Billions in Artificial Intelligence under the Test: Who Pays the Price for Unfulfilled Promises?
By: Abdullah Saeed Al Habsi
AI Investments Tested by Economic Returns
The losses seen in US tech stocks at the end of October were not merely red numbers flashing on trading screens. They brought back to the surface a question that has lurked for years behind the excitement surrounding artificial intelligence: To what extent can massive investments translate into real economic benefits?
In the trading session on Friday, October 30, 2026, the Nasdaq index dropped by about 2.86%, amid concerns over heavy spending on AI infrastructure development and the ability of companies to generate returns matching those expenditures. This does not mean the collapse of this industry or the fading of its future, but rather reveals a shift in investor sentiment, which no longer views the sheer scale of spending as proof of success.
This shift deserves attention that goes beyond the borders of the United States, particularly in countries seeking to develop their economies through technology while simultaneously facing challenges related to production, employment, energy, and financial resources.
How Does the Citizen Feel the Returns of AI Investments?
It has become easy to hear about a multi-billion-dollar project, a massive data center, or an agreement to develop smart systems. Yet a citizen following these news reports from home may not find a clear answer to a simple question: How will this reflect on their life?
If an institution spends large sums on a smart system, and a customer still waits for hours to complete a transaction, the problem has not been solved. If a factory uses advanced analysis tools while waste and failure rates remain high, owning technology has not yet translated into an economic achievement.
The difference here is between investing in the means and investing in the outcome.
Artificial Intelligence between the Needs of the Egyptian Economy and Production
In the Egyptian economy, as in many economies, there are areas that can benefit tangibly from smart applications. Analytical systems may help improve inventory management, transport planning, resource rationalization, and support for industrial and agricultural decisions. However, the viability of these applications is not measured by their names or modernity, but by their ability to address a specific problem that has cost an organization time, money, or effort.
It may be useful to imagine a medium-sized factory that decides to purchase an AI-based system to predict equipment failures. If the system succeeds in reducing sudden downtime and improving production, it may be a worthwhile investment even if its initial cost is high. But if it requires ongoing subscriptions, costly training, and complex maintenance without achieving measurable improvement, the project becomes a new burden on the factory.
The problem does not lie in the technology itself; it may be excellent in another facility. The problem lies in making the purchase decision before identifying actual needs and success criteria.
Small and Medium Enterprises and the Cost of Technology
The matter becomes even more critical for small and medium-sized enterprises. These organizations do not always possess the resources to endure costly trials. Every amount allocated to an unsuitable system may be drawn from the budget for labor, maintenance, expansion, or product quality improvement.
It is unreasonable for these companies to feel left behind simply because they have not purchased the latest AI applications. True progress does not mean every organization uses the same technology, but rather that it chooses what suits the nature of its business.
Hidden Costs after Purchasing AI Systems
There is another aspect that does not receive enough attention: the costs that appear after signing the purchase contract.
The new system may need organized data, more efficient networks, trained employees, and continuous technical support. It may depend on an external provider that can change prices or service terms. An organization may have calculated the initial startup cost, but failed to properly calculate the costs of subsequent years.
Therefore, feasibility studies should go beyond the contract value to cover the full cost of operation, including training, maintenance, information security, and the ability to transition to another system when needed.
The Responsibility of Governments in Evaluating Digital Transformation Projects
Governments and public institutions bear an additional responsibility. The resources they allocate to technical projects are tied to services people depend on, and a project’s success must not be reduced to its launch ceremony or the number of systems released.
Smart public service should be more precise, accessible, and equitable, allowing individuals to challenge a wrong decision and reach an official who can correct it. Otherwise, technical speed may turn into a new way to complicate old problems.
Conscious Investment Instead of Rushing into Technology
This argument does not call for reducing investment in artificial intelligence or delaying digital transformation. Falling behind in acquiring modern capabilities carries significant economic costs of its own. Rather, it is a call for conscious investment that does not confuse ambition with impulsiveness.
In some cases, the wisest decision may be to start with a limited project, test its results, listen to the employees who use it daily, and then expand if it proves successful. There should also be the courage to admit that certain applications have failed to achieve their goals, and that modifying or halting them is better than continuing to fund them simply to protect the institutional image.
From Billions in Spending to Results People Can Touch
Just as financial markets have started asking major technology companies what they will gain from billions in spending, institutions, governments, and corporations naturally need to ask themselves the exact same question.
Economies do not grow simply by possessing more advanced tools; they grow when those tools help produce greater value with fewer resources, deliver better services, or open up new opportunities for work and investment.
Artificial intelligence has become part of the economic future, but that future should not be written solely in the language of announced figures. It must also be written in the language of results that people can see.
Billions spent on promises may make big headlines, but the billions that turn into production, service, and knowledge are what build a stronger economy.
About the Author
Abdullah Saeed Al Habsi, MIoL
Member of the Institute of Leadership (UK)
Writer and researcher specializing in leadership, security, heritage, risk management, and operations rooms
A.S. Alhebsi
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