AI for Business Practice

Literature on the subject of big data and AI is frequently very technical and

informatics-focused. This book sees itself as a transmission belt that trans-

lates the language of business in the spirit of potentials and limitations. At

the same time, the technologies and methods do not remain to be a black

box. They are explained in the scope of the chapters on the basics in such a

way that they are accessible even without having studied informatics.

In addition, the frequently existing lack of imagination between the

potentials of big data, business intelligence and AI and the successful

application thereof in business practice is closed by various best practice

examples. The relevance and pressure to act in this area do happen to be

repeatedly postulated, yet there is a lack of a systematic reference frame and

a contextualisation and process model on algorithmic business. This book

would like to close that roadmap and implementation gap.

The discussion on the subjects is very industry-oriented, especially in

Germany. Industry 4.0, robotics and the IoT are the dominating topics. The

so-called customer facing functions and processes in the fields of marketing,

sales and service play a subordinate role in this. As the lever for achieving

competitive advantages and increasing profitability is particularly high in

these functions, this book has made it its business to highlight these areas

in more detail and to illustrate the outstanding potential by numerous best

practices:

• How can customer and market potentials be automatically identified and

profiled?

• How can media planning be automated and optimised on the basis

of AI?

• How can product recommendations and pricing be automatically derived

and controlled?

• How can processes be controlled and coordinated smartly by AI?

• How can the right content be automatically generated on the basis of AI?

• How can customer communication in service and marketing be opti-

mised and automated to increase customer satisfaction?

• How can bots and digital assistants make the communication between

companies and consumers more efficient and more smart?

• How can the customer journey optimisation be optimised and automated

on the basis of algorithmics and AI?

• What significance do algorithmics and AI have for Conversational

Commerce?

• How can modern market research by optimised intelligently?

Comments

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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