Imagine a world where every employee’s unique talents are perfectly aligned with their role, where organizations seamlessly adapt to changing skill demands, and where career growth is natural.  When implemented correctly, skills-based hiring and skills-based talent management can do that.  In this email, we have compiled critical points for HR leaders to consider.  I hope you find this email helpful.  I am also attaching a short pdf for your reference.

1.0 Two Aspects of Skills Architecture

Two major components exist within this context: skills-based hiring and skills-based management. While both initiatives are still in their early stages at many companies, champions of skills-based management appear to be slightly ahead of those focused on skills-based hiring.

2.0 Lack of skill granularity

Skills like Agile Frameworks need more depth. Businesses, especially in software, need more specific skills like code refactoring. (in software agile context). Draup excels in addressing this gap with precise skill differentiation.

3.0 Absence of workloads

Business contexts and Business Models need to be understood to define the relevant workloads. For example, Airbnb’s customer service is unique and requires specific skills. Unlike traditional hotels, Airbnb had to design systems for managing interactions between independent hosts and guests, emphasizing trust and communication.

4.0 Unclear Role of AI

Many skills architectures don’t consider AI’s full impact. AI’s value isn’t only in technical skills; it also enhances human roles. For instance, Nintendo’s approach, as described by Shigeru Miyamoto, focuses on creating products that combine human creativity with AI.

5.0 The need for HR to understand the overarching tech

HR leaders need to grasp how the data lake behind HCM systems functions. Collaborating with people analytics teams can help streamline and save time during planned integrations.

6.0 Skills Sequencing

Skills sequencing and simulations will be valuable in this context. For example, when a business requires Python, Flask, and FastAPI, our systems should have the intelligence to recognize that if someone has Python and Flask skills, they can quickly learn FastAPI.

7.0 Integrated Skills-Based Management and Skills based hiring

Skills-based talent management efforts should align with skills-based hiring practices, as this coordination will enhance the overall effectiveness of talent acquisition.

8.0 Peer benchmarks need to be across different Industry clusters

Today, it’s essential to compare across different industry groupings. For example, a pharmaceutical company operates as both a manufacturing and a tech company. Establishing the right clusters for comparison is critical for identifying emerging skills.

9.0 Skills Taxonomy can’t be extracted from HCM systems

Due to how HCM systems are structured, obtaining a straightforward extract of all skills is impossible. It may be beneficial to complement this with external data sets.

10.0 Frontline skills focus on productivity, while knowledge work emphasizes both productivity and innovation

Productivity is the main focus for frontline jobs, while productivity and innovation are essential for knowledge jobs.

11.0 Integration with People Analytics is beneficial

As Gen AI advances, we can leverage the skills infrastructure to create highly targeted, updated, and future-focused job descriptions.

12.0 Levels and its Impact

Understanding skill maturity across levels will greatly justify the need for distinct levels. Metrics such as Peer-level ratios will be helpful

13.0 Reskilling Time by Skill

Reskilling time varies by skill complexity: basic skills may take 1–3 months, while specialized skills require over a year. This framework helps in aligning training with role requirements.

14.0 Automation Impact by Skill

Certain skills that were complex in the past have now become easier. For example, Predictive Maintenance Setup used to be a very complex skill that has become easier due to Automation and AI impact.

15.0 Complete Set of Attributes

A blend of external and internal metrics will make the Enterprise’s skills architecture journey more impactful.

• Occupation
• Job Family
• Job Role
• Workloads and Sub workloads
• Core Skills
• Digital Products
• Soft Skills
• Skills Sequencing
• Neighborhood Skills
• Peer Level Congruency Ratio
• Complexity Congruency Ratio
• Digital/AI Disruption Index
• Talent Size
• Hiring Difficulty Indices
• Fully Futuristic Skills

Summary:  Draup has outlined the critical steps for successful Skills Architecture Journey