Introduction
Many companies have established structures for skill management in recent years. Competency models have been developed, skill matrices filled in, and tools implemented. On paper, it all looks promising. In practice, however, a different picture emerges: outdated data, low usage, and limited integration into development, planning, or project work.
The issue does not lie in the concept itself, but in its implementation. Traditional skill management is often handled in isolation, disconnected from core business processes, and rarely aligned with strategic goals. What’s missing is clarity, accountability, and tangible value for both employees and decision-makers. This is precisely where Advanced Skill Management comes in – with the aim of not just recording skills, but actively developing them, managing them, and making them usable across the entire organization.
In working with HR leaders, organizational development experts, and operational teams, we repeatedly encounter the same five structural barriers. Those who identify and address these challenges effectively can turn Advanced Skill Management into a true strategic tool, rather than a mere data collection exercise.
Skill Data Is Outdated
A common scenario: the skill matrix was completed a year ago. Since then, much has changed. New tasks, training sessions, evolving project roles. Yet the entries remain untouched. No one feels responsible for maintaining them, managers have little incentive to update data, and HR lacks access to reliable, up-to-date information.
This is exactly where Advanced Skill Management comes into play. It requires recurring processes, trustworthy data sources, and clearly defined responsibilities. Regular self-assessments, combined with feedback cycles or development conversations, help keep the data current. Even more effective is the automated enrichment of skill data through project tools, learning platforms, or insights from day-to-day collaboration.
Johnson & Johnson provides a strong example. The company uses AI-powered inference models to automatically identify skills based on employees’ role histories, activities, and learning behavior.
In a well-functioning Advanced Skill Management system, competencies are not only recorded. They are continuously developed, updated, and made visible across both the individual and organizational level.
Skill Management Is Not Connected to Daily Work
Many systems exist in parallel to reality. Skill data sits in a tool but has no link to role profiles, project assignments, or development initiatives. Employees see no benefit. Managers don’t either.
What helps is an integrated approach. Advanced Skill Management connects skill data with strategic HR processes such as career development, project planning, and performance management. Only when this information is actively used in everyday decision-making does it gain relevance and acceptance.
DHL offers a strong example with its internal career marketplace. The company actively uses skill data to match employees with relevant tasks, projects, and roles. The platform helps identify internal opportunities and encourages mobility based on actual competencies.
Advanced Skill Management creates value by ensuring that skills are not managed in isolation but are directly linked to real decisions and development opportunities.
Competency Frameworks Are Too Rigid
Centralized skill catalogs are designed to create consistency and comparability. In practice, however, they often become bottlenecks. Business units feel underrepresented. Emerging topics cannot be mapped. Maintenance becomes increasingly complex, while the overall value decreases.
What helps is a modular approach. A well-designed Advanced Skill Management framework defines a stable core of competencies while allowing flexibility for individual departments. Teams should be able to suggest and define their own skills without disrupting the overall structure. This way, the system remains both adaptable and aligned, without sacrificing clarity or consistency.
Skill Data Is Not Being Used
Even when skill data is well maintained, its potential often goes untapped. The information exists but is rarely used for strategic planning or operational decision-making. As a result, it loses its purpose.
What helps is visibility and clear use cases. HR teams need insights for succession planning. Project managers need skill matching to build effective teams. Leaders need transparency to guide individual development. A centralized dashboard or targeted reporting can make it easier to ask the right questions and find actionable answers.
Advanced Skill Management ensures that skill data is not just stored but actively used across the organization to support planning, staffing, and growth decisions.
There Is No Real Development Perspective
When skill management focuses only on data collection, it quickly begins to feel like a control mechanism. Employees are surveyed but not supported. This undermines both motivation and trust.
What helps is a development-oriented mindset. Advanced Skill Management must go beyond analysis and enable growth. Clear learning paths, relevant training opportunities, and visible internal career options all contribute to a system that supports individual progress.
Anyone who makes skills visible must also provide meaningful ways to strengthen them. Only then does the system move from static documentation to active development.
Case Example: How Johnson & Johnson and DHL Use Advanced Skill Management Strategically
Two international companies show what modern skill management looks like in practice. Their approaches differ, but they share one key element: they treat skills as a strategic asset for managing the organization.
Johnson & Johnson: A Future-Oriented Skill Ecosystem
In recent years, Johnson & Johnson has implemented a company-wide skills framework focused on strategic planning, individual development, and internal mobility. At the core are 41 “future-ready skills” used as a reference across all business units. These include competencies such as data literacy, automation skills, and digital process management.
What sets Johnson & Johnson apart is its use of AI to keep skill data continuously up to date. Through inference technology, the company automatically identifies skills based on employees’ role histories, project assignments, and learning activities. This data is not collected in isolation but is deeply integrated into the company’s HR technology infrastructure.
Managers gain real-time insight into the current capabilities of their teams. HR is able to analyze internal development potential more precisely. Employees receive personalized learning paths and career options based on their profiles. As a result, succession planning becomes more targeted, and internal mobility improves significantly.
Sources:
hyer – Johnson & Johnson’s Matchmaking Approach to Upskilling Employees
MIT CISR – Resolving Workforce Skills Gaps with AI-Powered Insights
Financial Times – AI tools and the future of skills
DHL: Skill Data at the Core of Internal Mobility
DHL takes a slightly different approach. At the center is a digital career platform that allows all employees to access internal job opportunities, projects, and learning programs. Matching is based on employees’ individual skills and interests, which are regularly updated and aligned with the requirements of open roles.
Managers can fill open positions with internal talent, without relying on external applications. At the same time, employees receive personalized learning suggestions that help them work toward their career goals. As a result, skill management is not perceived as a control tool, but as a real opportunity for personal growth.
The results are measurable: internal placement rates increase, employee turnover decreases, and recruiting efforts are significantly reduced. HR uses skill data proactively for workforce planning, succession management, and upskilling — not as a byproduct, but as a central planning tool.
Source:
Financial Times – Employers look to AI tools to plug skills gap and retain staff
What Other Companies Can Learn from This
Both examples demonstrate that Advanced Skill Management is not simply a digitalization project. It requires clarity around strategic goals, a practical and scalable framework, and the willingness to actively leverage skill data. The key lies in the consistent alignment of technology, organizational structure, and culture.
Companies looking to take the next step should ask themselves:
- Are we using skill data strategically, or are we just collecting it?
- Which roles, projects, or processes truly benefit from skill transparency?
- How regularly and reliably do we update our skills data?
- Do we offer internal development paths that are skill-based and actively supported?
The answers to these questions determine whether skill management remains a routine administrative task – or becomes a powerful lever for strategic talent development.
Conclusion
Advanced Skill Management begins where traditional models reach their limits. It connects competencies with roles, processes, and strategic decisions. It creates transparency, drives development, and empowers individuals to take ownership of their growth.
The technical foundation matters, but it is not the deciding factor. What truly counts is the mindset: skills are not just data points to be collected – they are a strategic resource to steer the organization. Companies that embrace this shift gain more than information. They gain clarity, agility, and long-term resilience.