• Understand generative AI's impact on MIS and SCM
• Learn effective prompting strategies
• Develop practical applications for teaching
• Identify research opportunities
• Create discipline-specific AI integration plans
Equips faculty with practical AI integration skills
Current AI Landscape
• Latest developments in generative AI
• Key players and platforms
• Industry adoption trends
• Educational implications
• Ethical considerations
Provides context for AI integration decisions
Faculty AI Usage Assessment
• Current adoption levels
• Common applications
• Implementation challenges
• Success stories
• Areas for improvement
Establishes baseline for workshop customization
2024 - 2025
Workshop Impact Metrics
Expected faculty adoption rate: 85%
Curriculum integration potential: 70% of courses
Student exposure to AI tools: 100% increase
The workshop is designed to accelerate AI adoption in academic settings, focusing on practical applications in both MIS and SCM disciplines.
Based on current academic AI integration trends - Gartner Education Insights, 2024
Discussion: Gen AI in MIS and SCM
MIS
Automated Report Generation
LLMs can generate comprehensive business reports by synthesizing data from various internal systems.
Saves time for managers by automating report creation.
MIS
Natural Language Database Queries
Allows users to interact with databases using plain language.
Enhances data accessibility for non-technical staff.
MIS
Intelligent Virtual Assistants for Internal Use
Chatbots powered by LLMs assist employees with HR inquiries, IT support, and policy information.
Improves internal communication and reduces support workload.
MIS
Enhanced Knowledge Management
LLMs help in organizing and retrieving information from vast corporate knowledge bases.
Quickly retrieves relevant information for better decisions.
MIS
Policy Analysis and Compliance Monitoring
Analyzes company policies and external regulations to ensure organizational compliance.
LLMs interpret stakeholder interviews and extract system requirements.
Improves accuracy in gathering system requirements.
MIS
Code Documentation
Automatically generates documentation from code or system specifications.
Automatically updates technical documentation.
MIS
Narrative Data Summaries
LLMs generate plain-language summaries of complex data analytics.
Makes data analytics insights accessible.
MIS
Anomaly Detection in Data
Identifies irregular patterns in data that may indicate errors or fraud.
Enhances data integrity and supports risk management.
MIS
On-Demand Help Systems
LLMs offer real-time assistance within MIS applications through natural language queries.
Reduces downtime and improves user proficiency.
MIS
Security Log Analysis
LLMs analyze security logs to detect potential threats or breaches.
Prompts response to security incidents.
MIS
Access Control Verification
Verifies user permissions and access levels using natural language processing.
Maintains data security through access control verification.
SCM
Demand Pattern Analysis
Analyzes social media, news, and market data to identify demand trends and patterns.
Improves forecast accuracy through multi-source analysis.
SCM
Supplier Performance Analytics
Monitors and analyzes supplier metrics, contracts, and communications.
Enhances supplier relationship management.
SCM
Risk Monitoring System
Processes global news, weather data, and economic indicators for risk assessment.
Enables proactive risk mitigation strategies.
SCM
Supply Chain Documentation
Generates and maintains SOPs, training materials, and policy documents.
Ensures consistent documentation across operations.
Image-to-Text and Text-to-Image Models
MIS
Document Digitization and Management
Image-to-text models convert scanned documents into editable text for document management systems.
Streamlines document digitization and management.
MIS
Automated Data Extraction
Extracts data from forms, invoices, and receipts into structured formats.
Reduces data entry errors and speeds up processing.
MIS
Visual Process Modeling
Text-to-image models create flowcharts and diagrams from textual descriptions of business processes.
Visualizes and optimizes workflows efficiently.
SCM
Automated Quality Inspection
Performs visual product inspections and defect detection.
Maintains consistent quality standards.
SCM
Document Processing System
Processes shipping documents, customs forms, and invoices automatically.
Speeds up documentation processing.
SCM
Supply Network Visualization
Creates visual representations of supply chain networks and processes.
Improves strategic planning and communication.
Speech-to-Text (STT) and Text-to-Speech (TTS)
MIS
Meeting Transcriptions and Analysis
STT models transcribe meetings and conference calls for record-keeping and action item tracking.
Accurately captures and stores meeting information.
MIS
Voice-Activated Information Systems
Enables voice commands for navigating MIS applications.
Enhances user experience with voice commands.
MIS
Audible Alerts and Notifications
TTS models convert critical system alerts into audible notifications.
Improves response times with audible alerts.
MIS
Data Entry Automation
Staff dictate reports or data entries, which are transcribed and inputted into systems automatically.
Facilitates hands-free data entry and reduces errors.
SCM
Warehouse Voice Operations
Enables hands-free inventory management and picking instructions.
