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Artificial intelligence has become a huge catch-all phrase.
But what does AI really mean for FP&A teams?
What will enterprise finance leadership jobs look like 5, even 10, years from now, and how will AI drive that change?
Precanto CEO and Co-Founder Sandeep Madduri and Kurt Shintaffer, Co-Founder and former CFO of Apptio and a Precanto board member recently shared their answers to such questions, and more, in a webinar moderated by Precanto’s Chief Revenue Officer and Co-Founder Meredith Hobik.
All three have worked deep in the trenches of finance before building, at Precanto, the support they feel is crucial to the development of modern, streamlined, and strategic enterprise financial leaders.
Read the highlights of their conversation below.
Why do you think AI and machine learning adoption is taking so long for finance teams?
Kurt Shintaffer: AI and ML have been slow to reach the mainstream, but if you think about it, it's been around for a long time in finance.
But the previous generation used AI in finance, which was really complex and expensive. And so because of that, it was limited to large companies.
You can all think of things like anomaly detection where businesses would look for errors or fraud and big financial data sets, or for consumer companies as they're thinking about forecasting their business. They apply macroeconomic trends to fine tune things based on consumer spending trends, or travel companies looking at weather forecasts. So there's been lots of use in the past, but that was typically internally built, big expensive systems applied to really big, complex data sets.
The difference is that we have new technologies, and we're not limited to doing AI on just stuff that's easy.
You mentioned the chief revenue officer type of use cases. That's pretty easy because we have a single data set from the CRM system based on a really structured way the opportunities move through the pipeline.
But most of the use cases that we are envisioning don't have that level of simplicity in its structured data. That’s no longer a limitation now because we have all these new tools to collect and cleanse the data and process large data sets. We have vendors that are providing all these out of the box capabilities, so we don't need to turn to a software architect or a data scientist to develop these ML or AI capabilities.
So I think we're going to see an explosion of AI use cases and finance because of all the new technology just being brought to bear.
Sandeep Madduri: One of the reasons why FP&A teams are slower in terms of adoption is because they don't like black boxes, right?
You think about how an FP&A team wants to trust a number, they want to be able to understand the underlying drivers, the assumptions that are going in there in terms of how a system is predicting something.
The second thing is, how will the system get better at predicting something over time? They also want to understand that, without which they cannot trust a number, without which they don't want to use AI and ML.
How can FP&A teams get comfortable and trust AI machine learning, especially as finance teams control the lifeline of a company: the financials?
Sandeep Madduri: I love that the phrase trust but verify because, you know, purpose-based solutions are for AI purpose-based solutions are critical, right?
Trust, transparency, explainability, and progression towards accuracy are super important. If there is a miss from a prediction standpoint, you want to understand what that miss is and how it's going to correct course – heal and get better over time.
Kurt Shintaffer: I think about the public cloud 10 or 15 years ago. I can remember when people didn't trust the cloud and thought it was just this niche thing. I remember talking to big enterprise CIOs who said, oh, I'll never use the cloud because I have some really unique business problems or unique scale, or we have so many IT folks that we can do this ourselves, but it wasn't long before they started to realize that they needed to use these public cloud services because AWS and Azure and all those hyper-scalers were just better at scale computing than they were.
Once they realized they can’t do it better themselves, and can’t be so risk averse, it allowed them to get more velocity in their businesses and keep them more productive.
AI is the next major business disruptor, and it's going to be something that we can capitalize on, but just like every adoption curve. There are going to be some laggards that have reasons for them not to adopt.
I would expect, just like the cloud, those laggards are going to be looking back years from now wishing they had moved faster.
Let's talk 5, 10 years from now. Where do you see the role of FP&A evolving with AI machine learning?
Sandeep Madduri: Finance teams spend about 25% of their time being strategic partners to the business. The remaining 75% of their time is spent gathering data, data wrangling, and then guesstimating a forecast. There’s very little time spent on business partnering. We think that in the next 5 to 10 years, finance teams stop spending 75 to 80% of their time in front of budget identifying numbers.
Kurt Shintaffer: I expect FP&A to be almost unrecognizable five years from now as compared to what it is today.There's the data, their speed, and there's access.
There's this daunting data challenge that we tend to identify every time we're trying to do some complex analysis using multiple data sources.
I can think of a number of examples where you have great ideas about what you think you can do, but then you realize the juice is not going to be worth the squeeze. Wrangling all the data, getting it into some places it can be used, doing all the correlation and cleansing, you just stop because it's not worth the effort.
But five years from now, I just don't think there will be many times where we have to turn away from an interesting idea because the data challenge is just too hard.
So I think that's going to be really liberating.
And its speed, that's kind of obvious. What used to take days or weeks is just going to take minutes or hours.
That's just going to really, really increase the velocity of the decisions that we can make. And if we can make faster decisions, we can drive business change more quickly and just be frankly better at our jobs and more effective as a business.
Access is another one. The current paradigm of how we get data from an app is just going to be a thing of the past. Anyone's going to be able to get answers, via text or voice prompts. For FP&A teams, I don't think you're going to need to build spreadsheets from the bottom up or generate all these reports and write all this narrative.
It's going to be an environment where we're free to drive business outcomes and not have to worry about the technical stuff.
Sandeep Madduri: I think another point, the way FP&A is going to be unrecognizable in the next five to ten years is in the ability to ask iterative questions.
One of the biggest challenges is when the C-suite asks a question about numbers, it's hard for them to iterate and ask a follow-up question without going back to the drawing board.
If you have the right AI and ML solutions in place, you can ask questions, and ask the next set of questions iteratively. and get the answers to that so that you're not waiting weeks or months to get to the next question. That's how we think about it.
Give us an example of Precanto's AI in action.
Sandeep Madduri: One of the things we're really proud of is our work with a public company, with over a thousand employees. The company was having a hard time getting accurate forecasts.
The team was spending more than six days trying to come up with a forecast number. AThey implemented Precanto to be able to create a forecast and plan in less than 48 hours.
The biggest difference for them was not only building trust, but being able to have more time as a team to focus on being strategic business partners to the rest of the business.
Learn more – book a demo.
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