AI Project Teams Are at the Mercy of Their Information Quality
Good data is the lifeblood of a successful artificial intelligence (AI) project. But collecting and parsing quality data can be time-consuming and costly. That leaves many AI teams facing a frustrating dearth of data.
of respondents in China expect AI to transform their business by 2021—the highest level globally
Amount expected to be spent globally on AI systems by the end of 2019. By 2022, that total is expected to more than double, to US$79.2 billion.
of organizations pursuing AI projects have run into problems with data quality, data labeling required to train AI initiatives and building model confidence.
AI professionals and data scientists report that training AI with existing data is more difficult than expected.
of executives say their companies have a clear strategy in place for sourcing the data that drives AI initiatives.
of executives say less than one-tenth of their company's digital budget goes toward AI.
Sources: Future in the Balance?: How Countries Are Pursuing an AI Advantage, Deloitte, 2019; Worldwide Semiannual Artificial Intelligence Systems Spending Guide, International Data Corp., 2019; What Data Scientists Tell Us About AI Model Training Today, Alegion, 2019; McKinsey Global Survey, 2018