Ali Abedi: Welcome everyone to our webinar on the role of data and automation in finance. We’re thrilled to have you with us today. I’d like to shift the conversation to a very important factor—the human factor. Question to you, Jawwad: we talk a lot about automation, but finance is still a very human-driven function. How do we make sure human judgment and experience stay central as teams become more data-driven?
Jawwad Rasheed: Yeah, it’s a really interesting question that comes up more often, particularly with the advent of AI solutions, where people say, “Well, what role is left for me to do?” or, “What role will be left for me to do in the advent of where the AI journey may take us?”
So there are many different perspectives. I’m happy to share my personal perspective on this. The fundamental comes down to how you provide challenge. How do you challenge outcomes? And outcomes can only be challenged if you know what to challenge in the first place. It’s a bit like trying to data mine—if you’re data mining and not knowing where to look for information, you’re going to be data mining everywhere. But actually, if you’re savvy in a domain or a spectrum, you’ll know naturally where to pivot to.
So for me, finance judgment is always going to be required to join the dots. And I’ll maybe provide the use of that in terms of the AI evolution and where that’s taking it.
So let’s take a savvy finance professional. A savvy finance professional will have a good grasp of financial data. They’ll understand the metrics, understand the implications, and they’re not going to consider numbers in a vacuum. They would appreciate hierarchical and account relationships, and have knowledge of drivers behind things like balance sheet KPIs. They may not know which drivers are more influential, but they’ll know what they are.
If we take the example of a language model—language models don’t necessarily have that instinctive understanding. But the model processes information that comes in as tokens. This uncovers statistical relations and produces outputs based on some recognized patterns.
So language models don’t have the—let’s say—typically don’t have the processing limits, or aren’t necessarily built with techniques that take away the human judgment. You’re going to need that, particularly with financial data, which is inherently very complex. It’s multidimensional. It’s a relationship.
And that needs to be presented, that needs to be managed in a form that at least is digestible to multiple models. Yes, there are techniques available that might improve the ability for those models to run—chunking, embedded-based retrievals, and the rest. But the inherent limitations aren’t necessarily alleviated.
The importance of data, for example, as the mechanism and the glue that helps improve the inference of those models, is going to be highly important. And the only people that understand that the best are the ones with the acumen in that domain—for finance professionals.
So for me, the role becomes the guardians of information—the ones that provide challenge to models and outcomes, the ones that are able to join the dots across a wider spectrum. Those that understand finance the best are the ones that can do that. Those that don’t are going to fall behind. And that’s, in a way, why I think finance acumen is even more important now than it was before. It doesn’t mean that it’s eradicated or taken away. It’s elevated to a point where more challenge needs to be provided.
Ali Abedi: Well said.
Tod Dillon: Can I just jump in with one comment on what you want, and from kind of the practitioning side of it? I mean, it might be trite, but you know, I think we’ve probably all heard the saying that, you know, AI is not going to take all our jobs. But the person who’s using AI or AGI more effectively will. And understanding what that means—I think, for me at least—I could see finance becoming much more of a hybrid role where it’s not just purely accounting or purely business analysis. It’s a combination of the financial acumen, but also the data science element and strategic development.
Whereas, Jawad said, like understanding what the data is telling me, knowing where to look for it, how to bring it all together, how to use the technology to more efficiently do what needs to be done. And I think it’s happening so rapidly, it’s important to kind of maintain a focus on, okay, what’s the end result? What are the questions we’re trying to answer? What are the things we’re trying to do? And how do you marry that traditional, if you will, finance skill set with more of a data science, more of a technological set, such that I could see, you know, looking at workforce, talent shift, and kind of emphasizing the combination of all the things versus just one thing where it’s a certain specialty.
Ali Abedi: Great, and that was a great segue for the next question, Tod, which is about the future of AI and automation. So how do you see automation and analytics evolving in the finance function over the next few years?
Tod Dillon: I mean, and I’m sure everybody, you know, is in the news, reading certain things. You know, I think, you know, like broadly speaking, I look at kind of automation just like I see it now, even personally and just organizationally, as kind of shifting from tasks to decision automation.
You bring in—if folks are familiar with Maslow’s hierarchy of needs, right? So it’s food, water, and shelter are kind of foundational things. And so for finance, that food, water, and shelter is closing the books, tax compliance, regulatory compliance. It’s the blocking and tackling. And we’ve automated a lot of that via RPA, whether it’s invoice processing, AP, AR, things like that.
But it seems to me, where the natural evolution—if you will—is moving towards real-time, like daily reforecasting models, where it’s, you know, finance doing much more proactive, prescriptive. And whereas, like, moving away from that basic automation and, you know, on the analytics side, a similar trend is, you know, monthly reporting. And, you know, we do—if I put out my monthly report for the month for April, if I put it out on business day one in May, it’s still backward-looking, right? It’s referential to what’s going on versus—and I think with analytics, seeing much more real-time, predictive, like in-the-moment, prescriptive analytics where that becomes the standard, where it’s—yes, we’ve got reporting for historical purposes, but that’s what that’s for. That’s for, you know, audit purposes.
But as AI evolves from just kind of the simple—and I’ll use air quotes—”simple AI” to generative AI, where we’re using AI—and I can say personally now, we use AI sort of for some niche uses—I could see it evolve into like full-on co-pilots, where it’s, you know, from whether it’s FP&A, risk management, treasury management, and audit, where, you know, you’re running a full-on audit internally before ever turning it over to a third—you know, a third-party outside accounting firm to come in and audit.
