And now, on to the show.
From Data Rails, this is FP&A Today.
Welcome to FP&A Today.
I'm your host, Glenn Hopper.
Today on FP&A Today, we're joined by Joyce Lee, CEO and Chief AI Strategist at Everenda Partners.
Joyce brings a rare combination of expertise to our conversation.
She's both a CFA charterholder and a computer science graduate with an MBA from Wharton.
Over her career, she's co-led multi-billion dollar investment strategies, advised global financial institutions and now works with boards and C-suites on AI strategy, governance and responsible adoption.
She also serves on the advisory board of OpenBB and co-authored the Athena Alliance AI Governance Playbook, helping finance leaders navigate the fast-changing world of AI.
Joyce, it's a real pleasure to have you with us today.
Welcome to the show.
It's great to speak with you, Glenn.
It's been great getting to know you over the months.
I know it's been a few months since we first spoke and I just.
Every time we talk, I just I have to say I absolutely love your background and focus as being both a CFA charterholder and a computer science graduate, and that MBA from Wharton.
I feel like you have the kind of the perfect package.
And I'm wondering, with that approach and insight, how would you say that unique combination has kind of shaped your career path?
Can you sort of walk us through both your educational and professional background?
Yeah, for sure.
I've been thinking about that a lot.
What's the through line of my career so far?
Because clearly, if you look outside in, it has three chapters.
The first one is computer science, engineering, doing analytics work for financial institutions.
That's my early career.
And then I switched lanes and then became an analyst and then portfolio manager, managing investments, including in different verticals and also type of firms such as hedge funds, loan only, mutual funds, ETFs you name it.
And then now I'm doing almost like the back to a little bit more technology and doing the intersection work between AI, finance and governance.
And the through line I came up with is I just have this curiosity and engineer sort of can do attitude.
I feel that there's always a solution to a challenge that's interesting enough waiting for us to solve.
That challenge early part of my career may be how to bring the data into you know, some of the finance suites help people to make decisions.
In the middle would be like how do I create value for our shareholders, our investors, to discover great companies And when they grow in their market value, the investors benefit as well.
And now it's more like bridging between how people should look at technology and how they can translate that into the problem they are solving, which may be business model, maybe how to unlock the potential of their labor force, or maybe just simply investment decisions and how to restructure their investment team structure as well.
I feel like that is just a storyline that gives me a lot of fun and that can do attitude.
You can also say very naive attitude for me to just say yes to a lot of these opportunities and have a lot of fun with it.
Yeah, and it is such an interesting time with technology, thanks to generative AI being so integrated into everything we do at a level that it never has been before.
Because truthfully, in the past that generation, the barrier to access, of being able to do real things with technology was the ability to code.
And of course, with your computer science background, you already had that.
But now people who couldn't code before they can get into vibe coding and access the power of Python and whatever other languages they're using in their everyday job.
I'm wondering, though and we're going to talk a lot about AI, obviously but were you tempted, or did you ever, when you were managing investment portfolios and doing your other work outside of computer science?
Did you ever think I can maybe automate this or do some modeling or some portfolio balancing or whatever it is?
Did you ever think about writing programs or did you apply the computer science when you were doing portfolio management?
Definitely all the time.
I may not always been doing it well, but this is always one of the questions I will ask for my colleagues or who are much more skills in programming, especially in the past, like dealing with big amount of data or highly complicated monitoring techniques.
I've always been assuming there's a better way to leverage technology on whatever we're doing.
I'll give you an example.
One of the things that we looked at as an investment team in the past was how do you get the unstructured data?
Of course, at that time, there's no GenAI.
So a lot of times we went into trade shows or we went into government sort of filing databases and get all these non-standard data sources.
And interestingly, that's where a little bit of technology can do a lot of mileage to uncover some of the interesting insights.
And when I was doing longshore strategy, we actually discovered a lot of sort of inflated financial claims or just maybe some of the questionable business practices.
