Recent research shows that organizations are feeling more confident about their AI plans, but many are not fully prepared when it comes to data quality and infrastructure. A new study from Precisely and Drexel University highlights a gap between how companies see their AI readiness and the reality of their data systems. This disconnect could slow down or hinder AI success as companies try to scale AI across their operations.

High Confidence Yet Underlying Challenges

According to the survey, over half of the respondents believe their organization’s infrastructure (87%), skills (86%), and data readiness (88%) are sufficient for AI projects. However, many leaders also admit that infrastructure (42%), skills (41%), and data quality issues (43%) are major hurdles. This shows a split between confidence and actual preparedness. Companies are moving fast with AI, but foundational issues remain, especially around data trust and governance.

While most organizations think they align AI well with business goals, only about a third actually measure success through specific metrics linked to key performance indicators (KPIs). Data quality continues to be a top concern, with more than half of leaders citing it as a critical factor for successful AI deployment. Without reliable data, AI systems can’t deliver the results companies expect.

The Growing Importance of Data Governance

Over the past couple of years, the market has shifted toward more autonomous, action-driven AI systems. This change puts even more emphasis on data integrity and governance. Leaders recognize that trustworthy, governed data isn’t just a nice-to-have—it’s essential for AI to work responsibly and effectively.

Many organizations are investing in better data management practices, but challenges remain. Data quality, infrastructure, and skills are still primary obstacles. The study suggests that companies focusing on integrated, governed, and contextualized data will be better positioned to turn AI ambitions into real business results.

Overall, the research underscores the need for organizations to balance confidence with concrete improvements in data practices. As AI continues to evolve, those that prioritize trustworthy data foundations will gain a competitive edge and reduce risks associated with autonomous systems.

Inspired by

• https://ai-techpark.com/study-ai-confidence-surges-while-readiness-lags-on-data-integrity/