Predictive analytics, grounded in artificial intelligence, data mining and statistical modelling, generates hypotheses and creates predictions about future outcomes. Applied to human resources management, it makes it possible to build predictive models to anticipate talent behaviours, identify skills under pressure and detect professional development opportunities. Myth or reality? This article takes stock of the current and future uses of AI for skills management.
Why Anticipating Skills Needs Has Become a Strategic Priority
85% of roles on the horizon of 2030 do not yet exist, according to France Travail estimates. HR managers must adapt to the emergence of new professions through reliable anticipation tools. Generative AI and predictive models use the analysis of unstructured data, generate hypotheses and enable the automatic interpretation of complex scenarios.
Increasingly Short Development Cycles
Skills management aims to develop employees and anticipate the evolution of their professional needs. The impact of new technologies does, however, accelerate the transformation of many roles and hasten the obsolescence of skills. Anticipating skills needs becomes indispensable for optimising the level of expertise and involving every employee in their own development.
Predictive analytics fits into this logic: it sustains the continuous training strategy by enabling every talent to update themselves in line with the established competency framework.
The Need for HR Agility to Remain Competitive
Human resources management involves anticipating the needs of both the organisation and its employees. Economic and social challenges, combined with the rapid pace of skills obsolescence on the labour market, are increasing this need for HR agility.
Forward-looking skills and employment planning (GPEC) now works alongside predictive analysis tools to meet this challenge. The aim is to train employees and develop emerging skills in the light of market transformations.
The Limitations of Traditional Talent Management Approaches
The primary challenge of conventional Knowledge Management methods is the difficulty of approaching competency in a dynamic way and integrating it into frameworks and job descriptions. The majority of organisations maintain closed competency frameworks, based on an analysis focused on the knowledge, behaviours and formal roles of a minority of individuals.
Through predictive analytics, the competency framework becomes dynamic and multivalent. It promotes the identification of transversal skills, beyond the anchors of age, qualifications or career path.
AI and Predictive Analytics: What Concrete Uses Exist Today in Skills Management?
Predictive analysis tools are already transforming the processes of talent acquisition and management in the HR domain. These applications harness the power of data to reveal knowledge at risk of disappearing and predict team performance.
Identifying Skills Under Pressure and Emerging Skills
Organisational roles are becoming hybrid and segmenting into responsibilities. They mobilise several specific competencies which, in the absence of a clear transmission policy, risk disappearing with retirements or internal mobility.
AI uses machine learning algorithms to explore large volumes of data and detect the weak signals corresponding to this situation: labour market trends, workforce movements and turnover rates, sectoral developments in the face of new technologies. A manufacturing organisation can thus anticipate significant needs for robotics and automation skills through the right analysis.
Predictive models also make it possible to predict the behaviours, learning capabilities and development aptitudes of employees. This capability provides a competitive advantage from the moment of recruitment and makes it possible to orientate the entire HR strategy.
Cross-Referencing HR, Market and Performance Data to Guide Decisions
Several business leaders acknowledge the absence of skills within their teams to navigate the transitions ahead. Managers in the manufacturing sector highlight a lack of preparedness for the consequences of the digital transition. Predictive analytics offers concrete levers for closing the gaps identified.
Skills management coupled with predictive analytics orientates training and provides a 360° view of the organisation's intangible capital. Skills-tracking tools and data visualisation then make it possible to define the nature of the recruitment to be carried out and to adjust development plans.
Current Use Cases: Internal Mobility, Development Plans, Workforce Planning
AI improves internal mobility through greater objectivity in competency analysis, tailored professional development recommendations and a clear view of internal opportunities. Technology eliminates classic bias and democratises access to skills development: algorithms focus on know-how and potential, creating a fairer environment.
Predictive analyses facilitate decision-making in the areas of recruitment, mobility, retraining and development plans, within the framework of workforce planning (GPEC).
Towards Augmented Skills Management: What the Future Holds
Deloitte's Human Capital Trends 2023 study puts into perspective the impact and challenges of predictive analytics applied to skills management. 93% of respondents recognise the importance of the competency-based approach, but only 20% of organisations consider themselves ready to meet this challenge. At the same time, 63% of organisational responsibilities are carried out in teams or in the form of projects not mentioned in the job description.
The Future Contributions of Generative AI and Predictive Models
Artificial intelligences will bring unprecedented personalisation to the approach of professional training. They will smooth the construction of individualised careers by combining the aspirations and preferences of talent with organisational objectives.
AI will strengthen the assessment of skills with a more reliable analysis of different levels, better consolidation of management appraisal, the instant identification of critical gaps and the suggestion of corresponding corrective plans. According to Gartner, 68% of managers already see a better match between their objectives and the aspirations or skills of their teams thanks to AI.
How to Prepare Your Organisation to Integrate These Tools
HR managers perceive the risk of competitive disadvantage without the adoption of AI solutions. A structured approach makes it possible to integrate predictive analysis tools within a clear framework. Several steps contribute to this: initiating a collaboration between the HR technology manager and the legal, IT and compliance teams to define the framework; involving Learning & Development experts to identify the real potential of AI; producing a detailed report for the leadership team; aligning the use cases for predictive AI with the organisation's strategic objectives. It is also important not to lose sight of the fact that technology will not replace talent if it becomes more capable than them at their current tasks.
The Challenges to Overcome: Ethics, Data Reliability, Change Management
The success of augmented skills management depends on its alignment with leadership, the quality of the data and transparent ethical standards. A progressive, staged approach, accompanied by continuous team training and the taking into account of field feedback, improves the adoption of tools.
An ethical approach respecting the principles of responsible AI and the protection of personal data is essential. Predictive analytics must always draw on human oversight for key decisions. Championed by leadership, this human-centred approach ensures the lasting integration of artificial intelligence into HR processes.
