Earlier this year, I wrote about fatal flaws in algorithms that were developed to mitigate the COVID-19 pandemic. Researchers found two general types of flaws. The first is that model makers used small data sets that didn't represent the universe of patients which the models were intended to represent leading to sample selection bias. The second is that modelers failed to disclose data sources, data-modeling techniques and the potential for bias in either the input data or the algorithms used to train their models leading to design related bias. As a result of these fatal flaws, such algorithms were inarguably less effective than their developers had promised.

Historical data – it's a bad system, but it's the best we've got, right? It's biased, out of date, and based on the flawed assumption that the future will look like the past. Historical data is far from ideal for training your artificial intelligence (AI) systems on. We've collated some expert advice to answer those questions for you here: "If you really want an AI system that delivers good business value, it's got to be forward looking, and therefore it must look at real time execution data." "If it's not then it's always looking in the rear-view mirror, which doesn't help me make good decisions which increase my revenue and decrease my cost."

Jeanne Ross is principal research scientist for MIT's Center for Information Systems Research. Because, as with enterprise systems, AI inserted into businesses drives value by improving processes through automation. An AI application might allow financial analysts to spend less time extracting data on financial performance, but it adds value only if someone spends more time considering the implications of that performance. Jeanne Ross is principal research scientist for MIT's Center for Information Systems Research.

Jeanne Ross is principal research scientist for MIT's Center for Information Systems Research. There is no question that artificial intelligence (AI) is presenting huge opportunities for companies to automate business processes. However, as you prepare to insert machine learning applications into your business processes, I'd recommend that you not fantasize about how a computer that can win at Go or poker can surely help you win in the marketplace. A better reference point will be your experience implementing your enterprise resource planning (ERP) or another enterprise system. Yes, effective ERP implementations enhanced the competitiveness of many companies, but a greater number of companies found the experience more of a nightmare.