Artificial intelligence (AI) may be job one for enterprise technology strategy but longstanding issues with data quality threaten to derail initiatives, according to a new survey of more than 800 data professionals.
Research from The Futurum Group paints a picture of an industry at a crossroads. Nearly three in 10 data teams cite building AI capabilities as their single most important objective, marking a decisive shift from AI as an experimental sideshow to the main event driving corporate data strategy.
“Enterprises are no longer just experimenting with AI; they are re-architecting their entire data strategy around it,” said Brad Shimmin, vice president and practice lead for data intelligence, analytics, and infrastructure at Futurum. “However, this aggressive push has exposed critical vulnerability, namely years of accumulated data debt.”
The survey identifies data quality, trust and governance as the biggest challenges facing data professionals, with 20% of respondents citing it a key concern. More troublingly, poor data quality also emerged as the leading cause of AI project failures, creating what Shimmin calls an “existential threat” to organizations’ top business priority.
This reality check has prompted a sophisticated response from enterprise leaders. Rather than choosing between innovation and infrastructure, companies are pursuing both simultaneously. The survey found 52% of organizations plan to increase AI investment, but nearly identical numbers are prioritizing data platforms and data quality tools at 41% and 40%, respectively.
“The market has matured beyond chasing future architectural trends,” the report found.
The findings also reveal a massive architectural shift underway, with 77% of organizations actively implementing, piloting, or seriously considering data lakehouse architectures built on open table formats. This move toward open systems reflects a strategic calculation to avoid vendor lock-in while preparing infrastructure for the scale and complexity that AI workloads demand.
Shimmin predicts that by the end of 2025, the primary bottleneck for scaling enterprise AI will shift from developing models to ensuring data readiness. In response, he anticipates most enterprises will focus on deploying what he calls an “AI-powered data control plane” to automate discovery, preparation, and governance tasks.
The transformation extends beyond technology to reshape professional roles. The survey found 73% of data practitioners report their work shifting toward more business-facing activities. As AI automation handles routine technical tasks, data professionals are evolving into strategic consultants responsible for connecting data assets with business outcomes.
Despite the challenges, the overall outlook remains bullish. More than half of organizations, 55%, plan to increase their data and analytics spending over the next six to 12 months. The survey indicates that 81% of respondents are already using or experimenting with generative AI for data workflows, driving the urgent need for infrastructure investments.
