Call For Papers

AIDSBA-2026 welcomes original research papers, case studies, and thought leadership contributions from academics, practitioners, and industry experts. Whether your focus is on transforming financial analytics, optimizing supply chains, enhancing customer experience, or addressing ethical considerations in AI, this conference is designed to inspire dialogue, foster partnerships, and shape the future of AI in business and economics.

AIDSBA-2026 invites original work from the following list of topics (but is not limited to):

1: Artificial Intelligence in Business Transformation

• AI in strategic decision-making
• Automation in finance, marketing, and HR
• Natural Language Processing (NLP) in customer service
• Computer vision for retail and surveillance
• Chatbots and conversational AI for business support
• AI-driven product recommendation systems
• Forecasting consumer trends with AI
• AI for competitive intelligence and market analysis
• AI for SME growth and digital adoption
• Case studies on AI adoption in enterprises

2: Business Analytics and Intelligence

• Predictive analytics in marketing and sales
• Business intelligence dashboards and visualization
• Churn prediction and customer lifetime value modeling
• Real-time analytics for agile decision-making
• Customer sentiment analysis and feedback mining
• Profitability and cost analytics
• Data monetization strategies
• Analytics for pricing and revenue management
• AI in consumer preference modeling
• Strategic KPIs powered by analytics

3: Operations Research and Optimization in the Age of AI

• Sustainable operations in the Age of AI
• Decarbonation and role of AI in sustainable operations
• Circular economy and sustainable operations in the Age of AI
• Net Zero Target and role of AI in sustainable operations
• Industry 5.0/4/0 in sustainable operations in the Age of AI
• Metaverse and sustainable operations in the Age of AI
• Linear, non-linear, and stochastic optimization
• Metaheuristics and evolutionary computation
• AI-driven logistics and transportation models
• Intelligent scheduling and resource allocation
• Game theory and agent-based modelling, Supply-demand optimization
• Constraint programming for industrial processes
• Simulation modeling for operational risk
• Multi-objective decision-making systems
• Operations planning under uncertainty

4: AI and Data Analytics in Supply Chain and Logistics

• Role of AI in resilient supply chain and logistics
• Decarbonation supply chain & logistics and role of AI
• Geopolitics risks in supply chain & logistics and role of AI
• Circular supply chain in the Age of AI
• Net Zero supply chain and role of AI
• Industry 5.0/4/0 in supply chain & logistics in the Age of AI
• Metaverse and supply chain & logistics in the Age of AI
• Disruptions in supply chain & logistics and role of the Age of AI
• Humanitarian Decarbonation supply chain & logistics and role of AI
• Sustainable procurement and role of AI
• Supply chain & Logistics 5.0 and AI
• Predictive demand and inventory forecasting
• Real-time logistics tracking with IoT and AI
• Blockchain for transparent and secure supply chains
• Supplier performance analytics
• Warehouse and distribution optimization
• AI for last-mile delivery systems
• Resilient supply chain modeling under disruption
• Environmental impact analytics in logistics
• Smart contracts and automation in procurement
• Human resource analytics in supply chain roles

5: Sustainability and AI

• Climate risk analysis and AI
• Climate financing and AI
• Role of AI and sustainable business
• Environment/Social sustainability and role of AI
• Sustainable SMEs business and AI
• Sustainable marketing practises and AI
• Role of HR in adoption of sustainable business practices
• Circular business practices and role of AI
• Decarbonized business and role of industry 4.0 technologies
• Resilient HR and marketing practices and role of AI
• Resilience, Equity, and Innovation and AI

6: Healthcare Analytics and Medical AI

• AI for disease prediction and early diagnostics
• Clinical decision support systems
• Medical image processing with deep learning
• EHR data mining and patient journey analytics
• Personalized treatment and precision medicine
• Real-time monitoring through wearable devices
• AI in drug discovery and repurposing
• Hospital operations and resource optimization
• Ethics and data privacy in medical AI
• AI in mental health analysis and interventions

7: Intelligent Finance and Economic Analytics

• AI for credit scoring and fraud detection
• Robo-advisory platforms and portfolio optimization
• Macroeconomic forecasting with AI models
• Blockchain and crypto-asset analytics
• ESG investment analytics
• Algorithmic trading and predictive modeling
• Banking and insurance risk modeling
• Customer segmentation in financial services
• Financial inclusion through AI innovation
• Real-time transaction monitoring and anomaly detection

8: Responsible AI, Data Privacy, and Regulation

• Algorithmic fairness and bias detection
• Transparency and explainability in AI
• Responsible AI design frameworks
• Ethical data collection and usage policies
• Governance and audit of AI models
• Regulatory compliance in different geographies (e.g., GDPR)
• AI risk management and accountability frameworks
• Privacy-preserving machine learning techniques
• Building trust in automated systems
• Societal impact of algorithmic decisions

9: Emerging Technologies and Future Trends

• Edge computing and real-time AI inference
• Federated learning and collaborative modeling
• Quantum computing in data processing
• Digital twins in manufacturing and operations
• AR/VR and immersive analytics applications
• 5G-enabled data ecosystems
• AI in climate change and sustainability analytics
• Multi-agent systems and decentralized AI
• Smart city infrastructure and urban analytics
• Cross-sector innovation case studies

10: Human-AI Collaboration and Skill Transformation

• Human-in-the-loop machine learning systems
• Augmented intelligence in decision support
• Reskilling for the AI-driven economy
• Behavioral analytics in workplace AI adoption
• Digital maturity models and transformation readiness
• Talent analytics and workforce planning
• Designing intuitive AI tools for non-tech users
• Organizational change management with AI integration
• AI in education and training systems
• Human-machine teaming and co-creation strategies

11: Foundations and Frontiers in Data Science

• Supervised, unsupervised, and reinforcement learning
• Data wrangling, cleaning, and pre-processing methods
• Feature engineering and model explainability
• Graph analytics and knowledge networks
• High-performance computing in data science
• Data ethics, bias, and fairness in AI systems
• Open-source data science platforms and tools
• Scalable ML pipelines in production
• Advances in neural architecture search
• Multimodal data integration (text, image, video)