Navigating the Agentic AI Investment Boom: Why Data Infrastructure Holds the Key to Realizing Value

The Agentic AI Budget Surge: Ambition Meets Reality

Enterprises worldwide are making substantial financial commitments to Agentic AI, with a striking 97% of organizations having allocated dedicated budgets according to Qlik’s comprehensive 2025 study. The investment scale is particularly noteworthy—39% of enterprises plan to spend $1 million or more, while 34% are dedicating 10-25% of their total AI budget specifically to agentic initiatives. This represents a significant shift from experimental funding to formal line items, creating heightened expectations for tangible returns by 2026.

James Fisher, Qlik’s Chief Strategy Officer, emphasizes the critical challenge: “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.” This disconnect between financial commitment and operational readiness defines the current landscape, where ambition outpaces infrastructure capabilities.

The Strategy Maturity Gap: From Planning to Measurement

Organizational approaches to Agentic AI have evolved significantly, with 69% of enterprises now reporting a formal AI strategy compared to just 37% in 2024. This represents nearly doubling strategic formalization within a single year, indicating rapid maturation at the planning level. However, this strategic advancement hasn’t translated effectively to value measurement frameworks., according to industry news

The study reveals a critical measurement gap: only 19% of organizations have established a defined ROI framework for their Agentic AI investments. This disparity suggests that while leadership recognizes the strategic importance of AI agents, most organizations lack the mechanisms to quantify their impact. As Erik Bradley, Chief Strategist at Enterprise Technology Research, notes: “As spend shifts from experimentation to line items, the constraints are classic enterprise ones: data quality, integration, governance and talent.”

The Implementation Timeline: Why Scale Remains Elusive

Current deployment statistics highlight the implementation challenge. Only 18% of organizations report fully deploying Agentic AI solutions, while 46% believe achieving meaningful scale remains three to five years away. This extended timeline reflects both technical and organizational hurdles that extend beyond simple model deployment.

Confidence in internal capabilities presents another significant barrier, with just 42% of respondents expressing confidence in their organization’s internal expertise to manage and scale Agentic AI initiatives. This expertise gap compounds implementation challenges and extends projected timelines for achieving operational scale., according to industry news

Data Infrastructure: The Critical Bottleneck

The study identifies data quality, availability, and access as the primary barriers to Agentic AI implementation, followed closely by integration challenges, skills shortages, and governance complexities. These findings underscore that the primary constraint isn’t model capability but rather what Fisher describes as “enterprise plumbing”—the fundamental data infrastructure required to support reliable AI operations.

This infrastructure challenge manifests across multiple dimensions: establishing trusted data pipelines, ensuring interoperability between systems, and creating governance frameworks that maintain data quality while enabling access. Without addressing these foundational elements, organizations risk limiting their Agentic AI initiatives to pilot programs rather than achieving scalable production deployment.

Risk Management: Navigating the Deployment Frontier

As organizations move toward broader implementation, risk considerations are shaping both pace and vendor selection. Cybersecurity emerges as the top concern, followed closely by output reliability and legal exposure. Explainability and auditability requirements also rank highly, reflecting regulatory and compliance considerations that influence deployment strategies., as previous analysis

These risk factors are particularly relevant for organizations operating in regulated industries or handling sensitive data. The emphasis on explainability suggests that black-box AI solutions may face adoption barriers unless they can provide sufficient transparency into decision-making processes.

Practical Implementation: Where Agents Deliver Early Value

Organizations are taking a pragmatic approach to initial Agentic AI deployment, focusing on areas where measurable outcomes and existing infrastructure support implementation. IT operations and software development emerge as the primary initial targets, with cost reduction identified as the top objective and productivity improvement as the key success metric.

These domains offer natural starting points because they typically feature established telemetry systems, clear performance baselines, and well-defined workflows. As Bradley observes: “The next year will be about turning tightly scoped use cases in IT ops and software engineering into durable, measured production.” This focused approach allows organizations to demonstrate value while building the expertise and infrastructure needed for broader deployment.

The Path Forward: Building Foundations for 2026 Scale

The study findings point to 2026 as a critical build phase rather than a broad rollout period. Organizations that succeed in scaling their Agentic AI initiatives will likely be those that prioritize data foundation work alongside their AI investments. This includes establishing robust data governance frameworks, improving data quality management, and developing the integration capabilities needed to connect AI systems with existing enterprise workflows.

Fisher’s recommendation captures the essential priority: “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.” This foundation-first approach recognizes that sustainable AI value depends as much on data infrastructure as on algorithmic sophistication.

As organizations navigate this transition from experimentation to operational scale, the differentiation between leaders and laggards will increasingly depend on their ability to address these fundamental data and integration challenges. The organizations that succeed will be those that treat data infrastructure as a strategic priority rather than a technical afterthought.

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