeing the functionality of Power BI and sort of the built in machine learning and the power automate and the, and the whole ecosystem now, especially the generative AI has been included. And, and so I’m like you, I’m just now kind of getting up to speed, but fortunately at this point in my career, I have a team that knows it way better than I do.
And, uh, <laugh> you can help. Uh, and, um, and the other thing that got me that you were talking about was Montecarlo simulations, because I love Monte Carlo simulations. It was actually, oh, okay. When I took, when I took CS 50 years ago, that was my final project for the class was, um, building a Monte Carlo simulation for, for financial forecasting. And so I love that. And I’m doing it in, uh, generative AI now. Mm-Hmm. <affirmative>. And it’s up until GPT-4. Oh, I wasn’t able to get it. There just wasn’t enough context window, not enough processing power and understanding what these in these models to do it. But now I’m, I do, I, I actually ran one this morning, so, uh, <laugh>, so love money Carlos simulations. I’m right there with you, <laugh>.,
Uh, probably a great transition into, because I’m very, I tech forward whatever, I’m a, I’m an evangelist for AI and what it can do in finance, and not just generative ai, but just using, you know, machine learning and all that. But I know you and I have talked about this a lot. And what do you, I mean, what are you seeing, what are your thoughts on integrating AI and machine learning into finance, and how do you see that transforming the industry in the coming years?
Laura Bloom:
We live, uh, we live in interesting times, don’t we? <laugh> There’s nothing really new, I think about AI per se. Um, it, you know, obviously it’s been a technology decades in the making. If you think back, it, it kind of had a moment back in the 2010s, uh, when BI and data science made its collective way into the corporate hive mind. But I think as a, as a technology, it really wasn’t quite fully baked yet. 2023 though, was definitely AI’s breakout year. Um, and following obviously the launch of chat GPT, everyone was jumping on the AI bandwagon <laugh>. Um, I will say it is, it has been awe-inspiring and humbling to see the velocity of innovations and machine learning. Hardly a week goes by where I’m not reading about some new business use case. And there’s also a lot of very interesting academic ones as well. I think it was back in June, I was reading an article <laugh> about researchers at MIT who were using machine learning, uh, to spot patterns and whale vocalizations, and they were able to uncover a phonetic alphabet <laugh> amongst blue whales, which is frankly mind blowing.
Um, who knew? Uh, but I, I’m listening now. You know, I think about these things, uh, just across the spectrum of business and academia. And I, you know, contrast it against a lot of the noise I’m hearing lately about how AI is overhyped and we’re in a bubble. Um, you know, I’m always seeing people make a reference between, uh, the AI hype of today versus the the nineties.com bubble. And I, I do think it’s worth pausing to think about that correlation for a minute, because if you’re thinking of AI on a short term basis, uh, as far as, you know, just getting a quick return on investment, people are probably correct. Wall Street’s probably correct. You know, you’re, you’re not going to recoup your investment very quickly. However, you know, I think you and I have talked about this before as Zennial, I am just old enough to remember the world before the internet and the world after the internet, and they are two vastly different places, you know, and at first it was kind of just like a, a hobbyist, um, idea, you know, the, the internet, these terrible web pages.
But very quickly there came this inflection point where, where there was sudden widespread adoption of the internet. And think about all the social change that it’s brought. I mean, how it’s toppled, you know, old venerable institutions. It’s created entirely new industries. It’s really democratized business, if you think about it, through the rise of the e-commerce and, and the globalization of both product and labor markets. I truly believe with all my heart that we are on the cusp of another massive technological and economic shift. And I remember way back a couple, uh, about a decade ago I was reading an article by, uh, Marc Andreessen and he was basically predicting that software was going to eat the world, and he was right. And I think, um, AI is, is going to be the new software in terms of its reach and impact on industry. Uh, I don’t think there’ll be a job or an industry that won’t be impacted by AI in some way, way.
Uh, I do think it’s worth remembering, you know, we are in the early days of the technology, so, you know, it, it’s really hard to predict how AI will ultimately be integrated into finance right now. I, you know, you see the best use cases in investment firms, insurance firms, banking, and so forth. Uh, really more the FinTech area over the short term. I do think AI will be, uh, utilized for more localized applications, you know, to, to automate recurring reports, um, to kind of uncover trends to make better investment decisions. You know, what new markets do we wanna jump into, what new products do we wanna, and of course, forecasting, there’s data that I think companies would like to pull into a forecast, but really can’t due to cost benefit, you know, customer data, competitor intelligence, economic trends and so forth. So definitely, I think over time, certainly over the next five to 10 years, we will see the role of finance evolve.
