Leveraging Generative AI for Job Augmentation and Workforce Productivity

The report examines the potential of generative AI to augment human capabilities and increase productivity, while highlighting that success depends more on people and culture than technology. It presents four future scenarios based on trust and technology quality, shares lessons from over 20 early adopters, and proposes a two-stage 'Enable and Engage' framework to help organizations scale GenAI deployment responsibly.

• GenAI is expected to significantly reshape the global labour market, with employers anticipating that advancements could affect up to 40% of total global working hours within the next five years.
• The report distinguishes between job automation, where GenAI fully performs tasks, and job augmentation, where technology supports or enhances human capabilities through human-machine collaboration to improve productivity and worker well-being.
• Productivity gains from GenAI are most evident in routine and repetitive administrative work; however, many organizations lack a clear plan for how to utilize the time freed up by these efficiencies.
• Successful GenAI deployment is primarily driven by human factors rather than the technology itself, requiring a cultural shift, trust, and the active participation of the workforce.
• Early adopters who are 'data-driven'—possessing established data quality, infrastructure, and governance—are able to develop and deploy GenAI solutions more quickly.
• The report identifies four near-future scenarios based on the uncertainties of trust and technology quality: 'High Hopes' (high trust, current quality), 'Broken Promises' (low trust, current quality), 'Lost Opportunities' (low trust, expanding quality), and 'Shifting Gears' (high trust, expanding quality).
• Organizations are managing GenAI risks—such as data breaches, bias, and ethical concerns—by implementing internal councils and maintaining 'humans-in-the-loop' to validate and verify outputs.
• Environmental sustainability is currently a secondary consideration in GenAI deployment, as most organizations acknowledge the energy-intensive nature of LLMs but have not yet developed specific strategies to address it.
• The proposed 'Framework for Action' consists of two iterative stages: 'Starting' (piloting and testing) and 'Scaling' (broad implementation), organized around 'Enable' (vision, infrastructure, governance) and 'Engage' (culture, skills, use case management) themes.

APA

World Economic Forum (2024). Leveraging Generative AI for Job Augmentation and Workforce Productivity. https://reports.weforum.org/docs/WEF_Leveraging_Generative_AI_for_Job_Augmentation_and_Workforce_Productivity_2024.pdf

Chicago

World Economic Forum. Leveraging Generative AI for Job Augmentation and Workforce Productivity. 2024. https://reports.weforum.org/docs/WEF_Leveraging_Generative_AI_for_Job_Augmentation_and_Workforce_Productivity_2024.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Leveraging Generative AI for Job Augmentation and Workforce Productivity |date=November 2024 |url=https://reports.weforum.org/docs/WEF_Leveraging_Generative_AI_for_Job_Augmentation_and_Workforce_Productivity_2024.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2024leveraging, author = {{World Economic Forum}}, title = {{Leveraging Generative AI for Job Augmentation and Workforce Productivity}}, institution = {World Economic Forum}, year = {2024}, month = nov, url = {https://reports.weforum.org/docs/WEF_Leveraging_Generative_AI_for_Job_Augmentation_and_Workforce_Productivity_2024.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }