The Agentic AI Investment Boom: Why Data Infrastructure is the Real Bottleneck

The Great Agentic AI Divide: Budgets vs. Execution

While artificial intelligence continues to dominate corporate strategy discussions, a new divide is emerging in how organizations approach Agentic AI implementation. According to Qlik’s comprehensive 2025 study, enterprises are pouring unprecedented resources into autonomous AI systems while simultaneously acknowledging that data infrastructure limitations will delay meaningful scale for years. This creates a fascinating paradox where financial commitment significantly outpaces operational readiness.

The Great Agentic AI Divide: Budgets vs. Execution
Financial Commitment Reaches Critical Mass
The Strategy-Execution Gap Widens
The Data Readiness Crisis
Risk Management Takes Center Stage
Practical Implementation Pathways
The Road to 2026: Building Foundations

“Enterprises are not short on ambition or funding. What’s missing are the data and analytics foundations that let agents work across the business with reliability and control,” observed James Fisher, Chief Strategy Officer at Qlik. His statement captures the core challenge facing organizations: the gap between AI aspiration and data reality.

Financial Commitment Reaches Critical Mass

The study reveals staggering budget allocations that signal Agentic AI’s transition from experimental technology to strategic priority. With 97% of organizations committing specific budget lines and 39% planning expenditures exceeding $1 million, the financial stakes have never been higher. More significantly, 34% of companies are dedicating 10-25% of their entire AI budget specifically to Agentic AI initiatives.

This level of financial commitment creates inherent pressure for demonstrable returns. As Fisher notes, “If you want Agentic AI to move the needle in 2026, invest first in trusted pipelines, interoperability, and a practical ROI framework your board believes.” The message is clear: substantial investment demands substantial results., according to industry analysis

The Strategy-Execution Gap Widens

While financial planning has accelerated dramatically, strategic maturity tells a more complex story. The study shows that 69% of organizations now report having a formal AI strategy, nearly doubling from 37% in 2024. However, only 19% have established a defined ROI framework to measure actual value delivery.

This discrepancy highlights a critical transition in AI governance. Organizations are moving beyond the initial “should we implement AI” phase into the more challenging “what did we actually achieve” evaluation stage. Without clear measurement frameworks, even well-funded initiatives risk becoming expensive experiments rather than value-generating operations.

The Data Readiness Crisis

Perhaps the most significant finding concerns the fundamental data infrastructure challenges preventing Agentic AI from achieving scale. The study identifies data quality, availability, and access as the primary barriers, followed by integration complexity, skills gaps, and governance issues.

This suggests that the constraint isn’t AI model capability but rather what Qlik describes as “enterprise plumbing” – the underlying data systems and processes that enable AI to function reliably across business operations. Until organizations address these foundational data challenges, Agentic AI initiatives will remain limited to pilot programs rather than achieving enterprise-wide impact.

Risk Management Takes Center Stage

As deployment considerations mature, risk management emerges as a critical factor influencing implementation pace and vendor selection. Top concerns include cybersecurity vulnerabilities, output reliability, and legal exposure, with explainability and auditability following closely behind.

These risk considerations are shaping not only how quickly organizations deploy Agentic AI but also which use cases they prioritize and which vendors they select. The most successful implementations will likely come from organizations that balance innovation ambition with robust risk management frameworks.

Practical Implementation Pathways

The study identifies IT operations and software development as the primary initial targets for Agentic AI deployment. These areas offer clearer outcome measurement through existing telemetry and baseline data, making them ideal proving grounds for more ambitious implementations.

According to Erik Bradley, Chief Strategist at Enterprise Technology Research, “Our data shows broad intent, but only a minority are ready to scale. The next year will be about turning tightly scoped use cases in IT ops and software engineering into durable, measured production.”

The Road to 2026: Building Foundations

With only 18% of organizations having fully deployed Agentic AI and 46% anticipating three to five years before achieving meaningful scale, 2026 appears positioned as a building year rather than a breakthrough year. The focus will shift from experimentation to creating the data infrastructure, governance frameworks, and measurement systems that enable sustainable scaling.

The organizations that succeed will likely be those that recognize Agentic AI as not just a technology implementation but a fundamental restructuring of how data flows through their operations. As the study suggests, the real work happens not in the AI models themselves but in the enterprise plumbing that makes them effective., as additional insights

For companies looking to capitalize on their Agentic AI investments, the path forward requires balancing ambitious vision with practical execution – investing in data quality and integration while simultaneously developing the governance and measurement frameworks that ensure these substantial investments deliver meaningful business value.