Measuring AI’s productivity impact is challenging without better data on time savings versus time taxes.
In Short
Measuring AI’s impact on productivity is difficult due to data scarcity and varying adoption rates among companies. Focusing on how AI saves time can clarify its productivity effects, necessitating improved data collection methods to relate time use to economic outcomes.
Measuring the impact of AI on productivity is challenging due to a lack of data.
The phrase “time is money” resonates in the context of AI, as time savings are vital for assessing technology investments.
AI’s effect on total factor productivity, which reflects value generation from resources, is a concern for both business leaders and policymakers amid stagnant productivity growth.
While new AI models emerge regularly, evidence of efficiency gains in economic statistics remains elusive. Surveys indicate that many companies are experimenting with AI, but adoption rates vary significantly across regions.
New technologies
Historically, the productivity benefits of new technologies can take time to appear in national statistics, illustrated by the example of electrification in American manufacturing.
Furthermore, measuring productivity is complex in sectors that do not produce standardised goods, making it difficult to quantify output quality in areas like legal or consulting services.
Focusing on time spent by workers and its implications can provide a clearer picture of AI’s productivity effects. Historical improvements in productivity stem from workers accomplishing tasks more swiftly.
AI has the potential to streamline time-consuming processes, prompting businesses to examine how employees currently use their time and identify areas of inefficiency.
Economists should develop better methods for data collection, such as tracking technology usage, to better understand how daily time allocation correlates with productivity value and AI’s economic impact.
