Economies love numbers. Politicians use them to judge whether reforms are working. Investors look for signals about future growth. Central banks rely on them when making decisions. Among all economic indicators, productivity has always held a special place. For decades, it has been seen as the engine of prosperity. If workers produce more in the same amount of time, economies grow richer. If productivity slows, growth slows with it. That logic survived the Industrial Revolution, mass production, globalization, and the rise of computers. Today, however, it is facing one of the biggest challenges in modern economic history.
According to EY Megatrends 2026 and Beyond, the world is entering a period where productivity may be growing faster than statistics can measure it. The issue is not poor data or flawed calculations. The problem is that the way value is created is changing so quickly that tools built for the industrial age are struggling to keep up with the digital economy.
For most of the last century, productivity was relatively easy to track. Factories produced more cars, steel mills produced more metal, and farms harvested more crops. Growth could be seen and counted. It came in tons, units, and hours worked. As economies evolved, services, software, finance, and knowledge-based work became increasingly important. Even then, cracks started to appear. Decades ago, economist Robert Solow famously observed that the computer age could be seen everywhere except in productivity statistics. Today, that problem is returning on a much larger scale.
The arrival of generative artificial intelligence has accelerated this shift dramatically. In a very short time, AI systems have learned to write reports, generate software code, create images, analyze information, and support business decisions. Tasks that once required hours or even days can now be completed in minutes. In some industries, efficiency is improving so quickly that it is beginning to reshape how work itself is organized. EY points to examples in healthcare, legal services, and scientific research, where AI is improving the quality of decisions while reducing the time needed to produce them.
Yet this is where a paradox emerges. A significant share of the value being created is barely visible in traditional economic data. If an analyst can prepare a report in twenty minutes instead of four hours, productivity may have increased dramatically, but statistics do not always capture the difference. If AI helps a doctor make a more accurate diagnosis without increasing the price of treatment, GDP may show little change. If software becomes smarter, faster, and more useful while selling for the same price, official data may record no improvement at all.
This is why EY argues that a new era of measurement is beginning. In the industrial economy, the main constraint was time and physical labor. In the digital economy, time is becoming less scarce. Artificial intelligence can perform vast amounts of intellectual work almost instantly. The real limits are shifting toward judgment, creativity, context, and the ability to make better decisions. In other words, value is increasingly created through the quality of outcomes rather than the number of hours spent producing them.
As a result, the meaning of productivity itself is changing. Traditionally, productivity focused on output relative to labor input. Today, attention is moving toward accuracy, relevance, and impact. At first glance, this may sound like a technical debate among economists. In reality, it goes much deeper. If countries no longer understand where value is being created, they risk misunderstanding how their economies are actually evolving.
The scale of the change becomes clear in the forecasts. According to IDC estimates cited by EY, artificial intelligence could contribute nearly $19.9 trillion to the global economy by 2030. At the same time, global GDP could increase by around 3.5%. Impressive as these figures appear, they do not answer the most important question: how much of the value created by AI will be visible in official statistics, and how much will remain hidden? As economies become more dependent on data, algorithms, digital platforms, and intelligent systems, this question becomes increasingly difficult to answer.
Not long ago, a company’s most valuable assets were factories, machinery, and buildings. Today, data, computing power, and algorithms are becoming just as important, if not more so. They generate value, but they are far harder to measure than traditional forms of capital. This creates a growing risk that economic growth is being systematically underestimated. Productivity may be rising faster than official figures suggest, while real changes in the economy move ahead of what statistics can capture.
For businesses, this means rethinking how performance is measured. Time spent and tasks completed are becoming less useful indicators. Speed of decision-making, quality of outcomes, and the ability to adapt are becoming more important. For executives, this means shifting from managing processes to managing results. For employees, it means that uniquely human capabilities such as creativity, judgment, strategic thinking, and understanding context become increasingly valuable.
The implications may be even more significant for governments. Productivity sits at the heart of economic policy. It influences expectations for growth, tax revenues, pension systems, and long-term competitiveness. If economic statistics become less reliable as a reflection of reality, the risk of policy mistakes increases. Governments may find themselves making decisions based on indicators that no longer tell the full story.
Artificial intelligence adds another layer of complexity. On one hand, it opens new opportunities. On the other, it requires enormous computing power, energy consumption, and investment in digital infrastructure. It raises questions about education, workforce development, and how the gains from productivity growth should be shared. Some countries and companies will adapt faster than others. Some workers will gain powerful new tools, while others will face the challenge of adapting to a changing labor market.
At first glance, productivity may seem like a topic reserved for economists and statisticians. In reality, it could become one of the defining economic debates of the next decade. For years, the world has been trying to measure a digital economy with tools built for an industrial one. As long as technological change was relatively gradual, that gap remained manageable. Today, the pace of change is accelerating, and the gap is becoming harder to ignore.
The central message of the EY report is both simple and unsettling. Artificial intelligence is not only changing how companies operate. It is changing how economic value is created. And if value changes, the way we measure it must change as well. The biggest challenge of the coming years may not be AI itself. It may be whether governments, businesses, and investors can recognize where value is actually being created before the old indicators become too disconnected from reality. In the age of artificial intelligence, the battle for productivity is gradually becoming a battle to understand where economic value truly comes from.
Shyngys Yerbolat, expert at EconomyKZ.org
