In September 2023, AvidXchange surveyed 500 finance executives at mid-market companies to learn about their priorities and plans for 2024. Our chief evangelist, Chris Elmore, led a discussion that summarized the findings on the latest episode of our “Net 30” podcast.
Elmore invited David Tareen, senior director of product marketing, and Gary Larson, director of growth readiness, to share their reactions to the survey findings and add insights. The three key trends the group chatted about were:
Continue reading for a summary of their conversation or click the player below to listen to the full episode. You can also stream our podcast on your favorite platforms, such as Spotify, Apple, iHeart or Pandora.
Finance at the Forefront of AI
Larson noted that the finance department is usually not the first to implement change, especially when it involves technology. But he’s noticed that finance departments are embracing AI, using it as a decision-making engine. Our study underscored this, with 72% of respondents saying their companies currently use AI.
He likened the advent of AI to the robotics boom in the automotive industry in the early 1980s. He said, “The jobs just shifted from some of the menial tasks to more important tasks that can’t be easily automated. When I think of automation, I think of making my job easier [and] doing the stuff I don’t want to do so that I can focus on more strategic things.”
Tareen agreed that AI is a good fit within the finance department because work often focuses on identifying patterns within large data sets. “People are good at things like intuition, creativity, right?” he said. “It’s very hard for computers to do that. But when you think about what computers are good at … It’s being able to consume a lot of data. You know, finance has a lot of data and then they predict and see patterns within that.”
Remote and Hybrid Work: Still Evolving
Most businesses today say that their workforce is hybrid, but Larson qualified: “It’s not a smooth hybrid.” Elmore agreed, “Technological-wise, [companies] haven’t gotten themselves together for this thing.” Our 2024 Trends Survey found that most companies have adopted hybrid work environments but there are still related hurdles to overcome.
Tareen said that, though hybrid may still be a challenge, it’s something that the organization needs to prioritize. Having an infrastructure that thoroughly supports hybrid and remote work will determine which organizations will be resilient in the next business disruption.
Larson noted that the pandemic sparked a beneficial shift in how finance leaders tackle challenges. “What we’re seeing is a shift in the mindset of leaders in how they’re strategically trying to solve for things where historically we would throw more bodies at a problem. I’ve started to see leaders be much more willing to look at technology and solutions that are not dependent on physical people doing something. It’s becoming one of the weapons or tools that leaders are thinking about earlier on in their decision-making.”
“Data Citizens” Drive Analytics
Access to real-time data analytics is helping finance departments make better decisions, enhance efficiency, identify opportunities and improve accuracy. Most of the business leaders we surveyed said they currently have access to real-time data in their organization.
Tareen advocated for leaders to identify passionate “citizen data scientists” to lead the charge and further data analysis efforts within finance departments. Larson agreed, “I love the fact that [the idea of a] data citizen is all about empowering people who are passionate about something versus waiting for somebody to be ordained and certified.”
Tareen recommended that organizations form a center of excellence to build an understanding of how organizations can leverage data to create advantages across their business.
If you’re interested in checking out the data from the survey discussed in this podcast episode, download our free report, “2024: Anticipating Tomorrow’s Trends.” To hear the full conversation, listen via the player below or follow the AvidXchange Podcast Network on your favorite streaming platform.
Please note: The “Net 30” podcast is designed for audio consumption. Transcripts are generated using speech recognition software and may contain errors. Please check the corresponding audio before quoting in print.
Hi, my name is Chris Elmore. I am AvidXchange’s Chief Evangelist and I’m the host of the Net 30 podcast. On the Net 30 podcast, we meet with industry leaders to unpack problems and solutions and talk about innovations that are impacting financial professionals. The best part about this? We’re going to do it all within 30 minutes. So let’s get into it.
Alright. Welcome to the podcast, David Tareen and Gary Larson. David, thanks for joining us today and tell folks a little bit about yourself.
So I have the product marketing mission here at AvidXchange. In previous lives, I’ve spent a lot of time at startups and established companies as well – you name it, any technology topic, artificial intelligence, machine learning, automation — a lot of other things. Again, happy to be on the show with you.
