1. Generative AI and Business Management: Background, Impact, and Insights from National Natural Science Fund Projects
With the rapid development of technology, Generative AI has become a hot topic in today’s society. It not only changes our daily lives but also has a profound impact on the field of business management.Generative AI can simulate human creative thinking, generating new content such as text, images, audio, and video, bringing unprecedented opportunities and challenges to enterprises.
In the field of business management, the application of Generative AI has penetrated various aspects such as organizational system design, business process reengineering, and business model innovation. However, the emergence of new technologies has also brought many questions, such as how to adjust the organizational logic of various internal elements of enterprises, how to use new technologies for value creation and innovation upgrading, and how to balance efficiency and risk.
To explore the applications and challenges of Generative AI in the field of business management in depth, the Management Science Department of the National Natural Science Foundation has established the project “Research on Scientific Issues in Business Management in the Era of Generative Artificial Intelligence.” This not only provides researchers with a broad research space but also offers a solid theoretical foundation and practical guidance for the high-quality development of enterprises.
2. Guidelines for Research on Scientific Issues in Business Management in the Era of Generative AI Directions
(1) Research on the Design and Management Mechanism of Digital Agent Systems Based on Generative AI
Generative AI empowers multimodal, interactive, intelligent digital agents, leading to the emergence of new models that integrate digital agent systems with human agents in rich business scenarios, posing new challenges for the design and management of business intelligence systems. Research content includes: 1. Research on the design mechanism of multimodal interactive digital agent systems based on Generative AI; 2. Optimization adjustments of digital agent system design based on human value alignment; 3. Research on the capability evolution strategy of digital agent systems based on Generative AI; 4. Research on multi-agent cooperation and interactive game mechanisms of digital agent systems; 5. Research on governance and risk prevention systems of digital agent systems.
(3) Research on Strategic Human Capital Resources of Enterprises Based on Generative AI
Generative AI technology has disrupted the traditional logic of knowledge creation, on one hand reducing the cost and threshold of knowledge production for enterprises, and on the other hand reshaping the process of value creation of human capital resources in enterprises, profoundly impacting the human capital value of knowledge workers and high-skilled talents. Research content includes: 1. Research on the generation mechanism of strategic human capital resources of enterprises in the era of AI; 2. The impact of Generative AI on the investment preferences of human capital resources in enterprises; 3. The process of value creation of strategic human capital resources of enterprises based on Generative AI; 4. Value assessment and allocation of human capital resources in enterprises based on Generative AI; 5. Ethical issues and governance of embedding Generative AI in the value chain of human capital resources in enterprises.
(4) Research on Accounting Information Generation and Disclosure Based on Generative AI
Generative AI has disrupted the traditional human-centered information generation and processing model, significantly enhancing the efficiency and output capabilities of large samples and multimodal data, while greatly lowering the threshold requirements for users’ professional knowledge and skills. Although Generative AI has brought higher efficiency in information generation, it has also exacerbated the production of false information, biased information, and homogenized content, potentially leading to information overload. Research content includes: 1. The impact of Generative AI on the content, methods, and efficiency of accounting information generation and disclosure in enterprises; 2. The impact of Generative AI on the collection, processing, and dissemination of accounting information by information intermediaries; 3. The impact of Generative AI on the decision-making process and effectiveness of accounting information users; 4. The impact of Generative AI on the identification and prevention of potential risks by regulatory and auditing institutions.
(5) Research on New Product Development Based on Generative AI
Generative AI possesses powerful capabilities such as real-world simulation, transfer learning, supercomputing, and rational autonomous decision-making. The organic integration of Generative AI with human intelligence helps achieve complementary advantages and collaborative evolution in the new product development process, promoting the production of more innovative and efficient new product development results. Research content includes: 1. The mechanism of integration of Generative AI and human intelligence in the new product development process; 2. Multisource heterogeneous data fusion for new product development based on Generative AI; 3. The role of Generative AI in the creative acquisition and association stages of new product concept design; 4. Market validation of new products based on human intelligence integration; 5. Research on the iterative optimization mechanism of new product development based on human intelligence integration.