Increases operational efficiency and safety.
SCM
Fleet Communication System
Manages driver updates and dispatcher communications through voice.
Enhances real-time logistics coordination.
Recent Applications
Data Management Systems
MIS
2023
Automated Data Quality Enhancement
Reduced data preparation time from months to days
Automated data-quality remediation through programmatic labeling
Enhanced integration of internal and third-party data sources
Implementation of data-centric AI tools significantly accelerated data quality improvement processes, enabling faster deployment of AI systems.
Source: "The art of data: Empowering art institutions with data and analytics" - McKinsey & Company, 2023
Enterprise Information Systems
MIS
2024
Integrated Business Systems Enhancement
25-40% reduction in administrative costs
20-50% reduction in data processing errors
Automated report generation and data classification
Enterprise-wide implementation of generative AI for automated documentation and data processing resulted in significant efficiency gains.
Source: "The network is the product: How AI can put telco customer experience in focus" - McKinsey & Company, 2024
Knowledge Management Systems
MIS
2023
Corporate Knowledge Base Enhancement
Reduced information retrieval time by 60%
Automated documentation generation and maintenance
Enhanced searchability and accessibility of corporate knowledge
Implementation of generative AI in knowledge management systems significantly improved information accessibility and maintenance.
Source: "The changing nature of teaching future IS professionals in the era of generative AI" - Journal of Information Technology Case and Application Research, 2023
Decision Support Systems
MIS
2024
Advanced Analytics Integration
Real-time data analysis and insight generation
Automated pattern recognition and anomaly detection
Enhanced predictive modeling capabilities
Integration of generative AI with decision support systems enabled more sophisticated analytics and real-time decision-making capabilities.
Source: "How Generative AI Can Support Advanced Analytics Practice" - MIT Sloan Management Review, 2024
Information Security Systems
MIS
2023
Enhanced Security Monitoring
30% improvement in threat detection
Automated security log analysis
Real-time anomaly detection and response
Implementation of generative AI in security systems improved threat detection and response capabilities while reducing false positives.
Source: "Derisking AI by design: How to build risk management into AI development" - McKinsey & Company, 2023
Demand Forecasting
SCM
2024
Cost Reduction Impact Analysis
Warehousing cost reduction: 5-10%
Administration cost reduction: 25-40%
Lost sales reduction: 65%
Recent implementations of generative AI in supply chain management have demonstrated significant cost savings and efficiency improvements across various operational areas.
Improved supplier selection through AI-powered analysis
Enhanced communication and collaboration with suppliers
Increased efficiency in managing supplier contracts and performance
Generative AI models analyze market conditions and recommend alternative suppliers, improving the efficiency and effectiveness of supplier relationship management.
Source: "How Generative AI Can Support Advanced Analytics Practice" - MIT Sloan Management Review, 2024
Sustainability
SCM
2024
AI and Blockchain for Sustainable Supply Chains
Enhanced visibility into products' origin and carbon footprint
Improved traceability and transparency in supply chain operations
Enabled more informed and ethical decision-making for sustainability
The integration of generative AI with blockchain technology optimizes supply chains for sustainability by providing insights into environmental and ethical factors.
Source: "Unleashing the power of generative AI to build autonomous supply chains" - HFS Research, 2024
Recent studies indicate a rapid acceleration in generative AI adoption within supply chain management, with a significant focus on enhancing operational efficiency and risk management capabilities.
Source: "Reshaping Business With Artificial Intelligence" - MIT Sloan Management Review, 2024 Update
SCM
2024 Forecast
Autonomous Supply Chain Integration
Investment projection: $13 billion by 2032
Growth rate: 46% CAGR
Focus on end-to-end automation
Recent implementation of generative AI in supply chain automation has shown significant promise, with major companies investing in autonomous systems that can self-correct and optimize operations in real-time.
Source: "Unleashing the power of generative AI to build autonomous supply chains" - HFS Research, 2024
Effective Prompting Strategies
Clear Context Setting
Define the AI's role clearly and specifically
Specify the desired output format
Provide relevant background information
State any constraints or requirements
Effective prompting begins with clear context setting, which significantly improves the quality and relevance of AI responses.
"As a database administrator, analyze this schema for normalization issues, providing recommendations in bullet points with specific SQL corrections..."
Specific Instructions
Break down complex tasks into clear steps
Use clear, actionable language
Include example outputs when helpful
Specify evaluation criteria
Detailed instructions help ensure the AI's output aligns perfectly with your needs and requirements.
"As a supply chain analyst, optimize this distribution network. Consider costs, delivery times, and sustainability metrics..."
