Samsung Electronics faces controversy over AI performance metric

Samsung Electronics is sparking debate with its new performance evaluation metric for employees utilizing generative artificial intelligence (AI). The company's DX division recently informed executives that it will unify performance measurement for its AI transition (AX) initiative under a single indicator: the Full-Time Equivalent (FTE) method. This approach aims to quantify how many individuals' work an employee can replace using AI tools.

The move has fueled concerns within the company that the new metric might serve as a precursor to workforce restructuring. Employees fear that the focus on replacing human labor with AI could lead to job cuts, rather than solely enhancing productivity. This concern is particularly pronounced as Samsung Group is reportedly the first among South Korea's four major conglomerates to fully adopt external generative AI across its group companies, potentially setting a precedent for the industry.

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We are concerned that the adoption of generative AI might be seen as a measure to determine how many employees can be reduced, rather than as a tool to enhance work efficiency.

Samsung Electronics has stated that the performance measurement for AI transition is a challenge currently being addressed by global leading companies. The company acknowledged that some aspects of the metric may be insufficient and plans to refine them based on real-world application cases. However, the internal apprehension remains that the FTE method, which primarily focuses on quantifiable output and labor input, may overlook qualitative aspects of work such as judgment, risk management, and overall work quality.

While Samsung maintains that the FTE method is intended to measure productivity improvements and is not a replacement for existing performance reviews, the company's decision to adopt a metric that directly translates AI usage into labor replacement figures has raised significant unease. The broader implications for the industry, should this evaluation system become an industry standard, are also a point of concern.

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Performance measurement for AI transition is an issue that global leading companies are currently contemplating. We plan to supplement any shortcomings in the performance measurement indicators based on field cases.