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Tuesday, August 22, 2017

Frequent Flaw of AI Implementation

Good cautious piece regarding implementation in the complex enterprise.  But hardly unique to AI.   Cae of any analytics, unless you are plugging a number into a strictly defined process control system. Results are statistical and often implemented inexactly by people.  So I remove 'fatal', its always a strong caution: how you implement the results into process.    Mention of ERP is also good here, its often part of a larger system, but rarely considered that way, was our experience.

In MIT Sloan Management Review
The Fatal Flaw of AI Implementation   by Jeanne Ross

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. 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 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 practitioners have 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 ERPs — those changes eliminate many nonspecialized tasks and create skilled tasks that require good judgment and domain expertise.   .... " 

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