Many executives are enthusiastic about the business potential of machine learning applications. But business leaders often overlook a key issue: To fully unlock the benefits of artificial intelligence, you’ll need to upgrade your people’s skills — and build an empowered, AI-savvy workforce.
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 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) system or another enterprise system. Yes, effective ERP implementations enhanced the competitiveness of many companies, but many other companies found the experience more of a nightmare. The promised opportunity never came to fruition.
Why am I raining on the AI parade? Because, as with enterprise systems, AI inserted into businesses drives value by improving processes through automation. But eventually, the outputs of most automated processes require people to do something. As most managers have learned the hard way, computers can process data just fine, but that processing isn’t worth much if people are feeding them bad data in the first place or don’t know what to do with information or analysis once it’s provided.
With my fellow researchers, Cynthia Beath, Monideepa Tarafdar, and Kate Moloney, I’ve been studying how companies insert value-adding AI algorithms into their processes. As other researchers and managers have also observed, we are finding that most machine learning applications augment, rather than replace, human efforts. In doing so, they demand changes in what people are doing. And in the case of AI — even more than was true with ERP systems — those changes eliminate many nonspecialized tasks and create skilled tasks that require good judgment and domain expertise.
For example, fraud detection applications may reduce the time that people spend looking for anomalies, but increase requirements for deciding what to do about those anomalies. 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.