Poor Data Quality Hampering Businesses and Their AI Plans

Why It Matters

Inaccurate data undermines AI effectiveness, leading to wasted resources and potentially harmful decisions, which threatens the ROI of AI investments across enterprises. Addressing data quality is therefore critical for firms aiming to scale autonomous AI agents and maintain competitive advantage.

Summary

A Salesforce State of Data and Analytics report finds that 84% of data and analytics leaders say their data strategies need a complete overhaul before AI ambitions can succeed, as poor‑quality, outdated or siloed data hampers insight generation. While 63% of business leaders claim to be data‑driven, only half can reliably produce timely insights and many draw incorrect conclusions due to missing business context. Pressure to deploy AI quickly is high—67% of leaders feel the urgency—but 42% lack confidence in AI outputs, and 89% have experienced inaccurate results from AI in production. Similar concerns are echoed in KPMG and Cloudera surveys, with data quality and governance identified as top barriers to expanding AI agents.