AI Product Value Assessment Model: An Interdisciplinary Integration Based on Information Theory, Economics, and Psychology

TL;DR

This paper presents a comprehensive AI product value assessment model integrating information theory, economics, and psychology to help enterprises evaluate AI investments systematically and avoid irrational decisions.

Contribution

It introduces a novel multi-dimensional evaluation framework combining interdisciplinary theories and a non-linear formula to quantify AI product value and guide rational investment.

Findings

The model effectively distinguishes successful from failed AI products.

Validation with 10 commercial cases supports the model's hypotheses.

The approach highlights the importance of positive and negative factors in AI valuation.

Abstract

In recent years, breakthroughs in artificial intelligence (AI) technology have triggered global industrial transformations, with applications permeating various fields such as finance, healthcare, education, and manufacturing. However, this rapid iteration is accompanied by irrational development, where enterprises blindly invest due to technology hype, often overlooking systematic value assessments. This paper develops a multi-dimensional evaluation model that integrates information theory's entropy reduction principle, economics' bounded rationality framework, and psychology's irrational decision theories to quantify AI product value. Key factors include positive dimensions (e.g., uncertainty elimination, efficiency gains, cost savings, decision quality improvement) and negative risks (e.g., error probability, impact, and correction costs). A non-linear formula captures factor couplings, and validation through 10 commercial cases demonstrates the model's effectiveness in distinguishing successful and failed products, supporting hypotheses on synergistic positive effects, non-linear negative impacts, and interactive regulations. Results reveal value generation logic, offering enterprises tools to avoid blind investments and promote rational AI industry development. Future directions include adaptive weights, dynamic mechanisms, and extensions to emerging AI technologies like generative models.