Mr. Krishna, IBM’s senior vice president of cloud and cognitive software, said about 80% of the work
with an AI project is collecting and preparing data. Some companies aren’t prepared for the cost and
work associated with that going in, he added.
Really?? Blaming the client for being unaware of the costs and challenges involved with collecting and massaging the data as the reason for so many halts (i. e., cancellations) of AI projects? It has nothing at all whatsoever to do with the Sales and Marketing teams either poorly communicating this information to the clients or even not communicating it all during sales pitches? Krishna should be fired for this blatant scapegoating.
https://www.wsj.com/articles/data-challenges-are-halting-ai-projects-ibm-executive-says-11559035800
International Business Machines Corp. executive Arvind Krishna said data-related challenges are a top reason IBM clients have halted or canceled artificial-intelligence projects.
Mr. Krishna, IBM’s senior vice president of cloud and cognitive software, said about 80% of the work with an AI project is collecting and preparing data. Some companies aren’t prepared for the cost and work associated with that going in, he added.
“And so you run out of patience along the way, because you spend your first year just collecting and cleansing the data,” said Mr. Krishna, who was interviewed at The Wall Street Journal’s Future of Everything Festival last week. “And you say: ‘Hey, wait a moment, where’s the AI? I’m not getting the benefit.’ And you kind of bail on it.”
Mr. Krishna didn’t name clients or say how many had halted projects.
One well known example of an AI project unraveling happened in 2017 at the University of Texas’ MD Anderson Cancer Center, which aimed to use IBM’s AI platform, Watson, to improve cancer care. An audit by the University of Texas showed the cancer center was using old data, among other issues.
A report this month by Forrester Research Inc. found that data quality is among the biggest AI project challenges. Forrester analyst Michele Goetz said companies pursuing such projects generally lack an expert understanding of what data is needed for machine-learning models and struggle with preparing data in a way that’s beneficial to those systems.
She said producing high-quality data involves more than just reformatting or correcting errors: Data needs to be labeled to be able to provide an explanation when questions are raised about the decisions machines make.
While AI failures aren’t much talked about, Ms. Goetz said companies should be prepared for them and use them as teachable moments. “Rather than looking at it as a failure, be mindful about, ‘What did you learn from this?’” she said.
Mr. Krishna said he couldn’t specify what percentage of IBM-related AI projects were halted over the past five years. But he said: “In the world of IT in general, about 50% of projects run either late, over budget or get halted. I’m going to guess that AI is not dramatically different.”
Responding to a moderator’s question about some of IBM’s perceived AI setbacks, including clients pulling the plug on projects and the relatively slow uptake of Watson, Mr. Krishna defended his company’s position in the market. He said AI project halts are “the nature of any early technology,” and added that IBM has some 20,000 AI projects world-wide in industries including banking, telecommunications and energy.
“I think 20,000 is not slow,” he said. “I think 20,000 projects is, what I would call, successful.”