Automating Financial Workflows and the Role of Human Judgment with Albert Lee

What do you see most people struggling with in finance with AI? Is it the prompting? Is it understanding the type of tasks they can do?

First of all, they do not know which tools to use. Amount of tools is just overwhelming. It's just a lot of to use, a lot of to learn. Second thing is that I think—

Meet Albert Lee, FP&A leader, AI finance coach, and corporate trainer with 13+ years of experience helping CFOs put AI to work in finance. I know in your training you focused a lot on Copilot. Why Copilot.

I think Copilot has its own place. It sits inside the Microsoft ecosystem and also the security, the data governance is best of the best. Copilot in Excel, so I can extract the information from the PDFs, thousands of pages of tens of annual reports, and then just make sure every figure is extracted, go to specific cells and specific location in the Excel template. So within like 10 minutes, you got a whole financial analysis or even investment analysis report ready.

So I'm curious, when you are training people on AI, how do you ensure they have sufficient data?

We have some custom GPT called—

Are you tired of being seen as just a spreadsheet person while others get a seat at the table? Well then, welcome to FP&A Unlocked, where finance meets strategy. I'm your host, Paul Barnhurst, a.k.a. the FP&A Guy. And each week I'll bring you conversations and practical advice from thought leaders, industry experts, and practitioners who are reshaping the role of FP&A in today's business world. Together, we'll uncover the strategies and experiences that separate good FP&A from great FP&A and will help you elevate your career and drive strategic impact. Today's guest is someone who's earned that seat at the table, and I'm thrilled to have on the show. Albert Lee, welcome to the show.

Thank you, Paul. Thanks for inviting me. Yeah.

Yeah. Excited to have you. So Albert's someone I've known for a few years. I've had the pleasure of chatting with him and really excited to bring some of his expertise and experience to the show. So a little bit about Albert. He's an FP&A leader, AI finance coach, and corporate trainer with over 13 years of experience spanning Big Four audit, global consumer brands, and Global 500 organizations. He has held FP&A manager roles at Merlin Entertainment Group and the Cookware Company, leading regional forecasting, profitability modeling, and process automation across APAC. Albert currently serves as a coach at the AI Finance Club, guiding CFOs and finance directors on integrating AI tools into finance workflows. He is also a corporate trainer for a Global 500 asset manager and has delivered sessions in partnership with CFA Society. He is also an MBA candidate from the University of Chicago Booth School of Business, a BBA alumnus from HKUST, and is a certified fellow CPA. Again, welcome. Love the background.

Thanks for having me here. And I just kind of met you in my home, but thanks for having me here. I love to be here because Paul, you know me for several years. Happy to be here. And then my honor to be a guest speaker. Yeah.

Well, excited to have you. So we're going to start, this is a question we ask every guest. So we're curious to see what your answer is here. From your perspective, what does great FP&A look like? How would you define it?

Actually, this is one of the questions I answered for CFI, Corporate Finance Institute. I think my answer is the same, like high-impact FP&A always connects the numbers to decisions. Like there's 3 perspectives. First, you got to know, understand the business, not just the profit and loss, but also the operations, marketing dynamics, and what keeps the CEO at night. And then secondly is that you need to generate insights that move the needle. And the third thing, and the final thing, I think is everyone, most of the people for our part is like, you need to build a relationship and trust with your business partner. This is my opinion.

Yeah, you definitely have to have that relationship of trust to be able to connect things. So appreciate that answer. Now we're going to spend pretty much the rest of the time digging into mostly AI. Right? Not that anyone's ever talked about AI lately. I've heard it's a big subject.

You got a lot of AI posts in the LinkedIn. You are too humble.

I've had some fun lately with my AI tips posts, kind of making fun of all the AI stuff we see, but I totally get it. I use AI all the time. It's a great tool. And where I'd like to start is, I know your training, you focused a lot on Copilot. Why Copilot? What led to Copilot training?

Actually, I use Anthropic one and also ChatGPT more because I do most of them like the deeper work for these two models. But I use Copilot for training is because when it comes to enterprise training, I think Copilot has its own place. Like it sits inside the Microsoft ecosystem and also the security, the data governance is best of the best. And also the licensing economics just makes a lot of sense at scale. I mean, I do not have any preference on any tools, but if you come to enterprise training, you got to be Copilot. Just what I think.