I do see finance pivoting more to a review, um, role in terms of, you know, looking for hallucinations, making sure, you know, you don’t wanna, you don’t wanna delegate, you know, your entire, um, set of financial statements to you, an AI model without having someone look it over and make sure that it’s accurate and complete. But then from there too, you know, obviously there’s gonna be, um, people required within finance to execute on strategic operating plans. There’ll always be a need, I think, for ad hoc reporting. And, you know, there might even be a potential to, for, for people in finance to develop the skill for prompt creation or even LLM training. So that’s over the short term. And then over the longer term, you know, you and I, and this is me preaching to the choir, <laugh>, um, you know, with a GI, I think is right around the corner.
There’s been a lot of talk about, of course, open AI and Project Strawberry. We’ll see what comes out of that. But then, you know, there’s a development now of AI agents, you know, which are these automated systems that in theory, at least at some point in the future, we’ll be able to conduct most of the processes of an organization. And that, that’s really mind blowing to think about. Um, so if and when that does come to reality, I, I do think there will be a contraction in the market. Um, and there probably will be some significant economic disruption, but I am, I’m very optimistic that similar to the rise of the internet, um, there will also be new opportunities within AI for people to take advantage of.
Glenn Hopper:
I agree. And you know, it’s funny, I, once I open the store, I’m like, I could spend the rest of the show talking about it, but I think the producers are gonna beat me up because I end up kind of being a, a one trick pony on this. So <laugh>, I do wanna cover some other aspects of, uh, of what we do, but I, all, all great and salient points, and I think you, you know, pretty much reflect the concern that I sort of hear around, uh, finance and accounting, um, you know, on, on all the groups I talk to and everything. And it’s, um, I don’t know, we are very much for generative ai. We’re very much in the nascent stages. I liken it to, I’m old enough to remember, um, a OL and prodigy Mm-Hmm, <affirmative> when you would actually have dial up internet at your 56 kilobytes a second, and you’d have to listen to the modem screechwhile it was connecting and all that. But if that’s where we are with this technology right now, imagine where we’ll be, you know, in the time from 1995 or whatever that was. So the next 30 years, what they’re gonna look like. Yeah, I mean, it’s gonna be, like you said, with the internet, completely different.
Laura Bloom:
It, it is the journey from dial up to, you know, um, these incredibly smart computers that you can wear on your, on your wrist or carry around in your pocket has been insanely swift <laugh>. So I think, you know, that, um, that ability to, you know, to share data quickly has, is really one of the, the major reasons why we’re able to see, um, you know, that higher velocity of innovation within ai. And I, I’m really excited to see where it goes. Everyone talks about the power needs and so forth, and they’re gonna have to create these big data centers and blah, blah, blah, blah, blah. It’s like, well, once upon a time you needed a whole building just for one computer, and now you can put it in your, so, um, you know, I would say like, let’s, let’s pause on the poo-pooing of AI and just kind of think about, you know, what the future looks like and how far we’ve really come in the last several decades.
Glenn Hopper:
Yeah. Great, great points. Alright, let’s, let’s spin this back around to the human side here, because your path to get to where you are now, I think it’s gonna be interesting for a lot of our listeners, and I, you know, you’ve had, as you described it, I mean, it’s a non-linear career path where you’ve been in different roles, different industries, different functions, and you know, I think if someone is listening to this show, or maybe they’re in accounting and they’re thinking about going to FP&A or they’re thinking about these transitions, I mean, what, what advice would you have for professionals who are looking to make kind of the similar trans transitions and how they could leverage their diverse experiences to, in order to be successful in finance?
Laura Bloom:
I think the most important piece of advice that I would have is to be flexible. Um, a lot of young people that I talk to get discouraged very easily because they don’t land the role of their dreams right away. Um, you know, I, as I mentioned, I started in contracts management and even though, you know, I kind of skipped over a lot of history there, it took me a very long time to find a public accounting firm that would hire me simply by virtue of the fact that, you know, it, it did take me eight years to pay my way through college. I was therefore, um, far older and far more experienced than their typical demographic. And so, you know, it was just a matter of knocking on different doors, um, until a no turned into a yes. And I think, you know, as I, as I coach younger people now, I always tell them, you know, if it’s a no here, it could be a yes somewhere else.
