This thesis is concerned with what AI is capable of in decision-making when involved in organizational decision-making processes or embedded in offered products that per-form decisions. It is also concerned with what is lost and what is gained through its use and which risks businesses face when applying it. It adds value to previous work conducted on challenges and risks by explaining these from a business perspective focusing on the economic implications for organizations. The resulting overview on chances and risks can serve organizations interested in AI investments to augment or automate decision-making in understanding the risk situation and potentials in this field.
In the first chapter AI is introduced in a comprehensible way for non-computer scientists and its relevance for business is outlined. Subsequently, decision processes and how humans and AI tackle them are explained which provides a foundation to under-stand respective strengths and limitations of humans and AI in decision-making. The third chapter explains how AI can be applied in decision-making in businesses processes and products that perform decisions providing benchmark examples. Autonomous driving and recruiting are presented as examples for decision automation and decision augmentation respectively on the basis of which benefits and challenges will be explained. Focusing on these examples aims at making the possible associated effects of using AI in decision-making processes more tangible and understandable for business professionals.
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
4.1 Autonomous driving
Autonomous driving is chosen as an example for automated decision-making with AI for two reasons. Firstly, autonomous driving is a topic with high media coverage causing extensive public discussions (Maurer, 2016, p.1; Beiker, 2016, p.194; Fraedrich & Lenz, 2016, p.621). Thus, in a survey conducted six years ago already 56% of 1,133 respondents indicated that they had heard about it (Wolf, 2016, p.112). Secondly, autonomous driving has a wide range of potential social, economic and environmental impacts affecting the whole population as vehicles are seen as everyday devices and closely connected to humans’ lives (Grunwald, 2016, p.656;. Gallardo et al., 2017, p.1). Special attention will be given to the economic impacts as these are of high interest for businesses. In business AVs promise to affect the profits of automobile manufacturers and suppliers positively (Meseko, 2014, p.24). Moreover, their effects go beyond this industry as AVs can improve productivity of the time spent of employees while commuting or travelling, create new business models, extend and diversify existing mobility concepts, optimize traffic flows and serve in the commercial field e.g. for deliveries (Schwarting et al., 2018, p.188; Wachenfeld et al., 2016, p.19; Lenz & Fraedrich, 2016, p.174; Bjørner, 2019, p.258).
In an open and dynamic road traffic system including many environmental factors such as the weather, driving requires a broad range of little complex decisions (Wachenfeld et al., 2016, p.453; Ramge, 2018, p.14). These range from a mechanical application of traffic laws through steering, accelerating and braking to plotting a trajectory in dilemma situations when an accident is unavoidable (Lin, 2016, p.69; Gherdes & Thornton, 2016, p.87; Flämig, 2016, p.372). Moreover, decision-making requires setting priorities e.g. to brake for safety reasons or speed up when another vehicle announces a passing maneuver by flashing (Ramge, 2018, p.14). According to the National Highway Traffic Safety Association (NHTSA) in the United States more than 90% of traffic accidents can be attributed to human errors and 33% of these are caused by decision errors (2015, p.2). AVs aim to reduce these human errors by implementing an optimized computer algorithm to perform decision-making (Gallardo et al., 2017, p.1).
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.
- Arbeit zitieren
- Anonym (Autor:in), 2020, Artificial Intelligence and Decision-Making Applications in a Business Context. Chances and Risks, München, GRIN Verlag, https://www.grin.com/document/974846
