Survey: Poor Data Infrastructure Creates Waste in AI Spending

Why It Matters

Weak data foundations inflate AI spend while suppressing measurable returns, threatening the profitability of digital transformation initiatives across the enterprise.

Key Takeaways

• 84% say data environments too complex to manage
• AI spending projected to rise 76% in two years
• Data‑mature firms see 84% AI ROI vs 48% for others
• Only 43% have predictive or automated infrastructure
• 96% need external data‑infrastructure support, but lack strategy

Pulse Analysis

The survey underscores a structural bottleneck: as AI adoption accelerates, many enterprises are still anchored to legacy data architectures that struggle with volume and complexity. Over four‑fifths of North American respondents admit their data environments are too tangled to manage effectively, a condition that not only hampers operational agility but also raises security concerns, with 57% citing difficulty detecting breaches. This growing data chaos arrives just as AI budgets are slated to climb 76% within two years, creating a widening chasm between spending and capability.

Data maturity emerges as the decisive factor in translating AI spend into tangible outcomes. Companies classified as data‑mature report an 84% likelihood of achieving measurable AI ROI, compared with less than half for organizations with fragmented data practices. Mature firms also prioritize data quality—cited by 75% of them—as a core success driver, and they are far more likely to have predictive, automated infrastructure (65% versus 27%). In contrast, only 43% of all surveyed firms possess such automation, limiting their ability to scale AI initiatives and manage risk.

The findings point to a clear market opportunity for vendors and consultants offering integrated data‑infrastructure solutions. With 96% of respondents acknowledging the need for external support, yet most lacking a coordinated strategy, firms that can deliver end‑to‑end data modernization—combining governance, automation, and security—stand to capture significant revenue. Executives should prioritize a roadmap that aligns data architecture upgrades with AI roadmaps, ensuring that rising AI spend fuels real business value rather than wasted expenditure.