We’re also joined by Gary Larson. Gary, tell folks about yourself.
Been at AvidXchange nine years and been just honored to be a part of just the journey. I was employee number 231. Wow. I’ve always been a part of the learning and development, whether it’s in sales or in talent, I’m just real passionate about infusing our culture and educating our internal and external customers.
There we go. So we have a learning person and a product marketer. And David, you going back to your experience a little bit, you did work in a startup that had a big push in artificial intelligence. And then you were at a fancy firm in Raleigh or Research Triangle Park. So, you’ve been around information and data and stuff like that for a long time.
I have, man. And it’s been really cool. I’ll just say that the coolest role that I had was in the startup. What we were doing without getting too technical is we were creating sort of a digital person. An algorithm, and then you could feed this algorithm anything you wanted from Excel spreadsheets, PowerPoints, structured data, video, audio, anything that you wanted.
And as the algorithm would take all this, it would learn it. And then you could ask it questions and it would just give you answers. So all of your sort of knowledge within your organization, safe and secure behind a firewall, and then you just converse with it, you don’t need a data science degree. It was actually really, really cool.
Well, let’s get into it. So AvidXchange conducted a survey, 2024 trends finance leader should think about. What we did is we surveyed over 500 finance execs in the middle market and came up with several priorities, and we’re going to talk about three of them on the podcast today. Artificial intelligence, hybrid remote workforce and data-driven decisions.
Gary, let’s get you in on this. So here’s the data points that we found out in our survey. 72% of financial leaders said that they’re currently using AI technology and over half, 60% said that they’re comfortable with AI technology. Do you believe that, Gary?
I think that that number of 72%, for me, it feels, as a whole, correct, but I know that when you look at the smaller and the smaller businesses get, the less comfortable they’re generally going to be with AI. It’s probably a newer thing for smaller companies versus bigger companies who have to have already started to embrace various types of technology.
So that number is really not too surprising to me. Finance generally isn’t the storming the beachheads of being the innovative leader in technology and in change. Generally speaking, my experience has been finance is kind of lagging with technology. But it’s been really interesting to see how they are actually kind of on the forefront in some of these spaces.
David, finance on the forefront. Is this blowing your mind or what?
No, I expect it. I mean, look, when you think about what are people good at and what are machines good at. People are good at things like intuition, creativity, right? My eight-year-old daughter can come up with, through using her imagination, she can come up with games out of the blue.
It’s very hard for computers to do that. But when you think about what are computers good at, what is an algorithm good at? It’s a lot of data, being able to consume a lot of data. Finance has a lot of data and then being able to predict and see patterns within that so I’m not surprised by it.
I think when I talk to customers, there’s a couple of different approaches. And most of the time where we get to is that it’s using that combination of people as well as algorithms usually gets you to the best result rather than saying, “Oh, we’re just going to use AI entirely for this problem.” That’s usually the wrong approach.
Yeah. And I don’t know, there seems to be a lot of noise about AI out there and there seems to be a lot of people who are a little bit nervous about their job being taken over by a robot.
So, yes, I think the concept that you’re describing, it’s generally, it’s called general artificial intelligence. What that means is that a system can learn from one set of data and then apply that to a completely different set of domain.
Right? People can do that very easily. I can learn one thing from driving a car. I can apply that to driving a motorcycle – very difficult for an AI to do that unless you’re talking about way in the future when we have this thing called general AI that can do that as easily as humans. And then, yes, maybe, but I think we don’t have to worry about that for a long time.
Okay, good. We just don’t have to worry about it. Gary, I mean, you’ve been around the HR game for a long time. How is this going to affect businesses, do you think?
So the way that I’ve seen it affecting the workforce is much like in the late seventies in the early eighties, when everybody in the automotive industry was worried about their jobs being taken away by technology and automation and robots, and we’re all going to be out of jobs. And what you actually find out is the jobs just shifted from some of the menial tasks to more important tasks that can’t be easily automated. I think when I think of automation, I think of making my job easier, doing the stuff I don’t want to do so that I can focus on more strategic things.
