What is the Productivity Paradox?
For more than 50 years, businesses have been using computers. They have spent a lot of money on computer technologies. But did all this investment in IT make a difference? Did it make people more productive?
Are companies that invest in IT more competitive? Even if IT can help organizations, the investment still relies on a strategy made by humans. Humans are not perfect, so the strategy might not be either.
But before jumping into such ideas, let’s have a look at the concept of the Productivity Paradox first to establish a common base.
In 1991, Erik Brynjolfsson published a groundbreaking article in the Communications of the ACM that explored an intriguing phenomenon: “The Productivity Paradox of Information Technology.”
Through comprehensive research and assessment, this paper opened up valuable conversations regarding how information technology affects productivity.
Through analyzing research on the influence of IT expenditure on efficiency, Brynjolfsson determined that the addition of information technology to business had not spurred productivity at all – hence, the baffling “productivity paradox.”
Drawing on concrete conclusions from this discovery, the author’s analysis leaves us with a perplexing paradox: how can IT have invested so much effort without producing any discernible results?
Though it is still too soon to affirm that its productivity contribution has been unsatisfactory, our inability to prove otherwise remains.
By categorizing the proposed explanations, four distinct groups have emerged.
1) Inaccurately assessing outputs and inputs,
2) Delays caused by learning and adaptation,
3) Reallocating and dispersing profits,
4) Poor utilization of data and technology resources.
After studying new evidence collected in 1998, Brynjolfsson and Lorin Hitt wrote their influential paper “Beyond the Productivity Paradox”, confirming that IT does indeed provide a positive advantage for businesses.
Additionally, they identified that the true benefits of using technology were not always associated with higher output; some intangible measures like the structural organization had improved as well.
Astonishingly, the effects of IT can differ greatly between businesses.
Generative AI for Business: Blessing or a Curse?
In a past article, I went through the potential of AI technology through artistic creativity, machine learning for medical applications, data prediction, self-driving vehicles, agro technology, and food substitution, etc..
Biased AI and the lack of ethics still missing in the domain, and regulation is still not yet covering all those new aspects (such as synthetic data) at the worldwide level, but the noise made by OpenAI and its product ChatGPT raised awareness about Augmented Working.
People can already project themselves with the ability to produce more output with the same level of input which defies the productivity paradox.
AI tools will become more prevalent in the workplace from factory safety to real-time dashboards for management.
In the long run, being able to cooperate and collaborate with intelligent machines will be an indispensable skill for our careers.
I would even argue that it could help protect us from facing redundancy due to automation. Working together with smart machines is a must if we want to stay competitive in tomorrow’s economy. It adds a layer of complexity to incorporate into the productivity paradox equation.
And it’s not over, as in 2023, organizations will be responsible for their carbon emissions and must devise effective ways to reduce the environmental impact of their activities.
This represents a double-edged sword – while AI algorithms have the potential to drive immense growth, they also necessitate considerable energy usage; from edge devices all through cloud networks that need power for AI technology to thrive.
Artificial Intelligence can aid in the mission for sustainability, however, if it is merely approached as a means to profitability without proper guidance and management, then this technology could be incredibly detrimental.
One report from 2019 uncovered that just training a single deep-learning model emits over 284 thousand kilograms of CO2! For these reasons, it’s vital to adopt AI responsibly for the environment.
Takeaways for CEOs:
- Develop responsible AI guidelines dedicated to generative AI.
- Identify the use cases that with generative AI will become a source of competitive advantage.
- They need to experiment and fully understand the depths of generative AI and how it will impact jobs. Based on that they will have to redefine their operating model and their organizational blueprint.
- They will need to implement a new change management model that takes care of the professional identity of the employees