Data, not offers, reveal what the maturity gap says about AI in Saudi enterprises

In the halls of “LEAP 2026,” robots and smart interfaces appear as if the transition of artificial intelligence to daily work may have been completed. But the picture inside Saudi enterprises is far less bright: the problem hindering implementation is not always the purchase of the tool or budgeting, but the data, systems and workflows that are supposed to make the tool useful.

According to the “AI Maturity Index 2026” by ServiceNow, 67 % of Saudi enterprises face a major challenge related to data not being ready for AI applications. The kingdom scored 50 out of 100 on overall maturity. The gap within the measurement is more significant than the average alone: leadership and strategy scored 56 points, while the AI-enabled workflow axis reached only 39 points. This does not prove a lack of ambition, but it does map the distance between announcing an initiative and embedding it in daily operations.

Data Is Not a Back-Stage PhaseWhen data are unstructured or not connected to existing systems, the model’s ability to deliver an actionable recommendation is limited, no matter how convincing its interface appears. The source notes that only 18 % of Saudi institutions have clear testing frameworks and risk management for these technologies, and only 13 % have managed to replace their legacy systems with modern, integrated platforms. Therefore, asking the vendor about the model’s capabilities is insufficient; the question must start inside the organization: which data will reach it, who will review their quality, and where the result will be tested before scaling.

The gap becomes more evident with agentic AI, that is, systems capable of executing a sequence of steps rather than merely assisting the user. The source says that 48 % of Saudi institutions have begun using these technologies, but only 10 % have used them to create fully automated, independent workflows. The difference here is practical: a tool that helps an employee with part of a task is not necessarily a system that redesigns the process or executes it from start to finish.

Rapid Spending Does Not Shorten Transformation TimeAI spending in Saudi Arabia recorded an annual growth of 124 % according to the source, with expectations that it will approach 20 % of IT budgets by 2027. However, spending alone does not address fragmented data, legacy systems, or the lack of testing frameworks. The index shows a link between governance and data-management maturity and a current return on investment of 161 % among the most mature institutions, with an expected 194 % within two years. These figures come from a study tied to a technology provider and should be treated as a measurement from that provider rather than a guarantee for any organization.

For teams in the Gulf, Egypt and the Levant following the Saudi experience, the message is not to lag behind new tools. The message is that priority setting is shifting: cataloguing data sources, assigning clear responsibility for their governance, and selecting a specific process for testing before discussing scaling. What the platforms showcase illustrates what AI can do. The value for the organization is determined by whether its data and processes are ready to allow it to do so.