IGNOU MBA MMPC-08: Information Technology for Managers
Unit 14: Artificial Intelligence and Digital Support System
Introduction
Artificial Intelligence (AI) and Digital Support Systems (DSS) are transforming business operations by automating tasks, enhancing decision-making, and improving customer support. This unit explores the fundamentals, applications, and challenges of AI and DSS in business management.
1. Artificial Intelligence (AI) Overview
1.1 Definition and Importance
- Artificial Intelligence (AI): The ability of machines to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.
- Importance: Automates repetitive tasks, enhances decision-making, and improves efficiency in various industries.
1.2 Types of AI
- Weak AI (Narrow AI): Designed for specific tasks (e.g., chatbots, recommendation systems).
- Strong AI (General AI): Mimics human intelligence across multiple tasks (e.g., advanced robotics, self-learning systems).
- Super AI: Hypothetical AI that surpasses human intelligence.
1.3 AI Technologies
- Machine Learning (ML): Algorithms that learn patterns from data.
- Natural Language Processing (NLP): Enables computers to understand human language.
- Computer Vision: AI systems that interpret visual data.
- Expert Systems: AI-based decision support tools.
2. Digital Support Systems (DSS)
2.1 Definition and Components
- Digital Support System (DSS): A computerized system that helps in decision-making using data analysis and AI.
- Components:
- Database Management: Stores structured information.
- Model Management: Provides analytical models for decision-making.
- User Interface: Allows interaction with DSS.
- Knowledge Base: Stores AI-driven insights.
2.2 Types of DSS
- Communication-Driven DSS: Facilitates collaboration (e.g., Microsoft Teams, Slack).
- Data-Driven DSS: Uses data analytics for decision-making (e.g., Tableau, Power BI).
- Model-Driven DSS: Utilizes predictive models (e.g., supply chain forecasting).
- Knowledge-Driven DSS: Incorporates AI to provide expert recommendations.
2.3 Applications of DSS
- Business Management: Financial planning, risk analysis.
- Healthcare: Medical diagnosis support.
- Supply Chain Management: Inventory and logistics optimization.
- Customer Support: Chatbots and virtual assistants.
3. Experiment: AI-Powered Decision Making
3.1 Experiment: Implementing a Simple AI Model in Python
Objective: Use machine learning to predict customer churn.
Python Code:
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
# Sample dataset
data = {'CustomerID': [1, 2, 3, 4, 5], 'Usage': [20, 50, 30, 80, 40], 'Churn': [0, 1, 0, 1, 0]}
df = pd.DataFrame(data)
# Train model
model = DecisionTreeClassifier()
model.fit(df[['Usage']], df['Churn'])
# Predict churn for new customer
new_customer = [[60]]
print("Churn Prediction:", model.predict(new_customer))
3.2 Experiment: Using a DSS for Financial Analysis
Objective: Utilize DSS to analyze sales data.
SQL Query:
SELECT Product, SUM(Sales) AS TotalSales
FROM SalesData
GROUP BY Product
ORDER BY TotalSales DESC;
4. Assignment Questions
- Define Artificial Intelligence and its key applications in business.
- Differentiate between Narrow AI and General AI.
- Explain the components of a Digital Support System.
- How does Machine Learning contribute to AI-driven decision-making?
- Discuss real-world applications of DSS in business management.
5. Self-Study Questions
- What is the role of Natural Language Processing in AI?
- How does AI impact digital marketing strategies?
- Explain the importance of expert systems in business management.
- What are the ethical concerns related to AI in decision-making?
- How do chatbots improve customer service efficiency?
6. Exam Questions
Short Answer Questions:
- Define AI and DSS.
- What are the main types of AI?
- Describe the role of predictive analytics in DSS.
- How do businesses use AI for customer engagement?
- Mention three real-world examples of DSS.
Long Answer Questions:
- Compare and contrast AI and DSS in business management.
- Discuss the applications of AI in supply chain optimization.
- Explain how digital support systems enhance decision-making.
- Describe the role of expert systems in AI-based decision support.
- What are the challenges businesses face when implementing AI-driven DSS?
Conclusion
AI and DSS are revolutionizing business processes by automating decision-making and improving operational efficiency. Understanding their principles, applications, and challenges helps managers leverage technology for competitive advantage.
Unit 14 on "Artificial Intelligence and Digital Support System" has been documented with comprehensive explanations, including theories, experiments, assignments, self-study questions, and exam questions. Let me know if you need any modifications or additional details!
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MMPC 08
