Data has long been considered the “Achilles Heel” of artificial intelligence. Data managers and their organizations have been pressing forward full throttle to build data estates that can handle the relentless AI surge. Yet, beneath all this activity is a persistent concern about the readiness of data environments to support enterprise-scale AI efforts.
That’s the word from the latest annual survey, the “2026 State of Data Integrity and AI Readiness” report, out of the Center for Applied AI and Business Analytics at Drexel University’s LeBow College of Business, in partnership with Precisely. Overall, perhaps not surprisingly, AI dominates the data strategies of organizations at this time. A majority of organizations, 52%, cite AI as the primary influence on their data programs.
At the same time, 43% of respondents feel data issues are the most significant barriers to the success of their AI initiatives. “Artificial intelligence has become the primary driver of organizational data strategies, yet a striking paradox has emerged,” the study’s authors assert. “While most executives claim they have the necessary infrastructure, skills, and data readiness for AI success, a substantial portion identify these same elements as their biggest obstacles.”
The survey reveals a striking contradiction between confidence about AI readiness and the reality of that readiness. Close to nine in 10 respondents, 88%, claim they have the necessary infrastructure, skills, data readiness, and governance for AI. In addition, 38% call out data quality as their biggest data integrity issue—down from 64% a year ago.
While most managers state they are “AI ready,” more than four in 10 cite issues with infrastructure, skills, data readiness, strategy, and governance. At the same time, 42% of data leaders cite technology infrastructure as a challenge, even though most claim their infrastructures are “AI-ready.”
Tellingly, 30% say their greatest missing expertise is the ability to deploy AI at scale. “The reality [is] ‘ready; often means basic capability, not enterprise-scale maturity,” the study’s authors said. “Having infrastructure in place doesn’t guarantee the ability to operationalize AI at scale.”
Data governance is a pivotal factor in driving trusted insights and enabling AI adoption, the survey finds. Forty-two percent say data governance has improved their organizations’ readiness to take on AI. Another 44% say data government initiatives have improved their data quality.
In addition, a majority of the organizations with governance programs surveyed, 72%, report high trust in their data, compared to 50% without governance programs.
While 71% report AI is aligned with their overall business goals, only 31% have actual metrics tied to key performance indicators. Still, 32% expect positive ROI from AI in the coming six to 11 months.
In terms of action to prepare data estates for the challenges ahead, a majority, 51%, are focusing on data quality. Another 50% are concentrating on assuring data privacy and security within their organizations. A similar number are focusing on delivering quality outcomes for organizational AI efforts.
Additional influences on data strategies include advanced analytics (42%), cloud adoption (41%), digital transformation (40%), workflow automation (40%), and data products (39%). IT-led initiatives such as modernization (35%), DataOps (26%), and data mesh/data fabric architectures (24%) also show moderate influence on data strategies as well.
These data management efforts are helping to ensure context within AI output. For example, almost all organizations (96%) are employing location intelligence and third-party data enrichment, employing location insights for targeted marketing, address validation and completion, delivery optimization, and risk management initiatives. These applications still have their challenges: Those using location intelligence struggle with privacy and security concerns (46%) and integration complexity (44%), while users of third-party data battle quality (37%), data privacy (33%), and compliance (32%) issues.