Leveraging Generative AI for Job Augmentation and Workforce Productivity 2024

Appendix: Scenario methodology The methodology used to derive the scenarios in Section 2 of this report was a systematic process involving workshops and trend analysis. Below are detailed steps describing the methodology. Step 1: Taking stock of trends An extensive inventory of trends and developments influencing GenAI-induced job augmentation, productivity and innovation. Over 100 trends were identified in this phase. Step 2: Identifying clusters of trends Individuals with mixed backgrounds, experiences, seniority levels and demographics were put into teams to cluster related trends. The 100+ trends were consolidated into approximately 30 clusters. Each cluster represented a group of related trends that together had a significant impact on GenAI- induced job augmentation. Trends were then rated based on this impact and those with above-average impact were identified as “drivers of change”. Step 3: Ranking by impact and uncertainty After clustering the trends and identifying 15 key drivers of change, each driver was assessed based on two dimensions: 1) the degree of uncertainty (i.e. likelihood of occurrence), and 2) its impact on GenAI-induced job augmentation, productivity and innovation (i.e. extent and strength of influence). Each participant individually plotted these, followed by a collective discussion and debate in efforts to map out the final matrix (impact against uncertainty). Step 4: Choosing core uncertainties From the high-impact and high-uncertainty quadrant, two core uncertainties were identified. The selection of these core uncertainties was crucial as they represent the most critical and unpredictable factors that could shape the future of GenAI-induced job augmentation. These core uncertainties form the basis for the scenario matrix, which are outlined in detail in this report. Step 5: Developing scenarios Using the core uncertainties as axes, eight distinct scenarios were developed, four for the near- term future and four for the distant future. The two uncertainties for the near-term future are 1) Improvement in applicability and quality of GenAI, and 2) Trust in outputs of AI. The two uncertainties for the distant future are 1) Large-scale adoption by organizations, and 2) Extent of GenAI deployment. 28