When my teenage son developed mysterious symptoms, I followed the same path anyone else would: I put his health in the hands of a team of medical professionals. Multiple myeloma is a rare blood cancer. It is so uncommon in 17-year-olds that it does not appear on diagnostic checklists.
Despite having no clear starting point to work from, my son’s doctors worked their way to an accurate diagnosis through a process of trial and error, bouncing ideas off each other and testing and discarding hypotheses until they could tell us what was wrong. The process felt inefficient and uncertain at a time when I wanted fast answers and cast-iron guarantees. But this messy and distinctively human approach saved my son's life.
AI promises to improve processes like this, replacing the fallible and unpredictable human mind with the analytic power of trained and tested algorithms. As someone who helps organizations implement AI technology, I know just how much potential it has to make processes and workflows more efficient. But before we start replacing human judgment at scale, we need to think carefully about the hidden costs that can come with productivity gains.
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