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2021 | Book

Introduction to Artificial Intelligence and the Nature of a Firm: Implications to Strategy and Strategy Implementation Read chapter Read first chapter

Artificial Intelligence and the Changing Nature of Corporations

How Technologies Shape Strategy and Operations

Authors: Prof. Dr. Tankiso Moloi, Prof. Tshilidzi Marwala

Publisher: Springer International Publishing

Book Series : Future of Business and Finance

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book explains how various forms of artificial intelligence, namely machine learning, natural language processing, and robotic process automation, could provide a source of competitive advantage to firms deploying them compared to those firms that would not have deployed these technologies. The advantages of machine learning, natural language processing, and robotic process automation in strategy formulation and strategy implementation are explored. The book illustrates the potential sources of advantage for the strategy formulation and strategy implementation processes, which can be derived from the deployment of each form of artificial intelligence.

Table of Contents

Frontmatter
1. Introduction to Artificial Intelligence and the Nature of a Firm: Implications to Strategy and Strategy Implementation
Abstract
This book consists of eight chapters dedicated at examining how various forms of artificial intelligence (AI) could shape a firm’s strategy. To assess how AI is likely going to shape a firm’s strategy, the book confines itself to AI forms such as machine learning (ML), natural language processing (NLP) and robotic process automation (RPA). After the introductory chapter, the book discusses a high-level overview of artificial intelligence in Chap. 2. It discusses strategy conceptualisation and formulation in Chap. 3. Chapter 4 discusses strategy implementation. Chapter 5 discusses machine learning as a form of artificial intelligence. It provides an understanding of machine learning as a starting point. The chapter further considers how machine learning (ML) could be used to shape strategy and strategy implementation. Chapter 6 discusses natural language processing as a form of artificial intelligence. It provides an understanding of natural language processing (NLP). The chapter further postulates how the NLP could be used to shape strategy and strategy implementation. Chapter 7 discusses robotics and robotics process automation as a form of artificial intelligence. Its aim is to determine how robotic process automation (RPA) could be used to shape strategy and strategy implementation. Chapter 8 provides a synopsis of the book.
Tankiso Moloi, Tshilidzi Marwala
2. A High-Level Overview of Artificial Intelligence: Historical Overview and Emerging Developments
Abstract
Historically, AI came into the spotlight driven by allies on the two sides of the Atlantic Ocean, the United Kingdom and the United States. In 1950, Turing put together and published Computing Machinery and Intelligence, which became a seminal work. In the United States, the concept of AI was inspired by science fiction known as Runaround created by Isaac Asimov. The DSRPAI was an important convention which propelled AI a step forward. There were still challenges as early computers could not store commands. They only executed them. Another early challenge in the computing world was that computers were unaffordable.
Since these early developments, AI has grown in leaps and bounds. Various technologies exist nowadays, including the natural language generation (NLG), the natural language understanding (NLU), speech recognition (SR), ML, virtual agents (VL), expert systems (ES), decision management (DM), deep learning (DL), robotic process automation (RPA), text analytics (TA), natural language processing (NLP), biometrics, cyber defence (CD), emotion recognition (ER) and image recognition (IR).
AI can be classified into strong and weak AI. The concepts of weak and strong AI emanate from Searle’s (The mystery of consciousness. Granta Books, London, 1997) objection to the argument put forward by the proponents of the idea that the relation between the brain and the body of a human being is identical in many respects to the relation between the hardware and software of AI and machine consciousness (MC).
Tankiso Moloi, Tshilidzi Marwala
3. A Brief Overview of the Firm’s Strategy
Abstract
This chapter provides a high-level overview of strategy by discussing the historical account of strategy, followed by a discussion aimed at understanding the concept of strategy. It further outlines and discusses processes that firms ought to engage in as they formulate strategy. From the discussions, it is apparent that strategy has long existed within living organisms. Strategy as a concept emanated from the need of the people to defeat their adversaries. The concept of strategy can be traced back to the period between 200 B.C. and 400 B.C. The first text that emphasised strategy was The Art of War by Sun Tzu, which was written between 400 B.C. and 500 B.C. From the business perspective, strategy refers to a way in which firms explain to their stakeholders how they will be moving forward. The firm strategy is generally viewed as a course of action that can help the firm in attaining their objectives. There are three aspects of strategy formulation. These are the corporate-level strategy, the competitive strategy (sometimes called the business strategy) and the parenting strategy. The growth or directional strategy outlines the growth objective of the firm and mainly on how to achieve this growth. The portfolio strategy outlines the portfolio of the lines of the firm, which assist in the diversification that the business should engage in. The parenting strategy explains how resources are allocated and managed, especially on areas to put more emphasis on. Strategy consists of three phases, namely, the diagnosis, formulation and implementation.
Tankiso Moloi, Tshilidzi Marwala
4. Strategy Implementation
Abstract
This chapter discusses strategy implementation. From the onset, it highlights some key definitions of strategy implementation. This discussion is followed by a brief distinction between strategy implementation and strategy execution. Some activities in strategy implementation are briefly outlined, and some factors that firms need to consider as they implement strategies are discussed. The chapter further outlines the typical pitfalls and mitigations for successful strategy implementation.
The terms strategy implementation and strategy execution are sometimes used interchangeably, even though some researchers argue that these are different processes. Planning, organising, leadership and control are essential activities for strategy implementation. To implement the strategy successfully, firms need to have the right people. They need to allocate adequate resources to provide support to the business units to execute the strategy.
As firms consider strategy, the executive layer of the firm must define the lines of communication throughout the firm. Firms further need to consider the systems, tools and capabilities that the firm has to be in a position to facilitate the implementation of strategies, and they will need to promote a culture of cooperation, responsibility and accountability.
