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More Than Half of Companies Adopting AI are Worried About the Reliability and Quality of Their Data, According to New Dun & Bradstreet Survey

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Businesses expect automation of tasks through agentic AI to be top use case, with one in three executives noting the opportunity to use agentic AI for strengthening data management

JACKSONVILLE, Fla., February 11, 2025--(BUSINESS WIRE)--Dun & Bradstreet (NYSE: DNB), a leading global provider of business decisioning data and analytics, today released findings from an AI survey it conducted that shows that a vast majority of organizations (88%) are now implementing artificial intelligence (AI), but more than half (54%) have concerns over the trustworthiness and quality of the data they are leveraging for AI.

"AI’s effectiveness – including explainability, transparency and relevancy – depends on the quality of the data it’s leveraging, yet our survey uncovered that only five in ten organizations believe their data foundation is where it should be for proper AI implementation," said Gary Kotovets, Chief Data & Analytics Officer at Dun & Bradstreet. "For AI planning, it is vital organizations ensure that their data is sourced from a clean, single source of truth that we call mastered data. At Dun & Bradstreet, we believe mastered data and AI are paramount to building effective solutions - our data is continually cleaned, updated, and validated to ensure it is as accurate, timely, and relevant as possible once it’s in the hands of our customers."

Other top concerns cited by survey respondents related to AI implementation include data security (46%), data privacy violations (43%), sensitive or proprietary information disclosure (42%) and data’s amplification of bias (26%). In line with these concerns, only half (52%) of organizations believe they have a good data foundation that will set them up for success with generative AI.

AI Implementation & Use Cases Vary

The survey also revealed that companies are at various stages of implementation, including exploration and research (29%), deploying AI solutions (25%), developing AI products (24%) and piloting programs/products (10%). Although use is widespread, two crucial roadblocks companies face as they integrate AI across functions are having trusted business data (33%) and navigating ethical and regulatory challenges (33%).

Other challenges faced by businesses irrespective of where they are in their AI implementation journey include:

  • Aligning on business prioritization and having internal subject matter expertise (tied at 31%)

  • Lack of explainability and interpretability of the technology (28%)

  • AI risk assessments (27%)

  • Showcasing ROI (25%)

  • AI transparency (25%)