Redefining Workloads for the AI-Driven Future: A Framework for Strategic Workforce Transformation

This week, we worked on a model to understand the impact of AI at each job role level. In the future workplace shaped by AI and automation, clearly defining workloads helps organizations strategically position their human and technological resources.? Our team arrived at a framework to decompose the jobs into the following categories of Workloads.?

Defining Workload Types:

• Directive Workloads: Clearly defined, rules-based activities typically suited for automation and AI-driven substitution.
• Feedback Loop Workloads: Activities involving regular interaction and refinement based on outcomes, typically requiring human judgment and oversight.
• Learning Workloads: Workloads focused on acquiring new knowledge, insights, or skills, relying heavily on human-driven exploration and creativity.
• Validation Workloads: Processes involving checking accuracy, compliance, and quality assurance, requiring human expertise and contextual awareness.
• Workload Iteration: Continuous cycles of refinement, improvement, and adaptation, strongly dependent on human intervention, creativity, and strategic judgment.
• Negligible Workloads: Activities that add limited value or can be phased out without significant operational impact.

Applying this framework to the role of a Company Secretary, the table looks like the following. (One aspect we realized is that we often take our focus away from key roles like company secretary, which are becoming critical and rapidly transforming workloads, so we attempted to run the models on Company Secretary job)?

Leveraging the workload-based methodology—categorized into Directive, Feedback Loop, Learning, Validation, Workload Iteration, and Negligible workloads—we translated the workloads into Skill categories (to clearly focus on the skills that are becoming critical now and in the future -see quadrant below)?

In roles like Company Secretary, the advisory and Governance aspects are becoming critical as AI attempts to automate data-driven aspects of the job.

Summary:? Draup’s model of looking at the role played by AI to impact different categories of workloads is a highly productive approach to transforming the future of work