Leveraging Generative AI for Job Augmentation and Workforce Productivity 2024

more advanced adopters, the focus for more complex GenAI use cases was around the internal organization. Among the more than 20 organizations interviewed, only four were found to be currently using LLMs in their services or products for customers. Combination of bottom-up and top-down The introduction and further rollout of GenAI require strong support by the leadership of an organization. However, due to the versatility of this technology, which can easily be applied to various tasks and processes, senior leaders also rely on the innovative capabilities of their workforce. It is in day-to-day practice where most use cases are identi fied and developed. According to this perspective, the most promising use cases are those embraced and championed by employees themselves. Careful scaling Based on conversations with various early adopting organizations, it is evident that many have moved beyond the initial experimentation phase. One of the key lessons learned by interviewees is the importance of not rushing the implementation process, which some consider to be a potential pitfall. They emphasize the need to develop and test GenAI solutions in small groups before rolling them out to the rest of the organization. This approach allows for any issues or shortcomings to be identified and addressed before wider implementation, preventing users from losing interest if things don’t work as expected. Additionally, attempting to move too quickly may create resistance by employees, as further discussed below. Advantages of data-driven organizations The organizations quickest to adopt GenAI in their workforce are those that could be described as “data-driven”. These organizations have been working on establishing robust data quality, data infrastructure, data governance and security measures for years. For example, they already implemented a data lake, a centralized place where vast amounts of business data are stored. This data lake serves as the foundation for processing and utilizing data for analytical purposes. While they may not necessarily be faster at identifying use cases for GenAI, they already have all the necessary components in place to develop and deploy quickly. With their existing data infrastructure and governance practices in place, these organizations could readily capitalize on the potential of GenAI for their workforce and business needs. Some interviewees also worked closely with (technology) partners to foster the development of GenAI within their organizations. Companies could also leverage these partners’ expertise and resources to further enhance and refine GenAI solutions for their workforce. Fear of disruption The unknown future is a driver in and of itself for the use of GenAI. Organizations interviewed recognize that they cannot predict what the future will hold, but they anticipate that GenAI will drive significant workforce changes, nonetheless. They want to pre-empt the potential disruption of their business by their current competitors or newcomers, so they start working on GenAI implementation, sometimes even without having a clear understanding of what it will ultimately yield. These organizations assume that the learning curve they are currently experiencing will pay off later, as they aim to develop a competitive advantage over those who deploy GenAI to their workforce later.Workforce productivity As discussed in Sections 1 and 2, it is currently difficult to determine the extent to which the use of GenAI may lead to significant productivity gains at the macroeconomic level. However, at the organizational level, some organizations do report such gains. For instance, one organization stated that it was able to automate requests that typically took two weeks to less than a few minutes. These gains are particularly evident in routine and repetitive (administrative) work. This aligns with the findings of previous research, which concluded that the potential for productivity improvement lies primarily in professions with this type of work. 42 It is notable that quite a few organizations among the interview sample do not have a clear plan for what their workers should do with freed-up time. Most of them do not currently measure the Insights on drivers of GenAI adoption: More than productivity gains3.2 17