Tech & Fun

 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...

 Chatbots are currently being boosted with the performance attribute AI. However, most bots at present are being implemented in a relatively trivial way. As a rule, certain keywords are scanned for on Twitter and Facebook, on the basis of which predefined texts or text modules are then automatically played out. Somewhat more intelligent are systems that automatically detect relevant text findings on the Internet and then put them together accordingly to form a post. This automatic form of content curation is also discussed under the term robot journalism. For the chatbots to be able to capture the posts accord- ingly, the in the meantime significantly advanced processes of natural language processing (NLP) which transform the running text into corre- sponding semantics and signal words, are used. Another approach is to connect the chatbots to knowledge databases. To the user, chatbots seem to be “intelligent” due to their informative skills. However, chatbots are only as intelligen...

 (Chat)Bots: Not a New Subject—What Is New? Bot, find me the best price on that CD, get flowers for my mom, keep me posted on the latest developments in Mozambique. —Andrew Leonard (1996) The topic of bots is new. Back in 1966, Joseph Weizenbaum developed with ELIZA a computer program that demonstrated the possibilities of communication between a human and a computer via natural language. When replying, the machine took on the role of a psychotherapist, worked on the basis of a structured dictionary and looked for keywords in the entered text. Even if this bot model as a psychotherapist only celebrated questionable success, such bots of the first generation with a firmly predefined direction of dialogue and keyword controlled are still used in many places. Especially in the past two years, bots have been experiencing a new quality and significance due to the fast developments of artificial intelligence, platforms, communication devices and speech recognition so that the unfulfille...

 esides designing and optimising corporate functions and processes, algorithmics and AI also have the potential to challenge and reinvent business models. Netflix, for example, owes its current success to the fundamental disruption of a business model from video-on-demand to streaming media. This way the company made it from the lower end of the market to the global market leader in no time at all, even ahead of the streaming portal of the giant Amazon. In addition to the production of their own series, Netflix won recognition in particular through the AI they developed, guarantee- ing maximum, dynamically adapted streaming quality, even if the Internet bandwidth is very low. As a result, the company was able to even prevail on markets with a rather underdeveloped infrastructure and establish itself at the top. Likewise, the agile start-up Airbnb is already threatening traditional industry leaders such as Marriott. Having started out as an idea of an inexpensive solution for budget...

 Typically 80% of the time in market research is spent on time-consuming tasks such as sampling, data acquisition and analysis, leaving only 20% for decisive detailed questions. By means of innovative big data and AI processes, this process can be automated so that market researchers have more time for really value-adding activities such as the interpretation of the analysis results and to derive recommendations and actions. Tomorrow’s market research will be oriented less towards samples and interviews but rather pursue a real-time census approach with automated analysis. By its very nature, market research is an extremely data-driven industry. Market researchers have always collected, edited and analysed particular data and then dealt with the interpretation of this data. In today’s fast-paced world, however, we are facing an enormous volume of data, we have already been juggling with zetta- or even yottabytes for quite some time. The global data volume is doubling every two year...

 As suggested by the afore-mentioned negative examples, certain risks are lurking in the background for companies that use algorithms in marketing. It is thus essential for companies to fully understand the algorithms applied and their limitations and for the algorithms to be used wisely. In addition, algorithms have to be supervised and controlled so that they are in harmony with the principles of the company and the image of the brand. Another aspect is the ever-increasing concerns of customers regarding their privacy, which can arouse mistrust of the use of algorithms. If the customer sees too much personalised advertising, this can be perceived as creepy, especially if the advertising is based on very deep insights into private information. This is also called overkill targeting and can reduce the success of the marketing strategy, The creepiness that the customer can experience emerges from an imbalance in the distribution of the information. The company advertising knows more...

 Many aspects in the last step of the marketing process, that of implementation and control, can be taken over by algorithms. Examples for the implementation of marketing strategies are, for example, the running of ads, the launching of a website or the sending of e-mails. As discussed previously, bots can display individualised Internet adverts. Bots can even take over the creation, personalisation and sending of marketing campaigns by e-mail. Even the creation of websites with the help of bots is possible, The Grid has been offering a private beta version for this since 2014 (Thomas 2016). The control phase at the end of the marketing process can be performed in both a qualitative and quantitative way and is essential. Factors that should be controlled are, among others, the reach of the campaign, marketing budgets, customer satisfaction, market shares and sales. Algorithms can be helpful in this case to measure the various factors and to make statements about the efficiency of t...

Algorithms, e.g. in the shape of bots, can be applied in all four steps of the marketing process. In the situation analysis, in the marketing strategy, in the marketing mix decisions and in the implementation and control. The situation analysis is meant to identify the customers’ unfulfilled wishes. Bots can be applied in the internal situation analysis of identifying the key performance indicator that provides information about the company’s strengths and weaknesses. In an external situation analysis, bots can search for certain keywords on the Internet to learn more about the customers and the competitors. Consumer behaviour can be observed and analysed with the help of bots. If companies use chatbots in customer service, bots can observe the courses of conversations and analyse them to obtain more information about the market and the customers. Bots can also hold interviews with certain customers or trend experts to conduct qualitative analyses. This can save both time and money as ...

 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...

 Nowadays, there already is a multitude of potential applications for marketing based on artificial intelligence. These potentials can, in principle, be subdivided into the dimensions “automation” and “augment” as well as on the basis of the respectively associated business impact. In the case of the augment applications, it is especially a matter of intelligent support and enrichment of complex and creative marketing tasks that are currently still performed by human actors. Artificial intelligence can, for example, support the marketing team in media planning or in the generation of customer insights (see the practical example  “The Future of Media Planning—AI as a game changer”). First and foremost, the augment potential is already more strongly developed in those companies that reveal a high degree of maturity in the AI maturity model. Planning and decision-making processes are also supported or already performed here by artificial intelligence. With regard to the automatio...