By doing that,
So I would say that benefited my career a lot the ability of always asking, even just asking the question can we do something differently with the technology available to us?
And I really look forward to convincing or encourage everyone else to think about that.
And in fact that's maybe one of the things that you also do.
A lot is encourage people to think about what can I do with technology these days that can either multiply my ability to do things or maybe discover something that I didn't know before.
Yeah, absolutely.
And I think I understand we're talking to senior leadership and finance and accounting professionals.
And I'm not saying that anyone... who's carved out a career with domain expertise in another area.
I'm not saying they need to go become a machine learning engineer or get a new degree in computer science, but I think it's important that we understand at some level what's going on under the hood, if we're gonna use AI.
And I think about where you are right now and I'm wondering and don't get me wrong I love there are so many finance leaders right now, and just across all professions and all industries, who are really leaning into generative AI and are getting very good at using it.
But a lot of times they don't take the time to look under the hood and say you know, they've figured out a good prompting strategy.
They've figured out good things that they can offload to AI, but they don't understand what's happening underneath the hood.
And with that engineer's mindset and computer science background you do have a little bit better of a read, or a significantly better read, I would say, because you understand sort of the engineering that's happening.
When you're talking to whether it's leadership or boards or anyone that's interested in rolling out AI.
Where do you draw that line?
And what are your thoughts on how much on the technical side we need to know versus just being good users?
Yeah, it's really interesting.
We've been talking about, you know, Gen AI, why is it different from past technology advancements?
And then I know you have a strong opinion on this as well.
I believe GenAI is really easy for a business leader or anyone to get onto.
Like they can start using it, they can start prompting and then they can even create this or leverage on the prompt library other people create and really be doing amazing things already.
However the curve.
So if you want to create you know truly value unlocking business strategy type of thinking, you have to go a step deeper.
You have to keep using it and keep thinking okay, what are the other things that other people are using it for?
And sometimes it doesn't have to be directly related to your business function.
Sometimes it's just the idea that you know you use it at life could be sparking an idea that you can use it at work.
It's more like that mindset that once you use it every day, you expand your sphere of.
Maybe I don't know the right analogy, but basically, if you think about you are sitting in the middle of a sphere, the more you use it, your sphere surface is going to expand.
You're going to have more interesting ideas and also you create this taste.
I know it's a little bit fluffy, but bear with me.
I do think for business leaders, a lot of times we develop that second level thinking based on our first level thinking, so that taste of what is a good idea, what's not a good idea in traditional business domains we're so used to it.
But on gen ai, if you can think about it similarly, you're gonna be able to be much more confident on determining what is the right ai initiative that your companies should consider.
What are some of the noise that is not really related to your true competitive edge of this business?
Therefore, you should pass.
So I do think that taste has to be developed by just grinding through all these daily usage, even though you don't know which one will give you that.
You know that 100 genius idea.
Yeah, and the other part of it.
So we talk a lot about if you can do all these calculations and forecast and generative AI and not in Excel.
Is Excel going anywhere?
I don't think it is and we talk about it all the time.
I don't care if you're in finance or data science or BI or whatever.
I mean, Excel, it's a perfect format to do data analysis in.
I think about my early career, I came up being an Excel warrior and really proud of all the formulas I could make and all that. but it's, you know, I was, I was been a CFO for a couple of decades now and I don't do as much in Excel anymore.
But in my point with all this is if you understand ways to manipulate data, if the, if the format is Excel or if it's R or whatever platform you're in, then you have more of an engineering mindset around it.
And you think.
You think about what's possible and whether you're writing the formula or not, you know the outcomes you can get.
And I sort of think all this vibe coding right now.
It's really cool if you can just talk to generative AI and have it build an app for you or whatever, but to think about a production-ready app and what you need to understand about it to be able to prompt more intelligently, even if you're not a great coder.
If you know constructs and you know this is a conditional loop and this is how it works and this is the way that I'm going to do this and this is sort of the overall architecture I want, then you can guide the prompts better.
