Big Data and Artificial Intelligence in Management. Disruptive Technologies as a success factor for decision-making

Table of contents

1 Introduction

2 Basics

2.1 Current economic environment of German companies

2.2 Information and data

2.3 Digitization and Big Data

2.3.1 Digitization

2.3.2 Big Data

2.4 Artificial Intelligence

2.4.1 Aspects of the history of origin

2.4.2 Definition of terms

2.5 Other relevant aspects

2.5.1 Algorithm

2.5.2 Internet of Things

2.5.3 Machine learning

2.5.4 Deep Learning

2.5.5 Cloud Computing

2.5.6 To the first category: "Robot-controlled process automation":

2.5.7 To the second category: "Augumented Reality"

2.6 Entrepreneurial Management 4.0

3 Analysis of decision-making in management

3.1 Key decision-making functions of management

3.2 Key factors in decision-making

3.2.1 Factor Analysis

3.2.2 Factor Experience:

3.2.3 Factor ethics and morality:

3.2.4 Factors of personal interests and relationships:

3.3 Human decision-maker versus artificial intelligence

3.3.1 Procedure

3.3.2 Factor Analysis

3.3.2.1 Aspects to the first research question:

3.3.2.2 Aspects of the second research question

3.3.2.3 Findings from the study

3.3.3 Factor Experience

3.3.3.1 Aspects of the first research question:

3.3.3.2 Aspects of the second research question:

3.3.3.3 Findings from the study

3.3.4 Factors ethics and morality

3.3.4.1 Aspects of the first research question:

3.3.4.2 Aspects of the second research question:

3.3.4.3 Findings from the study

3.3.5 Factors of personal interests and relationships

3.3.5.1 Study of the impact of personal interests

3.3.5.2 Study of the effects of relationships

3.3.5.3 Findings from the study

3.3.6 Cross-factor aspects

3.4 Decision-making insights

4 Analysis of the feasibility of artificial intelligence in practice

4.1 Basic needs

4.1.1 Supervision of the implementation

4.1.2 Hardware:

4.1.3 Software:

4.1.4 Intensive learning phase:

4.1.5 Input/basic data:

4.1.6 General conditions

4.2 Risks

4.2.1 Fundamental

4.2.2 Loss of control

4.2.3 Ethics and Morality

4.2.4 Privacy and Security

4.3 Insights into feasibility

5 Concluding remarks

Research Objectives & Core Themes

This work explores whether artificial intelligence (AI), by leveraging big data, can function as a success factor for corporate decision-making. The research investigates whether AI, compared to human decision-makers, can optimize existing value-creation processes or facilitate the development of new ones within the complex and dynamic landscape of modern management (Management 4.0).

• Technological impact of AI and big data on business management.
• Comparative analysis of human vs. AI decision-making based on factors like analysis, experience, ethics, and morality.
• Practical feasibility, including infrastructure, hardware requirements, and data acquisition.
• Risk management, specifically regarding control loss, ethical concerns, and data privacy.
• The concept of human-AI collaboration as an optimal future solution for management.

Excerpt from the book

Summary of Chapters

1 Introduction: This chapter highlights the increasing complexity of management decisions in a dynamic economic environment and introduces artificial intelligence as a potential success factor.

2 Basics: This chapter provides the theoretical foundation, explaining key terms like Industry 4.0, Big Data, and Artificial Intelligence, while establishing necessary terminology for the subsequent analysis.

3 Analysis of decision-making in management: This core chapter evaluates various factors influencing management decisions—such as analysis, experience, and ethics—and compares the performance of humans versus artificial intelligence in these areas.

4 Analysis of the feasibility of artificial intelligence in practice: This chapter examines the practical prerequisites for implementing AI in companies, discusses associated risks like loss of control and data privacy, and evaluates current feasibility.

5 Concluding remarks: This final chapter synthesizes the research findings, advocating for a cooperative model between humans and AI as an assistant to optimize management performance.

Keywords

Artificial Intelligence, Big Data, Management 4.0, Decision-making, Business Analytics, Industry 4.0, Digital Transformation, Ethical AI, Machine Learning, Data Privacy, Strategic Management, Human-AI Collaboration, Algorithmic Decision-making, Process Automation, Innovation.

Frequently Asked Questions

What is the core focus of this research?

The work investigates whether artificial intelligence, utilizing big data, can act as a crucial success factor in the decision-making processes of corporate management.

What are the central thematic fields addressed?

The research covers the economic environment of Industry 4.0, the technical definitions of AI and related technologies, the comparative analysis of human vs. AI decision-making factors, and the practical feasibility and risks of AI implementation.

What is the primary research goal?

The aim is to evaluate whether AI can lead to greater economic success in corporate decision-making either through more efficient processes or more effective decisions compared to human decision-makers.

Which scientific methods are employed?

The work is based on a comprehensive literature review, including expert studies, professional articles, and reports from renowned consulting firms, combined with a structured analysis of decision factors.

What topics are covered in the main section?

The main part analyzes decision-making factors such as analysis, experience, ethics/morality, and personal interests, juxtaposing human capabilities with those of artificial intelligence.

Which keywords characterize this work?

Key terms include Artificial Intelligence, Big Data, Management 4.0, Strategic Management, and Human-AI Collaboration.

Why is the "Eisenhower Principle" relevant to this work?

The principle is used to categorize tasks by importance and urgency, suggesting that AI can autonomously process "unimportant but urgent" tasks, thereby relieving management for more critical decision-making.

What is the main finding regarding AI's ability to handle "experience"?

While AI excels in processing large data sets, it currently lacks the human ability to abstract and transfer findings from unrelated scenarios, though it offers advantages by eliminating the risk of losing "institutional memory" when personnel leave.

What ethical risks are associated with AI in management?

The work identifies risks regarding inherent biases in data, potential for unethical dynamic pricing, and the difficulty of ensuring AI remains comprehensible and accountable to human oversight.