Dive Brief:
- Businesses are grappling with the effects of poor data management and governance as AI adoption advances, according to a Semarchy report published this week.
- Nearly all business leaders admit to encountering AI-related data quality issues, the survey of 1,050 respondents found. Decision-makers pointed to data privacy and compliance constraints, duplicate records and inefficient data integration as the primary culprits.
- These issues are hampering enterprise ambitions. Fewer than half of business leaders surveyed said their AI goals this year are attainable. Respondents said data woes are leading to decreased trust in AI outputs, project delays and increased costs.
Dive Insight:
Enterprise AI aspirations hinge on robust data management and governance frameworks.
“Many businesses are moving forward without first addressing these foundational data issues — creating major risks for adoption,” Craig Gravina, CTO at Semarchy, said in an email to CIO Dive. “AI ambition alone doesn’t translate to execution success without data readiness.”
CIOs have an important role to play in the process, according to Gravina.
To strengthen AI strategies, technology leaders should prioritize trusted, clean and well-integrated data rather than incomplete or duplicative information, Gravina said. CIOs should also champion a collaborative approach to governance.
Without a cohesive approach to adoption, businesses can run into problems. Nearly three-quarters of leaders blame poor prioritization and resource allocation for the disconnect around data management, citing investments in generative AI at the expense of data and analytics initiatives, according to a SoftServe report.
Enterprises continue to prioritize and invest in the technology even as they navigate roadblocks, putting even more pressure on technology leaders to find solutions promptly.
“CIOs are in a position to close the execution gap by ensuring AI is scalable, secure and aligned with both data and business objectives,” Gravina said.