So I think for us in our careers yes, we still need to have that domain expertise, and that's thing one.
And if it gets easier through AI to do our job, that's great.
But we still need to know the questions to ask and how to structure and guide the prompts.
If it's a chain of thought and all that.
So I don't know, it's got to be, I guess all that to say, are you doing any vibe coding right now?
Are you building anything that you're having AI write code for you?
Yes, yes.
Again, like if I don't do it, how do I know? what questions to ask.
And interestingly, I actually I want to extend your comments on the Excel modeling a little bit and then I go into the vibe coding.
So I used to look at a lot of pre-IPO companies and when they come IPO and there's a step you probably are familiar with but just for your audience, the sales side of investment banking analysts will have their model build up.
And then for us before the IPO.
We also, based on the communication with management, plus the financial filings, we also build up our own model.
So there's always this meeting where we'll compare our assumptions and our question, or our team members will question their assumptions.
They may try to defend their assumptions and that, back and forth, will make each side have their own decisions.
That ability to ask what's key questions that will influence that model.
You wouldn't ask like what's the you know tiny little detail that will affect the cell, that will lead to another formula.
You want to ask that.
You ask the most important levers, but how do you discover that?
How do you discover all these levers?
How do they work together?
Of course, in the form of excel formulas, but why one will lead to another and why the assumption difference will make a huge difference here, is developed over time.
And I do think that engineering mindset, or whatever you call it, analyst mindset that requires a little bit like more literacy than maybe not actually building the model.
I haven't actually building the model also for a long time, but I know what to ask.
I know, within 10 minutes of looking at model, I know what are some of the holes.
I will poke on right.
So, and going into the vibe coding.
Vibe coding to get started, again, getting started is super easy.
Within minutes, you will find something really amazing.
And you can especially show off to your kids that you are absolutely on the cutting edge.
That's very easy.
However, once you get there, how do you test, how do you evaluate?
That's why I think nowadays people tend to say eval is the key.
I think for board members or for C-level executives, that's the key criteria.
But unless you understand, or at least have that curiosity to learn to a certain level of literacy around AI, what AI can do and what AI might be able to destroy or create problems, you wouldn't be able to ask these very targeted questions that will influence your decision making.
So that linkage between the capabilities, the potential risk, is very critical for board members and executives to keep learning.
FP&A Today is brought to you by DataRails, the world's number one FP&A solutions.
Data Rails is the artificial intelligence-powered financial planning and analysis platform built for Excel users.
That's right, you can stay in Excel.
But instead of facing hell for every budget, month-end close or forecast, you can enjoy a paradise of data consolidation, advanced visualization, reporting and AI capabilities.
Plus game-changing insights giving you instant answers and your story created in seconds.
Find out why more than a thousand finance teams use Data Rails to uncover their company's real story.
Don't replace Excel, embrace Excel.
Learn more at datarails.com.
So this, I'll be very curious to hear what you are seeing in the market right now.
So last year, everybody was talking about AI, but nobody had budget.
This year, we're still, we're flying off the edge of the Gartner hype cycle on generative AI.
And I think though, we're starting to see a little bit of that slipping into the trough of disillusionment.
The super interesting thing around this.
I don't think it's because of any letdown in the technology.
The technology is moving lightning fast and it's better every day and there's new features.
For the arms race between the frontier providers is insane how quick everything's happening.
There have been studies, and this is one.
This is a time where I get jealous of the really big podcasts that they have like a producer in the room and they can ask for the stats and get all that.
Since I don't have anyone and I didn't prepare ahead.
You know, we've talked about it and I've written about it.
There's a lot of noise in the media right now about AI projects failing.
And you and I know the reasons for those.
And a lot of times it's, I would say the bulk of the time.
It's not because the technology it's bad, it's because maybe the desired outcome, what they were going out for, was unrealistic, given where technology is today.
But I'm wondering what you're seeing in the market right now, because there's this.
I think there's still that push from what I'm saying.