Tankiso Moloi, Tshilidzi Marwala
5. Machine Learning in Strategy and Implementation
Abstract
This chapter provides an overview of the concept of ML. This is followed by a discussion of the growing influence of ML and of the different forms of ML. A brief overview of deep learning is introduced, while the final part of the chapter explores ML in strategy and strategy implementation. It is apparent in the discussion that ML is a concept that addresses the question related to how to construct computers that are capable of improving automatically through experience. Over the years, ML has grown substantially. Its growth and progress could be attributed to online data availability and low-cost computation. Three forms of ML exist, namely, supervised learning, unsupervised learning and reinforcement learning.
In addition, two types of learning exist, namely, deep learning (DL) and shallow learning (SL). SL would typically contain the single-layer neural networks, whereas DL would contain many layers of neural networks. DL requires more computation power for forward or backward optimisation while training, testing and eventually running these neural networks. DL often outperforms shallow ML methods, which often consist of single neural networks. Firms applying ML in Big Data Analytics would gain an advantage in the variety of factors affecting strategy and strategy implementation.
Tankiso Moloi, Tshilidzi Marwala
6. Natural Language Processing in Strategy and Implementation
Abstract
This chapter discusses the concept of natural language processing (NLP), specifically how the NLP is applied. The chapter further outlines the typical functions of the NLP. In addition, it explores the NLP in strategy and strategy implementation.
The NLP is a branch of computer science, AI and linguistics that focuses on the interaction between computer programs and human language. There are three functions of the NLP, namely, machine translation (MT), text summarisation and sentiment analysis (SA). MT is a branch of computer linguistics that analyses the use of computerised tools to translate the context derived from this, from one language to the next. The language that is being translated is the human language.
Automatic text summarisation refers to a process of bringing about a succinct and meaningful summary of the text. SA is a way of finding out the polarity or strength of the opinion that is expressed in written text. The opinion could be either positive or negative. MT has three major approaches, namely, rule-based MT, statistical MT and neural MT. A rule-based system would typically require the expert to know both the source and the target languages. This knowledge is then used to develop syntactic, semantic and morphological rules to achieve the translation. The statistical approach uses statistical models based on the analysis of bilingual text corpora. The neural MT approach uses neural networks to achieve machine translation. There are two broad categories of text summarisation approaches, namely, extractive summarisation and abstractive summarisation. Furthermore, there are four popular types of SA, namely, the fine-grained SA, emotion detection, aspect-based SA and multilingual SA. The firm that deploys the NLP and ML in Big Data Analytics would gain crucial and better insights, setting the firm apart from its competitors.
Tankiso Moloi, Tshilidzi Marwala
7. Robotic Process Automation in Strategy and Strategy Implementation
Abstract
This chapter discusses the concept of robotics, which is followed by a discussion of the robotic process automation (RPA). The benefits of the RPA are also outlined in this chapter. The chapter further explores the RPA in strategy and strategy implementation. Robotics and RPA concepts are often confused. The confusion that occurs may be caused by the fact that there are some crossovers between robotics and RPA.
Robots are used to automate physical tasks, such as in manufacturing. So many types of automation have nothing to do with physical robots. Furthermore, so many branches of robotics have nothing to do with automation. Robots have consistent characteristics including a certain form of mechanical aspect. This aspect of a robot helps it complete tasks in the environment for which it is designed. They also would need electrical components that control and power the machinery, and they would typically contain some form of computer programming.
RPA can be viewed as bringing together business models, strategy, process, data, humans and robots. This is done in order to achieve efficiencies within the business processes. RPA has several benefits, including cost reduction, time efficiencies, better accuracy rate, improved governance environment, better customer advocacy and retention, improved checks and balances, increased speed and productivity, easy integration into existing technologies and super-scalability.
The firm applying RPA gains advantage as the performance tracking occurs on an ongoing basis and is timely and free of human intervention. Those charged with governance are able to harness this information and respond timely should the need arise to ensure that the strategic responsibilities and objectives assigned to relevant employees are on track. The firm applying RPA gains advantage of having a tool for eliminating human intervention and for integrating plans, processes and structures.
Tankiso Moloi, Tshilidzi Marwala
8. Synopsis to Artificial Intelligence and the Nature of a Firm: Implications to Strategy and Strategy Implementation
Abstract
This book examined how various forms of AI could shape the firm’s strategy. The book confines itself to AI forms such as machine learning (ML), natural language processing (NLP) and robotic process automation (RPA). Following the introductory chapter, the book discussed a high-level overview of artificial intelligence in Chap. 2. It discussed the strategy diagnosis and formulation in Chaps. 3 and 4 discussed strategy implementation. Chapter 5 discussed machine learning as a form of artificial intelligence. It sought to provide an understanding of machine learning as a starting point. It further considered how machine learning (ML) could be used to shape strategy and its implementation. Chapter 6 discussed natural language processing as a form of artificial intelligence. It sought to provide an understanding of natural language processing (NLP). It further hypothesised on how the NLP could be used to shape strategy and strategy implementation. Chapter 7 discussed robotic process automation as a form of artificial intelligence. It sought to determine how robotic process automation (RPA) could be used to shape strategy and strategy implementation.
Tankiso Moloi, Tshilidzi Marwala
Backmatter
Metadata
Title
Artificial Intelligence and the Changing Nature of Corporations
Authors
Prof. Dr. Tankiso Moloi
Prof. Tshilidzi Marwala
Copyright Year
2021
Electronic ISBN
978-3-030-76313-8
Print ISBN
978-3-030-76312-1
DOI
https://doi.org/10.1007/978-3-030-76313-8

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