Yeah, there's definitely a lot of your large enterprise companies, no question, have used Copilot. So totally understand that. That makes a lot of sense. You mentioned you use Claude, ChatGPT a lot in your personal life. So ignore training, ignore all that. Do you have a kind of favorite that you find yourself that you use the most when it comes to these tools right now?

Yeah, I have. I majorly use Claude because Claude's something, you know, every model has hallucination. When I think that it has hallucination, I will find ChatGPT to challenge Claude and find Claude to challenge back ChatGPT. That's what I call as cross-model validation. Yeah.

I think it's a good idea. I mean, I definitely use both and I use Copilot, ChatGPT, Claude, all three of those almost daily. I'd say Claude I use the most, but I definitely use all of them. And yeah, the cross-validation is always a good idea. So how did your journey go about? How did you start spending so much time kind of investing in AI in the work you do? Like what led to becoming a trainer?

First of all, I want to tell a story when I was still in corporate. I worked in a company, a Belgian company, and the finance function back then has been vacant for several months already. The handover, the documentation is very minimal. And then there are lots of reports that needs a lot of manual adjustment. I have nowhere to go, no one teach me, and nobody's there. The finance department is empty. I mean, I got to find a way. Then I turned to AI. I found GPT out of desperation, and very quickly it became a mentor. You know, it walked me through the manual workflows and even teach me how to use a tool called CData Python D365 Connector. That's a tool I never heard of and never used before. And then I just pull all the data directly from the ERP and auto-populate the Excel. That's an aha moment for me. I just never think of any tools can do that, but AI helped me a big favor.

Yeah. How much time did that save you?

Yes, maybe 80 to 90% because it's all automation to cover the manual work.

I know a lot of people that are finding great use cases, like you mentioned, to figure out how to automate the pulling of data. As one person said it, look, if you're still manually pulling it, you should be using AI more.

Sure. But you know, that's, uh, security issues for every model, so you got to figure it out anyway.

There are some situations where you may not be able to— security— like, you'll find this interesting, um, it was Chris Riley, he shared on LinkedIn the other week that one of the contracts he signed, it basically right in the contract, he had to agree for some modeling he was doing that none of their data could touch AI. Didn't, regardless of what security you had, that was just their policy. Like you couldn't, you could not use AI on the model. You could use AI to help you with something, but you couldn't give it any of the data. You couldn't even use Excel add-ins because they'd get the data. It was 100% locked down. So there's definitely times for security reasons. Whatever those reasons are for different companies.

The Belgian company I just mentioned, actually the head of IT stopped me from pulling all the data from the ERP and auto-populating the Excel. He said it's too risky. You've got to pass through the founder and the chairman. I just hold on it and I never try again.

Yeah, no, I get it. There's definitely, you know, people want their data all to stay within one tenant, one system. As soon as you start passing it different places, right, there's always more risk. So that's still a big concern, but I like what, uh, I know you know Glenn Hopper.

Yeah, I know, I followed him. So sure, sure.

All right, he writes a ton about AI and he says a lot of people use security at the same time as an excuse. He goes, the reality is AI is as safe as your SaaS products you're using, right? If you're on the cloud, they're SOC compliant, they're meeting all the different risk compliance. Now obviously that doesn't mean be stupid, there's still, you know, things you need to do. But I think sometimes because it's new, we use security as an excuse versus looking at it and saying, you know, weighing that risk against the other tools we have and saying, is it really higher?

I think so.

Yeah. I thought that was an interesting kind of perspective he gave, but interesting on the Belgian, the Belgian one.

I'm not sure. His insights is very good. I follow Henk Venkman. Yeah.

I agree. He's real. He's really good. So you did that at the Belgian company and you were like, wow, saw the benefit. How did you continue to kind of learn AI from there? What were the other kind of aha moments you had?

Another aha moment is like, I know Nicholas is very famous, Nicholas Boucher. So he is one of my friends and he has founded AI Finance Club and I joined AI Finance Club and I found that lots of the AI finance knowledge, I don't know how I don't know how it exists and I don't know how to apply. And then I just joined as an newbie and then he's incredibly kind to me. He referred a lot of clients to me and vouched for me when I have no record on it. So a lot of doors for corporate training, he actually opened the doors for me and I owe him a lot and appreciate for Nicholas's help here.