There's still that push from the top down of we have to do AI with nebulous, unclear what do AI means?
So that you know investors boards, senior management is pushing that down to their teams, telling them to do AI without clear goals and outcomes.
And then kind of, Those of us stuck in the middle are, well, what do you want me to do with this?
I can try to do it here, whatever.
So these sort of big capital investment projects are stalling, slowing down, not coming to fruition because of what I think, because of what I was just talking about, where people don't understand the technology and don't have clear goals around it.
But where we are seeing more success and more efficiencies happening at the front row or at the front lines rather, where employees are using the tools, sometimes with explicit permission from their companies, and sometimes they're just doing shadow ai off on their own and and using their own personal account.
But i'll break this into a couple of questions now that i've laid out all that exposition.
So the first question is when boards come to you right now, are you seeing a cooling off of or a fear around?
Okay, maybe we're gonna pump the brakes on AI projects.
What kind of AI questions are they asking right now?
What's your sense of people's mood and appetite for trying new AI projects right now?
Yeah, I think ROI question is number one still in boards' mind.
I wouldn't say boards would say, let's pause and review.
In fact, I do think if anything, boards now still have that urgency of let's figure this out.
Maybe what we have done give us valuable lessons.
Successes or failures of pilot projects are meant to give you insights that you can guide your decisions.
I would say ROI is still very much the big ask, but also I think the big change I'm seeing is the realization of closer attribution of these AI initiatives to business goals.
So what I mean by that is maybe in the past it would be.
Oh, let's think about how to you know, improve our productivity by 15, because that seems to be the magic number people throwing around.
But now maybe it's like okay, if you are, you know the chief revenue officer where your growth goes, how can AI help there?
If anything, it may not be AI helping.
It may be AI helping to track the marketing campaign and really be a little bit more efficient, Agilent on which marketing campaigns are doing the right things, and all that.
But regardless of what it is, it's linked to that business head as well.
So both the business division head function head would be able, would be asked to sign off on a certain AI goal and the KPIs of the AI team will be linked to that goal as well.
So I see that being talked about a lot more than I would say six months and 12 months ago, when at that time it was a little bit more like top-down.
Let's figure out how to improve productivity.
And I do think, going back to your point, understanding what AI can do and cannot do, where the AI risk might happen, really is part of very important input to drive that change as well.
Because now you can say it's not that easy to just bluff right?
It's more like how do you get measured will get done.
The other thing is I do feel there's this middle management dilemma.
I have a lot of sympathy on that group of talent because a lot of times, depending on whether the head of the division have a clear sense of what AI can do or cannot do, sometimes it's a little like difficult to communicate.
How do you go from that goal to what are the implementation?
Reality is
And especially, I think one of the area I would love to hear your feedback on that is the views on data.
Are we ready?
That's the first question.
Your head of division may have very different view as the middle management.
The second thing is even if we're ready on data, Is that expectation of our core competence or this treasure from our data?
Is that really true?
Our competitive edge or our core competence five years down the road?
Is it really coming from this data?
And the third thing I also feel like there's a lot of difference, especially in the finance.
Folks who FPA functions have to calculate the volume payback.
Is this understanding of?
Are we doing automation or are we truly leveraging AI?
So if you do the automation, it's easier to calculate the ROI and then sometimes it's much more attractive.
I have to admit.
But in order to really let's assume that AI really will play a huge difference in this business model, then there's definitely need to have a lot more communication, a lot more alignment, a lot more discussion, because Just simple calculation comparison, you may easily go with the process automation, which you are missing out.
A lot of other potentially interesting things.
Yeah, and it's so interesting because there is all this pressure and sometimes it can feel like if somebody doesn't dig in and figure it out and get down to like the brass tacks of what are we trying to do here.
And is it a rule-based workflow or where is AI in there?
It can feel like the blind leading the blind.
It's like everybody just telling each other we're going to do AI, we're going to do AI without defining what it means, or even necessarily defining what their end goal is, other than productivity increase or whatever the case is.
