Leveraging Generative AI for Job Augmentation and Workforce Productivity 2024

The unwritten future of GenAI in the workforce2 There remains a high degree of uncertainty about the future trajectory of GenAI in the workforce and the extent to which its potential for job augmentation and productivity growth may be realized. This section presents four different near future scenarios for how the deployment of GenAI in organizations could play out. Organizations, leaders and workers alike will need to consider these alternative futures when forming their hopes, expectations and strategies regarding GenAI adoption. “Amara’s law” states that observers tend to overestimate the short run impacts of new technology and underestimate the longer-term ones. The long-term impact of GenAI on productivity, augmentation and innovation remains uncertain. 35 While some current task- and firm-level use cases for tools like ChatGPT have shown a 40% decrease in task time and an 18% increase in quality, 36 experience has shown that it can take a long time for technology to become sufficiently widespread to affect productivity at an economy-wide scale. 37 Applying GenAI in tasks, processes and structures requires experimentation and finding and applying use cases – and this simply takes time. 38 This is also confirmed by the case study interviews in Section 3. Moreover, many tasks that humans currently perform, for example in the areas of transportation and manufacturing, are multifaceted and require real-world interaction, which GenAI is not currently able to improve upon. 39 The question is whether organizations will reach a point where massive scaling-up may take place, leading to productivity gains and job augmentation on a macroeconomic level in the longer term. Scenario thinking: navigating an uncertain future Studies of the future recognize its unpredictability and aim to anticipate and prepare for the impact of potential developments, in this case: the impact GenAI could have on job augmentation, productivity and innovation in the near future. The scenarios presented in this section are tools to navigate uncertainty and inform strategic decisions. They explore uncertainties and present possible outcomes of GenAI-induced job augmentation, productivity and innovation. These scenarios are not forecasts or idealized visions but illustrate what realistically could happen. This section presents four scenarios for the near- term future of GenAI based on future studies methods (Figure 2; see the Appendix for a more detailed description of the report ’s scenario methodology). Scenarios wer e developed through workshops and trend analysis, focusing on two key uncertainties that will shape the near future of GenAI-induced job augmentation, productivity and innovation: 1) trust in GenAI, and 2) improvements in applicability and quality of the technology. These scenarios are applicable to organizations that are exposed to GenAI, with leadership aiming to deploy GenAI, regardless of current workforce adoption or external influences. The analysis excludes situations where governmental entities may force or restrict GenAI use. GenAI-induced job augmentation and productivity growth: Two core uncertainties The first core uncertainty relates to the level of trust in GenAI, which refers to the confidence that employees and organizations have in GenAI-driven tools and their outputs. It also refers to the trust of employees in the organization, the technology provider and the government to prevent issues such as privacy breaches, exploitation and information leaks. As outlined in Section 1, trust is crucial for GenAI adoption and is influenced by different factors. The second core uncertainty relates to whether the applicability and quality of GenAI will continue to improve in the short term or remain the same. High applicability means GenAI tools are practical and useful across various use cases and industries. High quality means the outputs are accurate, reliable and have a low percentage of errors. When combined, these qualities make GenAI valuable and dependable. Improved applicability and quality would lead to new use cases, user models and functionalities, allowing GenAI to (further) augment and automate tasks, enhance jobs, create new industries and serve as a foundation for future technologies. 11