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HRHeadStart #88: LLMs and Future Jobs; Hypothesis Testing
The Talent Agenda
Accenture analysis states that about 60% of our total work time involves language-based tasks like general communication, sorting/organizing information, writing reports etc. What that means is that the longer-term impact of generative AI and Large Language Models (LLMs) on the world of work is potentially huge.
The World Economic Forum recently released the report “Jobs of Tomorrow: Large Language Models and Jobs”. It is based on an analysis of over 19,000 tasks across 867 occupations, assessing the potential for automation or augmentation of these tasks through LLMs. Some highlights:
The jobs ranking highest for potential automation are Credit Authorizers, Checkers and Clerks (81% of work time could be automated), Management Analysts (70%), Telemarketers (68%), Statistical Assistants (61%), and Tellers (60%).
Jobs with the highest potential for task augmentation emphasize mathematical and scientific analysis, such as Insurance Underwriters (100% of work time potentially augmented), Bioengineers and Biomedical Engineers (84%), Mathematicians (80%), and Editors (72%).
Jobs with lower potential for automation or augmentation are jobs that are expected to remain largely unchanged, such as Educational, Guidance, and Career Counsellors and Advisers (84% of time spent on low exposure tasks), Clergy (84%), Paralegals and Legal Assistants (83%), and Home Health Aides (75%).
In addition to reshaping existing jobs, the adoption of LLMs is likely to create new roles within the categories of AI Developers, Interface and Interaction Designers, AI Content Creators, Data Curators, and AI Ethics and Governance Specialists.
The analysis identified financial services, information technology, telecom and media as industries with the highest exposure to these technologies. Interestingly, for the overall HR function, the report identifies 22% of tasks that could be automated, with significant opportunities to augment HR roles through technology (as we have noted earlier as well).
We all tend to favour information that confirms our existing beliefs a.k.a confirmation bias. This can inhibit and adversely affect decision-making.
To make better decisions in HR (and otherwise), we need ways to pressure-test our assumptions, seek new information, check our biases and use evidence to make better decisions. A good way to do this is through the process of hypothesis testing i.e. creating a statement that is contrary to your belief (or that nullifies it) and then looking for evidence to reject the nullification. Check out this short article on how to apply this approach at work.
Slow, steady progress towards big goals can often be hard to observe in the near-term. Practice, patience and perseverance are the keys to success.