In recent years, the scene has become familiar in many organizations: employees mentally detached from work despite being busy, managers burdened with endless decisions, and senior leadership complaining about teams that "have room to maneuver" but hesitate in the face of bold choices. The paradox is that this occurs in an era of abundant tools—from meeting platforms to analytics and artificial intelligence—as if technology accelerates the channels but does not guarantee the quality of the content that passes through them. This is precisely what Kate Niederhofer (Chief Scientist and Head of Labs at BetterUp) attempted to establish with numbers in her article published by the World Economic Forum on February 4, 2026: the decline of "human skills" in the labor market was not a fleeting issue after 2020, but a sharp drop between 2019 and 2021 that has not returned to pre-pandemic levels even after nearly five years.

What is meant by "human skills" here? They are not "niceness" or "communication skills" in a superficial sense, but operational capabilities that enhance the value of work when it becomes complex: creativity and problem-solving, curiosity and lifelong learning, resilience and agility, and social influence and leadership. These skills manifest when ready instructions are absent: a new problem, a demanding client, a delayed project, or a technology that introduces a different way of working.

Economically, the important point is that the decline is not merely "motivational rhetoric"; it has a direct performance cost. BetterUp states that it has tracked trends in these skills and work performance among over 351,000 individuals between 2019 and 2025. The result: declines in "work effectiveness" across multiple dimensions, reaching about 6% in some aspects. To put it in perspective: if today's employees were evaluated by 2019 performance standards, nearly 75% would fall into the "lower performance tier" compared to their current position, meaning the benchmark itself has changed because overall capability has eroded.

Why did this happen?

The text does not reduce the cause to a single factor; rather, it describes a complex pressure: post-pandemic stress, economic anxiety, geopolitical tension, hybrid work dispersion (when teams are spread across multiple locations, times, and channels), increased burnout, changing generational expectations of work, and the declining role of education and training in building these capabilities. It adds a sensitive factor in 2026: “mental offloading to technology”—when we transfer part of our thinking to tools (including artificial intelligence), we may gain momentary speed, but we lose the “muscle” of practice in the long term.

The second paradox: the more these skills decline, the more valuable they become. The World Economic Forum links VUCA environments (which are volatile, uncertain, complex, and ambiguous) to the need for continuous curiosity and learning for workers to keep pace with the acceleration of change. In the age of artificial intelligence specifically, these capabilities become a “competitive advantage” because they are not easily replaceable; rather, they amplify the impact of technology when present: artificial intelligence handles cognitive routine, but humans set the direction, make judgments, and connect meaning to context.

Here emerges a dilemma the article calls “the visibility problem”: despite the importance of human skills, organizations invest less in them because they do not clearly appear in measurement and hiring systems; they are rarely mentioned in job descriptions and are not treated as fundable requirements. The article notes that they appear in only about 2% of job postings and that they declined during the pandemic years by about 4% and have not “bounced back” yet. Among these skills, curiosity and lifelong learning rank as the weakest competency among workers according to employer assessments—perhaps because building them is more challenging: BetterUp indicates that half of those with weaknesses in this skill may need about eight months of “coaching” to reach a basic level of competency, and achieving broad acquisition may extend to about 24 months.

Who is “taking the hit” the most? According to the article: Individual Contributors—those who work without direct management responsibility—have experienced the largest drop in creativity and problem-solving, curiosity and learning, resilience, and leadership/influence. The loss here does not stop at their performance; it rebounds on managers: a manager is expected to “produce” and “build people” at the same time, with larger and more geographically dispersed teams. In this context, the article mentions the phenomenon of Workslop: low-effort, low-quality outputs generated by artificial intelligence do not actually push work forward but transfer the burden of “filtering, verifying, and rephrasing” to the receiving party.

What is the solution?

The message here is critical: ready-made courses and quick workshops may open discussions, but they rarely lead to sustainable behavioral change. The alternative proposed by the article is a adaptive institutional system to restore “human transformation” within the organization—reaching everyone, especially individual contributors, and being long-term, tailored to roles and context, with “measurement intelligence” that captures signs of decline early. The article cites a peer-reviewed study on coaching interventions involving 391 participants that showed significant improvement in human skills, along with broader evidence indicating that the same interventions across more than 90,000 individuals helped in recovery post-pandemic and even surpassed 2019 levels in some groups.

How do we read this as an economy, not just as human development?
Read “skill” as capital: it is subject to shocks, requires time to re-accumulate, and directly affects productivity. Then ask: do we have a clear measurement of these capabilities, or are they “invisible” and thus unfunded? Do we rely on tools that accelerate outputs or on a system that enhances the quality of thinking? And finally: when we decide to invest in artificial intelligence, do we invest equally in the human who will judge the outputs and turn them into decisions? Because technological transformation without human transformation may buy speed… and sell capability.