Got it. So is that how you, was Nicholas how you kind of got into doing the training?

Exactly.

And what is it you like about doing training on AI? What do you enjoy about it?

I love to, you know, I'm an implementator and not just the one who want to speak. I want to measure the ROI. I want to make sure after the training everyone got benefits, got value. They cut a lot of time and then save a lot of manual works. And then every time I've finished the corporate training, I want to measure it. And that gives me a lot of feeling of satisfaction.

It's always nice when you know they use it and you're able to see real results. So what's your typical training look like? Are you training people to build agents, to use Excel, how to do analysis, write prompts? I mean, talk a little bit about what kind of training you're doing.

So I basically do two types of training. One is the AI finance coaching in AI Finance Club. We have an accelerator program, and then I coach every individual CFO and finance directors one-on-one for their projects for their AI finance. Real analysis and real case study. And another one is like I train the corporations, large and small corporations. I usually tell them how to do the AI finance in a governed way, how to keep all the audit trails, keep all the track records. You can trace back to the different figures. And at the same time, you also can save a lot of manual work and do a lot of automation. And sometimes I even teach them how to use agents, but agent is not my major case. Major case is still the LLM.

Yeah, so you're not doing a lot right now with agents. It's a lot more with kind of the LLM and—

A bit like the research agent or analyst agent in Copilot. And sometimes you can say in the cloud, I use Cloud Cowork sometimes for my personal business. Yeah, something like that. Yeah.

Have you used Copilot's Cowork yet?

Copilot Cowork, I'm still applying. You know, you got to pass through the Frontier program, you know. I'm still on the procedure, yeah.

I believe I'm part of the Frontier program from previous, and I've been meaning to look into Copilot Cowork. I haven't. I've used Claude Cowork for some things, and I need to use it for some more.

Claude Cowork is just amazing. Can do lots of work, can make judgment, can utilize the tools, can apply the judgment. I mean, amazing. Amazing tools.

Favorite use case so far for Cowork? What's the thing that's kind of blown your mind? You're like, oh wow, I don't have to do that anymore. I can let the tool do it.

For Cowork, sometimes I will use like, actually for Copilot Cowork, I'm still in a very beginning place, but I can tell you one function is like Copilot in Excel, something like this, or AddThis in Copilot, something like this. So I can extract the information from the PDFs. Thousands of pages of tens of annual reports and then just make sure every figure is extracted, go to specific cells and specific location in the Excel template. So within like 10 minutes, 15 minutes, you got a whole financial analysis or even investment analysis report ready. When back then, before AI, you need to do a lot of Many tasks, many stuff, like 2 to 3 hours. It just cuts a lot of time right now.

Yeah. And funny enough, I don't know if you saw this. There's a guy on LinkedIn. He shared, I want to say it was, I think it was $1,000. He gave $1,000 to ChatGPT, to Copilot, to Claude. He gave it all its preferences and said, you need to tell me what stocks I should buy and sell. And he's up 180% on Claude.

What the?

He's up on the others as well.

It's amazing, man.

Yeah, like 40 and 20 or something. And he's just 100%. He does the trades himself, so he doesn't have co-work where it's actually processing the trade, but he completely just follows whatever he figured. Okay, it's $3,000. If I lose it, I lose it. Let's see what happens. And I think in total, you know, he's made now, he's doubled like his money overall between the 3 different LLMs or something like that. It was, it may even more. He's been doing it for quite a while. And I was like, it's pretty amazing what they can do.

Got to learn from him, you know.

I know, I was like, I don't, I don't think I'm ready to give $1,000 to my LLM to spend.

That's a whole lot. That's a whole lot. I cannot pay for that much.

Although it could be fun just to— I'm tempted to just, uh, have it pick stocks and just track it for a few months without money type of thing and see how it does. It'd be interesting. Although, right, as soon as people start getting good at that The opportunity goes away because the market will start adjusting for it. You have to kind of be early. At some point it will, I, I would imagine, although, and you never know, cause it's probabilistic, right? Some people might win. Some people might lose. It'd be really interesting to do a study and I know we're off base from FP&A, but I think it's okay.

