"As the M.D. Anderson fiasco illustrates, IBM fell into the trap of over-promising and under-delivering. “IBM claimed in 2013 that ‘a new era of computing has e

"As the M.D. Anderson fiasco illustrates, IBM fell into the trap of over-promising and under-delivering. “IBM claimed in 2013 that ‘a new era of computing has emerged’ and gave Forbes the impression that Watson ‘now tackles clinical trials’ and would be in use with patients in just a matter of months,” Freedman noted. As to whether Watson will ever be useful in clinical situations? “This is hard,” opined Stephen Kraus, a partner at Bessemer Venture Partners. “It’s not happening today, and it might not be happening in five years. And it’s not going to replace doctors.” And that's the crux of the problem with Watson. IBM routinely promises that 'cognitive computing' (CC) can solve literally any data-based or science problem in your business' enterprise. But once you look closer at their methods, you find nothing special; CC is the same old bag of data mining tricks everyone has been using for decades. IBM's real problem: Watson brings nothing new to the table. At the Fortune 100 big pharma where I work we've gone through several rounds of evaluating IBM Watson's box of tricks for the value it might add to drug discovery in the chemical or clinical spaces. And each time we've taken a pass. IBM just hasn't shown that it has a track record of material successes in cutting edge AI, beyond what's available most anywhere. In particular, the plate tectonics of AI shifted with the advent of deep learning, which came along after IBM Watson won Jeopardy, and which still seems absent from their AI repertoire. Until CC demonstrates "competitive competency" in cutting edge AI and not more marketing jabber, I expect IBM Watson will continue to underwhelm.