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The adoption of Artificial Intelligence in companies is no longer a technological choice, but a profound organizational transformation. Often, the initial focus is on implementing tools, but this is only the “entry ticket to take part in the race”.
The real challenge for HR is to evolve the way people work in the right direction in order to 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. Artificial Intelligence, in fact, allows people to exercise a broader scope of autonomy in their work. It is therefore people and teams who translate purpose, strategies and decisions into value.
Potentially, Artificial Intelligence frees up Autonomy, which is the first Dominant Dynamic in organizational transformation. This dynamic urgently recalls the second Dominant Dynamic, namely the spread 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 in order to capture the potential value of this technology.
Challenges and critical issues: beyond technical skills
The change brought about by Artificial Intelligence is rapid and creates the 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. But within this data lies a counterintuitive reality: workers exposed to AI do not necessarily need technical specializations such as machine learning, but rather upskilling toward more heterogeneous skills.
OECD research also shows that, in the professions most impacted by technology, the demand for cognitive, digital and emotional skills has increased by around 8 percentage points.
What is needed is not technical “AI literacy”, but development for “AI-supported work”
The critical issues are not only educational but also psychological, because resistance to the adoption of this technology must be overcome. Internal resistance often stems from fear over job security and from distrust toward systems perceived as “black boxes”. In addition, issues such as algorithmic bias and data privacy remain central challenges in gaining employee buy-in.
Effective upskilling: between Skills Inference and Organizational Change
To close these gaps, upskilling must be treated as a strategic and systemic change, not as a simple catalogue of courses.
One set of solutions has leveraged the very potential of Artificial Intelligence in Learning and Upskilling projects that are both participatory and personalized. Here are some examples:
- Skills Inference (MIT Sloan): The use of AI itself to analyze employee data and map current skills and gaps against future roles with millimetric precision.
- Modular training and Microlearning: Learning broken down into 5-15 minute sessions ensures 76% higher information recall compared to traditional courses. Blended learning (AI combined with in-person moments; AI-personalized content and simulation-based learning) increases skill acquisition rates in some cases by 32%, while making it possible to reach proficiency levels in almost half the time.
- 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 assessment tool, the company achieved a 90% adoption rate for its learning platform.
- Harvard responded to the challenge by launching “Future Proof with AI”, a structured program that recorded completion rates three times higher than the 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 figure for HR is that the greatest gains, up to 30%, concern less experienced workers, suggesting that AI can act as an extraordinary accelerator for those at the beginning of their professional journey.
However, several sources, including McKinsey and MIT Sloan, emphasize that the success of AI Adoption also depends on leadership sponsorship and on a clear connection between training and internal mobility. Successful companies are those that “rewire” their operating models by placing people at the center. Because behind every AI system, there will be 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: the advantage will lie with those who are able to design upskilling programs that combine domain expertise and fluency in guiding 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 carried out with the support of AI. (also read Talent Management and AI)
This is the approach we promote at Base 9: through our AI Based Challenge© platform, we enable organizations and individuals themselves to observe concretely how people interact with AI, assessing not only the outcome, but the entire behavioral process.