If big data is the new oil, analytics is the combustion engine (Gartner 2015).

Data is only of benefit to business if it is used accordingly and capitalised. Analytics and AI increasingly enable the smart use of data and the associated automation and optimisation of functions and processes to gain advantages in efficiency and competition.

AI is not another industrial revolution. This is a new step on the path of the universe. The last time we had a step of that significance was 3.5 billion years ago with the invention of life.

In recent years, AI has catered for an immense leap in development in business practice. Whilst the optimisation and automation of production and logistics processes are focussed on in particular in the scope of Industry 4.0 , AI increasingly also addresses administrative, dispositive and planning processes in marketing , sales and management on the path towards the holistic algorithmic enterprise .

AI as a possible mantra of the massive disruption of business models and the entering of fundamental new markets is asserting itself more and more. There are already many cross-sectoral use cases that give proof of the innovation and design potential of the core technology of the twenty first century. Decision-makers of all industrial nations and sectors are agreed. Yet there is a lack of a holistic evaluation and process model for the many postulated potentials to also be made use of. This book proposes an appropriate design and optimisation approach.

Equally, there is an immense potential for change and design for our society. Former US President Obama declared the training of data scientists a priority of the US education system in his keynote address on big data . Even in Germany, there are already the first data science studies to ensure the training of young talents. In spite of that, the “war of talents” is still on the rampage as the pool of staff is still very limited, with the demand remaining high in the long term.

Furthermore, digital data and algorithms facilitate totally new business processes and models. The methods applied range from simple hands-on analytics with small data down to advanced analytics with big data such as AI .

At present, there are a great many informatics-related explanations by experts on AI. In equal measure, there is a wide number of popular scientific publications and discussions by the general public. What is missing is the bridging of the gap from AI technology and methodology to clear business scenarios and added values. IBM is currently roving around from company to company with Watson, but besides the teaser level, the question still remains open about the clear business application. This book bridges the gap between AI technology and methodology and the business use and business case for various industries. On the basis of a business AI reference model, various application scenarios and best practices are presented and discussed.

After the great technological evolutionary steps of the Internet, mobiles and the Internet of Things, big data and AI are now stepping up to be the greatest ever evolutionary step. The industrial revolution enabled us to get rid of the limitations of physical work like these innovations enable us to overcome intellectual and creative limitations. We are thus in one of the most thrilling phases of humanity in which digital innovations fundamentally change the economy and society.

If we take a look at business articles of the past 20 years, we notice that every year, there is always speak of the introduction of “constantly increasing dynamisation” or “shorter innovation and product cycles”—similar to the washing powder that washes whiter every year. It is thus understandable that with the much-quoted speed of digitalisation , a certain degree of immunity against the subject has crept into one person or the other. The fact that we have actually been exposed to a non-existing dynamic is illustrated by Fig. 1.1: On the historic time axis, the rapid peed of the “digital hyper innovation ” with the concurrently increasing effect on companies, markets and society becomes clear. This becomes particularly clear with the subject of AI .

The much-quoted example of the AI system AlphaGo , which defeated the Korean world champion in “Go” (the world’s oldest board game) at the beginning of 2016 is an impressive example of the rapid speed of development , especially when we look at the further developments and successes in 2017.

The game began at the beginning of 1996 when the AI system “Deep Blue ” by IBM defeated the reigning world champion in chess, Kasparow. Celebrated in public as one of the breakthroughs in AI, the enthusiasm among AI experts was contained. After all, in the spirit of machine learning , the system had quite mechanically and, in fact, not very intelligently, discovered success patterns in thousands of chess games and then simply applied these in real time faster than a human could ever do. Instead, the experts challenged the AI system to beat the world champion in the board game “Go”. This would then have earned the attribute “intelligent”, as Go is far more complex than chess and in addition, demands a high degree of creativity and intuition. Well-known experts predicted a period of development of about 100 years for this new milestone in AI. Yet as early as March 2016, the company DeepMind (now a part of Google) succeeded in defeating the reigning Go world champion with AI. At the beginning of 2017, the company brought out a new version of AlphaGo out with Master, which has not only beaten 60 well-experienced Go players, but had also defeated the first version of the system that had been highly celebrated only one year prior. And there’s more: In October 2017 came Zero as the latest version, which not only defeated AlphaGo but also its previous version. The exciting aspect about Zero is that, on the one hand, it got by with a significantly leaner IT infrastructure, on the other hand, in contrast to its previous version, it was not fed any decided experience input from previously played games. The system learned how to learn. And in addition to that, with fully new moves that the human race had never made in thousands of years. This proactive, increasingly autonomous acting makes AI so interesting for business . As a country that sees itself as the digital leader, this “digital hyper innovation ” should be regarded as the source of inspiration for business and society and be used, instead of being understood and repudiated as a stereotype as a danger and job killer.

The example of digital hyper innovation shows vividly what a nonlinear trend means and what developments we can look forward to or be prepared for in 2018. In order to emphasise this exponentiality once again with the board game metaphor: If we were to take the famous rice grain experiment by the Indian king Sheram as an analogy, which is frequently used to explain the underestimation of exponential development , the rice grain of technological development has only just arrived at the sixth field of the chess board.

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 optimisation of the digital and analytical value added chain to achieve sustainable competitive advantages. Smart computer systems, on the one hand, can support decision-making pro...