The landscape of human resources is shifting, driven by technological advancements, changing workforce dynamics, and evolving needs around the future of work. At Draup, we conducted a study isolating about 100 recent articles on the future of work and job descriptions asking for innovative HR skills. I am attaching a short deck for your review on the emerging workloads and skills in Human Resources.

An HR professional of the future will need to be a strategic partner, a change management expert, and a digital innovator. It’s about shaping the future of work itself. Gaining consulting skills is also becoming important for Talent Acquisition and Workforce Planning professionals.

Let us say the business demands skills like Python, Flask, and Fast API; our analysis should be precise to know that if someone knows Python and Flask, Fast API can be learned easily (Skills Sequencing).  We also need empowered consulting skills to have meaningful discussions with the business in such scenarios.  The Agentic Workflows in Talent Management, Predictive Retention, Gamification in Learning, Skills-based Hiring, Skills-based Talent Management, and the role of social media in Recruiting all demand different types of skills from HR.  Establishing common grounds across the rapid evolution of ideas will also fall within HR’s responsibility (mediation).  Interestingly, some new AI models are emerging in this space called the Habermas machine.  Experiments have shown that these models are very effective in conflict resolution, and I expect HR to use them in the future.

Here are some additional frameworks that HR needs to be very aware of for its future operating model.

• Fairness and Accountability Models: AI systems designed to minimize bias and ensure fair decision-making in hiring and promotions. Example: IBM’s fairness framework.
• Ethical AI in Decision-Making: Models like AI4People’s Ethical Framework focus on aligning AI with human values such as fairness and justice in HR processes.
• Debiasing Models: AI tools, such as those developed by Microsoft, aimed at reducing cognitive and systemic bias in hiring and performance assessments.
• Deliberative AI Models: Inspired by Habermas’s ideas, these models facilitate open communication and employee feedback in the workplace.
• AI Bias Reduction Frameworks: Examples include Incentive-Compatible Fairness, ensuring AI-driven HR systems promote pay equity and unbiased performance reviews.

Summary:  Draup has outlined future HR workloads and skill sets