Traditional job forecasting often requires extensive research cycles that can take two years or more to complete. Much of this analysis depends on prior market data and expert surveys, which may already be outdated by the time the study begins. As a result, by the time forecasts are published, new technologies—particularly in AI—may have already transformed industries and disrupted job markets. This time lag creates significant gaps between forecast predictions and real-world developments, limiting the accuracy and usefulness of conventional forecasts.

We leverage on current Large Language Models (LLMs) to forecast the automation potential of a role in the Education industry.

Methodology:
We begin by converting job descriptions into a standardized set of Intermediate Work Activities (IWA) based on O*NET classifications, utilizing our proprietary universal task translator query system to ensure consistency and accuracy. Once the tasks are properly categorized, we leverage advanced AI models and platforms to conduct detailed forecasts using our proprietary methodology. These models analyze task-level automation potential by drawing on the latest AI capabilities and knowledge sources.
Our approach offers significant advantages over traditional job forecasting methods. Most notably, it delivers results much faster than conventional forecasts, allowing it to keep up with the accelerating pace of AI advances. By harnessing the advanced reasoning capabilities and dynamic search functionality of current leading LLMs, our forecasts reflect the most up-to-date understanding of AI’s evolving impact on the workforce.

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