So whether it’s through public accounting or it’s, you know, through an AP role or a payroll role, or you know, any, any number of things, um, you know, keep an open mind about what role you take and then learn to craft your story. So for example, with contracts management, you wouldn’t think that would translate over into finance and accounting. However, as part of that role, you know, I was working with sales to build the sales bids, um, for new business. So that was, was, you know, very obviously finance related, and then also working very closely with accounting to give them the revenue expectations for commercial contracts and then the, um, the opex expenses for our, all of our, um, commercial properties. And so, you know, you find kind of like these, these little bits and pieces that really kind of fit into the role that you wanna move into, and you build your story around those.
I do realize that this is a tough market, um, to be making that sort of pivot in. So, you know, another piece of advice that I always give people is to, um, think like a, a creative and build a portfolio. Um, you know, people who are in artistic or marketing roles are often expected to come to interviews with examples of work that they’ve done in the past. And obviously, you know, for proprietary reasons, you can’t, you can’t show what you’ve done at other companies. And if you don’t have any experience, obviously you can’t do that in any event. Um, but what you can do is find business case studies online and you can build models. And then when you’re in these interviews, you can say, um, you know, you can tell them your story and then you can say, look, you know, I’ve built up this portfolio of models of financial models, maybe a three statement model or a revenue model, or even just a basic opex forecasting model. Um, I’d love to send my work over so you can see the quality of, of my work. And I, that’s something I’ve actually used to great effect. Um, 99% of the time finance companies are not expecting that, and it absolutely blows their mind. They get very excited to be able to see your work product ahead of time. So it’s definitely something I tell people to, you know, to consider and keep in mind. Um, and then also, oh, go ahead. Sorry.
Glenn Hopper:
Well, no, I was gonna say that’s, you know, like you said, creatives do it and, uh, programmers do it. You have your GitHub site, it’s like, here’s stuff I’ve built. And when you go for a, a finance interview and you’re talking about models you’ve built, it’s very different. It’s, you know, it’s almost like qualitative versus quantitative. It’s, I can tell you about it. Mm-Hmm. But what if I could actually show you what I did? So that’s a, a great point. And I do, um, I want to hit on a couple more things before we run out time here. And one of them is, you know, as you talk about, um, making these pivots and everything, like in order to build the models, you have to have this foundational skillset. And I’m thinking about what, you know, what you’re saying and what you think today, kind of what these critical skills are. Lemme make this a two part question, <laugh>, what you think these critical skills are that are needed today in FP&A and what, you know, people can kind of do to develop these skills. But going back to our earlier conversation, do those skills change as AI becomes more pervasive in our profession?
Laura Bloom:
Great questions. I, I think the answer to your first question might surprise some younger people, not so much. Um, more experienced fp and a, uh, professionals, the most important skills that you need in fp and a are not in fact technical skills. They are in fact, soft skills. The first one I would say is communication. It doesn’t matter if you have an Ivy League school degree and you can speak 10 languages, if you cannot, uh, communicate the results of your analysis in a clear and concise manner, you’re not gonna get a seat at the table. And I think this is something that people in accounting and finance really and truly struggle with. We have a lot of cognitive bias. You know, we think that everyone has the same level of knowledge and understanding as we do. And we, you know, we also have a tremendous amount of enthusiasm for the data.
So we wanna give everyone the gory details of the analysis, but that’s not, that’s really not what most people are looking for, particularly at the senior level. You know, they want the high level details. So I would say, you know, good effective communication, particularly up the ladder, is, is not an inherent skill for most people. Um, you know, just taking from my, from my own learnings, you’ll always wanna come prepared. Make sure you spend time, you know, reviewing the work that you’ve done and be prepared to, to speak to it at a higher level. Don’t wing it. Um, this is something I learned from a manager very early on in my career. If you don’t know the answer, don’t try and BS your way through it. Just tell them that you will look into that particular question after the meeting and they’ll get back to you.
That helps establish credibility. Um, you know, if you’re really, if it’s something you’re really struggling with, you know, hire a coach. Um, go to Toastmasters, form a practice group, uh, even find a mentor or a manager that you can practice with. And then one last tool I have found to be very effective for myself is called the, the ladder of abstraction. I’m sure computer science majors will know exactly what I’m talking about, but it’s essentially a way of thinking about information, uh, from going from a very detailed level to a, to a more, um, broader conceptual level. And I, I highly recommend people look into that. And then in addition to communication, um, you know, I think people really need to spend time, um, with, I think what most people call critical thinking skills or problem solving. Uh, again, as you move up the corporate ladder, you are expected not just to produce the data, but then to assess those scenarios, weigh the pros and cons, and then make defensible decisions.
