While 74% of businesses plan to invest in AI initiatives this year, less than half (46%) are confident in their data quality. This is according to a survey of 1,050 senior business leaders across the US, UK, and France by Semarchy, a global leader in master data management (MDM) and data integration.

This contrast highlights a critical challenge that businesses face as they rush to adopt AI without ensuring data readiness. Nearly all (98%) have encountered AI-related data quality issues, primarily due to data privacy and compliance constraints (27%), high volumes of duplicate records (25%), and inefficient data integration (21%).

These challenges have resulted in decreased trust in AI outputs for 19% of businesses - a critical risk to AI credibility and adoption. Additionally, companies report delays in new project deployment (22%) and increased costs (20%). 

The disconnect between executive ambition and AI execution reality is glaring. Fewer than half (46%) of business leaders believe their AI goals for this year are realistic and achievable. This drops to just 35% for CDOs. This scepticism likely stems from intimate knowledge of the data challenges they must overcome for AI to deliver real value. 

Craig Gravina, Chief Technology Officer at Semarchy, said: "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."

"The next steps here are logical: look at the business case for AI closely and assess the readiness and risk of their data before jumping in headfirst. We need to reshape the way businesses view enterprise data and AI ecosystems in order to fuel AI-driven innovation while ensuring transparency, security, and governance."

Gilles CORCOS, CIO Sales & Marketing at Elis, said: "By strategically combining the capabilities of Artificial Intelligence (AI) and Semarchy Master Data Management (MDM), we have established a self-reinforcing cycle of data quality improvement. This synergy allows our data stewards to redirect their focus from time-consuming, repetitive tasks towards more strategic, high-value activities. This not only enhances the overall quality and reliability of our data but also translates to substantial time and cost savings for Elis."

Jean-Yves Falque, Founder and Executive Chairman at Apgar, said: "AI is a social and industrial revolution in motion, and every company must embrace it to stay competitive. Yet, too many initiatives fail due to a lack of digitalization and insufficient governance and data quality management.

Laying a strong foundation is essential to building trust and ensuring an effective, responsible rollout. Unlike BI, AI will democratize data usage and bring data quality and governance to the core of business operations. Like any transformation, it requires education and commitment, empowering every 'data citizen' to contribute to making it a true value driver-essential for the long-term success of their company."

The study reveals ambiguity around corporate AI leadership and governance. While 37% of CIOs, 30% of CTOs, and 23% of CEOs consider themselves chiefly responsible for AI strategy, only 15% of Chief Data Officers (CDOs) share this view. Just 6% of CEOs believe CDOs should be chiefly responsible for AI initiatives - a surprisingly low figure given AI's heavy reliance on data quality and governance. 

Gravina added: "AI leadership shouldn't be driven by siloed thinking or short-term priorities. Instead, it should be based on a range of expertise and experience and, of course, high-quality data. The research shows that just 7% of organizations have a cross-functional team driving AI strategy. A collaborative approach is essential to align technical capabilities with business objectives, but strong data leadership remains critical."

Businesses planning to accelerate AI adoption this year must also contend with ethical, regulatory and security concerns. Fewer than half (45%) are actively working to mitigate AI bias, while a similar number (47%) admitted that employees use non-private AI environments, including to complete tasks involving sensitive company data.

The full report can be accessed on the Semarchy website at: https://semarchy.com/resources/semarchy-bridging-ai-data-gap-report/