A People Analytics Framework for Skills-Based Workforce Transformation

This week, I had the privilege of presenting at the People Analytics event in London, where I explored a topic that’s becoming increasingly urgent: how HR leaders can extract real, measurable ROI from AI-driven transformations. While AI tools and platforms are evolving rapidly, true transformation doesn’t begin with technology—it begins with skills.

Over the past decade, organizations have invested significantly in skills-based talent systems. Yet, for many, tangible progress in areas like skills-based hiring or developing a skills architecture remains limited. Beyond a handful of well-known role transitions, we still struggle to cite credible, scalable examples, especially when it comes to non-obviousskill transitions.

This gap has prompted us to rethink the approach and distil it into a framework that can guide HR and talent leaders forward. At Draup, we believe a successful skills transformation strategy is built on four essential pillars:

1. Identify Required Skills (Now and in the Future)

Organizations must first define which skills are critical today and which will matter for now and in the future (2030 is a good framework) —mapped by job family, Job roles, and occupation. Draup supports this with deep, real-time labor market insights, helping HR teams anticipate shifts and prepare ahead.

2. Assess Existing Skills in the organization 

The next step is understanding your internal talent inventory. This means going beyond titles to analyze employee skill sets—whether through self-assessments, manager input, or external data. Draup plays a supporting role in this  this by leveraging external profiles to infer internal employee capabilities at scale.

3. Determine workforce transition potential

This is often the most complex step. Identifying which employees can transition into future roles—and how—requires a nuanced, industry-specific understanding. For example, while biopharma professionals working on small-molecule discovery may be close to large-molecule R&D, the path isn’t straightforward. Draup’s peer role analytics and transition data help uncover these non-linear pathways, bringing clarity to mobility planning.  For example, in industrial manufacturing, CNC machine operators may seem well-positioned to move into robotic automation roles, but the shift requires a solid foundation in programming and systems integration, not typically part of their current scope.  There are many examples like this.

4. Prioritize Micro-credentials

Strategic workforce planning often underestimates the power and value of micro-credentials in identifying targeted learning.  SWP should partner with L&D on this as this is very much an intelligence effort.   Certifications and micro-credentials offer high-ROI opportunities to bridge critical gaps. Draup helps organizations pinpoint which micro credentials truly matter, minimizing credential overload while maximizing impact.

This framework is designed not just to structure workforce transformation efforts but to ground them in real data and actionable insights. I look forward to continuing this conversation with many of you and welcome the opportunity to share how Draup is partnering with leading organizations on this journey.