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...

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- ...

What is Data Protection and Data Integrity

As a matter of principle, when it comes to data protection, a differentiation must be made between personal data and data involving companies. As soon as inferences can be made to a specific individual and single data levels are being worked at, a moment has to be taken to consider: What is being processed? Is there already a business relationship? Which permissions or legal consent elements are at hand? Customer data may not be collected without permission and may also not be resold. Anybody who acts carelessly here can quickly render themselves liable to prosecution. In principle, the following applies however: Almost anything is possible with the customer’s consent. This is the reason why Facebook can act with the data to such an extent, because consent has been given, even if only few users have probably fully read and understood the Terms of Use. Likewise, a relatively far-reaching data processing in the scope of an ongoing customer relationship under the motto “for our own p...

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