Do I really want to spend an hour and a half writing a job description when I can have ChatGPT do that, then I customize it? And then that gives me two hours back to then go and do something else strategic instead of trying to make sure that my grammar is correct. So, AI in the, in the same way that we have spell check, in the same way that we have predictive text, or the way that we use Waze, Maps, and it’s making your life easier.
But I do think that the conversation around what are the limits to it? And when, when do we start potentially hurting ourselves as people? Are we becoming so dependent on technology that we don’t know how to do our job? Like, could your kid find his way home or her way home without Google Maps?
There’s things that we need to be able to continuously be able to do, but there’s also work that we’re paying some people a lot of money, especially in the finance space, and they’re spending their days doing a lot of manual things and potentially making errors that could be done automated.
And now I could take that person who has a master’s degree in finance and accounting and use them towards this new acquisition strategy we’re trying to roll out.
So to that point, let’s talk about use case. You see a little bit of a segue here and, and, and in our survey, five use cases identified 67% of people surveyed said they’re going to use AI for customer service, 64% said fraud detection, which I think is kind of interesting. 64% said risk management and to kind of Gary’s point, 57% said they’re going to use it for investment management and 52% said they’re going to use it for automation. So you got customer service, fraud detection, risk management, investment management and automation.
Anything stick out to you in any of those use cases?
Yeah. So on fraud detection, I mean, AvidXchange uses techniques such as anomaly detection, whereas you see transactions going and what an average looks like from different suppliers and things like that, as soon as you see an anomaly that raises a flag may not be fraud, but it’s an early detection. So that I think makes a lot of sense.
The one that was surprising to me, quite frankly, is how low automation scored. Yeah. And I think when people think about automation, they’re like, ah, done that. We automated workflows a long time ago, but the truth is the pace of change is so fast these days that if you have a static workflow, it’s going to break down because there’s a lot of changes in your data sources, data structures and things like that.
What artificial intelligence brings to the table is machine learning. So even though an input has changed, the machine learning algorithm can make the best decision on what changed, how it changed, what I need to pick up to continue that workflow. So I think automation, I was surprised how low that was. And there’s a lot more potential on automation from an AI perspective.
When I used to do a lot of initial calling, I would ask people if they’re automated in their accounts payable process and they would say yes. And then I would ask them another question, which is, do you have any paper?
And they go, “Yeah, tons.”
I’m like, “Okay, well, yeah, so there is a little disconnect on that.”
Good, good conversation. I think we’re going to be talking about this AI thing for a long time. I think people are kind of nervous about it, a little dubious about it. They’re kind of thinking, well, is this a toy or is it for real? And we seem to kind of be in the middle of, in the middle of that.
What do you think, David?
Yeah, no, I think any time you bring a new innovation where you don’t completely understand what it can and cannot do into the finance industry with lots of regulation and lots of compliance, I think you should be nervous, right? I think there’s the explosion in education is because yet the technology seems easy.
But the data science underneath that understanding which model to apply and get a really good result and get the right ROI that I think takes a lot of skill. So I think that makes a lot of sense to me.
When I looked at this result, I think that when I look at customer service, I know what that is and I know what that pain feels like.
Fraud detection, risk management, investment management. I can, as a normal consumer, I can consume what I would like to improve around that. When I think about automation, it starts to feel very fuzzy to me. And it’s like, eh, I really don’t understand what I would automate. I know what I would automate with customer service.
And I know what I would like to do with AI with some fraud detection, but I’m not sure what automation is or means. And I think to Chris’ point, when we ask some of our prospects about if they’re automated and they say, “Yeah, we have, we have these spreadsheets that we type stuff into and it has calculations in it. And then we’ll send this and we’ll do that.”
And their version of automation sometimes is working off a spreadsheet or scanning things into like a Dropbox. So I think for me, the big gap there, Chris, is around education of their world and what they think is either a automated, or they don’t even realize that some of the ways that they work can be drastically improved.