It's okay.

Take 50 people with each of those tools, take some money and like track it over 2 years. And just see how it changes over time and what, and what happens and how good the return is. I think it'd be an interesting study because it's going to give different answers to everybody if they were all using the same prompts, started from the same place. How divergent and different would everybody be at the end of a year or 2 years? It would be really fascinating to see.

I think so. Yeah, kind of interesting.

Yeah, yeah. And I imagine we'll see those type of studies.

It's Just a matter of time.

Yeah, exactly. All of it's a matter of time. So I know you do the Finance Club. You've also done some enterprise and startup training. Do you have a preference? Do you like, you know, the coaching, the one-on-one, the startups, the big enterprise companies, or does it matter? Do you have one you prefer?

To be honest, there's no preference for me because I can tell you the reason. Because like, I think smaller companies, they serve as assets. To the same AI capabilities the big enterprises are deploying because like the technology has just go to a point that's already like 20 to 30 people business can run workflows that is already at a Fortune 500 budget or level. So I don't want to be the guys that's only taking the big logos. If just some small team want to get serious on the AI practice, AI finance practice, I'm just willing to have kingdom with them and for them. Yeah, as well.

Yeah, that makes a lot of sense. I could— at the end of the day, they're all, for the most part, they're all using the same tools, they're all using the same models. Sometimes they may have their own customized AI in some of these companies, but they're still using more than likely either ChatGPT or Anthropic's model, maybe an open source like Llama or, you know, some of these others out there. But the backend models are all pretty similar. Like Claude's model, it isn't, its answers are better in certain areas. I don't think they're better in others versus, you know, Gemini or ChatGPT. But right now I think most people feel like it's the most complete in that it's the easiest to use the way they've built skills and the way you got Cowork, Code, and Claude just all in the desktop app. It just, I don't know that it's the package right now is the best. I think, you know, by the time we release this episode, that could change. True, true.

But one model, you know. Yeah, yeah.

I mean, I don't know if you saw that, but they're on pace. So last quarter, Anthropic did— from the numbers I've seen, they did $4 billion in revenue.

Yes. This quarter, they're on track to do $10 I believe they already surpassed OpenAI, right? I'm not sure, but I, I read some news saying they're gonna—

I think in this quarter they'll pass OpenAI. OpenAI is around 30-something.

I think so. I think so.

I believe.

I think so.

So if they did 10 on a run rate basis, I don't think they've passed them in a— on a yearly basis. Yeah, but if they continue on a run rate, I believe by next quarter they would have a big— they'd be bigger than OpenAI.

Yeah. Everyone's talking about the cloud, you know, right now. Everyone's talking about them.

Yeah, and I'm— it's amazing how many companies are switching to cloud. And, you know, a friend of mine made a good point. Many people know David Fortin as we talk AI, and I'll get your thoughts on this. But, you know, he said, look, the reality is, sure, cloud's great and you can switch, but Copilot can probably meet 90% of your needs. And the bigger problem is your people don't know what they're doing. Pick a tool, train them, get benefit, give it some time. Don't just switch to the latest tool because everybody says it's the best right now. Your thoughts?

I think I'm kind of tool agnostic. I don't have any preference on any tools. Just I want to pick the tool that I like to use and I am comfortable to use. That is the best for me. I don't care its functionalities or functions or the power. I just want to use the one I am most comfortable with.

Yeah, no, and that makes sense. And so I think, you know, you kind of agree it's a good point that companies need to be careful. Switching for switching's sake usually doesn't work well because, hey, I want the latest technology, right? If you did that with an ERP, it'd be a nightmare. We've all been through an ERP implementation and it's not something you change lightly. Yes, it's a lot easier to switch AI, at least right now. Will that be case in 2 years when you've, you know, built it into all your processes? I don't think it will be as easy, but fascinating to watch. So what do you find is the biggest thing people need help with? Is it the prompting? Is it understanding the type of tasks they can do? You know, building workflows? What do you see most people struggling with in finance with AI?

