What AI Competencies Do Your Finance Team Really Need?

This article is co-written by Thomas Schultz and Anders Liu-Lindberg 

Click "subscribe" to receive a notification every time a new article is published!

Are you starting to consider using AI to solve some of your business pains? That’s probably a clever idea but know that these projects must be carefully planned. You also need an approach to building in-house competences as opposed to getting outside help. Two weeks ago, we described how to solve business pains with AI as opposed to just starting random projects simply because you must have AI.

This week we’ll go into more details about what needs to be in place before you start on your AI journey. Specifically, for the finance function we’ll discuss what you can do yourself and what not. It’s time to look at step two in our five-step model for succeeding with AI. 

2. Think in terms of specific projects instead of general knowledge 

Most people these days have general knowledge about AI. At least we can start to imagine what it can do for us and that’s why we feel we must have it. However, general knowledge won’t get you far as soon as you start to be specific about what business pains to solve and how to go about it. 

Challenge: We often meet businesses that think that general in-house AI-competences are needed. “We need to build an AI-resource center with skilled data scientists”. Now – this may be a promising idea for some organizations. Amazon.com would not be where they are today without focusing on data science as an in-house resource. However, for most companies that’s probably not the way to go.

Let’s offer an analogy: Most businesses have a fleet of company cars. However – we’ve never met a company insisting on having and in-house car manufacturing facility or even their own lease agency. They buy from car makers or lease from agencies. You don’t need to build your own AI-solutions unless you are the new Amazon of course.  

Fix: Start by solving specific AI-suitable business pains by using external experts or available business centric solutions. Cash in the benefits business wise and in terms of improved understanding of AI. Then solve the next business pain and cash in. Continue until it’s evident that you will benefit from building an internal competency center. 

What we’re saying is that you need to prove that it works at a large scale before it makes any sense to build up competences internally. Once you’ve proven the value there’s also money to invest in building competences, if it makes sense to you. 

Off the shelf products can get you far 

Looking at the finance function we’ve observed companies where they simply took off the shelf solutions down from the internet or from vendors and applied it to their operations. One example saw a finance function use Alteryx to replace all human forecasting in their commercial finance function. Instead of humans doing the forecasting and corporate arguing if it would hold true or not countries and markets now had to argue why the AI made forecast would not hold true. In most cases the AI forecast proved much more accurate than the human forecast.

This is not surprising as we know human forecasting is biased. We’ve seen this done elsewhere too using in-house competences. We’ve also seen the first chat bots in Finance being able to answer simple queries rather than doing manual look up yourself.

AI is coming, and it doesn’t have to be complicated. Yet, don’t start by building a huge in-house center of excellence. Start with specific projects and prove that it works. Then we talk from there!

Have you had any success with some specific AI projects? Then we’d love to hear about it and maybe even feature your example. Don’t hesitate to reach out in the comments or via direct message.

This is the sixth article in a mini-series about RPA and AI. Read previous articles in the series below. From next week we begin to discuss how to succeed with AI.

How To Make Robots A Part Of The Finance Family?

Why You Should Only Robotize Standard Processes 

Robots and Humans. A Marriage Made In Heaven Or Hell?

A Tale Of Robots: From Assembly Lines To Knowledge Workers?

Robots Must Solve Business Pains To Be Successful

You can read previous articles about robotics and other stories about finance transformation below.

Blip. Blop. Accounting Robot. Are You Ready?

Are You Ready For Robotics Process Automation?

Have You Met Your Robot Accountant Yet?

Robots Are The Future Of Analytics

Your Robot Accountant Has A Name, It's Dixie

What Defines A Finance Master?

The CFOs Roadmap To Transforming Finance

How Finance People Can Be More Successful

The New Career Path For Finance Professionals

I also encourage you to take a tour of my past articles on finance transformation, finance business partnering and not least “Introducing The Finance Transformation Nine Box” which is really the starting point for the transformation. You should join our Finance Business Partner Forum which is part of the Business Partnering Institute's online community where we will continue to discuss this topic and you can click here to follow me on Twitter.

Anders Liu-Lindberg is a Senior Finance Business Partner at Maersk supporting our largest product and I have more than 10 years of experience working with Finance at Maersk both in Denmark and abroad. I am also the co-founder of the Business Partnering Institute and owner of the largest group dedicated to Finance Business Partnering on LinkedIn with more than 7,000 members. My main goal at Maersk is to show how to be successful with business partnering and drive value creation as a trusted partner. I am the co-author of the book “Create Value as a Finance Business Partner” and a long-time Finance Blogger with 28.000+ followers.