Most corporate executives recognize that artificial intelligence (AI) is already reshaping the business and financial landscape. Further, they know the pace of AI-based change is certain to continue accelerating. 

They are not, however, necessarily confident of either their ability to soundly execute their company’s AI-related strategies, or their data-readiness “to ensure the reliability of AI outputs,” according to a new report. 

At least, so claims a new report commissioned by AI company Teradata and conducted by market research firm NewtonX. The report is based on a quantitative study of C-suite executives and AI decision-makers at large companies (at least 1,000 employees and $750 million in annual revenue). 

Among those surveyed, 40% said they don’t think their data is ready to achieve accurate outcomes, according to the report. “That’s not much better than a coin-flip difference between trusting AI outputs and not,” the report said. 

Further, seven in 10 study participants said their company’s AI strategy is misaligned with their overall business strategy. Only 56% said their organization has a clear strategy for AI. And, while 73% of respondents said they view their company as an early adopter, just 27% said they view themselves as leading AI adoption in their industries. 

AI is most frequently used at the departmental level, according to the report. Only 12% of responding executives said they have deployed AI company-wide. 

How to instill internal confidence that AI deployments can have big impacts? The best way, according to Teradata’s report, is through successful use cases. But there may not be enough of those to do the job. “Most AI proofs of concept fail to make it into production,” the report said. It added that most executives expected to see a return on AI investment within six months (58% said so) or a year (84%). 

However, survey takers pointed to several barriers to AI success, topped by a shortage of skills. 

Almost half (46%) of the respondents saw business leaders as the most important drivers of increased trust in AI. The top strategies for doing so included encouraging a culture of experimentation (57%) and demonstrating a clear vision for AI and its potential impact on the organization (53%).