Topic number two is a topic that I’m absolutely sick and tired of talking about. Are you all ready? Hybrid workforce. And Gary, I thought I’d start with you. Like I said, a lot of HR experience, but our data has found in the survey that 65% of financial leaders work in a department where they work one to four days in the office.
And 27% say that their in-person, like in-office strategy is dictated by collaboration. But 29% said that the technology is inadequate. And 19% said improper onboarding and training for digital skills has created some tremendous hurdles. Gary, kind of what jumps out to you with all of that, all of those numbers?
The interesting thing about, like, the hybrid workforce is that, yes, we are hybrid. However, it’s not a smooth hybrid. And what I mean by that is, the return to office. How many days a week are you expected to be in the office? Who’s coming in? When? Are we sharing workspace?
All of those things are still, we’re kind of in this awkward place where businesses are like, “Hey, we’ve invested millions of dollars in this infrastructure. We want people back in the office.”
And you have employees going, “I get that, but we’re just as productive, if not more productive being remote.” So even though the chart says, “Hey, we have a hybrid model of three to four days a week.” The reality is if we were to double-click into that, we would find a lot of variation in the direction that companies are going.
With moving – trying to move their workforce into office more or potentially less. But the thing that encapsulates all of this is the use of technology to be able to manage all of the amount of work that we do. It’s forever changed and it, and it won’t go back. The fact that we’re meeting even when we’re in the office, half to two-thirds of my meetings are still on Teams or in Zoom with other people in the office. It’s a really weird, different place.
I completely agree with Gary. It’s a weird, different place. But the thing is, the reason why we’re talking about this hybrid workforce thing is because of the pandemic. Now, it might not be a pandemic next time. It might be something else. Right. They just need to be ready for it. What’s, what do you think about that?
I think one of my favorite things to do is talk to our customers, and even in such a regulated and compliance-specific industry like finance – no two customers are alike. You’ve got different industries and that will really just drive the hybrid type of environment that that company promotes. But I think the question, Chris, I think you’re spot on and it’s what Gary said as well, is that who’s going to be resilient? When the next disruption happens, who’s going be scrambling to automate to get rid of paper to be that flexible so that they don’t have that disruption or their competitors will be more ready and get out of that disruption looking more whole? So I guess it’s, it’s, it’s, you’re right. It’s going to be getting ready for the next disruption and who’s going to be more resilient.
Alright, Gary, this is your opportunity to predict the next disruption what’s it going to be?
I do think it will be very similar to a COVID type thing where maybe not on the scale where, where people are as sick as it was, but I think people are much more hyper-aware of their wellbeing and not wanting to come into the office. So in the event, let’s say that the virus broke out in the early stages of that, even before it potentially even becomes a pandemic, you’re going to see people completely shut things down and not wait until things get bad and almost creating a global shutdown.
No, I think, I think that’s a, that’s an incredible, I don’t think I’ve heard that before, but that makes a lot of sense to me that because people are so in tune to this, that something on a significantly less, hopefully magnitude will still drive that amount of disruption. That’s a good point, Gary.
It’s a head-scratcher. Why? First of all. We’re still talking about this. And then second of all, people technologically-wise, they haven’t gotten themselves together for this thing. It’s almost like we have this new normal where it’s like, well, maybe I shouldn’t automate that back-end process, or maybe I shouldn’t update the collaboration system, or maybe I should just wait until something happens, but I just don’t think that that’s a good idea.
Oftentimes businesses, corporations, even people on their personal level, they’re going to focus on what’s next. What’s right in front of them when the pandemic was, what’s next? It was right in front of us. People reacted. Now it’s not, people are focusing on other priorities. I think what I’ll say is – and this ties back to a little bit on AI, analytics and what we’ve talked about earlier is folks who think about decision making about automation more strategically, they will be the ones taking action today. And I think that will build the resiliency that they need for whatever happens. Hopefully nothing ever happens, but if it does, then I think that’s what’s going to happen.
Something’s going to happen.
I think it’s what we’re seeing is a shift in mindset of leaders in how they’re strategically trying to solve for things where historically we would throw more bodies at a problem, and I’ve started to see leaders be much more willing to look at technology and solutions that are not dependent on physical people doing something.
