A Task-Based Approach to Generative AI: Evidence from a Field Experiment in Central Banking

We examine how generative AI impacts productivity across the task-based framework using a field experiment at the National Bank of Slovakia. In our experiment, we randomly assign generative AI access to central bank employees completing workplace tasks that mirror the theoretical task-based framework. Our results indicate that generative AI access leads to large improvements in both quality and efficiency for the majority of participants. We find a strong complementarity between generative AI and non-routine work, both on average and for most participants. We also find some support for generative AI as both cognitive-biased and specialist-biased, though smaller in magnitude than our tests of routine-biased. While workers in routine jobs experience larger individual performance gains, generative AI is less effective for the routine task content of their work. The mismatch between generative AI’s task- versus worker-level impacts is economically large, and results from a simulation exercise suggest the organization can increase output by 7.3% by changing how workers are assigned to tasks in the presence of generative AI. Additionally, we find differences in how the benefits of generative AI relate to worker skills: low-skill workers benefitmost in terms of quality while high-skill workers benefit in terms of efficiency. Our findings provide empirical support on generative AI and task-level complementarities, with important implications for how generative AI will impact workers, organizations, and labor markets more broadly.