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A recent report from Precisely highlights an interesting paradox: 87% of organizations believe they are ready for AI, yet at the same time, 40% of the leaders reported that data, skills, and infrastructure remain the biggest obstacles.
Precisely’s fourth annual State of Data Integrity and AI Readiness report reveals a growing disconnect in how organizations perceive their AI preparedness.
This confidence gap is already affecting execution. While 71% of respondents said their AI initiatives are aligned with business goals, only 31% reported having metrics tied to business KPIs. This underscores a lack of accountability as organizations attempt to scale AI beyond pilots.
The report identifies a lack of data readiness as the most significant barrier to aligning AI with business objectives. Despite significant investment in data enrichment and location intelligence, many organizations still struggle to trust the data feeding AI systems.
“The 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,” said Dave Shuman, Chief Data Officer, Precisely.
“As AI systems become more autonomous, data integrity is no longer a nice-to-have; it’s 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.”
In terms of solutions to some of the challenges, Precisely points to data governance as a key differentiator. The report states that in the last 18 to 24 months, the market has reached an inflection point, where AI is shifting to agentic systems. This has widened the gap between organizations with a clearly defined data strategy and those that do not.
Leaders that value and prioritize accurate and contextual data, backed by strong governance, are more likely to successfully execute and scale AI initiatives. Nearly three in four (71%) of organizations with a data strategy and data governance program report high trust in their data, compared to 50% without it.
Almost all the organizations surveyed (96%) report that their organizations invest in location intelligence and third-party data enrichment to add context to their data for AI initiatives.
In addition to the data readiness challenges, the report shows that more than half of the companies are struggling to close the AI skills gap. Only 38% feel very prepared in terms of staff skills and AI training.
The most sought after AI skills include the ability to deploy AI at scale (30%) and expertise in responsible AI and compliance (29%), translating business needs into AI solutions (28%). It’s worth noting that many companies misunderstand AI skills. Earlier this week we covered what it really needs to close the AI skills gap.
“The skills gap isn’t about a lack of talent in one area, it’s about the need for professionals who can operate across data, business strategy, and AI governance simultaneously,” said Murugan Anandarajan, PhD, Professor and Academic Director at Drexel LeBow’s Center for Applied AI and Business Analytics. “That reality has major implications for how organizations and universities prepare those entering the workforce for the era of Agentic AI.”
(Marko Aliaksandr/Shutterstock)
The findings suggest that AI readiness challenges are compounding rather than isolated. Gaps in data governance, data quality, and skills are reinforcing one another, making it harder for organizations to move from AI experimentation to enterprise-scale deployment. As AI systems become more autonomous, these foundational weaknesses increase operational risk and limit the ability to realize consistent returns from AI investments.
While AI systems continue to become more sophisticated, the success of AI initiatives still depend on the fundamentals: robust data governance integrated tightly with AI, system data quality monitoring and improvement, and comprehensive AI talent development. The report also recommends having a contextual layer to enable more accurate predictions and actions by AI systems.
Precisely argues that the “window for honest assessment is now”. Those who are not able to fix fundamental issues may concede ground that would be hard to catch up with later.
The report was conducted in collaboration with the Center for Applied AI and Business Analytics at Drexel University’s LeBow College of Business. The findings are based on a survey of 500+ senior data and analytics leaders across large enterprises in the U.S. and EMEA.
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