Adapting to AI's Influence on the Global Workforce

We have been reflecting on and developing a strategic thesis on the impact of AI and Gen AI on the global workforce. The work done by Gen-AI: Artificial Intelligence and the Future of Work is quite helpful to initiate the thinking (Cazzaniga, M., Jaumotte, F., Li, L., Melina, G., Panton, A. J., Pizzinelli, C., Rockall, E. J., & Tavares, M. M. (2024). Gen-AI: Artificial intelligence and the future of work.)

Almost 40% of the global employment is exposed to AI. However, exposure to AI does not necessarily mean job elimination. In several cases, productivity from AI can further cement the human role of AI and drive up wages. An example job in this area is a UI/UX experience modeling job. Even though there are a lot of tools, new workloads like intention mapping drive up human value and will further drive up wages.

Here is an interesting graph from the IMF report. ( you will see high exposure complimentary, high exposure – low complimentary and low exposure)

In this context, there are several questions in the leaders' minds.

1. How are we trending in terms of headcount and wage growth, and what do these trends indicate about our organizational health and competitiveness?
2. Are we effectively developing skills that foster innovation, and how can we measure the impact of these skills on our overall innovation capabilities?
3. What should be our organization's optimal shape and size to ensure efficiency, agility, and scalability in response to market demands?
4. How should we evaluate the impact of the convergence of various technologies on our business processes, competitive advantage, and future growth opportunities?
5. Do we have a strategic and effective location strategy that aligns with our business objectives, operational needs, and market presence?
6. Do we have the right vendors and partners in the ecosystem to partner across the projects?

To develop a comprehensive framework to address these questions, we need to create the following assets:

1. Job Deterioration Model: Understand how AI is impacting workloads across Jobs
2. Future Framework of Jobs: Develop a framework that considers the convergence of technologies such as AI, robotics, and AR/VR.
3. External Skills Mapping for Hiring: Identify and map skills that are difficult to hire for specific jobs.
4. Location Frameworks by Functions:? Developing framework for Cost Optimization and Innovation centric global locations
5. Learning Frameworks for Training: Create resources to support skill enhancement during transitions.
6. Project Frameworks: To enable a Pixelated workforce and leverage gig work and other forms of labor

Summary:?The impact of AI on jobs will differ based on the type of job and geographic location. Companies urgently need to create multiple labor models to address critical workforce issues.