Enterprises Face Data Hurdles Despite Surging Investment in Agentic AI, Study Finds

Budget Commitment Meets Implementation Reality

Enterprise investment in Agentic AI has reached a critical tipping point, with 97% of organizations having committed budget to these initiatives, according to the Qlik 2025 Agentic AI Study. The research, conducted by Enterprise Technology Research (ETR), indicates that substantial financial resources are being allocated, with 39% of enterprises reportedly planning to spend $1 million or more and 34% dedicating 10-25% of their total AI budget specifically to Agentic AI projects.

Analysts suggest this level of budgetary commitment transforms Agentic AI from experimental technology into a formal line item, creating expectations for measurable returns by 2026. However, sources indicate that despite strong financial backing, significant operational challenges threaten to delay widespread implementation.

The Data Readiness Gap

While funding is abundant, data infrastructure appears to be the primary constraint on progress. The report states that data quality, availability, and access represent the most significant barriers to implementation, followed by integration challenges, skills gaps, and governance concerns.

“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,” said James Fisher, Chief Strategy Officer at Qlik, according to the study findings. “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.”

Implementation Timeline Extends Beyond Expectations

Scale remains a distant goal for many organizations, with only 18% having fully deployed Agentic AI solutions to date. The analysis indicates that 46% of enterprises believe achieving meaningful scale is still three to five years away, pushing widespread implementation into the 2028-2030 timeframe.

Confidence in internal expertise appears to be another limiting factor, with just 42% of organizations expressing confidence in their team’s capabilities to manage Agentic AI initiatives. This suggests that 2026 will function more as a building phase rather than a period of broad rollout for most enterprises., according to further reading

Strategic Maturity Versus Value Measurement

The research reveals significant progress in strategic planning, with 69% of organizations now reporting a formal AI strategy compared to just 37% in 2024. However, value measurement frameworks lag considerably behind, with only 19% having established a defined ROI framework for their Agentic AI investments.

Analysts suggest this disparity indicates a shift in governance focus from whether to implement AI to what organizations are actually gaining from their investments. The maturation of strategic planning without corresponding measurement capabilities could create accountability challenges as budgets increase.

Risk Management and Initial Use Cases

Top concerns cited in the study include cybersecurity vulnerabilities, output reliability, and legal exposure, with explainability and auditability following closely behind. Sources indicate that risk management considerations will significantly influence both implementation pace and vendor selection criteria.

Initial implementation appears to be concentrating in areas where measurement is more straightforward. According to the report, IT operations and software development represent the most targeted initial applications, with cost reduction as the primary objective and productivity improvement as the key success metric.

Erik Bradley, Chief Strategist at ETR, noted that “As spend shifts from experimentation to line items, the constraints are classic enterprise ones: data quality, integration, governance and talent. 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 Path Forward

The analysis concludes that Agentic AI has clearly crossed the budget approval threshold and moved into formal operating plans for 2026. However, the transition from pilot programs to run-rate operations depends heavily on resolving fundamental data infrastructure challenges rather than advancing model capabilities.

Until organizations can successfully integrate governed, high-quality data into existing workflows without introducing additional risk, most Agentic AI initiatives will reportedly remain limited to proofs of concept rather than becoming standard operational practice.

References & Further Reading

This article draws from multiple authoritative sources. For more information, please consult:

• http://en.wikipedia.org/wiki/Qlik
• http://en.wikipedia.org/wiki/Data_quality
• http://en.wikipedia.org/wiki/Artificial_intelligence
• http://en.wikipedia.org/wiki/Analytics
• http://en.wikipedia.org/wiki/Data_integration

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