The framework for optimal division of labor
We systematically analyze each stage of work to identify the optimal division of labor between humans and machines—enhancing performance, reducing costs, and expanding capacity.
What's needed to perform the job
How the work gets done
What the job produces
What's Needed to Perform the Job
The expertise, training, and competencies required to perform the job effectively.
Understanding and adherence to laws, regulations, and organizational policies.
The ability to convey information, persuade stakeholders, and build relationships.
The cognitive ability to analyze situations, identify solutions, and make decisions.
The physical, digital, and informational resources needed to complete work.
How the Work Gets Done
The process of evaluating options and choosing courses of action.
How data and information are collected, analyzed, and transformed into insights.
The generation of novel ideas, solutions, and approaches to work.
How tasks are sequenced, dependencies managed, and work orchestrated.
The ability to adjust to new situations, learn from experience, and improve performance.
What the Job Produces
The tangible products, documents, or artifacts produced by the work.
The assistance, guidance, and problem resolution provided to stakeholders.
The choices made and guidance provided as outcomes of the work.
The measurable results and value created by the work.
The sharing of expertise, insights, and learning with others.
Apply this framework to your organization with detailed job decompositions that identify specific Human + Machine collaboration opportunities.
| Feature | Towers (WTW) GGS | Mercer IPE | Korn Ferry (Hay) Method | Jo |
|---|---|---|---|---|
| Methodology | Points-based; analytical and consistent approach to defining job size across an organization. | Proprietary, customizable point-factor system; assesses value created within organizational context. | Widely accepted point-factor approach; measures and compares jobs based on contribution to the organization. | Decomposes jobs into Input → Throughput → Output stages to evaluate Human vs. Machine suitability for augmentation. Focuses on optimizing the division of labor. |
| Key Factors Evaluated |
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| Purpose / Outcome | Establishes job grades based on size/level, often tied to compensation structures. Typically uses up to 25 grades, adaptable based on org size/complexity. | Establishes job value within context, often for compensation and internal equity. Flexible and tailored sizing. | Establishes job grades based on contribution, widely used for compensation benchmarking. Adaptable to varying company sizes. | Not a sizing/grading methodology. It's a way to look at work objectively to identify where Automation, AI, RPA, or Robotics will add value. It assesses the expected friction during implementation and informs strategies to preempt it. Applicable to any job. |