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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Possible Limitations of AI-Based Bots

The examples above already show the present-day potential of AI-based bots. At present, these systems are still in an early stage and still have certain limitations and potentials for optimisation. Twitter Bot Tay by Microsoft Most bots at present are reactive service bots. Engagement bots that actively interact with the users as market and brand ambassadors go one step further. The most famous example here is the chatbot Tay by Microsoft. Microsoft removed Tay from the web apologetically within one day. The example shows that the uncontrolled training of bots by the community can lead to fatal consequences. AI systems still have to learn ethical standards. It thus becomes apparent that even bots require a kind of guideline. Like a journalist has to observe editorial guidelines, bots have to observe certain standards. The next generation of AI-based bots must control and create the possible room for communication. IBM Watson has been able to celebrate quite a few respectable resul...

Robot Journalism Is Becoming Creative

Algorithms are able to automatically search the Web for information, pool it and create a readable piece of writing. In addition, data-based reports in the area of sport, the weather or finances are already frequently created automat- ically today. Recently, for example, merely a few minutes after Apple had announced their latest quarterly figures, there was a report by the news agency Associated Press (AP): “Apple tops Street 1Q forecasts”. The financial report deals solely with the mere financial figures, without any human assistance whatsoever. Yet, AP was able to publish their report entirely via AI in line with the AP guidelines. For this purpose, AP launched their corresponding platform Wordsmith at the beginning of 2016, which automatically creates more than 3000 of such financial reports every quarter, and which are pub- lished fast and accurately. It is no longer that easy to distinguish between whether an algorithm or a human has written a text. Another exception of rece...

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...

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