And I've had this semantic battle that I'm just not ready to let go of.
But, you know, 2025 this year, everybody calls everything an agent.
And that's, you know, great for marketers.
They can call this chatbot an agent.
They can call this workflow an agent and all that.
But an agent is a very specific tool in artificial intelligence.
It is something that has agency, like you and I have agency.
An agent is something that you tell it to go do it and it goes off and does it and, unless it has a problem, it performs all the calculations and doesn't come back to you until it's done.
So if you call your chatbot an agent or if you call whatever tool you have that's not truly an agent, then you're watering down how significant that is and also you're messing with people's expectations where they hear from various media oh, we could just have an agent do that.
Well, it's not that simple, and i think that's why a lot of these projects are failing is just unrealistic expectations and a lack of understanding of what ai can and cannot do, and and the only reason i would think of going back and like sort of conceding on calling things that that we know are not agents and just allowing them to be called an agent If it looks like an agent to the end user, does it matter if it's actually an agent or if it's just an orchestrated workflow?
I don't know.
I mean, if I tell my computer to go off and do some task, and I don't know, the Rube Goldberg machine that's going on in the background that's doing all the calculations and decision trees and typical automation flow, and then it comes back when it's done, to me it seems like an agent, so I'm going to call it an agent.
So maybe that's where I could let it go.
But at the board level, when you're talking to them, I feel like they probably don't want to hear that distinction right?
I mean, how do you?
I guess I just I can't get past the need for us to have technical understanding, but also someone to be that interpreter and go do it.
But I think if you don't have that interpreter and you don't have someone with that deep level of understanding, that's why these projects are failing.
So I don't know if boards are hearing that, if they're aware, if they care or what their take on it is.
The short answer, at least I haven't met someone who really care about that distinction.
Of course, when the CTO or the CIO went before them got asked about these type of questions, then it will come up.
But in terms of just proactively say, are you doing the Asian with true autonomy, true tool use and true ability to learn from the past decisions?
I haven't, at least I haven't encountered.
That doesn't mean there's none.
I'm just saying it's not common.
But I do think one of the reasons is a lot of board members, especially in the more traditional, I would say, regulated industries, assume this is far away from the core business functions of this business.
So they tend to assume that agents are used more in, let's say, go-to-market lead generation marketing and maybe some of the email.
In casual life, it would be like, booking a flight ticket or whatever.
But in reality we are seeing more and more the agents becoming a core confidence, at least workflow, in some of these businesses.
And it will be interesting to see what will get the board members' attention.
My bet would be they will be very worried about the risk associated with it.
Because when you say agency, it's great for people to have agency, but once you assuming machine has agency, the risk alarm bells will just start ringing.
And who will be able to stop an agent before they do something that's unexpected or above the guardrail?
So we already mentioned vibe coding.
Actually, one thing I really like to see, and I would like to attend when I have time, is to go to these type of hackathons where these AI agent startups not even startups, they are already like hundreds, at least dozens of millions of revenue in size to present them as a tool for developers.
And where they're focusing on.
I also know this, for agents are moving from what agents can do in terms of workflow, like no-code, low-code workflow you put together an agent, build an agent to now very much focus on what are the guardrails?
How do we monitor, how do we get almost AI response to some of these unexpected behaviors?
How can we create audit trails so that, even though agents may create all these desirable outcome, but in case something happens, we can offer all these audit trail logs to anyone who's checking that?
And lastly, I would just say also if you assume not just yourself have agents right and then your counterparts have agents as well, how is your position, How is your business position to talk to or sell to or even deal with these type of agent counterparts?
Are you really accommodating as opportunity, or are you sort of shutting them out because the risk metrics
I would love for us to have more discussion about that, but I would just say not yet.
Yeah.
I mean, and what agents can do when they start interacting with each other.
I know I think Google just came out with an agent payment protocol and there's a lot of protocols being built around it now.
And it is funny to think we're.
You know, if you're designing robots.
