Leveraging Generative AI for Job Augmentation and Workforce Productivity 2024

by 50% for one-third of their job tasks.10 As respondents interviewed as part of the research for this report highlighted, to realize these potential productivity gains it is important to capture time saved as value at an organizational level. Moreove r, GenAI has the potential to augment human workers by enhancing their skills and capabilities, ther eby increasing their productivity and enabling new and diverse forms of value creation.11 For instance, GenAI may augment human capabilities in creative tasks, though it does not currently surpass human creativity on its own.12 Research also suggests that GenAI may help narrow productivity gaps between lower- and higher-skilled workers.13 GenAI’s potential to enhance productivity may vary across countries, industries and organizations. At a country level, more developed economies may face higher disruption risk due to prevalence of knowledge work, but they are also better equipped to adopt GenAI more quickly and at scale.14 Many of these countries also face a decrease in labour supply, which may boost the demand for new technologies such as GenAI to seek efficiency improvements.15 Emerging economies may similarly benefit from productivity growth by addressing infrastructure constraints and shortages in basic digital skills. Early research indicates that GenAI may disproportionally boost productivity for workers with less experience or skill, thereby reducing entry barriers to the digital economy. 16 At an industry level, exposure to GenAI-driven task automation and augmentation varies widely across sectors, with not all industries being equally impacted or standing to benefit from GenAI. As described above, previous research has identified which tasks are most exposed to LLMs, highlighting their higher or lower potential for automation or augmentation. For example, one recent study found that software developers from three large technology firms increased the number of tasks completed by over 26% using GenAI. 17 When these exposure levels are aggregated at the industry level, it becomes evident that the impact of GenAI may vary significantly across industries. For instance, the technology and financial sectors could face substantial task automation, while the healthcare and education sectors may benefit more from task augmentation. 18 Importantly, as discussed in Section 3 of this report, productivity growth is not the only driver for organizations to deploy GenAI. Many also expect improved quality of work and better work experiences for their employees, increasing employee engagement and talent retention. Historically, slow and inconsistent adoption of AI technologies has restricted their impact and effectiveness. 19 As of mid-2024, only 12% of workers report that they use GenAI at work on a daily basis. 20 Current barriers to GenAI uptake encompass concerns related to trust, skills acquisition, change in culture and unclear business value. Trust Trust is a crucial factor that must be considered when embracing new technologies. GenAI models are sometimes referred to as “black box” systems due to the complexity of their algorithms, raising concerns about the outcomes they generate and transparency. 21 In line with this, CEOs see cybersecurity, spread of misinformation, legal or reputational damage, and increased levels of bias as primary concerns related to the adoption of GenAI. 22 In addition to bias and discrimination, workers are specifically worried about the lack of oversight, transparency, explainability and accountability. 23 To build trust and facilitate the ethical use of GenAI, there is strong demand for transparency and responsible deployment. Increasingly, organizations are implementing responsible GenAI principles to build trust in decision-making processes by improving explainability and mitigating risks. At both national and supranational levels, some territories are tightening regulations on AI to promote trust and ethical use by setting clear boundaries and enforcing accountability. For example, the European AI Act includes the “human-in-the-loop” principle, emphasizing human accountability in decision making. This principle may help to increase trust in GenAI by establishing accountability and respecting human values. 24 At an industry level, concerns have been raised about a comparatively small number of industry players holding significant influence over the development of GenAI as well as its regulatory environment. 25 Government regulation is partly aimed at creating a level playing field where all parties follow the same regulations and criteria. Nevertheless, inconsistent AI laws worldwide could also have the reverse effect, disadvantaging organizations that are operating in the most strictly regulated jurisdictions. Current barriers to scaling GenAI adoption in the workforce 1.3 9