Economic AI Literacy: A Source of Competitive Advantage

Economic AI Literacy: A Source of Competitive Advantage

Dirk Nicolas Wagner (Karlshochschule International University, Germany)
DOI: 10.4018/978-1-7998-5077-9.ch008
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This chapter introduces the concept of economic AI literacy as a source of competitive advantage in a world where artificial intelligence (AI) complements and transforms business models. The purpose of economic AI literacy is to allow for enhanced strategic decision making in firms that either offer and/or use AI. Data and information goods, economics of networks, and economic agents in artificially intelligent firms are introduced as basic elements of economic AI literacy. To illustrate application, the case of TensorFlow and related cases are presented. The discussion highlights the strategic relevance of economic reasoning in the light of the expected effects of AI on business transformation.
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It is backstage where the new key actor in business prepares for its role. And this is where it will remain while the play changes. It changes with the introduction of Artificial Intelligence (AI) as the new actor who represents a general-purpose technology (Trajtenberg, 2019). Based on a cluster of technologies that includes Machine Learning, Deep Neural Networks, Big Data, Internet of Things and Cloud Computing AI has finally left the research laboratories and begins to rapidly establish itself in business practice, to cross borders between industries, and to create a constant flow of new possibilities on the fly. The play changes because there is a pattern change (Kruse, 2015) in how organizations work with machines. In short, AI assists by providing predictive analytics, it augments by providing prescriptive analytics, and it substitutes human work by fully automating business activity (Lepenioti et al., 2019). It does take over knowledge work but it does so in unprecedented ways. Typical for software, AI operates “behind the scenes” (Jennings et al., 2014, p.85). Not only does its reasoning largely remain a black-box for its users. More often than not, users do not even notice their interactions with an AI, which is for example illustrated by the fact that an ordinary smartphone user is likely to have hundreds of thousands of server contacts per week (Hill, 2019). Protected from foreign eyes, AI uses its place in the cloud to learn. This is a new property for a machine. The learning takes place centrally but with the help of and in the presence of decentralized and massively parallel action. This is highly economical. AI is designed to be highly results-oriented.

A new type of actor who represents a general-purpose technology, who acts behind the scenes and who rapidly learns centrally based on decentralized activity: these are the ingredients for an economic pattern change that poses a substantial challenge to business leaders and to their organizations: How to adopt, co-operate but also compete against a technology that

  • interacts with business through an exponentially growing number of sensors and user interfaces, making it omnipresent,

  • pulls the strings together centrally in the cloud and without further eye-contact,

  • on each encounter puts more knowledge to use, and

  • operates more and more autonomously without taking breaks in a highly economical fashion?

Leading tech firms who are at the forefront of AI development for business purposes early on realized that with AI on board machines are no longer ‘handled’ but need to be ‘organized’ and ‘managed’. They identified the discipline of economics as a puzzle piece to fit in between computer algorithms and business processes and they started to hire economists (Athey & Luca, 2018). This serves to explain the effects of the deployment of AI and to develop constraints as well as incentives for machines to act, coordinate, and collaborate in desired ways. The Economics of Artificial Intelligence (Agrawal et al., 2019) have rapidly gained importance.

Key Terms in this Chapter

Information Good: Good that is non-rival in consumption. Production may involve high fixed costs but low marginal costs.

Agency Problem: Problem that arise in situations where the principal cannot directly ensure that the agent acts in the principal's best interest.

Economic AI Literacy: Basic knowledge of the economics of artificial agents and of organizations and markets that consist of artificial agents.

Information Asymmetry: Situation in an economic transaction where one party has more or better information than the other.

Network Effects: A good or service exhibits network effects if the value to a new user from adopting it is increasing in the number of users who have already adopted it.

Cognification: The process of making objects or systems smarter and smarter by connecting, integrating sensors, and building software/artificial intelligence into them.

Shared Mental Model: Enables groups of individuals to achieve a common interpretation of the environment and to derive joint prescriptions as to how that environment should be structured.

Increasing Returns: The more a technology is adopted, the more experience is gained with it, and the more it is improved.

Exclusion Principle: Applies to goods where the owner may exclude others from the use unless they pay.

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