The Productivity Puzzle: Gains and Growing Pains

Estimates of AI’s productivity impact are varied. A recent analysis by the Center for Economic Policy Research (CEPR) suggests a modest 0.29% productivity increase in firms that have deployed AI over the last three years, though nearly 90% haven’t yet detected any measurable gains. More optimistic projections from EY-Parthenon estimate a 1.5% to 3% productivity boost over the next decade, particularly in technology, finance, consulting, legal, and accounting sectors. Employee perceptions paint a more positive picture: a Workday report (Computerworld) indicates that almost three-quarters of Spanish professionals feel more productive with AI, saving an average of 1-3 hours per week. However, this time savings isn’t without caveats. A significant portion is spent reviewing and validating AI-generated outputs, and a concerning trend is emerging: a potential for “silent burnout” among younger workers (ages 25-35) who are increasingly becoming “full-time AI auditors.”

This shift in workload isn’t necessarily about reducing effort, but rather redefining it. As Adolfo Pellicer, VP and General Manager of Workday Iberia, explains, “AI is radically changing the type of tasks and how we approach them, transforming our way of working towards a model of high efficiency.” He emphasizes that the employee’s role is evolving from executor to supervisor or “editor-in-chief,” a transition that requires critical thinking, not simply administrative burden. But what happens when the validation process becomes more time-consuming than the original task? Is the promise of AI-driven efficiency simply shifting the workload, rather than alleviating it?

The Intensification of Work and the Risk of Demotivation

The potential downsides extend beyond mere time expenditure. Reports highlight a risk of work intensification, where accelerated paces and self-imposed increased workloads lead to burnout, cognitive fatigue, and ultimately, diminished quality. ActivTrak’s State of the Workplace report points to a potential for demotivation stemming from a failure to fully realize AI’s potential, noting that organizations have invested heavily in reducing workload but often neglect resource reallocation.

Magalí Riera, Director of the Master’s Degree in People Management, Talent and Digital Transformation at UNIE University, echoes this sentiment. “We’ve moved from a model of direct task execution to one of supervision and improvement of content created by the tool.” She stresses the importance of “ethical and responsible use,” beginning with verifying the accuracy of AI-generated content. This validation process, she argues, is contributing to the “silent burnout” as professionals lose their direct involvement in the creative and execution phases of their work, potentially leading to disengagement and a loss of confidence in their own abilities.

Iñigo Fernández, CEO of Technology by PageGroup Spain, emphasizes the importance of proper AI training. “The results depend heavily on the initial information you provide, and many people are using AI without fully understanding how to use it effectively.” He notes that AI is proving particularly helpful in automating repetitive tasks in non-tech sectors, requiring minimal review, while technology roles demand more extensive validation. He hasn’t yet observed widespread mental fatigue, but acknowledges the potential for it to emerge as roles shift. He points to a concerning trend: senior employees increasingly taking on both the AI prompting and review roles, potentially hindering the development of junior staff.

Mitigating the Negative Effects of AI Integration

Successfully implementing AI requires not only a strategic plan but also proactive measures to address potential drawbacks. Fernández stresses the importance of communicating the value of human contribution to teams. “If employees perceive their work as purely administrative review without added value, frustration is likely to increase.” The focus should be on the productivity gains that allow employees to dedicate time to more strategic and fulfilling tasks.

Riera adds that professionals shouldn’t become mere revisers of AI-generated content. They should combine AI assistance with critical tasks that require direct human participation to maintain intellectual stimulation. Pellicer emphasizes the need for ongoing training: “By investing in people and teaching them to use AI effectively, we transform what could be a source of stress into a strategic resource that makes work more enriching and sustainable.” He concludes, “Transparency about how algorithms are used is the only thing that calms human uncertainty.”

Ultimately, the success of AI integration hinges on a fundamental shift in mindset. It’s not about replacing human workers, but about augmenting their capabilities and empowering them to focus on higher-value tasks. But can organizations truly achieve this balance, or are we on a path towards a future where efficiency comes at the cost of employee well-being?

Frequently Asked Questions About AI and the Future of Work