(6) Research on Multimodal Data Insights and Personalized Marketing Content Generation Based on Generative AI
Multimodal data insights based on Generative AI help comprehensively understand marketing leads, deeply mine marketing knowledge, and guide AI to generate marketing theory-driven personalized content, such as text, audio, images, and videos, for effective interaction with consumers. Research content includes: 1. Marketing theory-driven multimodal data insights; 2. Marketing theory-driven multimodal data fusion; 3. Personalized marketing content generation based on Generative AI; 4. Cross-modal marketing content generation based on Generative AI; 5. Multimodal marketing strategy optimization based on Generative AI.
3. Suggested Topics for National-Level Project Applications in 2025
1. Focus on Specific Industries or Types of Enterprises
Viewpoint:Different industries and types of enterprises have different characteristics and needs when applying Generative AI. For example, the manufacturing industry may focus more on optimizing production processes, while the service industry may emphasize enhancing customer experience.
Reason:The business processes, market environments, and competitive situations of specific industries vary, which leads to fundamentally different ways in which Generative AI operates and the issues it faces. For instance, in the healthcare industry, when using Generative AI for disease diagnosis assistance, it is essential to consider not only the accuracy of the technology but also to adhere to strict medical ethics and regulatory requirements. Therefore, conducting research focused on specific industries can deeply explore the unique application value and management challenges of Generative AI in that field, making the research results more targeted and practical.
2. Address Actual Business Pain Points
Viewpoint:Research topics should closely revolve around the actual business pain points faced by enterprises in applying Generative AI, such as cost control, quality improvement, and customer churn.
Reason:Enterprises encounter various practical problems during their operations, which are often the most concerning and urgently needed issues to resolve. If the research topic can directly address these pain points, the research results will be more readily accepted and applied by enterprises. For example, for e-commerce companies, customer churn is a critical issue. Researching how to use Generative AI to analyze customer behavior data, predict customer churn tendencies, and propose effective retention strategies is a topic that is both practically significant and can bring direct economic benefits to enterprises.
3. Emphasize the Integration of Interdisciplinary Knowledge
Viewpoint:The combination of Generative AI and business management involves multiple disciplines, such as computer science, mathematics, psychology, etc. The topics should reflect the integration of interdisciplinary knowledge.
Reason:Generative AI itself is a technology-intensive field that requires knowledge from computer science and mathematics to support its algorithm development and model construction. Business management, on the other hand, involves knowledge from management, economics, psychology, and other areas to understand enterprise operations and consumer behavior. Only by organically integrating knowledge from different disciplines can we comprehensively and deeply study the application of Generative AI in business management. For instance, when researching personalized marketing, both computer science knowledge is needed to process multimodal data and psychology knowledge to understand consumer purchasing motives and decision-making processes.
4. Consider the Forward-Looking Nature of Technological Development Trends
Viewpoint:Research topics should have a certain forward-looking nature, able to foresee the future development direction of Generative AI technology and its potential impact on enterprises.
Reason:Technology is evolving rapidly, and today’s cutting-edge technologies may become widely applied in the near future. If research topics are limited to the current level of technology, the research results may become outdated in a short period. For example, the integration of quantum computing technology with Generative AI may lead to a tremendous enhancement in computing power, profoundly impacting data processing and decision-making in enterprises. Paying attention to such technological development trends in advance helps conduct research with long-term value and provides reference for the future development strategies of enterprises.
5. Emphasize Operability and Verifiability
Viewpoint:The research content involved in the topics should possess operability and verifiability, capable of being validated through empirical research or experiments.
Reason:The research results of national-level projects need to be scientific and reliable. If the topics are too abstract or lack operability, it will be challenging to conduct effective research and validation. For example, when researching the mechanism by which Generative AI enhances employee satisfaction in enterprises, data can be collected through questionnaire surveys, field interviews, and experimental comparisons to validate research hypotheses. Such research results are more persuasive and easier to be recognized by both academia and industry.
In summary, the combination of Generative AI and business management is a field full of challenges and opportunities. Through in-depth research and exploration, we hope to provide strong support for the high-quality development of enterprises while promoting theoretical innovation and practical progress in the discipline of business management.
From the suggested topics in the guidelines, it can be observed that there is a trend in both industry and academia: marketing is significantly influenced by Generative AI, and it can even be said to be facing fundamental changes. In the field of marketing combined with Generative AI, there are numerous research questions, which also means many opportunities will emerge. This is undoubtedly an excellent opportunity for marketing scholars, and everyone must seize it.
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