Aims

Frontiers in Business and AI is an international, interdisciplinary open-access journal dedicated to promoting the deep integration and dialogue between artificial intelligence technology and traditional business management disciplines. This journal aims to publish two types of research: one is the cutting-edge exploration that utilizes AI technology to innovate, challenge, or expand traditional business theories; the other is the application research that drives the innovation and adaptation of AI technology based on the practical needs of business.

This journal provides a platform for scholars, entrepreneurs and technical experts to jointly establish new paradigms, theories and practices in business management in the era of intelligence.

 

Scope of Submission

This journal is dedicated to promoting the deep integration of artificial intelligence and business management. The specific scope of soliciting contributions includes, but is not limited to, the following eight major thematic areas and their numerous sub-disciplines:

 

Topic One: Strategy, Leadership, and Governance

This theme explores how AI can reshape the core logic of enterprise strategy formulation, the decision-making patterns of top leaders, and the corporate governance structure.

AI-enabled competitive strategies: Dynamic pricing based on real-time data and algorithms, market entry strategies, and reconfiguration of competitive advantages.

Algorithm-driven M&A and investment decisions: Using AI for target selection, value assessment, and post-investment integration prediction.

Corporate governance in the AI era: The role of algorithms in board decision support, and the ethical risk governance framework of AI.

Strategic foresight and scenario planning: Using generative AI and simulation technology to predict industry disruptive changes and future scenarios.

AI leadership: The collaboration model between managers and AI systems, and the leadership and empowerment capabilities for AI projects.

 

Topic Two: Marketing and Customer Journey

This theme focuses on how AI can achieve ultra-personalized customer insights, automate marketing processes, and reshape the entire customer experience.

Ultra-personalized marketing: Customer segmentation based on deep learning, real-time next-best-action recommendations, and optimization of omnichannel experiences.

Comprehensive application of generative AI in marketing: Automated advertising creativity, personalized product descriptions, virtual influencers, and dynamic content generation.

Predictive customer analysis: Customer lifetime value prediction, precise churn warning, and win-back strategies.

Application of voice and image AI in retail: Intelligent shopping assistants, visual search, customer traffic analysis, and immersive virtual try-on.

Conversation-based marketing and business: Intelligent customer service based on large models, shopping advisors, and 24/7 customer interaction.

 

Topic 3: Operations, Manufacturing and Supply Chain Management

This topic explores how to utilize AI to achieve self-optimization of operational processes and build an intelligent, efficient and resilient supply chain system.

Autonomous Supply Chain: Based on AI-driven demand sensing, autonomous logistics, intelligent warehousing and full-chain autonomous optimization.

Industry 4.0 and Intelligent Manufacturing: AI-driven predictive maintenance, digital twins, computer vision-based detection of quality defects and flexible production scheduling.

Process Intelligence and Automation: Discover bottlenecks through process mining techniques and utilize AI to achieve end-to-end business process automation.

Sustainable Operations: Utilize AI to optimize energy consumption, reduce waste, track carbon footprint and implement circular economy.

 

Topic 4: Organizational Behavior and Human Resource Management

This topic focuses on the profound impact of human-machine collaboration on organizational structure, employee behavior, skill requirements, and human resource practices.

Algorithm Management and Employee Experience: Employees' perception of algorithm monitoring, the impact of algorithm fairness on work engagement, and human-machine collaborative performance.

AI-driven Talent Management: Resume screening based on natural language processing, AI interviewers, personalized learning development path recommendations.

Skill Reshaping and Future Workforce: Identification of key skills in the AI era, employee retraining, and analysis of skill gaps.

Design and Management of Human-Machine Hybrid Teams: Building trust in hybrid teams, communication patterns, and leadership challenges.

 

Topic 5: Finance, Accounting and Risk Management

This topic explores the transformative applications of AI in high-risk, high-data-density areas such as financial modeling, transaction execution, and audit risk control.

Fintech Frontiers: Intelligent investment advice, high-frequency algorithmic trading, AI credit rating, and blockchain-supported smart contracts.

Predictive Risk Management: Real-time identification and stress testing of market risk, credit risk, and operational risk using AI.

Automated Accounting and Auditing: The application of AI in invoice processing, financial report generation, and continuous auditing.

Financial Fraud and Anti-Money Laundering: Complex financial crime identification based on graph neural networks and anomaly detection models.

 

Topic 6: Information Systems, Technologies and Data Foundations

This topic focuses on the technical platforms, data governance, and the organizational adoption process that support AI business applications.

The adoption and absorption of enterprise AI platforms: Organizational, technical, and cultural factors influencing the successful deployment of AI.

AI as a Strategic Information System: The theory and case studies of using AI capabilities as a source of core competitive advantage.

Data strategy and governance: Data quality management, data privacy and security, and data asset valuation for AI-oriented data.

AI project management and agile development: Lifecycle management of AI projects, MLOps practices, and cross-functional team collaboration.

 

Topic 7: Innovation, Entrepreneurship and Business Ecosystem

This topic explores how AI lowers the threshold for innovation, develops new business models, and reconfigures the industrial ecosystem.

AI-driven business model innovation: Data-driven platform transformation, product-as-a-service, outcome economy, and other new models.

AI entrepreneurship and investment: The unique growth paths, valuation methods, and challenges faced by AI startups.

Open innovation and AI ecosystem: AI capability opening based on API economy, developer ecosystem construction, and collaborative innovation.

AI application in R&D and innovation management: Utilizing AI to accelerate drug discovery, materials science, and the design of new products.

 

Topic 8: Ethics, Regulations and Social Impact

This topic forms the foundation of AI commercial applications, exploring how to ensure its development is responsible, sustainable, and in line with social interests.

Interpretable AI and Algorithm Accountability: Achieving transparency and explainability of algorithms in high-risk business decisions.

Algorithm Fairness, Bias and Discrimination: Detecting and mitigating biases in algorithmic decisions in areas such as recruitment and credit.

The Macroeconomic and Social Impact of AI: The reshaping of employment structure, income inequality, regional economy and global value chain by AI.

Global AI Policies and Compliance: Comparative study of AI regulations in different jurisdictions and their impact on corporate strategies.