Here's How To Test If Your AI Solution Will Be A Success

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

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Does this sound like you? You’re a tech geek and on top of all the latest developments in the market. When it comes to Artificial Intelligence you’re like a fish in the water and know exactly what solutions you can leverage to achieve significant benefits in your finance function. In brief. You ROCK AI! Any takers? Probably not. Most of us don’t have a good idea about how AI solutions could deliver benefits to Finance. What’s the best way to figure it out? Test it! That’s exactly what we’ll teach you how to do in this fourth article about bringing AI into the business and the finance function. Here’s the third step in our five-step model for succeeding with AI.  

3. Do a feasibility study on the back of a napkin 

A terrific way to see if you can grasp the full extent of a problem is to try to solve it on the back of a napkin. If it can’t be described in simple terms with simple suggested solutions, you likely don’t understand it well enough yet. This is also the challenge with AI. 

Challenge: The AI-part of AI-projects is the “easy”-part. That makes it very easy to make a small prototype that gives promising results. However – the road from simple prototype on curated data to a real life, operationally implemented solution is often long – and you are not able to predict beforehand, what you will be facing moving from prototype to implemented solution. 

Fix: Do the “Thomas Schultz” – 10 step AI-test on a napkin: 

• Step 1: Can you identify an “object of interest” – i.e. the object you want to be able to predict something about: A credit card transaction, a customer, a bank account, a bank transfer, etc.
• Step 2: Does an improved analysis or a more correct handling of the “object of interest” have large positive consequences business wise or are a lot of resources used on manually handling the “object of interest”?
• Step 3: Is it easy to define a meaningful and simple categorization of the “object of interest” that makes sense in business terms: Credit card transaction [fraud score], customer [high/low] long term value, bank transfer [money laundering yes/no], etc.
• Step 4: Pretend that you already (miraculously) have the AI-solution in place – being able to predict exactly what you want it to do. How will you use the new knowledge? What will you do that is different from what you do today? (a clear answer is needed)
• Step 5: Training data for labeling: AI-models needs training. Do you have access to historic data about your “object of interest” and the needed classification? Do you have a record, i.e., of fraudulent credit card transactions, loss-generating customers, money laundering cases? If not – can you generate some training data?
• Step 6: Training data attributes: For your “object of interest” – do you have data on ALL the attributes that might explain why something happened historically. Do you know the right things about your customers to be able to build a good AI-model (based on knowledge about loss- and profit generating customers?). If shoe size is the most important loss/profit predictor and you don’t have any data on shoe size – it’s game over.
• Step 7: Did you do your business case on the napkin as well? Does it promise business success if the AI-project is a success?
• Step 8: Are you sure you need AI to solve the problem? Is it really that complex of problem?
• Step 9: Are you sure that you cannot just download some piece of software that will do the job? Image recognition, speech-to-text translation, etc. All problems, that have been solved already (by AI). Just download and use!
• Step 10: Are you sure you are legally allowed to do what you want to do in terms of GDPR and privacy and ethics? Joining a multitude of data from many diverse sources is often a prerequisite for AI-projects.

Does it feel more tangible now to evaluate whether AI will be good for your business and finance function? At least you now have a structure to follow that’ll organize all the thoughts you’re currently struggling with. As you can imagine from the sketchy examples we’ve already given there’s a significant potential for the finance function in AI. 

Are you ready to do some testing? 

Yes, we still have more steps to go in our model but already now you should be ready to test if AI could be the answer to some of your business challenges. Just identify an “object of interest” and get started. It is not that hard. If you have examples of how AI already helped your finance function solve business challenges it would be great if you could share. Not just the final solution but also how you went about building the business case, the software, and the organizational change efforts. More real use cases mean even more AI in Finance.

This is the seventh 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

What AI Competencies Do Your Finance Team Really Need?

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 29.000+ followers.