Future of Innovation: Research
Thought and practice leadership
AI job disruption forecast
How will artificial intelligence (AI) change the way we work?
The Future of Innovation Lab x Lee Kuan Yew Centre for Innovative Cities has begun co-forecasting with AI to advance its job forecasting methods. This is advantageous over traditional job forecasting methods, which often require extensive research cycles that take up to years to complete.
We convert job descriptions into a standardised set of Intermediate Work Activities (IWA) based on O*NET classifications, utilising our proprietary universal task translator query system to ensure consistency and accuracy. Once the tasks are properly categorised, we leverage advanced AI models and platforms to conduct detailed forecasts using our proprietary methodology.
In this blog series, we share insights from our job forecasts. Feel free to reach out to find out more and collaborate.
Issue 5: Data Scientist
It is important to future-proof data science careers by focusing on human-centric skills such as domain expertise, stakeholder communication, and ethical oversight—areas where AI is still far from matching human judgment.
Issue 4: Accounts Assistant
While automation will likely streamline much of the transactional workload for Accounts Assistants, human skills will remain vital to maintaining relevance in an AI-driven workplace.
Issue 3: Executive, Quality Service Management
The role of Executive, Quality Service Management is expected to undergo significant transformation. Our insights underscore the importance of proactive workforce planning.
Issue 2: Digital Marketing Manager
Our insights suggest that while digital marketing managers are likely to see automation reshape analytical and technical functions first, uniquely human skills like strategic planning, creativity, and stakeholder communication will be key to staying relevant in an AI-driven future.
Issue 1: Conventional Job Forecasting vs LLMs-Powered Models
Traditional job forecasting often requires extensive research cycles that can take two years or more to complete, creating significant gaps between forecast predictions and real-world developments, limiting the accuracy and usefulness of conventional forecasts. In this issue, we leverage current Large Language Models (LLMs) to forecast the automation potential of a role in the education industry.