You design robots to be human form because you want them to operate in a world that was designed for humans.
So if you have a, you know two foot tall something on wheels with one arm, you know one mechanical arm or whatever, it can do a lot but it can't manipulate around the way that humans can.
So, you know, that's in the physical world and what you're designing.
But then if you're designing digital agents, it's kind of funny to think that the whole idea of a UI around a website and the way we navigate apps and the internet and everything is very human centric and it's very inefficient for agents.
So it's going to be interesting to see what happens on the web as more and more like I think I've.
It's funny you mentioned that it's always the demo for personal use.
It's always booking a flight or booking dinner reservations and all that.
And it's.
But if you think about how complicated it is going to the Delta website and navigating you know where to from and then looking at the different options and the different flight class options and then the seat options and all that, it's a very cumbersome way to navigate when really an API that just went in and looked at the database of available flights and seats and all that would be much easier.
So if you're in business right now, how much are you planning for this sort of agent-run internet and how much your focus on it is, when you can't even get an agent to reconcile your credit card accounts or whatever?
I do think for FP&A colleagues, there are two things that people are more and more have to consider.
So one is maybe from a product side of things.
If you sell AI type of products, Agents means the pricing strategy will be very, very different, right?
So outcome-based or even action-based, usage-based.
One thing for sure, it's not going to be seat-based.
So how do you model that out?
And especially with a lot of uncertainty and not a lot of existing playbook yet.
That's from if you sell these type of products.
I think if you, as a PA professional, can think about ways to think through that, create a framework around, that is a great way to stand out and differentiate yourself.
Just my opinion.
But if you are buying these type of products on the other side like your cost side of things also could be more and more important in your cost structure as well.
So that's something also to consider.
I'm pretty sure that Glenn, in the future episodes, he will talk about that a lot.
So I look forward to listening to those.
I want to talk a little bit more about governance But before I do I wanna put a pin in my question about sort of the direction from boards and the latest studies that have shown all these AI projects failing.
A lot of the numbers I see over and over are CFO says yep, AI is strategic, we have budget, we're ready to go forward.
And then you ask how many are doing pilots and it's some percentage.
But then if you ask how many have gone beyond pilots and are actually using AI at scale in production, that number drops precipitously.
And, you know, it's also it's a very nascent technology and, you know, finance and accounting.
We're not you know, we're meant to be risk averse.
So we're not going to be out on the bleeding edge.
But what is it more than timing right now?
What's why?
Why is there such a gap between call it aspiration and execution on AI implementation?
Yeah, I know you went say yourself, but I do feel like your article on this topic, on your Substack, was excellent.
So I would highly recommend people to track down your Substack.
But my opinion, other than the normal, you know the timing and all that, people management, change management.
I do want to bring in two points that I feel are relevant but less talked about.
So one is you do have that first phase of you know, using AI, adopting AI as a co-pilot or a chatbot.
Sometimes it's even built internally to customize for the internal use case.
And that was good, but until it's not, the chatbot as an interface is very rigid.
It creates another workflow.
And initially, maybe people are benefiting from that chatbot's help.
But with AI's capability continue to develop.
People would love to have embedded AI into their existing workflow instead of having another window copy-paste and copy-paste back.
So that really affects the utilization rate a lot.
And that's also a natural way of progression, I guess, for the technology to go from a traditional UI, which is a we call it traditional but two-year-old UI type chatbot, to now it's like agent or embedded, completely native type of tools.
I do think that transition, depending on your company's stage.
Sometimes people are stuck in between like how do I move from a very canned, very easy to understand type of chatbot to a much more powerful but also has a little bit more friends, or integration work type of projects.
So the other thing that I feel people are now not talking about it enough is the way of implementation.
So in the past, maybe, again, like we chose to, there are two ways.
One is to buy this very generic use, but very powerful chatbot.
The other way is we have this secret formula of great data.
We have to build on our own and therefore hire an army of talents and build our own.