And then I mentioned the workforce talent shift. So I guess the one last one—I was chatting with Liz Bearce, our Head of Marketing, this morning about it—you know, around governance and trust. And that’s one that I think about a lot, and I’ve been thinking about a lot lately. Because, you know, as part—as a CFO—I, you know, I view my role as I’m the financial steward of the company. I’m responsible for the financial health and well-being of the company, but that encompasses also risk, risk management, regulatory compliance, and all that.
And I think, when I think about it with regards to AI and governance, you know, as AI does more—and I think it’s, I don’t want to say it’s inevitable, but I think it almost is inevitable—I think it’s going to be crucial for finance and finance professionals to focus on governance. Meaning, like, not just data quality, auditability, but AI ethics, AI rules, explainability. Such that—back to Ali, your question on workforce and, like, workforce acceptability—it’s ensuring people are comfortable, not only with what AI is doing or, like, automation is doing or analytics are doing, but what it’s being used for, what the results are.
Such that, you know, eventually, do we see KPIs for AI performance, AI accuracy, data quality, and all those things? And I’ll liken it to—and I was joking with Liz about this morning—I liken it to thinking about autonomous vehicles. You know, right now, using Waymo as an example, Waymo is currently operating in a number of cities. And the last I was reading about them, they’re operating, and they do autonomous taxis, and they’re doing upwards of a quarter-million rides per week in Los Angeles, Phoenix, and San Francisco.
There’s a company—Kodiak Technologies—in Texas that’s actually doing truck driver autonomous trailer trucks driving from Houston to Dallas, and they’re in test mode. But if you ask the average American, “Are you comfortable with autonomous vehicles being ubiquitous?” I think most folks are like, “Boy, that makes me a little nervous. Like, what are the guardrails—like literal and figurative—what are the guardrails around that operation today?”
And we think about that and say, “Okay, what’s going to happen, and what is it going to look like in five years?” And are we planning for that? And so, like, tying that back to finance as well—finance, we do certain things today, and we do it in a certain way. I think the technology is going to change all of that. What’s that going to look like in five years? To the extent we can know, that is probably limited. But how do we plan for that?
And I just—I think a lot about that. And that’s in the governance space, and how, you know, how it affects where it goes and how we manage it as finance professionals.
Ali Abedi: Great insights, and Jawwad, given your experience and expertise, what should finance leaders be doing today to prepare for that future?
Jawwad Rasheed: Well, I think a lot of the effort comes back to reinforcing foundational capabilities. And if organizations are to advance, they need to carefully consider how they’re setting up the core structures underneath them in order to realize what lies ahead.
So the things that you can do, things that we can be very actionable on—finance leaders can absolutely and should be doing much more to empower finance professionals with capabilities that do make them more autonomous or self-service.
So Tod’s point around, you know, becoming more autonomous—I mean, there is a vision. We can kind of see this one day where accounting can be autonomous, auditing can be autonomous, and the concepts of periods may disappear. We may not even need a period. It just moves to a view that you have at any point in time—takes away month-end, quarter-end, the rest. And that’s a wonderful vision to have, right, in terms of how much burden is reduced.
But the way that you do that is, we have to continue driving automation. You have to continue increasing self-serve capabilities and making people aware of what’s available to them. I think maintaining that effort is where a lot of challenges lie. How do you maintain a snowball effect, where you start in one place and extrapolate and continue to drive that momentum across the organization?
You’ve got to find incentives to do that—motivating employees as well to repurpose what they do and drive high-value outcomes. Comes with time from the top. So, you know, what—I want you to think differently about your role. I’ll incentivize you to do so. I want you to bring forward ideas. You’re going to be compensated for that. You’re going to be seen as a champion, and the spotlight is going to be shown on you. So that’s a very, you know, top-down message that needs to be managed and delivered.
Absolutely needs to be some control and governance, right, around how a lot of this is advanced. People are very worried at the pace, and that’s a natural thing in terms of concern. So what is the data governance practice that needs to be enforced? How does that align with AI governance? How does that align with wider practices, policy, and procedures? Still needs to be a strong emphasis on that.
We do come back to a key point around—how do we focus on data quality to help rebuild trust so that we can realize new opportunities that AI will bring? That requires someone also to assess: Well, what are the right applications? What are the right tools and technologies that would align to finance persons and use cases?
There isn’t a silver bullet. Everything’s going to have an appropriate tool, technology, or application that’s aligned to it—that would improve. And someone needs to, or a collection of people, or consensus needs to be achieved as to: Well, what’s right for what application?
There’s a role creation in there as well, I think, when it comes into AI governance, but particularly for finance that are very front and center—with all of the information available to them, with the prevalence of other systems, applications that have grown organically and inorganically.
So, good to think big, start small. Think about how you can pilot solutions. Alteryx works with all of our partners, and Lydonia, obviously, are well placed to support how we help customers go from small and scale and do that in a controlled and managed way. But have some state in mind—and I don’t want to call it end state—but let’s say a more mature state. Know what you’re moving to.
And that’s kind of aligned to, let’s say, top-down hierarchy, KPIs, saying: Well, how does this thing that we’re doing align to a key metric that we’re looking to achieve throughout the full lifecycle of the organization? Otherwise, it becomes so detached it doesn’t become aligned to a goal and sense of something your organization is trying to achieve.
Have that mature state of mind. Know how it aligns to the hierarchical KPIs and where you live in that equation across the organization. Yeah, and continue to, you know, experiment and pioneer, innovate, and encourage those to do so around you.
Ali Abedi: Thank you. Thank you both. Tons of insight here. So thank you all again for joining us, and huge thanks to you, Todd and Joel, for sharing your time and insights. And if you’d like to keep the conversation going, feel free to reach out on that. Have a great rest of your day.
