AI Budgets Soar, But Data Readiness Lags: Qlik 2025 Study

A significant gap exists between financial investment and practical implementation for Agentic AI across major corporations, according to a recent industry study. While nearly all large enterprises have secured budgets, a mere fraction have successfully deployed these systems at scale. The primary obstacles preventing widespread adoption are not related to funding or ambition but stem from foundational data challenges. Issues with data quality, accessibility, and integration are creating a bottleneck, slowing down the transition from pilot programs to full-scale operational use.

The research reveals that an overwhelming 97% of organizations have committed financial resources to Agentic AI initiatives. A substantial portion of these companies, 39%, plan to invest one million dollars or more, while over a third are dedicating between 10 and 25 percent of their total AI budget to this area. This level of commitment has firmly established Agentic AI as a standard line item in corporate budgets, creating an expectation for tangible results in the coming year.

Strategic planning for AI is becoming more formalized, with 69% of respondents confirming they have a defined strategy, a notable increase from 37% just one year prior. However, the ability to measure the value derived from these investments is lagging. Only 19% of organizations have a concrete framework in place to calculate return on investment, indicating a shift in corporate governance from questioning the need for AI to demanding proof of its financial impact.

The journey to full-scale implementation is expected to be a long one. Currently, just 18% of enterprises report having fully deployed Agentic AI solutions. Nearly half of the surveyed leaders believe achieving meaningful scale is still three to five years away, a timeline compounded by the fact that only 42% feel confident in their internal team’s expertise. This suggests that 2026 will primarily be a year focused on building foundational capabilities rather than launching widespread rollouts.

The single greatest impediment to progress is data infrastructure. Data quality, availability, and access top the list of barriers, followed by integration complexities, a shortage of skilled personnel, and governance concerns. The constraint is less about the power of the AI models themselves and more about the underlying “enterprise plumbing” required to make them function reliably.

With deployment comes a new set of risks that organizations must navigate. The highest concerns among business leaders are cybersecurity threats, the reliability of AI outputs, and potential legal exposure. Issues of explainability and auditability are also significant considerations. How a company manages these risks will directly influence the speed of adoption and the criteria used for selecting technology vendors.

The initial applications for Agentic AI are predictably pragmatic. IT operations and software development are the most frequently targeted areas for early deployment. The primary goal in these domains is cost reduction, with productivity gains serving as the key performance indicator. These areas are attractive starting points because they often have established telemetry systems and performance baselines, making it easier to measure the AI’s impact.

As spending transitions from experimental budgets to core operational expenses, the challenges are familiar ones for large organizations. The next twelve months will be critical for converting limited, well-defined projects in IT and engineering into stable, measurable production systems that deliver consistent value.

(Source: MEA Tech Watch)