I think the people who are struggling with data, first of all, they do not know which tools to use. The tools, amount of tools is just overwhelming. It's just a lot of to use, a lot of to learn. And then they don't know how to pick which tools. Second thing is that I think the evolution is so fast. Back then, 2 years ago, we are talking about LLM and now we are talking about agent and agentic AI become a very hot word. And sometimes I don't think a lot of things you need to use agentic AI. But sometimes people just want to do the things using the most hyped tools or the hypewords rather than using the most suitable tools. So that's what I think in the current situation.

Yeah, I mean, I think a great example, I was on a call and somebody's like, I have the standard process where I need to pull some data and do some different things. Can I use AI for that? Like, why would you? You have Power Query, it's deterministic, just set up your steps and run it each month. And it was like they wanted to use AI. Well, that's a cool new thing, so I could figure out how to use it. And I just said, I wouldn't in that case. Just, you already have a tool that you know works, you know you're going to get the exact output you want, and you can automate it. And I think sometimes people think, but I got to show I'm using AI. And at the end of the day, any good leader could care less what the tool is. If you get the right answer. You're doing a good job. It's saving time. You're being productive, right? If you're doing the things that a good employee does, I could care less if you call it AI or whatever you call it. And I think sometimes people need to remember that because we feel like, oh, I got to be on the bandwagon. Yeah, you should be using AI, but that doesn't mean it's the right tool for many situations.

And you know what? Actually, I'm a big fan of Power Query and Power BI. So, um, If I can solve the issues by only using Power Query or Power BI, I would not use AI. Just no need.

I mean, I'm 100% with you. I'm a huge Power Query fan. I mean, there's definitely times when you don't need AI or use AI to help you write the code in Power Query, because at the end of the day, as I've talked a lot about on the show and you know, right, generative AI is probabilistic.

Exactly.

You're not going to get the same answer every time. You need those deterministic pieces in there. And Power Query is deterministic. If I give it the same data and the same code, I'm going to get the same output whether I run it one time or a million times. Never going to be the case with generative AI without a deterministic step in there. And so that's something I've been preaching, you know, quite a bit is helping people realize You need to think about the amount of variability you could have in the process, and that will change how you build it. I imagine you've had some of these conversations with people you're talking to.

Yep. Actually, uh, we have a program called AI Finance Accelerator, a base promotion, you know, but, um, for the Nicholas AI Finance Club, you know, um, in week 4 to week 6, we have, uh, teaching on something about probabilistic versus deterministic. So for myself, I will use the draft of strategy plan and the planning and also the generation of code. I may use some probabilistic model like LLM, like Claude and GPT. But for the calculation part, the factual part, the context part, I need to use Python. I cannot use LLM. So it's like a killing combo for Python plus the ChatGPT or Claude. It's a killing combo. I mean, you cannot wholly rely on each one of them.

I mean, 100% agree. So yeah, we're on the same page there. So I'm curious, when you are training people on AI, how do you ensure they have sufficient data? The other challenge is you have to have enough data to produce good outputs from AI.

Actually, we have something called some custom GPT called a fictitious data generator. So you just describe the situation, the data you want, how many rows, how many cells, and then you give it a context, give it the requirements, it will generate for you. But of course, I mean, it's not as good as the real world situation because real world data, you got a lot of messy stuff. You got to import from and export from the ERP, a lot of different sorts of data. We cannot mitigate that, to be honest, but we try our best to, to provide the fictitious data.

Yeah, so you're often doing the training on fictitious data, but what kind of— what guidance do you give them about the real world? Because we all know real, real data is messy. There's a lot of companies that are struggling to get good output from AI because data challenges. So what would you say to people on that front?

Actually, for real-world data, I usually go to the corporate training part because it is not really included in the AI Finance Club. And we have real data to be trained in AI Finance Club, but sometimes you've got to deal with the confidentiality and security. So corporate training, when I sign the NDA, the non-disclosure agreement, I will teach people how to do this real data stuff. So it's go back to my framework of the model, like how to automate the data using the governance and also audit trail, something like this. But a lot to talk about, so I don't think we have enough time. So about that.

Yeah.

Yeah.

So how do you think about just high level, what's kind of your framework that you teach? Just kind of what's the basic idea?

