EY Vice Chair Calls Human Judgment a Key Skill in the AI Era — Fortune

EY Global Vice Chair for Alliances and Ecosystems Julie Teigland wrote in an op-ed for Fortune that as artificial intelligence capabilities for companies grow, skills that technology does not possess are becoming increasingly important. Among them, she named setting goals, assessing context, and accountability for decisions.

According to Teigland, companies may fail to realize up to 40% of the potential productivity gains from AI because of gaps in investment in employee development. She noted that training programs often focus on the technical use of AI tools but pay insufficient attention to evaluating results and the ability to intervene when a system is operating incorrectly.

AI does not replace human accountability

The author believes that technologies are effective at performing repetitive tasks and identifying data, but people must set goals, give information meaning, and respond to changing circumstances. She emphasized that using AI should improve work efficiency rather than transfer to algorithms decisions that require human judgment.

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Teigland also cited a World Economic Forum survey, according to which analytical thinking is the most sought-after core skill among employers: about seven out of ten called it essential. According to her, employers expect workers not to blindly delegate tasks to systems, but to be able to verify and question AI results when necessary.

Collaboration between people and systems

In the EY representative's view, hybrid workflows should not be built as a rigid sequence in which AI first completes a task and a person merely receives the result. Instead, people and systems should be involved at different stages of a shared process.

As an example, Teigland cited beverage manufacturer Lion. According to her data, cooperation with partners made it possible to speed up responses to customer inquiries by 75% and improve the operating cost metric in the HR function by approximately 30%. Daikin, for its part, used AI for code generation and automated testing during the implementation of an ERP system, while employees monitored exceptions and high-risk scenarios. Teigland estimated that this accelerated project delivery by approximately 30%.