How to Make Your Company Ready for Machine Learning

By James Hodson

Machine learning refers to a machine’s ability to fulfill objectives based on data and reasoning. At present, however, we have neither the data nor the understanding necessary to build machines that make routine decisions, as well as human beings.

But machine learning still offers huge potential. How can managers incorporate it into their organization’s daily decision-making and longer-term planning? How can a company become machine-learning ready?

Catalog your business processes. Look for procedures and decisions made frequently and consistently, like approving or denying loan applications. Record whether the loan was approved; the data used to make that decision; and any other information about the circumstances behind it (Who made the decision? At what time of day? How confident did they feel about it?). This is the kind of data that can be used to fuel machine learning in the future.

Focus on simple problems. Automation and machine learning will work well in cases where the problem is well defined and well understood, and where the available data exemplifies the information necessary to make a decision. A good problem for machine learning is identifying a fraudulent transaction. The question “What makes customers feel happy?” is vaguer and thus more challenging—not the place to start.

Don’t use machine learning where standard business logic is sufficient. Machine learning is useful when the set of rules is unclear, or follows complex, nonlinear patterns. If you want transparency and reliability, go for the simplest possible approach that meets your performance criteria.

If a process is complicated, use machine learning to create decision support systems. If the objective is too unclear to define with respect to the data, try to create intermediate results to help your teams be more effective. You can think of machine learning as part of the hierarchical decision-making path.


James Hodson is CEO of the AI for Good Foundation and chairman of the Financial Data Science Association.


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