New research from Precisely and the Center for Applied AI and Business Analytics at Drexel University\u2019s LeBow College of Business reveals gaps in operational maturity, data trust, and skills as organizations look to scale AI enterprise-wide\u00a0<\/p>\n
Precisely, the global leader in data integrity, today released the findings from its fourth annual\u00a0State of Data Integrity and AI Readiness\u00a0study. Conducted in collaboration with the\u00a0Center for Applied AI and Business Analytics at Drexel University\u2019s LeBow College of Business,\u00a0the research is based on a survey of more than 500 senior data and analytics leaders across large enterprises in the U.S. and EMEA and highlights a growing disconnect in how organizations perceive their AI preparedness. Leaders overwhelmingly believe they are ready for AI, yet their responses point to foundational gaps that could significantly undermine AI success.<\/p>\n
\u201cThe research shows that confidence in AI does not automatically translate into ROI. Organizations are moving quickly, but many are doing so without the trusted, governed data foundations required to scale AI responsibly. That disconnect represents what we call the Agentic AI Data Integrity Gap, and it introduces significant risk,\u201d said Dave Shuman, Chief Data Officer, Precisely. \u201cAs AI systems become more autonomous, data integrity is no longer a nice-to-have; it\u2019s a business imperative. Organizations that invest now in integrated, improved, governed and contextualized Agentic-Ready Data will be best positioned to turn AI ambition into measurable business results.\u201d<\/p>\n
Access the full report here:\u00a02026 State of Data Integrity and AI Readiness.<\/p>\n
Disconnect Between Agentic-Ready Data Perception and RealityAI continues to be a top priority with 52% of survey respondents citing AI as the primary influence on their data programs, and 85% of respondents reporting adoption of\u00a0Agentic\u00a0AI by their organization.\u00a0Agentic-Ready\u00a0Data is imperative for return on investment (ROI). However, the study conducted in late 2025 identifies key areas of concern around infrastructure, skills, and data readiness as organizations look to move from AI pilots to full-scale implementation.<\/p>\n
Key findings include:<\/p>\n
A number of leaders confidently report having the necessary infrastructure (87%), skills (86%), and data readiness (88%) for AI, but many also admit infrastructure (42%), skills (41%), and data readiness (43%) are their biggest obstacles.<\/p>\n
Most organizations claim they connect AI well with business goals, yet only 31% have actual metrics tied to key performance indicators (KPIs).<\/p>\n
43% of leaders cite data readiness as the most significant barrier to AI alignment with business objectives and more than half name data quality as the most common data integrity priority.<\/p>\n
Data Governance Is a Key DifferentiatorOver the past 18 to 24 months, the market has reached an inflection point, with AI shifting to action-driven,\u00a0agentic\u00a0systems. The research highlights a stark divide between organizations with a clearly defined data strategy and those that do not. Leaders that prioritize accurate, consistent, and contextual data, supported by strong data governance, report far greater confidence in their ability to execute and scale AI initiatives.<\/p>\n
71% of organizations with a data strategy and data governance program report high trust in their data, compared to 50% without.<\/p>\n
63% have established some form of AI governance, but those that have integrated AI governance into existing data governance programs are showing even greater levels of success.<\/p>\n
96% report that their organizations invest in location intelligence and third-party data enrichment to add context to their data for AI initiatives.<\/p>\n
32% of leaders with a data strategy and governance already in place expect positive ROI from AI in just 6-11 months.<\/p>\n
Skill Shortages PersistIn addition to the data readiness challenges, more than half of organizations (51%) also cite skills as a top need for AI initiatives, while only 38% feel very prepared in terms of staff skills and AI training. Key areas where AI skills are lacking include:<\/p>\n
Ability to deploy AI at scale (30%)<\/p>\n
Expertise in responsible AI and compliance (29%)<\/p>\n
Translating business needs into AI solutions (28%)<\/p>\n
AI model development and basic AI literacy (27%)<\/p>\n
\u201cThe skills gap isn\u2019t about a lack of talent in one area, it\u2019s about the need for professionals who can operate across data, business strategy, and AI governance simultaneously,\u201d said Murugan Anandarajan, PhD, Professor and Academic Director at Drexel LeBow\u2019s Center for Applied AI and Business Analytics. \u201cThat reality has major implications for how organizations and universities prepare those entering the workforce for the era of Agentic AI.\u201d<\/p>\n
The Shift to Agentic AI Raises the Stakes for Data IntegrityAgentic\u00a0AI transforms how work gets done by enabling systems that don\u2019t just generate insights and content, but take action, interpreting signals, making decisions, and executing workflows across the enterprise. The study underscores how unprepared many are at the data foundation level. This lack of preparedness has resulted in an\u00a0Agentic\u00a0AI Data Integrity Gap, the gap between where enterprise data is today and what\u2019s required to safely and effectively power\u00a0Agentic\u00a0AI at scale.<\/p>\n
Organizations that successfully close the gap focus on Agentic-Ready Data strategies built on unified, easily discoverable data; trusted third-party enrichment; continuous updates; and strong governance, transparency, and automation; driving trust, control, and efficiency at scale.<\/p>\n
For a comprehensive deep dive into the 2026 State of Data Integrity and AI Readiness findings, attend the webinar \u201cThe 2026 State of Data Integrity and AI Readiness\u201d on February 25, 2026, and hear directly from experts at Precisely and Drexel LeBow\u2019s Center for Applied AI and Business Analytics or access the full report:\u00a02026 State of Data Integrity and AI Readiness.The post Study: AI Confidence Surges While Readiness Lags on Data Integrity first appeared on AI-Tech Park.<\/p>\n","protected":false},"excerpt":{"rendered":"
New research from Precisely and the Center for Applied AI and Business Analytics at Drexel University\u2019s LeBow College of Business reveals gaps in operational maturity, data trust, and skills as organizations look to scale AI enterprise-wide\u00a0 Precisely, the global leader in data integrity, today released the findings from its fourth…<\/p>\n
The post Study: AI Confidence Surges While Readiness Lags on Data Integrity<\/a> first appeared on AI-Tech Park<\/a>.<\/p>\n<\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-container-style":"default","site-container-layout":"default","site-sidebar-layout":"default","disable-article-header":"default","disable-site-header":"default","disable-site-footer":"default","disable-content-area-spacing":"default","footnotes":""},"categories":[4,1],"tags":[3],"class_list":["post-766","post","type-post","status-publish","format-standard","hentry","category-ai","category-ai-and-ml","tag-ai"],"yoast_head":"\n