The adoption of Artificial Intelligence in companies is no longer a technological choice, but a deep organizational transformation. The initial focus often falls on the implementation of tools, but this is only the "ticket to enter the race”.
The real challenge for HR is to evolve the way people work in the right direction to fully seize the opportunities offered by this technology.
It is important to highlight the crucial characteristic of Artificial Intelligence applied to work, namely that Artificial Intelligence “autonomizes” cognitive work. Automation comes with Agents. In fact, Artificial Intelligence enables people to operate within a broader perimeter of autonomy in their work. It is therefore individuals and teams who translate purpose, strategies and decisions into value.
Potentially, Artificial Intelligence unlocks Autonomy, which is the first Dominant Dynamic in organizational transformation. This dynamic urgently calls for the second Dominant Dynamic, namely the diffusion of qualified and up-to-date Know-How at every organizational level. In other words, Upskilling. See our article on People Strategy.
AI-enhanced autonomy at work makes it essential to define systemic Upskilling pathways if organizations want to capture the full potential value of this technology.
Challenges and critical issues: beyond technical skills
The change brought by Artificial Intelligence is rapid and creates a real risk of a significant skills gap.
Already in the 2024 report “AI and the changing demand for skills in the labour market” by the OECD, it emerges that around 40% of companies identify the lack of skills as the main barrier to AI adoption. However, these data reveal a counterintuitive reality: workers exposed to AI do not necessarily need technical specializations such as machine learning, but rather upskilling towards more heterogeneous skills.
OECD research also shows that in the professions most impacted by technology, demand for cognitive, digital and emotional skills has increased by about 8 percentage points.
What is needed is not technical “AI literacy”, understood as the development of people for 'AI-supported work.
The challenges are not only educational but also psychological, as there are resistances to adopting this technology that must be overcome. Internal resistance often stems from concerns about job security and distrust towards systems perceived as "black boxes". In addition, issues such as algorithmic bias and data privacy remain central to gaining employee buy-in.
Effective Upskilling: between Skills Inference and Organizational Change
To bridge these gaps, upskilling must be treated as a strategic and systemic transformation, not just a catalog of courses.
One stream of solutions has leveraged the same potential of Artificial Intelligence in participatory and personalized Learning and Upskilling projects. Here are some examples:
- Skills Inference (MIT Sloan): Using AI itself to analyze employee data and accurately map current skills and gaps in relation to future roles
- Modular training and Microlearning: Learning broken into 5–15 minute sessions ensures a 76% higher retention rate compared to traditional courses. Blended learning (AI combined with in-person sessions; AI-personalized content and simulation-based learning) can increase skill acquisition rates by up to 32%, while significantly reducing the time needed to reach proficiency.
- Among the most significant cases, Johnson & Johnson used AI to identify 41 “future-ready” skills across 4,000 technologists. By presenting the process as a personal development tool rather than an evaluation system, the company achieved a 90% adoption rate for its learning platform.
- Harvard addressed the challenge by launching “Future Proof with AI”, a structured program that achieved completion rates three times higher than average.
- Empirical research confirms the benefits of upskilling: a study published in The Quarterly Journal of Economics showed that the use of AI assistants in customer support increases average productivity by 15%. The most interesting data for HR is that the greatest gains (up to 30%) are seen among less experienced workers, suggesting that AI can act as a powerful accelerator for early-career professionals.
However, several sources – including McKinsey and MIT Sloan – highlight that the success of AI adoption also depends on leadership sponsorship and a clear connection between training and internal mobility. Successful companies are those that “rewire” their operating models by putting people at the center. Because behind every AI system, there is always a person guiding it to generate value.
Conclusion: a new paradigm for people development
In summary, AI is not only changing the skills required, but is redefining the very concept of talent and Upskilling: competitive advantage will belong to those who can design effective upskilling programs that merge domain competence and fluidity in driving intelligent systems. The challenge for HR is to move from theory to practice, measuring how people use AI and how talent is expressed when real tasks are performed with AI support (see also Talent Management and AI).
This is the approach we promote at Base 9: through our AI Based Challenge© platform, we enable organizations and individuals to observe concretely how people interact with AI, evaluating not only the outcome but the entire behavioral process.