By Jared Council
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.
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.