- Four out of five managers say they were less likely to value human employees after using AI
- AI still requires human oversight, and many struggle to fully trust it
- Poor and even negative ROI continues to plague many
A new study by Globalization Partners has revealed that more than four in five (82%) business leaders say they are less likely to value human employees after using AI tools, relegating human workers as secondary assets to more capable systems.
This sentiment differs from the current situation, where 60% of the 2,850 senior executives surveyed agreed that humans still lead work operations, with AI merely serving as a productivity enhancer.
The difference could mean that while humans remain integral today, managers may place less emphasis on the human workforce in the future as AI gets more work done autonomously.
AI affects how much top managers value their human employees
The shift is likely to position humans as AI leaders rather than administrative workers, with two in three (69%) now spending more time than ever before monitoring and reviewing AI-generated work. The sense of lack of trust also still lingers, with only 23% fully confident in AI’s accuracy and 61% concerned about legal accuracy when using AI on sensitive documents.
But while some managers see AI as a human replacement, many others are still unhappy with their returns. Three-quarters (73%) say ROI has not met expectations, and 16% even report negative ROI. As a result, about seven in 10 executives say they are prepared to cut AI budgets this year if targets are not met.
Separately, Gartner VP analyst Padraig Byrne explained, “AI is everywhere, but most organizations are still figuring out how to monitor and trust these systems.”
Giving a sneak peek at where companies might be going wrong, the research firm suggested that those building AI agents without strong semantic and contextual data foundations are most likely to see hallucinations, unreliable output and biases.
Together, the two reports indicate that while leaders increasingly see AI as inevitable, many still struggle to trust it.
Looking ahead, Gartner calls for the implementation of model monitoring policies to deliver quite quality metrics and an increased focus on infrastructure to handle high-volume model telemetry.
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