AI investment is surging—but data challenges are holding businesses back
74% of organizations plan to invest in AI this year, yet fewer than half feel confident they can meet their AI goals. A big reason? Data quality issues. Without a strong data foundation, AI initiatives risk failure before they even begin.
Semarchy surveyed more than 1,000 senior business leaders in the US, UK, and France, to examine where AI ambition and data reality are misaligned and what leaders must do to close the gap.
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The disconnect between AI ambition and reality
New research from Semarchy highlights the critical gap between AI aspirations and execution. While companies are eager to embrace AI, data privacy concerns, duplicate records, and poor integration are creating roadblocks that limit AI’s effectiveness. Business leaders recognize the need for stronger data governance, cross-functional collaboration, and a strategic approach to AI adoption—but many don’t know where to start.
What you’ll learn:
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- The top data challenges preventing AI success—and how to solve them
- Why AI leadership is unclear in most organizations, and who should take ownership
- How businesses can turn data quality into a competitive advantage for AI
74% of businesses
are investing in AI initiatives this year
98% of organizations
have experienced data quality issues that are affecting AI adoption
46% of business leaders
believe their AI goals are ahievable
“The enthusiasm for AI adoption from businesses is clear, but our findings reveal a troubling gap between ambition and reality. Businesses are eager to embrace AI, yet many lack the data quality necessary for success. Deploying AI at scale on a bad foundation will only magnify business risks and lead to wasted investments.” – Craig Gravina, Chief Technology Officer at Semarchy
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