So for example, I can give you some example like, you know, something like garbage in, garbage out, garbage out. You know, sometimes you need to audit before you automate. For example, when you point to a datasets. You've got to describe the data, where it comes from, who owns it, how often it's refreshed, and what known issues it has. And then you start to start narrow. The second step is try to— don't try to feed AI all the data lake from your ERP. Provide a clean, well-understood dataset like a single P&L export from the GL and then build a workflow around it and then expand it bit by bit by bit in the workflows. And then the last step is to build the verification into the prompt itself. I teach people to ask AI to flag inconsistencies, missing values, and anomalies as a part of our outputs. So not just produce the answer, but also do the data quality check by using LLM itself.

DLMs are great for checking things. It's always good when you load some data, first ask it to review the data and just ask questions about it. It's a good way to start. Like, I don't know if you've seen this, Copilot just added in Excel plan mode. Have you seen that yet?

Yes, I have seen that, but I did not use that to be disclaimer. Yeah, I did not use that yet, but I know that's a function.

Yeah, so I used it. So one of the cases we tested when I did my Mod Squad series for financial modeling is we had it, we had all the tools build a deferred revenue schedule, and then I started having them build it and provide instructions. Well, this time, instead of my big long prompt that was really detailed, I tried a really short prompt and I used Plan, and it brought up a number of things that I didn't have in my typical deferred revenue schedule that were really helpful. So I was really impressed. I got a better end product than I did with my detailed prompt that I had worked on forever because there were just things that I wasn't including. Now, I could have added them to my prompt and asked for them,, but I hadn't necessarily thought of it in what I was doing. And so it was, I was impressed with, I've only used it kind of that one time, but I was impressed with the job it did. And I think we'll see more and more of that, right? We've seen a lot of tools do that of, I think all the agents will eventually have some kind of plan mode first, because the reality is if you just build, rarely are you going to get what you want on the first try, and you're much better to go through all the questions, go through the planning, really get into detail, you're going to get a lot closer than if you just let it build with, you know, one prompt.

I gotta check it out.

Yeah, yeah, I would love to get your thoughts once you've had— yeah, you know, one thing you say, I know when you train AI, you say AI is a copilot, not an autopilot. Talk a little bit about that.

I think anyone use, uh, AI seriously knows, um, it's intuitively Because in my opinion, AI is like your analytical copilot. It helps you navigate, watch the dashboard, and spot the blind spots you miss. But the wheel is still in your hands, right? The accelerator, the brake are still in your hands. So you will not just let a copilot decide where the plane is going to land. That's my opinion. And that's my idea. And I like the suggestion, the definition, and also the accountability. For my opinion, it must stay with the humans.

Got it. Yeah, no, your example of the pilot, right? Pilots— a lot of the plane is flown by the machine, by the automation, but the pilot is there the whole time, and ultimately the pilot makes the call. So, all right, another question I have for you. We talked about data. But what's the key with AI to having kind of good verification, a governance layer, right? We all know AI can hallucinate. We know there are mistakes it can make, and ultimately we're responsible for it. So how do we make sure we have a good kind of verification governance layer? Any advice there?

I would give advice for 3 dimensions: verification, explainability, and governance. No, verification is knowing what to check, like, um, You don't need to check those low-risk stuff. You just check the high-risk stuff like the key financial figures, the citations, because AI hallucinates sometimes, and time-sensitive data because the data keeps changing all the time, and then casual claims and scenario assumptions. For explainability, just you should be able to understand the data and rephrase it to the people who don't know. So use simple English to rephrase it. If not, you just shouldn't put your name on it because an unexplainable conclusion is just not useful. The first thing and the final thing I think is the governance. I personally have an RYG model. R means red, Y means yellow, G means green. So green means that AI can run by itself. Yellow means AI drafts, human reviews. While for the red, I think humans can lead while AI only advise. These are my three-step approach.

Makes a lot of sense, right? There's certain things where it's like, all right, I'm comfortable letting it run. If there's mistake, it's not a big deal. Or, or I've run it 50 times, it's been right every time. I'm comfortable enough. This is low enough risk versus the other side where this is going in front of the board or this is going public. I better double and triple check all the numbers before I let it out. So it's always good to have a framework. I like that. And red, yellow, green, everybody knows that. That's a simple one to use. So thank you for sharing that. So I'd love your perspective. We see a lot of promises that AI can do everything, right? We see a lot of marketing hype. We see a lot of vendors probably over-promising. From your point of view, what's realistic to do with AI? Where does it struggle? How do you kind of think about that?

