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Disparity Between AI Confidence and Readiness in Businesses

A recent study conducted by Precisely, in collaboration with the Center for Applied AI and Business Analytics at Drexel University's LeBow College of Business, sheds light on an intriguing paradox in today's corporate landscape: confidence in artificial intelligence (AI) far surpasses actual readiness and operational maturity. The findings from the fourth annual State of Data Integrity and AI Readiness report reveal significant gaps in essential areas such as infrastructure, data trust, and skill sets, as organizations strive to implement AI strategies on a larger scale.

The survey gathered insights from over 500 senior data and analytics leaders from large enterprises across the U.S. and EMEA. Surprisingly, while a substantial majority of leaders express confidence in their AI readiness, their insights reveal foundational weaknesses that could jeopardize the success of their AI initiatives.

The Confidence vs. Reality Gap

According to the report, 52% of respondents consider AI to be a primary influence on their data initiatives, with 85% indicating that their organizations have begun adopting agentic AI systems. Despite this confidence, many leaders reported significant obstacles affecting their AI implementations. For instance, although 87% claimed they had the necessary infrastructure and 86% felt equipped with the right skills, a troubling 42% admitted that infrastructure was a major barrier, while 41% felt insufficiently skilled to execute AI strategies effectively.

Furthermore, only 31% of organizations could tie AI initiatives directly to key performance indicators (KPIs), highlighting the need for organizations to establish robust metrics aligned with business objectives. Notably, 43% of leaders pointed to data readiness as the most significant barrier to aligning AI projects with business goals, and over half identified data quality as a critical area requiring improvement.

The Role of Data Governance

As AI systems transition towards more autonomous capabilities, the relevance of strong data governance becomes increasingly apparent. The study illustrates a stark contrast in confidence levels between companies with a well-defined data strategy and those that lack one. Approximately 71% of organizations with proactive data governance reported high trust in their data compared to 50% of those without such strategies in place. Moreover, organizations that have integrated AI governance into their overarching data governance approaches enjoyed even higher rates of confidence.

With 96% of participants indicating that their organizations invest in location intelligence and third-party data enrichment, the emphasis on enriched and contextual data for AI applications is clear. Furthermore, 32% of leaders with established data strategies anticipate positive returns on investment (ROI) from AI within just six to eleven months, showcasing the potential benefits of investing in solid data infrastructures.

Addressing Skills Shortages in the AI Landscape

The report also uncovers concerning trends regarding the availability of AI skills across organizations. More than half (51%) identified a skills shortfall as a critical need for their AI initiatives, while just 38% felt very prepared in terms of personnel capabilities and training. Key areas cited for skill shortfalls included:

• - 30% lack the ability to deploy AI solutions at scale.
• - 29% need greater expertise in responsible AI practices and compliance.
• - 28% struggle to translate business needs into effective AI solutions.
• - 27% face challenges in AI model development and basic AI literacy.

Murugan Anandarajan, PhD, the Professor and Academic Director at Drexel LeBow's Center for Applied AI and Business Analytics, emphasized that addressing this skills gap requires a new breed of professionals capable of bridging data management, business strategy, and AI governance.

Moving Toward Agentic AI and Closing Data Integrity Gaps

The shift toward agentic AI is fundamentally reshaping organizational workflows, enabling AI systems to perform not only data analysis but also decision-making and task execution autonomously. This evolution mandates a strong foundation in data integrity, as shortcomings can lead to serious repercussions. Precisely's research identifies this as the Agentic AI Data Integrity Gap—the disparity between the current state of enterprise data and the requirements for successful and safe deployment of agentic AI at scale.

Organizations that aim to successfully bridge this gap must prioritize developing Agentic-Ready Data strategies. These strategies should focus on unifying easily accessible data, leveraging reputable third-party data enrichment, and maintaining strong governance, transparency, and automation capabilities.

For professionals and businesses eager to delve deeper into these insights, a webinar titled 'The 2026 State of Data Integrity and AI Readiness' is scheduled for February 25, 2026. The discussion promises to address the core findings and facilitate expert dialogue on how organizations can better prepare for the future of AI. Access to the full report is available for further exploration of the key themes discussed in this vital study.

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For further information on Precisely and their products, visit www.precisely.com.