This is no longer a rhetorical question. It is a strategic one, and many organizations are already answering it—often without saying so out loud. 

The future of work did not arrive with an explosion. It crept in quietly, through pilot projects, productivity tools, and “experimental” AI initiatives. While leadership teams debated ethics and governance, algorithms were already learning how to screen candidates, write strategies, optimize workflows, and make decisions faster than any committee ever could. 

This is not just another technology cycle. It is a redefinition of value. 

A conversation with Manlio Ciralli, SVP Central Marketing at The Adecco Group, helped clarify several key dimensions of how a global leader in the labor market is approaching this shift. 

“The real challenge AI brings to organisations is not technology, it’s about leadership. 

AI will radically redefine how work is done, how skills are built, and how value is created. Used well, AI can remove repetition, reduce cognitive overload, and act as an amplifier of human intelligence. Used poorly, it risks creating distance between people, decisions, and responsibility. Ethics AI and governance are crucial to create a safer and more inclusive world. 

To me, one of AI’s most powerful opportunities lies in knowledge and employability. AI can democratize access to learning, personalize training at scale, and continuously upgrade skills, making employability a dynamic capability rather than a static credential. In a world where roles evolve faster than job titles, this matters more than ever. 

But technology must be understood for what it is: a transformation shift, rather than a new tech tool. AI is changing how people learn, how decisions are made, and how organizations operate. Treating it as “just another system” misses the point and creates several risks.The ethical question is therefore central. AI should automate what is repetitive and predictable, so humans can focus on judgment, strategy, creativity, and care. It should strengthen proximity, not erode it; increase accountability, not destroy it. 

The future of work will not be defined by how advanced our algorithms will be but by how intentionally we design the relationship between technology, people, and purpose.” 

AI is no longer something companies merely use. It is something companies are increasingly built around. This shift forces a deeper reflection on what work means, who truly matters, and how power shifts within organizations. 

In this new landscape, many companies believe they are “early.” In reality, they are already late. The question is no longer whether AI will transform work, but whether leaders will guide that transformation—or be quietly shaped by it. 

The first wave of AI—generative systems such as ChatGPT, Claude, and Gemini—revealed the power of conversation, content creation, and cognitive automation. The second wave will be deeper and far more operational. 

AI agents are systems that do not merely respond; they act. They read and rank CVs, optimize supply chains in real time, coordinate workflows, and interact autonomously with one another. These systems monitor, learn, remember, and execute multi-step plans with minimal human intervention. 

The result is not replacement, but recomposition. Work is being broken down, redistributed, and reassembled across hybrid teams where humans and AI operate together. Productivity rises, coordination costs drop, and many micro-tasks disappear entirely. 

This is why the real transformation is not technological—it is strategic and cultural. 

As AI capabilities accelerate, organizations tend to fall into three broad categories: 

• AI-native companies, built from the ground up around data, automation, and algorithmic decision-making 

• Adaptive companies, attempting to integrate AI into legacy structures—often with significant friction 

• Passive companies, waiting too long and slowly becoming irrelevant 

The winners are not those who “use AI,” but those who rethink their entire value architecture: processes, roles, governance, and decision logic. 

In practice, transformation typically unfolds in three phases: 

1. Automation of repetitive tasks 

2. Integration of AI into critical workflows 

3. Redesign of organizational structures into fluid, AI-enabled micro-teams 

Most companies remain stuck in phase one—optimizing yesterday’s processes instead of designing tomorrow’s organization. An Italian study shows that only around 25% of companies have integrated AI into their industrial plan as a strategic priority with structured governance. (Source) 

Labor market signals are already clear. Demand is rising for AI and machine learning experts, data specialists, cybersecurity professionals, fintech engineers, and software developers. At the same time, roles centered on routine activities—data entry, administrative work, basic accounting, ticketing, and operational support—are declining. 

International reports estimate that around 28% of jobs in OECD countries are at risk of automation, particularly those characterized by routine and easily codifiable skills. (Source) According to Randstad’s Global In-Demand Skills Report 2025, demand for AI and automation professionals increased by 39.6% in 2025 compared to 2024, with a persistently high vacancy rate for advanced skills. (Source) 

By 2030, 39% of core skills will change. AI literacy, data analysis, adaptability, and critical thinking are becoming baseline requirements rather than differentiators. But skills alone are not enough. (The World Economic Forum's (WEF) ‘Future of Jobs Report 2025’) 

As AI increasingly takes over execution, the human role moves upward—from production to judgment. 

The most valuable human contributions now lie in the ability to: 

• Ask the right questions 

• Interpret and challenge AI-generated outputs 

• Assess ethical, social, and strategic implications 

• Make complex decisions under uncertainty 

This is not about “learning how to use AI.” It is about rethinking professional identity. 

New hybrid roles are emerging: prompt strategists, AI ethics officers, decision architects, AI orchestrators—roles that combine technical understanding with critical thinking, synthesis, emotional intelligence, and values-based judgment. In other words, what becomes scarce is not intelligence, but meaning. 

There is, however, a quieter risk beneath the productivity gains. As AI reduces cognitive effort, organizations risk sliding into uncritical thinking: excessive trust in automated outputs, binary simplification of complex problems, and the gradual delegation of the “why” and the “how” to machines. When answers are instant, the ability to question erodes. 

Recent studies published in 2025 show that a significant share of young professionals do not verify AI-generated content, implicitly assuming its accuracy. This phenomenon—known as automation bias—is also increasingly visible in organizations, where managers rely on AI-produced reports without fully understanding their assumptions, limits, or biases, turning decision-support tools into substitutes for human judgment. (AI & Society, 2025; EDPS, 2025) 

The labor market is evolving faster than education systems can adapt. By 2027, according to the Future of Jobs Report del World Economic Forum, nearly 44% of job skills will change, forcing continuous reinvention. New learning models are emerging: corporate universities, vertical academies, micro-credentials, and lifelong learning ecosystems. The organization itself becomes a learning platform. 

In this context, HR is no longer about managing people, but about designing systems in which humans and AI can thrive together—transforming recruiters into talent strategists, HR business partners into workforce architects, and interviewers into curators of potential. And the CV? It is no longer the story. It is merely the starting point. 

AI does not pause for strategy workshops. It does not respect org charts. And it does not care how long it took a company to approve its last change initiative. Right now, decisions about work, talent, and value creation are being delegated—sometimes consciously, often by default—to systems optimized for speed, scale, and efficiency. Without intentional human design, these systems will also redefine fairness, opportunity, and the meaning of work. 

That is why the true competitive advantage is no longer technology. 
It is judgment. 

The organizations that will lead are not those that automate the most, but those that decide what should never be automated. They will invest not only in AI capabilities, but in human ones: critical thinking, ethical reasoning, and the courage to challenge machine-generated certainty. 

Across all sectors, one thing remains constant: humans will not disappear—but their leverage will change. And the leaders who understand this today will not merely adapt to the next wave. They will decide where, and how, it breaks.