use case



Accenture has estimated that by 2035 AI technologies could increase labor productivity 40% or more, doubling economic growth in 12 developed nations.  AI’s immediate impact on profitability is improving individual efficiency and productivity.  Bottom line, without embracing AI as a corporate finance strategy, companies risk losing large market shares and slowly becoming irrelevant.


AI is a transformational asset.  When deployed it allows analytics technologies to spot relationships between variables that humans are simply incapable of seeing.


One of the biggest problem that AI currently faces in becoming a transformational asset for mid-size companies, is that data is collected randomly all over the firm and hope for the best by letting a deep learning algorithm find a golden nugget. This naïve approach is called ‘data alchemy’, coined by Lukas Vermeer of


Lukas says When people ask me how to extract useful information from all this data that we have, I tell them they need a data alchemist, not a scientist. True innovation starts with asking Big Questions. Only then does it become apparent which data is needed to find the answers we seek.”


The correct approach is to first think hard about a good use case where your firm can create value out of your data.  We must define the problems we want to solve, procuring the right data, hiring people with the skills to make sense of the data, and empowering them with the appropriate technology. 



Step 1: What Business question keeps you up at night?

·      You have problems checking the quality of steel you are procuring?

·      Your customer churn rate is too high?

·      Your manufacturing process deviates from the plan all the time?


Step 2: Assess the use case’s business case

Assessing without emotions the real value of a use case for AI is key. For instance there is a very large European re-insurance company which has a +100 people strong AI Lab. They get requests from business units all the time with ideas and issues like the above. But the first task this team learned to do is to evaluate (together with the business unit) the actual business case of that use case. Will it create €1M in savings or rather just €50K?


Step 3: Technical capabilities in-house

The deeper you are in a tech delivery part of a business, the higher the tendency to develop it all in-house. And that makes sense for mission critical algorithms which are the core of the firm but not general functions in sales or supply chain: For example, Shell would not want to build AI tools to optimize their supply-chain or HR functions, but it would want to develop its own AI system for interpreting seismic imaging to detect oil.


And often the best solution for such functions can be easily found by external AI providers who are fast and nimble (and hungry for projects). Take this example from a recent Economist article: ‘At Leroy Merlin, a French home-improvement retailer, managers used to order new stock on Fridays, but defaulted to the same items as the week before so they could start their weekend sooner. The firm now uses algorithms to take in past sales data and other information that could affect sales, such as weather forecasts, in order to stock shelves more effectively. That has helped it reduce its inventory by 8% even as sales have risen by 2%, says Manuel Davy of Vekia, the AI startup that engineered the program.’



So to summarize: AI is a transformation-enabled assets whose function is to improve profitability and foster innovation.  This asset is spearhead at the CFO level but implemented throughout all business functions of the company, from financial model to supply chain, customer experience, production, sales, marketing, platforms or human resources.  Successful AI implementations require a holistic understanding of all business units’ operations and pain points. Being a profitability factor as important as capital or labor, AI must be an executive-managed asset. These profitability factors are a companies biggest assets and should be lead by the CFO or CEO. 



Although it may seem inevitable that such transformational asset  will be adopted “en masse”, the reality is more nuanced than that. Financial executives want smarter, faster decisions, but the balancing of data, people, and technology when it comes to transforming a business to an AI-driven predictive analytics model is difficult and requires AI enabled-processes and structures


Implementing this technology requires an ideological shift for businesses, not just capital investment. We propose to put the most analytical person in your company in charge of AI: your CFO. They are experienced dealing with numbers, expect a solid business case before making any investments and also understand the pain points in your organization.


The Age of AI is the heart of corporate financial futures. Get ready now!