Agentic AI Readiness Index 2026: The Gap Between Investment and Data Maturity

The 2026 Agentic AI Readiness Index measures the extent to which corporate data environments are prepared to support agent-based AI workloads and initiatives in a production environment. The results show that only 15 percent of companies are fully prepared to deploy agent-based AI (Agentic AI) in production, even though nearly 60 percent report investing tens or hundreds of millions in this area. The index is based on a survey of 400 data experts in the United States, the United Kingdom, the EMEA region, and the Asia-Pacific region.

Data Requirements for Agentic AI

Companies are evaluated based on the key data requirements that Agentic AI needs for reliable operation. Among the most important are data timeliness, data provenance, governance, and interoperability. Agent-based AI systems are designed to plan, execute, and implement business processes autonomously. This increases both the benefits and the risks for companies.

Deficits Turn into Failures

When AI systems are put to productive use, shortcomings in data quality, governance, and interoperability can lead to operational failures. This significantly limits the potential for secure, large-scale automation using AI. „Most companies fail with AI not because of the models, but because their data isn’t ready,“ says George Fraser, CEO of Fivetran. „Companies are deploying agent-based AI on top of fragile pipelines, a lack of traceability, and systems that were never designed for autonomy. This doesn’t lead to better results—it leads to faster failures.“

"Most companies don't fail at AI because of the
It's not about the models, but rather that their data isn't ready.“

George Fraser,
CEO,
Fivetran

Actual adoption is outpacing readiness: 41 percent of companies are already using agentic AI in production—despite significant gaps in data reliability, governance, and interoperability. Data issues are the biggest obstacle: The most frequently cited hurdles to achieving Agentic AI goals are data quality and provenance (42 percent), regulatory requirements and data sovereignty (39 percent), and security and privacy risks (39 percent). Interoperability is crucial to success: 86 percent of data leaders state that platform extensibility and interoperability are important or critical. Among them, 17 percent believe these are essential for AI and data decisions. Nevertheless, many companies feel constrained by fragmented system landscapes and vendor lock-in. Data integration platforms are cited as the greatest source of lock-in risks.

Data Infrastructure as a Limiting Factor

The results highlight a broad industry trend: The more autonomous artificial intelligence systems become, the more data infrastructure becomes a limiting factor. According to Gartner, up to 60 percent of all AI projects could be abandoned due to a lack of AI-ready data.

The report measures readiness using the Agentic AI Readiness Index: These are composite scores that evaluate a company’s data foundation in key areas such as data timeliness, data provenance, governance, and interoperability. The average maturity level among respondents is approximately 61 to 62 percent. This shows that most companies still need to close critical gaps in order to achieve a return on investment from their AI investments. Companies that consider themselves fully prepared not only have a significantly greater sense of security but also demonstrate clear advantages in their operations. (Source: Fivetran)