Algorithmic Business—On the Way Towards Self-Driven Companies

The effects and implications of algorithmics and AI affect the entire corporate value added chain. According to the focus of the book, the “business

layer” of the AI business framework has foregrounded the “customer facing” processes and functions. In this chapter, the potentials for the entire

corporate value creation are briefly described. It will be shown that artificial intelligence can change the way of working in classical company areas

both sustainably and radically: By using artificial intelligence, companies can

not only exploit efficiency and productivity potentials but also cater better

to customers and thus create added value. In addition, the significance of the

ideas and potentials of so-called Conversational Commerce (Sect. 4.2) for

internal company functions and processes will be illustrated and explained

(Conversational Office). Finally, the areas of marketing, market research

and controlling (as relevant cross-sectional function) will be described and

explained in more detail. Furthermore, algorithmics and AI also have the

potential of reinventing business models; these topics will also be treated in

this chapter. Finally, it will be investigated whether it makes sense to install

the position of a chief artificial intelligence officer in companies.

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Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1): 1. First you would look at what spam typically looks like. You might notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to come up a lot in the subject. Perhaps you would also notice a few other patterns in the sender’s name, the email’s body, and so on. 2. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. 3. You would test your program, and repeat steps 1 and 2 until it is good enough. Since the problem is not trivial, your program will likely become a long list of com‐ plex rules—pretty hard to maintain. In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually fre‐ quent patterns of words in the spam examples compared to the ham e...

What is Machine Learning

The term machine learning (ML) as a part of artificial intelligence is ubiq- uitous nowadays. The term is used for a wide number of various appli- cations and methods that deal with the “generation of knowledge from experience”. The well-known US computer scientist Tom Mitchell defines machine learning as follows: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Mitchell 1997). An illustrative example of this would be a chess computer program that improves its performance (P) in playing chess (the task T) by experience (E), by playing as many games as possible (even against itself ) and analysing them (Mitchell 1997). Machine learning is not a fundamentally new approach for machines to generate “knowledge” from experience. Machine learning technology was used to filter out junk e-mails a long time ago. Whilst spam filters that tack- ...

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