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ABSTRACT
This pilot study presents an innovative artificial intelligence (AI) model to predict lawyers’ appraisal ratings in a law firm. Methodology development was based on an 11-years database comprising multiple descriptors from 229 lawyers. The AI model builds upon law firms’ tournament, simulating lawyers’ career competition to predict performance rankings. Within a one-year lag, the accuracy of the model was approximately 88%. With two- and three-year lag times, the predictions show only a minor drop in performance. Benefits of this in-silico strategy involve decreasing the frequency of appraisals linked with considerable time and resource savings. By highlighting the most relevant performance predictors in the firm, practitioners may identify bias in appraisals and realign talent management with business strategy. This longitudinal study aims to pilot predictive research for AI models in talent management in law firms. Future research may lead to predictive models supporting talent strategies and practices.
Acknowledgments
We would like to express our gratitude to VdA, the law firm where we developed this research. We wish to acknowledge the work developed by our late colleague Inês Costa Lopes, whose original work inspired us, kept us focused and motivated until the end of this study and to whom we dedicate this paper.
Disclosure statement
No potential conflict of interest was reported by the author(s).