Artificial Intelligence and Decision-Making Applications in a Business Context. Chances and Risks

Table of Contents

1 Introduction

2 Artificial Intelligence

2.1 Definition of Artificial Intelligence

2.2 Development of Artificial Intelligence

2.3 Expectations on Artificial Intelligence

2.4 Artificial Intelligence technologies

2.5 Relevance of Artificial Intelligence for business

3 Decision-making

3.1 Human decision-making

3.2 Artificial Intelligence in decision-making

4 Applications of decision-making with Artificial Intelligence in business

4.1 Autonomous driving

4.2 Recruiting

5 Chances and risks

5.1 Chances in terms of cost reduction or increase in revenue

5.1.1 Improved efficiency

5.1.2 Increased decision quality in outcomes

5.2 Risks in terms of cost increase or decrease in revenue

5.2.1 Lack of social acceptance

5.2.2 Legal issues

5.2.3 Ethical issues

5.2.4 Decreased decision quality in outcomes

5.2.5 Technological errors and limitations

5.2.6 Workforce transition

5.3 Trade-offs concerning Artificial Intelligence-powered decision-making

6 Conclusion

Objective and Research Focus

The primary objective of this thesis is to provide a comprehensive analysis of the capabilities of Artificial Intelligence (AI) within organizational decision-making processes, specifically examining both the potential benefits and the risks faced by businesses. By focusing on a business perspective and economic implications, the research addresses the fundamental question of what is gained and lost when AI is deployed to either augment or automate decisions, ultimately helping organizations evaluate their AI investment strategies.

• The distinction between decision augmentation and decision automation in business processes.
• Evaluation of AI potential through the lenses of cost reduction and revenue increase.
• Identification of critical risks, including social acceptance, ethical, and legal challenges.
• Case study analysis of autonomous driving and human resources recruiting applications.
• Economic trade-offs inherent in AI-powered decision-making.

Excerpt from the Thesis

Summary of Chapters

1 Introduction: Introduces Artificial Intelligence in the business context, outlining its growing relevance in augmenting and automating decision-making.

2 Artificial Intelligence: Provides a foundation on AI definitions, its development history, expectations, technologies, and its significance for business.

3 Decision-making: Explores the theoretical models of human decision-making and how Artificial Intelligence can be integrated into these processes.

4 Applications of decision-making with Artificial Intelligence in business: Presents practical examples of AI in business, specifically focusing on autonomous driving and recruiting.

5 Chances and risks: Analyzes the economic impacts of AI adoption, covering efficiency, decision quality, social acceptance, legal and ethical issues, and workforce transition.

6 Conclusion: Synthesizes the findings, highlighting the trade-offs of AI and offering recommendations for organizations regarding AI investment and risk management.

Keywords

Artificial Intelligence, Decision-making, Decision Augmentation, Decision Automation, Autonomous Driving, Recruiting, Machine Learning, Economic Implications, Artificial Neural Networks, Risk Management, Social Acceptance, Ethical Issues, Legal Issues, Workforce Transition, Business Strategy.

Frequently Asked Questions

What is the primary focus of this thesis?

This thesis examines the capabilities of AI in organizational decision-making, focusing on the economic opportunities and potential risks for businesses.

What are the central thematic fields?

The core themes include the technical basics of AI, the theoretical models of decision-making, and the application of these concepts in autonomous driving and human resource recruiting.

What is the core objective of the research?

The objective is to provide a business-oriented overview of AI to help organizations determine where to effectively invest in AI-driven decision-making tools.

Which scientific methods were used?

The work utilizes a comprehensive literature review and develops a benchmark based on inter-industry and cross-departmental analysis of AI applications.

What does the main part of the thesis cover?

It provides an introduction to AI, explores human versus AI decision-making models, and presents case studies on autonomous vehicles and recruiting processes to illustrate chances and risks.

Which keywords define this work?

Key terms include AI, decision-making, automation, augmentation, autonomous driving, recruiting, and the economic trade-offs associated with these technologies.

Why is "social acceptance" identified as a critical risk?

Social acceptance influences purchasing behavior and brand reputation; a lack of it, especially in critical areas like safety-sensitive autonomous driving, can hinder market adoption.

How does AI specifically impact the recruitment process?

AI helps in sourcing and screening candidates by identifying patterns and ranking applicants, though it faces challenges regarding unconscious bias and transparency.