And I think a, a lot of success around that comes from learning to ask questions. A lot of people are afraid to speak up, learning to ask questions, starting to ask the right questions, and also learning to take a step back and look at the bigger picture. Um, consider what the upstream and downstream impacts of the data and a whole, the ladder of abstraction I just mentioned is, is also a problem solving tool. And you can also use, uh, McKinsey has a great problem solving framework. So if it’s something you know, that you feel like this is not a strong skillset, you can use some of these structured formats to kind of break problems down and say, okay, this is the analysis that I came up with. Now how do I create, um, my recommendations from that? So that’s the first, I would say that’s the first part of your question.
And then to the second part of your question, I mean, that’s really the million dollar question, isn’t it? Um, everyone is talking about upskilling and reskilling, but again, because we are, as you said in the nascent stage of this technology, it’s very difficult to know. I think what other technical skills will be required. Um, certainly communication and problem solving skills will always be in vogue. And I know it’s difficult for a lot of people to exist with a high level of ambiguity change is very stressful. And a lot of people right now I think are dealing with a, a great deal of existential anxiety about the coming of ai. And if I can be vulnerable for a moment, you know, I am, I am personally as apprehensive as I am, excited about the, the technology, and we are privileged to live in a time of rapid technological change.
I would say the best thing that that people can do is have a growth mindset and, um, you know, make sure that you are always investing in continuous learning. You know, stay up to date on the latest developments in your industry. Um, be willing to pivot in your role. You know, if, if, um, AI is rolled out and it requires different skills, you know, be willing to step in and, and get your hands dirty. Um, and to your point earlier, you know, become an internal resource for the company, um, whether that is through, you know, machine learning or AI courses, um, doing what I’ve done in the past, which is fine tuning LLC models as a finance or accounting expert, or even just going out to demo AI software. You don’t have to own a business to be able to do that. Um, all those things will really keep, uh, help you stay kind of on the, the front end of the AI wave and help you stay relevant in that rapidly changing market.
Glenn Hopper:
Great advice. Um, and I guess, you know, the way I think about it is I, you know, I don’t know when we get to the different levels of open ais, um, you know, there there sort of hierarchy of, of a GI, but right now and maybe the foreseeable future, I see it as treat generative AI as if that’s your intern and so now if you have an intern doing the work for you, how are you gonna provide additional value? So to your point, you’re checking, you’re verifying, but also now, how am I adding strategy in? Because when you get out, get your head out of the weeds of, you know, just, uh, data entry and, and doing the sort of the basic data transformation. Now how can I level up? So I’m, I don’t know, I’m, I’m maybe overly optimistic, but I’m, I’m, I see that it being a tool that we use, not one that’s gonna replace us.
Laura Bloom:
Exactly. And I, I think a really good place to start is, is copilot. Almost every company uses Windows in some capacity. Um, copilot is a very low stakes AI assistant, <laugh>, um, I already use it for email, um, for creating, um, you know, uh, summaries out of Microsoft teams and, um, starting to un you know, try and understand how it can be applied in Excel as well. So, you know, if, if nothing else start there and kind of, you know, grow from there, sky’s the limit.
Glenn Hopper:
Well, in our last few minutes, I do want to go, uh, e even more on the human side, on, on the personal side. And we always like to find out something new and interesting about our guests. So I’m gonna put you on the spot here. Um, tell me what is something that not many people know about you, maybe something we couldn’t find by just Googling you really quick.
Laura Bloom:
<laugh>. Um, you know, I am, as I said earlier, before we started, I am a huge nerd. Um, I love the sciences, I love STEM. I spend, uh, a stupid amount of time at the Denver Museum of Nature and Science. Uh, they have a 60 minute in space class that I attend, uh, pretty much every month. Um, my house is very space forward, as you can probably tell by the <laugh> the artwork behind me. Um, I’m also something of a, an amateur geologist. I have a pretty sizable gem and mineral collection <laugh>. Um, I’ve just kind of had a, a lifelong passion for, for rocks. Um, but yeah, taken all together, I love the sciences and, um, you know, I, I enjoy being able to learn about, um, the universe that we live in, you know, whether it at the, uh, the geological processes here on earth or, um, space exploration and, uh, all the exciting things that are going on, um, over <laugh> and above our heads.
Glenn Hopper:
I can somehow picture like that all incorporating its way into your approach to modeling and digging in on numbers, sort of that scientific curiosity, applying also to the numbers. So I could see that being an adjacent interest. So that’s great <laugh>. Um, you know, and another thing that we didn’t even talk about, but I’m gonna throw it out here. You know, I know that you in recent years have been really dialed in on go to market finance, and I, I talked about it in your introduction, but we didn’t talk about, um, uh, really the details of, of what you do, uh, in that area. So I guess, you know, maybe a way to sort of make reference to that, uh, in our, in our few room minutes left in a few minutes left. Tell me kind of, what is your, I guess we’d say what’s your favorite go-to-market metric? Like, or, or what appeals to you in, in that focus?
