Leveraging Generative AI for Job Augmentation and Workforce Productivity 2024

Executive summary Generative artifi cial intelligence (GenAI) has the potential to drive significant improvements in workforce productivity at the level of tasks, organizations and economies. Delivering those gains depends, among other things, on the deployment of GenAI to augment jobs, i.e. to partially perform tasks in such a way that technology effectively supports or enhances human capabilities through human-machine collaboration. Drawing on a review of existing research, scenario analysis and case studies of early adopters, this report proposes a framework for action that fosters job augmentation. Global context What sets GenAI apart from previous developments in artificial intelligence is its ability to widen access to the use of AI and eliminate the barrier of specialized knowledge. GenAI has the potential to contribute to economic and productivity growth by creating effi ciencies through freeing up working time spent on lower-value tasks to engage in higher value-added activities. Moreove r, GenAI has the potential to augment human workers by enhancing their skills and capabilities, thereby increasing their productivity and enabling new and diverse forms of value creation. However, GenAI’s potential to enhance productivity may vary across countries, industries, and organizations. To effectively deploy GenAI in the workforce, organizations must also address a range of factors including trust, skills, culture and the demonstration of business value from GenAI investments. Scenario analysis With such a fast-moving technology, it is hard to predict how even the relatively near-term future will play out. To help think through the possibilities, it is useful to think in terms of scenarios based on two key uncertainties that will shape the near future of GenAI-enabled job augmentation, productivity and innovation. 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 as well as employee trust in their employers, technology providers, and governments. 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. Any combination of these two dimensions is possible, leading to very different outcomes. A world where trust is low—either because GenAI does not progress significantly from today; o r, conversely, because of concern over its fast progress and potential for job disruption—is one which misses out on the opportunities of productivity gains and job augmentation. A world of high trust but limited improvements in GenAI contains significant risks; while one where both trust and quality and applicability improve in tandem is likely to see the biggest gains in workforce productivity and job augmentation. Insights from early adopters The four near future scenarios outlined provide a useful background to insights derived from interviews with more than 20 early adopters from a wide range of industries and regions across the world. These organizations are pursuing GenAI partly out of confidence in productivity gains. They also believe that GenAI will impr ove the quality of work, and the experience of their employees. A dif ferent motivation is a desire to pre-empt the potential disruption of their business. The organizations quickest to adopt GenAI in their workforce are those that could be described as ‘data-driven’. They emphasize the need to develop and test GenAI solutions in small groups before rolling them out to the rest of the organization, allowing for issues to be identified and addressed before wider implementation. They also put significant emphasis on risk management, including designing processes that have ‘humans in the loop’, forming internal committees or councils that establish internal rules, standards, and frameworks and assess use cases and consider sustainability implications of using GenAI at scale. To identify the potential for workforce productivity gains and job augmentation, early adopters combine both bottom-up and top-down approaches, with str ong support from leadership and reliance on the innovative capabilities of their workforce. It is in day-to-day practice where most use cases are identified and developed. According to this perspective, the most promising use cases are those embraced and championed by employees themselves. Framework for action Combining insights from the scenarios and lessons learned from early adopters, the report proposes an actionable framework for promoting job augmentation and workforce productivity growth with GenAI. Focusing on factors within an organization’s control, it is designed to be useful both to organizations just starting out on their GenAI workforce deployment journey as well as those seeking to scale existing efforts. 4