But now both sides have something else to desire, because the general use sometimes cannot, again like cannot be fully integrated and fully enhanced, fully harvesting the potential of that business, and also doesn't make you differentiate right.
Everyone is using the same thing.
Like, why would you be so different?
And then the other side, building on yourself, and it's even has even more.
I would say that MIT article also mentioned it has even higher failure rate because your talent either cannot keep up or there's also a risk of overspending or budgeting where your talent or your allocation may not be moving into the most cost-effective way.
And we all know that.
You know the techniques of AI model training has been changing so much over the last two years.
So that conversation, either at board level or at C-level, or even at maybe a layer below C-level, it's a build versus buy.
But there's another way, which is partner.
Or maybe also the small new ways like Acquire, but I would say Acquire is rare, but partnership becomes a lot more interesting now versus 12 months ago.
If you look at some of these AI labs, but also some of the AI startups and AI companies, they adopt this huge advantage of domain knowledge of your space specific industry and then they can work with you much more effectively.
And then when we build together, sometimes it's the best solution.
And that could actually bridge for some firms bridge between those two phases, like the chatbot generative chatbot phase and the tool that's really tailor-made for their business potential.
Yeah, and as you're talking through all that I think about, I spent a lot of my career focused on the SMB space and I think about where they are with generative AI usage and where MidCap and Enterprise certainly any public companies and anyone with compliance issues where they have to be with AI.
And you'll see reading LinkedIn posts and articles.
SMB adoption is pretty interesting right now because they don't have the same compliance issues.
They don't have the audit, They're just.
You know, they're founder-led and they found ways to automate things.
And if it's 74% right, that maybe that's close enough.
And they, you know, they can't reproduce it or whatever if it saves them two days of money.
And you know obviously I'm talking very small businesses there.
But it's interesting right now for companies.
Enterprise companies are the ones that have the budget to do this and typically will have the data to make it valuable, and they will have a higher level of data maturity.
But I think that the fears and sort of some large companies are more agile than others, but the fears around, whatever that fears they have of AI, is it gonna be wrong?
Is it gonna hallucinate?
Is it gonna steal my data?
Is it gonna leak information?
I think that that's part of what's hindering adoption at the enterprise level.
And one of the questions I get all the time, which is really to me, it's a pretty straightforward thing to solve for.
I mean, I know you use AI in areas where it is appropriate.
If you are doing something that needs a deterministic outcome, don't use probabilistic generative AI to solve for that.
Use a rule-based system or whatever.
And then it's a matter of just logging.
What are the prompts?
What are the responses?
Whatever, just a log so that you can go back and replicate the parts that you can.
But I'm on the compliance side, and maybe this is a path.
And I want to talk about your AI governance playbook.
But what are you seeing or how are you advising public companies on making sure that the ways they use AI are repeatable scalable, auditable and understandable for whether internally or external audit?
Yeah, there's definitely not one playbook for all.
But from a board angle it's almost like you don't implement anything before you have the risk framework and guardrails in place for big businesses, because the reputational and business risk and legal compliance risks are highly expensive and very hard to recover from.
So I would say that's definitely just the guideline for every businesses.
But I do want to go back to one of the things about the risk, or maybe the fear, of adopting AI because of these, all these risks.
There are two things that, again, like one is literacy thing, right?
Understanding where the techniques are these days to separate some of the tasks.
That needs a very definite outcome.
And that could be, you know the, again like using AI agent a little bit broadly.
Now they can run a certain part of Python code which always produce the same result based on the same formula.
So I think there's a very easy to bust type of misconception about GenAI is that if we do anything about GenAI, the whole machine is black box.
No, it doesn't have to be.
There are sweet spots where the black box or the hallucination is great, is useful.
But there are certain part that you want to be that very, very reliable, then you can still link these that you are very carefully written.
And that is something, you know, a lot of people who are new to Gen AI didn't know.
So I would say that's a very, that's something that people should know so that it's not like it's unsolvable.
