Poor data quality causing majority of artificial intelligence projects to stall

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A majority of enterprises engaged in artificial intelligence and machine learning initiatives (78 percent) said these projects have stalled—and data quality is one of the culprits—according to a new study from Dimensional Research.

Nearly eight out of 10 organizations using AI and ML report that projects have stalled, and 96 percent of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence, said the report, which was commissioned by training platform provider Alegion.

For the research, Dimensional conducted a worldwide survey of 227 enterprise data scientists, other AI technologists, and business stakeholders involved in active AI and ML projects.

Data issues are causing enterprises to quickly burn through AI project budgets and face project hurdles, the study said.

Other findings of the survey: 70 percent of the respondents report that their first AI/ML investment was within last 24 months; more than half of enterprises said they have undertaken fewer than four AI and ML projects; and only half of enterprises have released AI/ML projects into production.

A majority of the organizations surveyed (81 percent) admit the process of training AI with data is more difficult than they expected; 76 percent address this challenge by attempting to label and annotate training data on their own; and 63 percent try to build their own labeling and annotation automation technology.

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