Role-Based Prompting
MIS
MIS Role Prompting
Business Analyst: Requirements analysis and system design
Database Administrator: Schema optimization and query performance
IT Project Manager: Timeline and resource allocation
System Architect: Technical infrastructure design
Specifying MIS roles helps frame the context and expertise level for AI responses.
"As a business analyst, review these user stories and identify potential system integration points..."
SCM
SCM Role Prompting
Logistics Manager: Transportation and distribution optimization
Inventory Specialist: Stock level management and forecasting
Procurement Officer: Supplier selection and negotiation
Supply Chain Strategist: Network design and risk management
Using specific SCM roles helps focus the AI on relevant domain expertise and methodologies.
"As a logistics manager, analyze these shipping routes and suggest optimization strategies..."
Chain-of-Thought Prompting
Problem Decomposition
Break down complex problems into manageable components
Identify key variables and relationships
Create logical sequences for problem-solving
Build step-by-step solution approaches
Chain-of-thought prompting helps tackle complex problems by breaking them down into logical steps.
"Let's optimize this warehouse layout step by step: 1) Analyze current flow patterns, 2) Identify bottlenecks..."
Solution Validation
Verify logical consistency in each step
Check against original requirements
Test edge cases and assumptions
Review implications of proposed solutions
Systematic validation ensures solutions are robust and meet all requirements.
"Verify this database design meets ACID requirements by checking: 1) Atomicity, 2) Consistency..."
Advanced Prompting Strategies
Few-Shot Learning Approach
Provide examples of desired input-output pairs
Demonstrate patterns through multiple examples
Include edge cases and special scenarios
Show variations in acceptable outputs
Using examples helps the AI understand exactly what kind of output you're looking for.
"Here are three examples of properly normalized databases. Follow these patterns to normalize the given schema..."
Best Practice
Output Formatting
Specify exact output structure needed
Define templates and formats
Include required metadata
Set clear style guidelines
Clear output specifications ensure responses are immediately usable without reformatting.
"Generate a technical specification following this template: [Section 1: Overview, Section 2: Requirements...]"
A visual research engine that creates interactive mind maps from academic papers and research materials. Features include connection identification between papers and concept visualization.
Enables visual exploration of research connections
Converts various content formats (YouTube, PDFs, documents, URLs, emails, recordings) into structured mind maps using advanced language models. Supports multiple LLM backends including GPT-4 and Claude.
A research assistant platform that processes academic papers and research documents to provide verified, source-based answers to specific queries. Includes comprehensive summarization capabilities.
Facilitates source-verified research inquiry
Hands-on Prompting Exercise
Beginner
Research Paper Analysis
Learn to extract key information from academic papers using structured prompting with PDF documents.
⏱️
Duration: 20 mins
👥
Format: Individual
📄
File: PDF/Word
Exercise Steps:
1. Upload your research paper
2. Extract key sections
3. Analyze methodology
4. Identify research gaps
5. Generate future research directions
"Analyze this research paper and provide: 1) Key findings summary, 2) Methodology assessment, 3) Research gaps, 4) Potential future research directions. Format the output as a structured report with clear sections..."
Intermediate
Course Material Enhancement
Transform existing lecture notes and syllabi into enhanced learning materials with AI assistance.
"Review these papers and create: 1) A comparison matrix of methodologies and findings, 2) Thematic analysis, 3) Research gap identification, 4) Synthesis of key contributions to the field..."
Intermediate
Grant Proposal Enhancement
Improve grant proposals with AI-assisted analysis and recommendations.
"Review this grant proposal and provide: 1) Impact strengthening suggestions, 2) Methodology improvements, 3) Budget optimization recommendations, 4) Timeline refinements, focusing on NSF/NIH standard criteria..."
Advanced
Semantic Search Setup
Create a semantic search system for your research papers and documents.
"Help me create semantic search queries for my research papers that can: 1) Find related methodologies, 2) Identify similar findings, 3) Discover research gaps, 4) Connect related works across different papers..."
Wrap-up and Next Steps
Bridging Theory and Practice in the AI Era
Key Takeaways
Integration of AI across MIS and SCM disciplines
Practical prompting strategies for academic use
Research opportunities in AI implementation
Pedagogical approaches for AI integration
Next Steps
Implement AI tools in course materials
Develop discipline-specific AI guidelines
Create AI-enhanced assignments
Foster collaborative AI research projects
Impact Metrics
85%
Expected Faculty Adoption
70%
Course Integration
100%
Student AI Exposure
Resources Available
Workshop materials, prompts library, and implementation guides
Support Network
Faculty community and ongoing technical assistance
"Embracing AI in academia isn't just about adopting new tools—it's about reimagining education for the future."