Predictive analysis based on artificial intelligence, data mining, and statistical modelling generates hypotheses and creates forecasts about future outcomes. This groundbreaking technology is now being applied to human resources management to build predictive models.
In theory, you can anticipate employee behaviour, identify critical skill gaps, and uncover opportunities to develop career pathways. But how does this play out in practice? This guide cuts through the hype to explore the current and future applications of AI in addressing skills needs.
Why has anticipating skills needs become a strategic priority?
According to estimates from France Travail, 85% of the jobs that will exist by 2030 do not yet exist today. HR leaders must adapt to the emergence of new roles by relying on robust forecasting tools. Generative AI and predictive models harness unstructured data, generate hypotheses, and enable the automated interpretation of complex scenarios.
Ever-shorter cycles of change
Your organisation must empower its people through effective skills management. Yet the impact of new technologies is rapidly reshaping many roles, demanding swift adaptation to remain competitive.
Anticipating skills needs has become essential to optimise expertise and actively engage every employee in their own development and career progression.
Predictive analysis strengthens your continuous learning strategy by enabling each employee to align with the defined skills framework.
The need for agile HR to stay competitive
Human resources management depends on anticipating the needs of both your organisation and its people. Economic and social pressures, combined with the speed and complexity of skills obsolescence in the labour market, are intensifying the need for agile HR.
Strategic workforce and skills planning now works hand in hand with predictive analytics tools to meet this challenge. The goal is clear: to train employees and develop emerging skills in response to evolving market demands.
Limitations of traditional talent management approaches
The main challenge HR teams face with traditional knowledge management methods? The complexity of approaching skills as dynamic elements and integrating them into job profiles and competency frameworks.
Most organisations maintain closed competency frameworks. These are typically built on an analysis focused on the knowledge, skills, behaviours, and formal roles of a small minority of individuals.
With predictive analysis, your competency framework becomes dynamic and multi-dimensional. It supports the identification of transferable skills—beyond age, qualifications, or career history.
AI and predictive analysis: how are they being used today in skills management?
Predictive analytics tools are already transforming talent acquisition and management processes within HR. These AI applications harness the power of data to uncover at-risk knowledge and forecast team performance.
Identifying critical and emerging skills
Roles within organisations are becoming increasingly hybrid and are now broken down into specific tasks. They draw on multiple specialised skills which, without a clear knowledge transfer strategy, risk being lost through retirements or internal mobility.
AI uses machine learning algorithms to analyse vast amounts of data and detect weak signals that point to this very situation:
- Labour market trends
- Workforce movements, team needs, and turnover rates
- Sector developments in response to new technologies
With the right analysis, an industrial company can anticipate a significant need for robotics and automation skills.
Predictive analytics models also make it possible to forecast behaviours, the ability to acquire emerging skills, and development potential. This capability gives you a competitive edge from the point of hiring and helps shape your entire HR strategy.
Bringing together HR, market, and performance data to guide decision-making
Many leaders acknowledge that their teams lack the skills needed to navigate upcoming transitions. In the industrial sector, managers specifically highlight a lack of preparedness for the impact of digital transformation. Predictive analysis provides a powerful springboard to close these identified gaps.
Skills management, combined with predictive analysis, helps steer training efforts and provides a 360° view of your organisation’s knowledge and capabilities. Data visualisation, supported by a skills tracking tool, then enables you to define the nature of recruitment required.
Current use cases: internal mobility, development plans, strategic workforce and skills planning
AI is transforming internal mobility through:
- Greater objectivity in skills assessment
- Personalised career development recommendations
- Clear insight of internal opportunities for your talent
The technology behind predictive analysis removes traditional biases and democratises access to skills development. Algorithms focus solely on know-how and potential, helping to create a fairer environment.
Predictive analytics around skills make decision-making easier in areas such as recruitment, internal mobility, reskilling, and development planning. They form a key part of strategic workforce and skills planning.
Towards augmented skills management: what the future holds
Deloitte’s 2023 Human Capital Trends study highlights both the impact and the challenges of applying predictive analytics to skills management. While 93% of respondents acknowledge the importance of a skills-based approach, only 20% of organisations consider themselves ready to meet this challenge.
At the same time, 63% of tasks in organisations are carried out in teams or through projects that are not reflected in job descriptions. In the age of AI, skills management provides HR with a valuable framework to better structure internal policies.
The future contributions of generative AI and predictive models
Artificial intelligence will bring a new level of personalisation to professional learning. It will streamline the building of individual career paths by aligning employees’ aspirations and preferences with organisational goals.
AI will enhance skills assessment through:
- More reliable analysis of different proficiency levels
- Stronger consolidation of manager evaluations
- Instant identification of critical gaps
- Targeted suggestions for corrective action plans
AI will provide better tools to evaluate training and adjust upskilling strategies. According to Gartner, 68% of managers already observe improved alignment between their objectives and the aspirations or skills of their teams thanks to AI.
How can you prepare your organisation to adopt these tools?
HR leaders are already anticipating a competitive disadvantage if their organisation fails to adopt and implement AI solutions quickly. However, HR directors must prioritise a structured approach to integrating predictive analytics tools—built on a clear and consistent skills framework:
- Establish collaboration between the Head of HR Technology and the legal, IT, and compliance teams to define a clear implementation framework
- Involve Learning & Development experts to fully understand the real potential of AI
- Deliver a detailed report to the HR leadership team and the CHRO
- Clarify fact from fiction—AI will not replace talent, even if it outperforms them in certain current tasks
- Align predictive AI use cases with your organisation’s strategic objectives
We recommend adapting this framework to the realities of your organisation to achieve stronger performance outcomes.
Key challenges to address: ethics, data reliability, and change management
The success of augmented skills management depends on alignment with leadership, high-quality data, and transparent ethics.
Choose a gradual, well-supported approach by rolling out your solutions in stages. Ongoing training for your teams and incorporating feedback from the field will help improve adoption.
Adopt an ethical approach by upholding the principles of responsible AI and protecting personal data. Predictive analysis must always be guided by human oversight when it comes to key decisions. Led by senior leadership, this human-centred approach ensures the sustainable integration of artificial intelligence into HR processes.
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