HBR Staff
AI does not need to fail on a global scale to cause enormous damage — to individuals, companies, and societies. Models frequently get things wrong, hallucinate, drift, and can collapse. Good AI comes from good data, but data quality is an enormous organization-wide issue (and opportunity), yet most companies have neglected it. Companies need to understand the nuances of the problem they’re trying to solve, get the data right (both by having the right data for that problem and by ensuring that the data is error-free), assign responsibility for data quality in the short term, and then push quality efforts upstream in the longer-term.
Twenty years ago, mortgage-backed securities and collateralized debt obligations were all the rage. These new financial products were, initially, a wonder: they helped put millions of people into homes and make billions for banks. Then things went horribly wrong, and they nearly tanked the global economy.
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