Can we trust the machine for financial planning and analysis?
Anders Liu-Lindberg
Leading advisor to senior Finance and FP&A leaders on creating impact through business partnering | Interim | VP Finance | Business Finance
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Ask yourself how to make things happen not if they’re possible. Because anything is possible today at least when it comes to using technology in FP&A. However, we’re yet to see a technology revolution, and whereas 60%+ of transactional finance processes are now automated the rate of automation is much less in FP&A. This is also the simple driver of spending up to 70% of our time on data, reporting, and analysis.
However, one thing is for the machine to predict the future yet it’s another for trusting it to lock in your bonus target. And companies still have bonuses. Some may have switched to more aggregate bonus schemes while a leading few have completely skipped them. And while skipping them may sound crazy you’ll be hard-pressed to find studies that show a high correlation between bonus schemes and performance.
Still, most companies use bonuses and as an individual, you feel you have a need to be in control of the number you’re being measured on. This seems like a natural need, and it certainly also was a theme when we got together with leading companies in the FP&A space to talk about FP&A practices fit for the Next Normal. Let’s discuss some of the issues involved in trusting the machine.
Should we trust the machines?
It’s a complex question obviously that goes beyond the pure technicalities of producing the numbers. You can easily point to moral and ethical dilemmas. However, we’re simple and practical people so we’ll skip the philosophical question for another day.
Instead, let’s focus on what we know. Leading companies are today using machine learning to produce forecasts that are more accurate than what they ever got bottom-up from the human hand. This is a fact. And they’re much more efficient doing it this way too!
And this should not come as a surprise to us. Why? Because machines are not biased. They are not sandbagging. And they’re certainly not playing politics. They provide an objective view of what future performance will be given everything we know at a certain point in time. That doesn’t mean we cannot change that of course through the actions we take but all things equal this is the most accurate view on future performance.
We CAN trust the machine. The question is if we want to? Seen from our perspective it comes down to accountability. Can we be accountable for delivering a number that we’ve had no control over in any way? Most people would probably say no. However, it’s likely due to being rooted in old processes and beliefs rather than being a logical assumption for how things work today and in the future.
We cannot answer this question for you, but we could challenge you and say “why not”. Wouldn’t it be time better spent to focus on how to improve performance the most rather than on how to have the most negotiated… excuse us accurate number? Let’s instead talk shop and focus on moving forward the business. That is guaranteed to create more value than discussing what the number should be.
It’s a huge mindset change to start trusting the machine and leaving your compensation in its hands. However, the alternative is simply to deliver worse performance. Feel free to challenge us on this notion but we’re willing to promise that it will lead to better performance in the long run if you let the machine do the predictions.
Are you ready to make the shift?
First you need to build a model that works of course. That will take time as you’ll need to train it and it will need to compete against your current practices to convince people that it’s more accurate. Once you’ve done the overall testing you can try to have two parts of the business run on different models. One uses the old bottom-up budget model and the other goes by the machine predictions without much challenge.
We wouldn’t be surprised at all if the machine-driven part of the organization performs the best. Of course, the humans might get lucky and win round one. Play ten rounds though and the machine will win out! We strongly advocate starting trusting the machine today. Then you can stop sandbagging and play politics while not letting your own biases get in the way. What’s not to like?
How much do you use machine predictions in your organization, and would you be willing to completely let go of the control of the forecast? If not, what’s stopping you and what do you think is the best way to optimize performance across the company? We’d love to hear from you so give us a shout-out in the comments.
This was the eighth article in the series "Planning as we know it is dead". You can read the previous articles in the series below.
While you await future articles why not read our latest series "FP&A Transformation Talks" below.
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You can read a lot more articles about FP&A, Business Partnering, and Finance Transformation below. It all start's with “Introducing The Finance Transformation Nine Box” where you set the ambition for your transformation. You should join the Finance Business Partner Forum, which is part of the Business Partnering Institute's online community.
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From Business Partner To Working Within The Business (part of an article series where I interview finance professionals about their careers in FP&A and Business Partnering)
Is Your Product Optimized For Value Creation? (part of a toolbox series where we look at what tools FP&A professionals should leverage to drive value creation)
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Anders Liu-Lindberg is the co-founder and a partner at the Business Partnering Institute and the owner of the largest group dedicated to Finance Business Partnering on LinkedIn with more than 10,000 members. I have ten years of experience as a business partner at the global transport and logistics company Maersk. I am the co-author of the book “Create Value as a Finance Business Partner” and a long-time Finance Blogger on LinkedIn with 75,000+ followers and 150,000+ subscribers to my blog. I am also an advisory board member at Born Capital where I help identify and grow the next big thing in #CFOTech.
Controller Manager | FP&A Manager | Planning Manager | Finance Manager | Strategy Manager
2yI disagree on some points regarding using machines, especially on this paragraph: "And this should not come as a surprise to us. Why? Because machines are not biased. They are not sandbagging. And they’re certainly not playing politics. They provide an objective view of what future performance will be given everything we know at a certain point in time..." Machines are very biased, as biased as their programers, or the source of data they are reading. You also state that they will plan "given everything we know at a certain point". This is only true if you program the machine to use that data. No AI will use all data in the universe to predict something, it will use the data you tell it to consider, therefore having a good human that understand that variables that may or may not have correlations (causal or not) with the variable you are trying to predict is going to be important until machines learn how to do it, which is very far from reality right now. Since no humans know all the variables that are relevant, some are being created right now we we have no idea (new tech, new geopolítica, new virus and so on). Since some are new machines don't have historical data to check for correlation and causality.
Helping FP&A Professionals provide value to their businesses | Founder of The FP&A Guy | Host of 3 popular Finance podcasts | Microsoft MVP
2yLove it Nicolas Boucher.
GP at Born Capital | CFOTech Investor, Advisor & Entrepreneur. Follow me for posts about CFO insights and FP&A.
2yIn order to trust the machine it's crucial to understand how it came to the prediction. I am a strong believer that algorithmic forecasting has huge benefits over manual forecasting (just think about reforcasting if a fundamental variable has changed), but the management reports need to be able to transparently breakdown how the forecasts have been generated, so management can follow the logic and provide feedback to the data scientists if needed
I teach Finance Teams how to use AI - Keynote speaker on AI for Finance (DM me if you need help)
2yAt the end the machine is another team member and should be considered like an employee: train the machine, coach it, review its work and step after step give it more responsibility but always be there to support it and take ownership of the result of its work!