 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 i...

 Computer vision describes the ability of computers or subsystems to identify objects, scenes and activities in images. To this end, technologies are used with the help of which the complex image analysis tasks are divided among as small sub-tasks as possible and then computed. These techniques are applied to recognise individual edges, lines and textures of objects in one. Classification, machine learning and other processes, for example, are used to determine whether the features identified in an image probably represent an object already known to the system. Computer vision has multifaceted applications, among them the anal- ysis of medical imaging to improve prognoses, diagnoses and treatment of diseases or facial recognition on Facebook, which ensures that users are automatically recognised by algorithms and are suggested for tags. Such sys- tems are already used for security and surveillance purposes for the identi- fication of suspects. In addition, e-commerce companies such...

 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- ...

 Methods and Technologies In the following, the various methods and technologies are briefly outlined and explained. Symbolic AI Since the conference at Dartmouth College in 1956, a variety of different methods and technologies have been developed for the construction of intel- ligent systems. Even if neuronal networks and thus the approach of sub-symbolic AI dominates today, the field of research was dominated by the symbolic approach for a long time. This “classical” approach by John Haugeland called “Good Old-Fashioned Artificial Intelligence” (GOFAI) used defined rules to come to intelligent conclusions depending on the input. Up to the AI winter of the 1990s, “artificial intelligences” were developed by program- ming and filling control equipment and standards and databases to then be able to access them in practice. To this day, a large number of search, plan- ning or optimisation algorithms and methods from the times of symbolic artificial intelligence are applied in modern ...

 Despite the great AI successes of recent years, we are still in an era of very formal, machine AI. Figure shows that the underlying methods and technologies have not fundamentally changed since the 1950s/1960s to today. However, due to the increased amounts of data and computer capac- ities, the methods could be applied more efficiently and successfully. The so-called deep learning approaches brought about an immense leap in qual- ity. These massive gradual improvements to “machine learning on drugs” allow us to perceive a quasi-principle leap in AI that does not actually exist in this way. The systems are still learning according to certain rules and set- tings, patterns and distinctive features. The next important step in the evolution of AI is the ability of the sys- tems to learn autonomously and proactively to a wide extent. The first promising learn-to-learn approaches were applied in the AlphaGo example described. In addition, there are numerous promising research approache...

 The subject of AI is nothing new—it has been discussed since the 1960s. The great breakthrough in the business world has failed to appear, but for a few exceptions. Thanks to the immensely increased computing power, the methods can now be massively parallelised and intensified. Innovative deep learning and predictive analytics methods paired with big data technology facilitate a quantum leap of AI potential benefits for business applications and problems. In the last ten years, the breakthrough with regard to the applicability in business practice has succeeded due to this further devel- opment. At present, the discussion is, on the one hand, shaped by hardly realistic science fiction scenarios that postulate computers taking over man- kind. On the other hand, there is a strongly informatics-/technology-laden discourse. In addition to that, there are singular popular science publications as well as articles in the daily press. The latter adhere to the exemplary level without holis...

 Big Data—More Than “Big” A few years ago, the keyword big data resounded throughout the land. What is meant is the emergence and the analysis of huge amounts of data that is generated by the spreading of the Internet, social media, the increasing number of built-in sensors and the Internet of Things, etc. The phenomenon of large amounts of data is not new. Customer and credit card sensors at the point of sale, product identification via barcodes or RFID as well as the GPS positioning system have been producing large amounts of data for a long time. Likewise, the analysis of unstructured data, in the shape of business reports, e-mails, web form free texts or customer surveys, for example, is frequently part of internal analyses. Yet, what is new about the amounts of data falling under the term “big data” that has attracted so much attention recently? Of course, the amount of data avail- able through the Internet of Things (Industry 4.0), through mobile devices and social media has ...

 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 ind...

 In the early phases of the industrial revolutions, technological innovations replaced or relieved human muscle power. In the era of AI, our intellectual powers are now being simulated, multiplied and partially even substituted by digitalisation and AI. This results in fully new scaling and multiplication effects for companies and economies. Companies are developing increasingly strongly towards algorithmic enterprises in the digital ecosystems. And it is not about a technocratic or mechanistic understanding of algorithms, but about the design and optimi- sation of the digital and analytical value added chain to achieve sustainable competitive advantages. Smart computer systems, on the one hand, can support decision-making processes in real time, but furthermore, big data and AI are capable of making decisions that today already exceed the quality of human decisions. The evolution towards the algorithmic enterprise in the spirit of the data- and analytics-driven design of business ...

 Artificial intelligence (AI) has catered for an immense leap in development in business practice. AI is also increasingly addressing administrative, dispos- itive and planning processes in marketing, sales and management on the way to the holistic algorithmic enterprise. This introductory chapter deals with the motivation for and background behind the book: It is meant to build a bridge from AI technology and methodology to clear business scenarios and added values. It is to be considered as a transmission belt that translates the informatics into business language in the spirit of potentials and limitations. At the same time, technologies and methods in the scope of the chapters on the basics are explained in such a way that they are accessible even with- out having studied informatics—the book is regarded as a book for business practice. AI and the Fourth Industrial Revolution If big data is the new oil, analytics is the combustion engine (Gartner 2015). Data is only of benefit ...