The other thing is, I do think for startups and also for some of the existing consulting or technology companies, that providing that guardrail, providing that sort of data security governance layer is is part of their core business offering.
And if we think about the infrastructure companies, some of the AI data layer companies, that's the whole starting point of their businesses.
So definitely there are solutions.
Of course, going forward.
I'm pretty sure we're seeing new things that are coming up that needs guardrails to be amended.
But I do think that we're not looking at something that we cannot solve.
And lastly oh I forgot to mention one thing is now people realize one of the biggest risk for AI implementation or AI strategy is to lock in with one vendor.
And fortunately, now people are very used to the vendor has to be, you know, exchangeable.
If we have to, you know, exchange models or exchange some of the other data sources, that should be very modular.
Of course, that creates challenges for, you know, the vendors.
How do you... hold on to these customers.
You have to keep up with the newest and the safest practice.
Yeah.
Challenge for vendors and challenge for the end users when open AI changes their model and makes changes and don't tell anyone ahead of time.
So you're scrambling to plug it into a swap it out and plug it in.
Yeah.
I can completely relate there.
God, there's so much more I want to talk about, but I do want to be, and just time has flown by.
I think a couple of questions I want to hit before you go though.
Um, Because so many people seem a little bit stuck around rolling out AI right now.
What is one piece of advice that you'd give a CFO or finance leader who really they have been directed to?
They want to lean in, they want to use AI, but they just don't know where to go.
What advice would you give them at this point?
Again, this is very case by case, but I do think one overwhelmingly good advice, I hope, is to really focus on.
You know, five years down the road, where do you think your business competitive edge would be?
And then walk that back.
I think for a lot of business leaders, especially finance leaders, who are so used to think about NPVs and just think about the you know go and then discount it back.
And what needs to happen now?
And that actually is easier to get people aligned than you know what's going to happen next six months or 12 months.
And I do think that the more you can bring people on the same page on that what really make our business click will be better.
Yeah.
And I think that longer time horizon makes people think more strategically than tactically, because in an AI arms race, being tactical is very difficult when you're like oh, we were using Gemini, but now Anthropic does this.
So we need to pivot and do that.
It just makes it very difficult to try to react to the latest.
But if you are thinking strategically and focus towards that long term, hopefully you've got a goal that moves.
That's right.
That also allows you to be very firm on where you're going, but very loose on how to get there.
Wow.
So we're at the time of the show where I get to the boilerplate questions that we ask everyone.
So we'll close it out with these.
So I'm sure you've heard the show before, but we ask everyone What is something that maybe most people don't know about you, something they wouldn't learn from just checking you out on LinkedIn or other social media?
I'm actually an introvert.
I got very nervous going to conferences, cannot strike a conversation with a stranger.
So I will hope people reach out to me, but I enjoy one-on-one conversations a lot.
And definitely I would highly encourage you to reach out to me if you feel what I'm saying here adding some value to you and would love to get connected.
That's great.
And as I too am an introvert, which is crazy to say for a podcast host, but I'm doing three podcast recordings today and it's going to be exhausting.
I'll just go sit in like a sensory deprivation chamber after this is done.
But to your point, though, if I were talking to three people at once, it's a lot.
But these one-on-one conversations are fantastic.
So I'm really, yeah, I'm right there with you on that.
So everybody's favorite question and I know you probably write a lot more or would use programming a lot more than Excel, but we have talked a lot about Excel
So What is your favorite Excel function and why?
Yeah, I already hinted on that.
It's XMPV because I just keep thinking about that lens a lot.
Yeah, yeah, excellent.
Well, I guess, before I let you go how, and we'll put links in the show notes if our listeners want to reach out and get in touch with you, what's the best way for them to follow and connect with you?
Yeah, I'm quite active on LinkedIn.
Definitely reach out. to me there.
I also have a sub stack.
You can check it out.
The link also is on my LinkedIn profile.
So, well, Joyce, thank you so much for coming on the show.
This has been a blast.
Thank you, Glenn.
