The past decades have witnessed major AI technology developments. The profound social and economic changes brought about by the deployment and advancement of AI applications in producing goods and services, transportation and logistics, or service provision have triggered an intense debate on the present and future impact of AI on society. 

UNESCO remarks that AI is a deeply transformational technology: research shows that it could contribute USD 13 trillion to the global economy by 2030, increasing the global GDP by about 1.2 % annually. 

The innovative landscapes of AI emerging from these studies reveal similar patterns; the largest increase in AI took place in the last five years and is dominated by China, Japan, South Korea and the United States. Although AI developments are mainly concentrated in the telecommunications and software services, and electronics manufacturing sectors, there are clear signs that almost all other industries are increasingly exploiting the opportunities of a new degree of automation brought about by AI technologies. 

To understand the role of AI in labour productivity, researchers at the Eurasian Business Review conducted a study on “The impact of artificial intelligence on labour productivity.”

While there is a consensus among researchers about the rising trends and transformative nature of AI, the speculative interpretations about its economic impact and value for productivity are less conclusive, echoing the concerns synthesized in the popular Solow’s paradox “You can see the computer age everywhere but in the productivity statistics”. A more positive stream of literature claims that the disruptive content of AI technology, leveraged through the automation of tasks, reduction of uncertainty, the recombination of existing and generation of innovations, will have a productivity-enhancing impact. 

AI and productivity 

The recent upsurge of innovations in technologies related to AI and robotics spurred an intense debate on their consequences on interlinked social outcomes such as growth, productivity, employment, earnings and inequality. 

According to a research by OECD, jobs with the highest risk of being automated make up 27% of the labour force on average in OECD countries, with eastern European countries most exposed.

Classical economic theories predict that, ultimately, economic growth depends on technological change and innovation.  

More recent theories, such as skill-biased technological change, posit that technological innovation may lead to wage polarization through relative increases in the demand for skilled workers with respect to unskilled ones and possible job losses through the automation of tasks. 

Data and variables 

A Mckinsey study states that Generative AI is a step change in the evolution of artificial intelligence. As companiesrush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions. 

Using firms’ patent portfolios to measure the stock of prior knowledge for new knowledge production relies on several considerations. Many previous studies resume R&D expenditures as a measure of firms’ effort to innovate that presumably translates into new knowledge.  

Yet, R&D expenditures are typically aggregated measures retrieved through firms’ balance sheets, having thus no purpose in studies aiming to focus on specific types of knowledge inputs, as this study does for AI. As with any other general-purpose technology, AI is transversal, i.e., cutting through many scientific disciplines and used in an increasing number of sectors. 

Impact of AI 

As the ongoing debate on the impact of technological development in AI and robotics on productivity offers contrasting predictions, empirical studies can offer important insights.  

There is a presence of technological opportunities for economic activities and types of businesses typically characterized by comparatively low capital intensity, organizational complexity and patenting activity.  

Mckinsey report states that using generative AI in just a few functions could drive most of the technology’s impact across potential corporate use cases. In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. 

Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue. This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies. 

Smaller, more agile AI-patenting firms may have been able to readjust faster and introduce AI-based applications in their production processes at a scale allowing the creation of a significant impact on productivity.  

The larger, more diverse patent portfolio of non-AI technologies, by contrast, still dominates the productivity-generating process of the larger and more complex firms, which could take longer to fully exploit in its value chain and upskill the existing workforce to benefit from their AI inventions fully.  

The low economic maturity of AI technology is also confirmed by the finding that a significant effect is only observed in the more recent years of the sample.  

Most firms patent more in non-AI than in AI technologies. Yet, there are clear signals of technological specialization among SMEs and service firms. More than a quarter of the SMEs and more than 22% of firms operating in services only record AI patenting activities and have no patents in other fields.