According to the World Economic Forum, by 2030, technology is expected to displace 92 million jobs while creating 170 million new ones. On paper, the balance is positive. Yet this projection comes with a critical condition that is often underestimated: 39% of today’s core skills will change within the same timeframe. This reality does more than underscore the urgency of continuous learning: it reveals a deeper shift already underway. AI has moved well beyond the copilot paradigm.
In advanced organizations, AI systems now execute workflows, trigger decisions, and orchestrate processes with punctual human intervention. The human role increasingly operates at a supervisory layer: setting intent, validating outcomes, managing risk, and redefining objectives as conditions evolve. This fundamentally alters how talent must be developed. In an environment shaped by rapid AI evolution, the capability that matters most goes far beyond tool mastery. What becomes critical is cognitive adaptability: the ability to reason with systems that evolve continuously, operate probabilistically, and introduce non-deterministic outcomes.
This shift forces a redefinition of learning itself. Traditional enterprise training models assume a stable future state, a set of skills that can be acquired and applied over time. AI invalidates that assumption. Organizations now operate in a prefigurative environment, where no fixed skills map remains relevant for long. In this context, mental plasticity becomes a competitive advantage, enabling organizations to overcome organizational lag and continuously realign processes, incentives, and mindsets at the same pace as the technology.
To address the growing gap between AI capability and organizational readiness, Globant designed AI Talent Shift: a structured learning and adoption framework created for organizations operating in AI-rich environments. Rather than focusing on tools, AI Talent Shift aligns human, technical, and cultural dimensions to accelerate real-world adoption. It is built to help organizations redesign how people learn, collaborate, and make decisions alongside increasingly autonomous systems.
As Mayra Botta, Learning Manager at Globant, explains, “We are not just learning how to use a tool. We are facing a change in mindset, in attitude, in how we relate to uncertainty and what comes next.”
Turning Cultural Readiness into Measurable Adoption
Many AI transformation efforts prioritize strategy, architecture, and tooling. While these elements are essential, they are insufficient on their own. Advanced AI environments require an operating system that enables experimentation, rapid feedback, and human oversight at scale.
AI Talent Shift was designed precisely for this challenge. Drawing on Globant’s experience deploying AI across complex organizations, it provides a repeatable model for accelerating adoption, one that treats learning as a continuous, cooperative process rather than a one-time intervention. As Javier Scher, SVP Technology and Head of Education AI Studio at Globant, notes, “AI should be understood as a cognitive infrastructure. The challenge is not access to the technology, but how organizations reorganize decision-making around it.”
Critically, success cannot be measured solely by participation. In talent management, the most relevant metric is application, not completion. AI Talent Shift, therefore, prioritizes learning and adoption metrics that reflect real organizational impact:
- Applicability (ROL): Measures how participants apply what they learned in real work situations, directly connecting learning to execution.
- Relevance and Suitability (PA): Evaluates whether content is perceived as practical, current, and immediately applicable to daily work.
- Completion (COM): Acts as a signal of engagement, motivation, and the effectiveness of the learning experience.
- Experience and Satisfaction (NPS): Captures overall perception and advocacy, key indicators of sustained adoption.
Together, these metrics shift the conversation from training delivered to capability built, a distinction that becomes increasingly critical as AI reshapes operating models.
AI Talent Shift in Practice: Key Learnings from the Field
The applicability of this approach has already been tested beyond internal environments. In partnership with CCIFA (the French-Argentine Chamber of Commerce), Globant hosted a live webinar focused on AI Talent Shift and mindset change at scale. The discussion surfaced several consistent insights across organizations and industries:
- AI maturity is uneven, but mindset gaps are universal. Even organizations with advanced AI deployments struggle to scale impact without cultural and cognitive alignment.
- Instrumental training plateaus quickly. Teaching tools without reframing decision-making and accountability leads to short-lived gains.
- Psychological safety is an execution enabler. Teams that feel safe experimenting with AI adopt faster and surface value sooner.
- Human oversight is a design choice, not a fallback. High-performing organizations intentionally define where AI executes and where humans intervene.
- Adoption accelerates in community. Learning becomes more durable when it is shared, discussed, and reinforced collectively.
As Mayra summarized during the session, “Artificial intelligence has stopped being a trend and has become transversal to all roles and all industries,” adding that “the logic of learning a single tool and feeling set for years no longer works.”
The Next Phase of AI Transformation
Looking ahead, the direction is clear. AI will continue to evolve. Roles will continue to reconfigure. The true differentiator will not be access to technology, but the ability to institutionalize adaptability, turning mindset into organizational capability.
For advanced organizations, the next phase of AI transformation is no longer about experimentation. It is about making adaptability repeatable, measurable, and scalable. And that work starts now.
Globant’s Education AI Studio helps organizations redesign learning for AI-native environments, enabling AI adoption to become a scalable organizational capability. Through frameworks like AI Talent Shift, the Studio accelerates AI upskilling via collaborative, cohort-based learning, delivering measurable impact within weeks and preparing teams for AI-driven ways of working.