I think AI is good at something like information synthesis, like pull the most important few things from several hundred pages of annual reports in just minutes. Second is like first draft generation and also multi-angle framework. Sometimes you've got to build from 0 to 70 points stuff. It's very difficult back then when there is no AI, but right now it's very easy. So the points for human to be here is to improve from the 70 points to 95 points. This is the good thing that it can go. And also, I think it also accelerates some of the repetitive manual tasks. For the things it struggles, I guess, is some factual accuracy under pressure, because if you just have time limits, you ask the AI the wrong thing, it's just going to hallucinate. And then the second thing is that the context sensitivity, like the same data points can be used, can generate different things in different macro environments and industry cycles. And also, I think some Something this accountability is also struggling with right now at this current moment. But I think it has its own pros and cons. I think the most honest framing is like AI is a powerful efficiency multiplier. It amplifies whatever the user brings because like if a strong analyst using AI, it will be much, much stronger while the weak analyst using AI, it will make the mistakes faster. That's what I think.

Makes sense. Appreciate that. All right, so we're going to move on from AI to— I have kind of some standard questions I ask everybody got FP&A. In your opinion today, what's the number one technical skill FP&A professionals should master?

I just answered back then, it's like the Power Query. I'm a big fan of Power Query. I'm very contrarian. I think most of the people will say Excel or modeling, but I think it matters a lot, but in modern FP&A, Power Query is the skill that turns you from someone who waits for clean data to someone who creates and owns the clean data. So you can pull, you can transform, you can refresh the data automatically. Your monthly close gets lots of faster and your variance analysis gets much faster. And then you can use the time saved to make even better decision. This is the most important thing. Love it.

I, I'm a huge Power Query fan. Uh, you know, interesting, we've started to see lately a lot more answers around data thinking, system thinking with AI, you know, which part of gets back to being able to clean and model data, which Power Query is something that can help a lot with that. So we're seeing more and more answers that kind of think center around data design, system thinking, than just Excel and modeling. It's interesting to watch it all change, but Power Query got me promoted. I'm a huge fan of Power Query, so I can appreciate that answer. What about softer human skill? What would you say is number 1?

I think communication, like specifically storytelling with data, like the What's So What Now What framework by a lot of storytelling gurus like Sophan Hamid, you know, What's So What Now What framework. I think most of the analysts will start at What's like, here are the numbers, and then what numbers there are. But I think the good analysts will go to so what, so why it matters, while the great analysts will go to now what, what we should do, and what's the call to action, what's the recommended action for the, for the situation. So that separates an FE&A analyst from the true business partner. That's what I think.

Yeah, communication is always huge. That's a very common answer we get. The framework you mentioned is one we use in my training all the time. The what, now what, so what. It's a great— or what, so what, now what. Great framework. Next one I'm curious to see, and I think I know the answer based on your previous answer. If Excel removed one feature tomorrow, which one would cause you the most panic?

You know what the answer is, man. Power Query. Power Query.

Power Query.

Yeah, Power Query. Yeah.

I knew, I looked at that one and I said, based on the answer a minute ago, I'm like 99% sure I know exactly what you're talking about.

Exactly, exactly. Power BI, no-brainer, no-brainer for me.

Yeah. Okay. So if you had to pick a second one, which would be number 2?

Number 2, maybe the VSTACK or XLOOKUP. Just my preference. No, no reason. No reason. Just my preference.

Yeah, no, that's, that's good. We'll go with that one for number 2. I was just curious to see what you'd say after Power Query. All right. So we have a get to know you section. This is where I ask kind of some fun questions of each of our guests to get to know you a little bit better. So if I said you could have dinner with one person alive in the world today, who would you take to dinner and why?

Let me ask you, what do you think? Who do you think I will have dinner with? I think you know the answer as well. Yeah. Just give you some tips. You know that. Yeah.

High on my list would be Warren Buffett. I think he'd be fascinating to go to dinner with. If Nelson Mandela was still alive, I'd love to have dinner with him. A lot of people I could list. Davis Smith, actually, right now. You have no idea who he is. Most people don't. He, he started the company Cotopaxi. It's a Corporation B benefit corp. And his goal with the company is to alleviate what they call not just poverty, but I think they call it severe poverty, which is you make less than $1 a day. And so they use areas that are very poor to source all their materials and just an amazing guy. I've always really respected him. So he'd be my dinner.