Laura Bloom:
I don’t know that they have a favorite specifically, but I will say in my mind the most important GoTo metric is your cash runway. Um, and for those that don’t know, it’s the amount of time that a company has before running out of money. And it is probably the most important indicator of a company’s sustainability. Most companies should have about 12 to 18 months of, uh, cash runway. And that’s extremely important, particularly, you know, at the startup level for SaaS companies, when as they start to run out of money, um, they need to do one of three things. They either need to become profitable, which is very difficult to do in the early stages. They need to get, uh, another cash infusion, or they have to lower their cash burn rate, um, by decreasing opex, whether that’s through a reduction in force, uh, a pullback on sales and marketing spend, or even maybe a delay in ap. Uh, that’s, that’s something that I’ve always worked very closely with clients on. And, um, I would definitely say, you know, that’s, that should always be front and center, um, in any go-to market discussion.
Glenn Hopper:
So that was an easy one for you because that’s, that’s where you’ve been, uh, been working. I know we talked before the show and I, I, I feel like you didn’t want to answer this, but I’m gonna, I’m gonna ask it anyway ’cause we ask everyone, we gotta know we’re, we’re <laugh>, we’re collecting this information, we’re <laugh>, <laugh>, um, um, but it’s just, it’s data that we don’t know what we’re doing with, so I dunno, but we’re <laugh>. We do ask everyone. Um, what is your favorite Excel function and why?
Laura Bloom:
The reason why I didn’t wanna answer that is because there are so many good candidates <laugh> in Excel. So I will answer it by saying, by saying this, one of my biggest pet peeves is watching analysts painstakingly, um, do a sum across a, a, a time series, uh, <laugh> for, you know, months, quarters, years, whatever the case may be. So I always like to introduce people to the combination of SUM Filter um, so that, you know, they can create this dynamic, uh, formula that will just do that work for them. And then you don’t run into an opportunity for errors. Um, and then I would say just if we’re talking in a, on a pure volume standpoint, the function that they use the most, um, probably, I don’t know, half, half, a hundred times, uh, a day is X lookup. I love X lookup. Um, I started out with V lookup and H lookup way back in the day, and X lookup just came to shake everything up. So, yeah, I think those together are, are very important parts of a, of a dynamic model. See,
Glenn Hopper:
See, great answer. You didn’t, you didn’t want to answer it first, but you, you nailed some good ones. And, uh, some filters is, is a great one. I don’t know since I’ve been hosting the show, I haven’t heard that one, but I de I definitely get that. And then X lookup. Everybody loves that. And if people are still using vlookup, you wanna be like, dude, let me show you a trick, <laugh>.
Laura Bloom:
I know it’s, we, we, we’ve advanced. But you know, it’s amazing even, you know, with Excel, um, people complain about it being a legacy tool, but it’s evolving all the time.
Glenn Hopper:
Yeah, and it’s still, I mean, all of us, I’m like, I love RI love Python, I love all these other tools, but my excel’s like my comfort place. It’s like, we’re gonna start here and do it. <laugh>
Laura Bloom:
<laugh>, exactly. There’s no better tool I can think of for ad hoc analysis. Obviously you don’t wanna be doing your full planning and forecasting cycle in Excel. That that gets very difficult very quickly <laugh>, but for ad hoc analysis and just, you know, smaller, um, projects, I think it’s, it’s, um, absolutely unbeatable.
Glenn Hopper:
Well, Laura, I really appreciate you coming on the show. I’m glad we, you know, after some, some false starts that we finally got, uh, got to get it recorded. It’s well worth the wait. And I, I thank you for all of your, uh, excellent insights and uh, and information that you provided. And I do, before you go though, I wanna make sure, uh, we can let our listeners know how can people connect with you and learn more about your work and, and communicate with
Laura Bloom:
You. I am always happy to make new connections on LinkedIn, so please do reach out and send me a connect request. Um, I don’t care what your title is, I will connect with you and, uh, you know, for people who are interested in working with me or who would like me to demo a, a software product, please feel free to reach out, um, to my email address directly. That’s contact@impactfpna.com and the website, uh, impact fpna.com is coming soon.
Glenn Hopper:
Thanks again, Laura.
Laura Bloom:
Thank you, Glenn. It’s been a great pleasure speaking with you.
