At a time when "Wall Street" and major global financial institutions have begun directing concerned questions about the gap between massive capital expenditures (CapEx) for artificial intelligence technologies and the volume of actual revenues realized; the numbers in the Kingdom of Saudi Arabia were drawing an accelerating path.

The adoption rate of artificial intelligence in private sector entities jumped to 33.1% by the end of 2025.

We are here facing a critical economic paradox: the inevitability of digital transformation to increase efficiency, in confrontation with the "sticker shock" that has begun to worry boards of directors globally. At "Yamama Insights", with our philosophy based on "To understand life… we interpret numbers", we stand before this paradox to pose the most important question:

"How do we transform these technologies from a cost center into a real driver of rapid economic and social return (SROI)?"

Beyond the 33.1% Jump: Expanding the Automation Frontier

Artificial intelligence algorithms are no longer the monopoly of technology company server rooms. Data from the Central Department of Statistics for 2025 reveals a structural shift in the Saudi economy. Despite the leadership of telecommunications and financial sectors (61.1% and 52.9% respectively), the real economic story lies in "traditional sectors".

  • Utilities and Mining Operations: The jump of the electricity and gas supply sector to 35.4% means the transition of artificial intelligence from the phase of "technical experimentation" to the phase of "field operational efficiency", where predictive models are used to reduce waste and improve supply chains.
  • Education and the Knowledge Economy: The reach of adoption rate in educational institutions to 51%, with the qualification of over 13,000 teachers, represents an investment in human infrastructure, and is an early indicator of reducing traditional training costs and raising output quality.

And when comparing this performance to the average of OECD member countries of 20.2% for the same year, we find that the Saudi market exceeds the global average by a margin of 13 percentage points, approaching the rates of Scandinavian economies.

Global Concern Escalates: The Steep Cost of "Intelligence"

On the other side of the picture, global capital markets are experiencing a state of "perceptual correction". Published reports and their links listed below point to restlessness among CEOs about the operating costs of generative artificial intelligence (Generative AI). The intensive consumption of cloud computing, data processing costs (Inference Costs), and subscription fees for tools like (Copilot and ChatGPT Enterprise) that require high monthly fees per user, have begun to pressure profit margins. Financial markets are asking today:

When will productivity growth cover the cost of these investments?

This global concern imposes on local institutions, whether for-profit or nonprofit, the necessity of adopting strict governance models before injecting money into technologies they may not actually need.

The Size Gap: The Competitive Efficiency Challenge

OECD data indicates that 52% of large companies use artificial intelligence compared to only 17.4% of small companies. This "algorithmic gap" signals a distortion in competitiveness. Large companies have the financial capacity to absorb initial AI costs, while small and medium enterprises (and with them, emerging nonprofit entities) may be excluded from the market if they do not have supported digital infrastructure, which is explained by intensive Saudi government investments within "Vision 2030" to democratize access to these technologies through the provision of national cloud computing at competitive prices.

Conclusion: Wisdom in "Humanizing" and Governing Numbers The jump to 33.1% is undoubtedly a victory for the speed of economic response in Saudi Arabia. But the lesson learned from global market volatility is clear: adopting artificial intelligence for the sake of adoption (FOMO - fear of missing out) is a guaranteed financial drain. The next phase requires transitioning from measuring "adoption rate" to measuring "usage efficiency". Critical sectors, headed by the nonprofit sector, are required to govern their technology investments to ensure that every algorithm purchased actually contributes to maximizing economic returns, and more importantly, deepening sustainable societal impact.