Workforce Planning insights from Draup's upcoming report

Draup is currently developing a comprehensive labor market report focused on the impact of AI, which is scheduled for completion in July. Once finalized, we will circulate it to you. In the meantime, here are some of the key findings we’ve documented so far.

Summary of Key Findings for Workforce Planning

1. AI is Restructuring Work, Not Just Automating Tasks
   AI agents have evolved beyond providing assistance. They now execute tasks independently. This alters job structures from linear workflows to hybrid human-AI collaborations.
   Workforce implications: Redesign jobs around decision points and AI integration zones, rather than static duties.

2. Middle-Skill Roles Are at Greatest Risk
   While much attention is on frontline automation, the report emphasizes that mid-skill roles are more structurally vulnerable. These jobs are loosely defined and often skipped over in upskilling plans.
   Workforce implications: Clarify the purpose, scope, and training pathways for these roles before they become obsolete.

3. Expertise Formation Pathways Are Disrupted
   With AI taking over the “easy tasks,” junior employees are deprived of opportunities to learn by doing.
   Workforce implication: Develop new models—such as simulation, scenario review, and AI output critique—to enhance judgment and experience.

4. LLMs Flatten Knowledge Asymmetry
   Generative AI reduces the advantage of those who simply know more. Access to high-quality answers is now democratized.
   Workforce implication: Shift hiring and training focus from factual recall to contextual interpretation and application.

5. Human Value Centers on Framing, Ethics, and Adaptability
   AI can solve well-formed problems. But humans remain essential in defining the problem, considering edge cases, and applying judgment.
   Workforce implication: Elevate framing, ethical reasoning, and ambiguity navigation in skills taxonomies and leadership models.

6. The ROI Flywheel of Human-AI Collaboration
   Enterprises that combine automation with role redesign and capability uplift generate compounding returns: cost savings, better quality, faster learning.
   Workforce implication: Anchor transformation programs in this cycle: automate → recompose roles → elevate talent → reinvest.

As AI automates tasks, the value of human skills like creativity becomes more pronounced—but also more misunderstood. We need to define these skills with greater precision and translate them into role-specific capabilities to guide hiring, development, and performance.

Here is an example table of what Creativity means across Job families