At a Glance

  • As companies plan to expand AI applications, leaders are refocusing on how data is managed and governed, according to a Harris Poll released Wednesday. The market research firm, on behalf of data intelligence platform provider Collibra, surveyed more than 300 technology decision-makers earlier this year.
  • Nearly 90% of surveyed leaders said protecting data privacy is their top concern in AI initiatives. More than four-fifths of decision-makers said data ownership has shifted over the past year as AI work has increased.
  • "Previously, data responsibility might have stayed at the domain level," Stijn Christiaens, co-founder and chief data citizen at Collibra, told CIO Dive. "Now, because of AI, it gets much more executive-level attention."

Deep Dive

Data has been enterprise-critical before the recent wave of AI, but now business leaders are eager to get governance efforts on track. Executives do not want AI efforts to go to waste, and poor data management can make AI goals even more elusive.

Implementation hurdles have already hindered progress. According to a report released earlier this year by Informatica, two-thirds of enterprises said they are still stuck in generative AI pilot phases, unable to turn experiments into production applications. About three-fifths of leaders said they face pressure to accelerate project progress.

"Many of the risks associated with AI are data risks: poor quality, ownership, privacy," Christiaens said. "If you only focus on the opportunity... and don't consider the risks, that's a big mistake."

CIOs can help organizations upgrade data processes before wasteful spending accumulates and pilot projects stall by leading assessments of existing practices and identifying areas for improvement. Analysts told CIO Dive that technology leaders should also communicate with other C-suite executives, emphasizing the importance of data lineage, diversity, and quality.

Companies must consider compliance and legal implications when updating data practices. For example, high-risk use cases require more safeguards than low-risk ones. Companies such as The Wendy's Company and EY have also turned to synthetic data to strengthen privacy protection.