Application of a Model Life Cycle Concept to Investments in Artificial Intelligence Evaluation on the Example of Large Language Models

Abstract

The life cycle of an artificial intelligence model is the object of research. The purpose of the study is to develop a model life-cycle methodology that describes the economic content of the investment process in artificial intelligence technology. During the study, both general scientific methods such as analysis, synthesis, comparison, abstraction, induction and deduction were used, as well as project methodologies of the life-cycle, employed as the basis for the value creation life-cycle of the model. The analysis was based on identifying the necessary stages of model development in terms of the CRISP-DM methodology and determining the features of each of them in terms of cash flows. Modified versions of the model life-cycle containing risk assessment, including model risk, were also taken into account. In the process of research, the proposed generalized model life-cycle methodology was specified for a specific AI technology — large language models. As a result of the study, the author proposed a three-stage model. The possible optionality between the stages and the characteristics of cash flows are described. It was concluded that an investment project for the development of AI contains several real options — abandonment, reduction, expansion and replacement. For large language models, the life cycle structure and possible optionalities are preserved. The peculiarity is that the value creation process involves cash flows from different areas of application of the model in business processes. The results of the study are of practical importance for medium and large businesses engaged in the independent development of AI models and/or applying them to their business processes. The proposed concept of the model life-cycle can also be used to develop a methodology for evaluating investments in AI using real options.