A new global study from the IBM Institute for Business Value highlights a growing tension in enterprise AI adoption: organizations are ambitious about AI, but their data is often not ready to support it. Based on responses from 1,700 Chief Data Officers (CDOs) and senior data leaders across 27 countries, the research shows that while AI is increasingly integrated into technology roadmaps, most organizations struggle to turn data into measurable business outcomes.
AI Ambition vs. Data Readiness
According to the survey, 81% of CDOs say their organization’s data strategy is aligned with technology infrastructure—up sharply from 52% in 2023. Yet only 26% express confidence that their data can support AI-driven revenue streams. Barriers such as accessibility, completeness, integrity, and consistency remain persistent hurdles, preventing enterprises from fully leveraging AI capabilities.
“Enterprise AI at scale is within reach, but success depends on organizations powering it with the right data,” said Ed Lovely, VP and Chief Data Officer, IBM. “Organizations that get this right won’t just improve their AI—they’ll transform how they operate, make faster decisions, adapt to change more quickly, and gain a competitive edge.”
Key Insights From the Study
- The evolving CDO role: Once primarily data custodians, CDOs are now expected to be business strategists. 92% of respondents say success depends on focusing on business outcomes, but only one-third strongly agree they can clearly articulate how data drives results.
- Competitive advantage through data: 84% of CDOs report that unique data products have delivered competitive advantages, while 78% prioritize leveraging proprietary data to differentiate their business.
- AI-data gap persists: While 81% prioritize AI investments, only 26% are confident their unstructured data can generate business value. Many organizations are still early in developing datasets and governance frameworks for AI agents.
- Culture and talent challenges: Data democratization is seen as essential—82% say data is wasted without broad access, and 80% say democratization accelerates decision-making. Yet talent gaps are widening: 77% struggle to fill key data roles, and only 53% feel recruiting efforts meet skill needs, down from 75% in 2024.
Implications for Enterprise AI
The study underscores a critical lesson for enterprises pursuing AI at scale: ambition alone isn’t enough. Success requires robust, high-quality data, strong governance, and a culture that empowers employees to leverage it effectively. For organizations struggling to close the AI-data gap, prioritizing data infrastructure, talent development, and integration with AI workflows will be essential.
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