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BusinessDay: AI’s productivity paradox

Monitoring the impact of automation and AI investments requires well-defined goals and objectives, sound technical knowledge, and high-quality data.

By Johan Steyn, 8 March 2023

Investments in machine-intelligent automation and artificial intelligence (AI) have the potential to transform businesses and unlock new levels of productivity and efficiency. However, many organisations find that these investments often do not deliver the anticipated value.

According to a study by McKinsey Global Institute, only 25% of the work done by humans can be automated using present technology. Another study by Gartner suggests that 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them. And a survey by Deloitte reported that only 38% of respondents reported that their organisation had achieved moderate to substantial benefits from their automation initiatives.

As far back as 1993, Erik Brynjolfsson from the MIT Sloan School of Management, wrote about the phenomenon that investments in information technology do not appear to necessarily lead to increases in organisational productivity.

In his famous and controversial research paper, “The Productivity Paradox of Information Technology: Review and Assessment”, the author argues it can take several years for organisations to fully integrate and optimise IT systems and that the benefits of IT may not be immediately apparent. One of the most widely cited scholars studying the economics of information systems, Brynjolfsson’s views are still relevant today, even with the emergence of new technologies such as robotic process automation (RPA) and AI.

Why do so many new technological initiatives in business fail to live up to the hype? The reasons are not difficult to grasp but are often overlooked by business leaders.

The lack of clear goals and objectives is one of the main reasons why automation and AI initiatives fail to produce the expected value. Companies may invest in these technologies without having a clear knowledge of their goals or with accurate expectations. Without well-defined goals and objectives, it can be challenging to monitor the impact of automation and AI investments and make educated decisions about where to concentrate resources.

Automation and AI technologies require high-quality data to perform properly. If the data used to train these technologies are insufficient, wrong, or biased, the outcomes could be unreliable or even harmful. Companies are required to guarantee that their data is of good quality and relevant to the task at hand.

Installing and maintaining automation and AI technology demands technical knowledge. Companies may find it difficult to recruit or hire qualified people to deploy and manage these technologies, or they may lack the internal resources to build the requisite skills. Without sufficient technical knowledge, firms may struggle to properly integrate smart technological platforms.

Automation and AI technologies frequently necessitate substantial modifications to corporate processes and workflows. Workers may oppose these changes, or they may lack the necessary skills or training to use new technology efficiently. To gain the full benefits of these investments, businesses must be prepared to effectively handle these cultural and organisational changes required.

In “The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies”, Brynjolfsson writes that “to invent something is to find it in what previously exists”. In the age of AI and smart automation, business leaders should not forget the basic principles around the value of human workers, the right choices around systems and the not-so-common sense approach to technological investments.

• Steyn is on the faculty at Woxsen University, a research fellow at Stellenbosch University and founder of


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