That was like the last resort. It was like, “Oh, I have nothing else to do. I might as well think about technology because Bob can’t physically come into the office and process my checks.” It’s becoming one of the weapons or tools that leaders are thinking about earlier on in the decision-making.
Well, okay. So good job, Gary. Good job, David. You’ve teed me up very well for the last and final topic. Data-driven decisions. So, 66% of leaders say that they do have access to real-time data. But here’s my question, David. Are people really making data-driven decisions with real-time data or is it just kind of noise?
I think I want to say that I make data driven decisions. The truth of it is that oftentimes there’s gaps in data. There’s less data than I want. So at the end of the day, it’s a mix, right? I will say that there’s the concept of absolutely not enough data scientists to help us with all the data that is out there.
So there’s this new movement of having a citizen data scientist. And that’s a term that’s been coined in the industry and that’s, those are people who don’t have formal education to become a data scientist. They may not have a statistics degree or econometrics degree, but they’re curious and they’re dangerous enough to get onto some of these tools that are now available that let you manipulate data and run algorithms and be able to predict the future.
So I think it’s really for financial leaders, they should look at who are those curious data scientists, citizen data scientists on their team and how can they use those skills to improve those metrics that you just talked about?
I love, I love that. Gary, data citizens. Are you a data citizen?
No, no, I’m not, but I do love the fact that that data citizen is all about empowering people who are passionate about something versus waiting for something, somebody to be ordained and certified what you’ll generally find is that if people are passionate about something, they’re just as reliable and as credible and often as good of a performer as someone who has a certification or degree in something.
So, I love tapping into that. I also think that you asked about the real-time data. Yeah, is it being used? And I can tell you, within AvidXchange and also within the organizations that I’m just connected with that real-time data is absolutely driving decision-making from real-time staffing where I’m looking at performance of how my call center reps are doing every hour, and I’m looking at their performance minute over minute, average call handle time, performance, all of those things, and then I can make decisions to say, “Hey, guys, I think we need to re-index or we need to do some things here.”
Or even you could see it in the restaurant industry where my wife leads the company, and they’ll look at real-time data and say, “Hey, I think based on the turn rate of our tables or the average reservations or these things that are coming in, we can actually let a couple of hostesses go early or we need to see if we can call somebody to bring them in.”
Gary is spot on. As this relates to financial industries. You talked a little bit about empowering people who are curious. When we talk to our customers, I think some of the folks who’ve done it really well and are getting real benefits from data and analytics are the ones that have sort of formed a center of innovation where they pair up formal skills, the data scientists that they have with those citizen data scientists, just curious people who are good at data, good at tools.
And you sort of create that center of excellence, that center of innovation, and then all your analytics projects, your data projects, just run it through that center of excellence. And you will see the skills just grow because it’s real problems. It’s people who are curious learning from people who have formal skills and most of our successful customers are doing it that way. I think that’s best practice.
That’s fascinating. Well, so we surveyed and asked what are some data that is being analyzed in the accounts payable teams and 75% said invoice data, invoice status, date, due date, things like that. 67% said measuring invoices and payment workflows – that’s to Gary’s point, the whole efficiency of the process.
And David, you had mentioned something earlier in the podcast about AI doing a good job about looking at workflows and really optimizing and then 60% say tracking, which this one, by the way, I think it’s kind of interesting tracking, purchasing patterns are always fascinating.
Yeah, I think there’s so much more we could, we could do here. We did, there’s so much more that we actually do here at AvidXchange for some of the folks who are listening to this. One of the areas where there’s so much potential, like you just said about purchasing. Most of your suppliers will offer you a discount based on your payment terms. Analytics is used for predictions right and optimization, you can have an algorithm that’ll say, “Hey if you make your payments in this pattern then you will end up maximizing what’s in your contracts and maximizing those discounts. And this is the timing of when I want you to make those payments, so there’s all of those possibilities.
Food for thought. David, Gary, thank you so much for joining me today.
Love it, thank you so much for having us!
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