Nearly half of workers using AI unsure of productivity gains
A study conducted by The Upwork Research Institute, has revealed that AI is contributing to employee burnout, by increasing their workload. The research involved interviews with 2,500 global C-suite executives, full-time workers, and freelancers. While 96% of the executives feel AI is enhancing productivity, 77% of workers using AI tools think their workload has gone up. Notably, 47% of workers using AI reported they don't know how to achieve the expected gains in productivity.
Rising productivity demands lead to employee burnout
The escalating productivity demands are causing burnout among full-time employees. One in three full-time employees stated they are likely to quit their jobs in the next six months, due to feeling overworked and burnt out. The majority of global C-suite leaders (81%) admit they have increased demands on their workers in the past year. This has resulted in full-time employees feeling burned out and struggling with their employer's productivity expectations.
Freelancers excel amid AI-driven productivity demands
Contrary to full-time employees, freelancers are successfully meeting and often exceeding productivity demands, outperforming their counterparts. C-suite executives reported improved well-being and engagement among freelancers. They also noted that freelancers have doubled outcomes for their business in areas like organizational agility (45%), quality of work being produced (40%), innovation (39%), scalability (39%), revenue and bottom line (36%), and efficiency (34%).
Upwork Research Institute proposes strategies for AI integration
Kelly Monahan, Managing Director of The Upwork Research Institute, stated, "Our research shows that introducing new technologies into outdated work models and systems is failing to unlock the full expected productivity value of AI." She suggests a fundamental shift in how leaders organize talent and work to fully utilize AI's potential. Monahan proposed strategies including investing beyond the tech stack, bringing in outside experts for AI projects, rethinking productivity measurement, and moving toward skill-based hiring and workflows.