I am a big fan of Warren Buffett, but I may choose another person. I watched his basketball game since I was very young, since I was a teen. I think I would choose Tim Duncan, a basketball legend in NBA league.

Definite legend. I'm a big Utah Jazz fan, so I can remember some good matchups against him and Karl Malone.

I watched him play when I was a kid. What I admire is really his championship, is his emotional stability. Every time he failed, he lost to Lakers, he got back up and won the champion again. I mean, he crossed 3 decades of basketball era and 3 different playing styles and 3 different rosters. He still win the champion. I mean, the constant was him, the composure, the leadership, the ability to make whatever and whoever was around him better. So I will just have a dinner with him. I want to.

Yeah, he had 3 different teams. You had David Robinson with this first title. You had Parker and Ginobili with the next 3. The last one, the leader of the team was really Kawhi Leonard in that last one.

Kawhi Leonard, yeah, exactly. Exactly. Yeah.

You know, and then the others were aging. So there's really kind of been, yeah, 3 different. I agree with you. That's an interesting, I hadn't thought about that. All right. So next one here, if you could have any superpower, what superpower would you have?

That's a pretty challenging one. I'll choose time traveling because I can tell you the reason, because I want to go back to my younger self. You know, when I was young, when I was still in corporate FP&A in maybe different companies, different F& A department, really don't know how to handle people and relationships. I rubbed colleagues in the wrong way and said things that I shouldn't have said. And also I didn't appreciate everyone who worked with me enough because like, I know they have their bad vibes. Sometimes I just say something that is not appropriate. So if I could go back, I want to teach and coach my younger self how to treat people differently, with more patience, with more grace. Because the technical skills you can pick up anytime, but the people skills, it's gone for a lifetime, you know, lifetime learning. Yeah.

Last one. Do you have a favorite like movie or TV show you like to watch?

For movie, there's one. For TV show, there's one. So for movie, I will choose The Pianist. I forgot who the main character is, but it's The Pianist. So touching for me to see The Pianist. For the TV show, I think it's Game of Thrones. Kind of hard to beat to share the storytelling ambition, but Even though people say the ending is not too good, but I'm still a big fan of Game of Thrones, you know, amazing.

I mean, I will admit I've never seen an episode, so I'll take your word for it.

Yeah, indeed.

If you could offer one piece of advice to our listeners to be a better FP&A business partner, what advice would you give?

Got to be one, right? I think it's the understand the operations from the operation partner or the sales partner, the business partner you have. In the sales team or operation team, because, you know, sometimes figures got to be very cool, very cold, you know, you need to understand how to build a relationship. You've got to know the real world situation from the business partners, from the operation partners. You've got to know the news of the whole story. You cannot just know about numbers. The whole story is in the operations, is in the sales, is in the business. So you got to know from the others, not from numbers, I think. Yeah.

I agree with you. I think that's really good advice. So as we wrap up here, last question, if someone wants to get in contact with you, maybe, you know, questions about training or just reach out to you, what's the best way for them to do that?

You can search in the LinkedIn search my name, Albert Lee, FCPA. I'm the Albert at ASEAN, and then send me a connection request. Just mention you heard my podcast and I will always reply and connect with you.

Right. Perfect. Well, thank you so much for joining me, Albert. As always, fun to chat. Appreciate you sharing some of your experience, and I'm sure it is late for you, so we'll let you go so you can enjoy your evening. Thank you so much for coming on, Albert.

Appreciate it, Paul. See you.

That's it for today's episode of FP&A Unlocked. If you enjoy FP&A Unlocked, please take a moment to leave a 5-star rating and review. It's the best way to support the FP&A Guy and help more FP&A professionals discover the show. Remember, you can earn CPE credit for this episode by visiting earmarkcpe.com, downloading the app, and completing the quiz. If you need continuing education credits for the FPAC certification, complete the quiz and reach out to me directly. Thanks for listening. I'm Paul Barnhurst, the FP&A Guy, and I'll see you next time.