Software enables completely new inspiration in the creative process By keeping AI software development entirely in-house, Audi demonstrates competence in a crucial emerging field Leveraging artificial intelligence (AI) in all departments: this is the goal Audi has set itself on its way to becoming a data-driven company. With FelGAN, the company now employs software that uses artificial intelligence to open up new sources of inspiration for designers. Creative people are always on the lookout for inspiration. The same is true of the designers who create new wheels at the Audi Design Studio in Ingolstadt. But where to find untapped sources of inspiration?

Now that machines are interacting with people and integrating into the chaos of the wider world, everything becomes more uncertain. The computers are out of their boxes. Computers haven't changed much in 40 or 50 years. They're smaller and faster, but they're still boxes with processors that run instructions from humans. AI changes that on at least three fronts: how computers are made, how they're programmed, and how they're used.

Across a broad spectrum of sectors, artificial intelligence (AI), digital, the Internet of Things (IoT), process automation, and other technologies are shifting value from manufacturers and distributors to companies that operate end-to-end platforms and provide outcomes as-a-service. For many enterprises, that means constant change and disruption--and a growing threat of market obsolescence. Leaders, revising their five-year plans every quarter, are seeking ways to constantly reinvent their companies to stay ahead of the pack, with competitors of varying capabilities and scale and customers who expect more for less. And for many companies, the answer is large-scale global transformation. Indeed, 80 percent of CEOs in one study claim to have transformations in place to make their businesses more digital; 87 percent expect to see a change in their operating models within three years.1

Most would agree that the best value creation is about growing the economy in a sustainable way, managing a balanced and inclusive society, making sure there are enough jobs for its citizens, prosperity, health and education, and guiding the country towards increased social wellbeing and stability. This is complicated in the present world. We live in a world that is changing at a fast pace, a pace never seen so far in human history. We now live in a period of technology data-driven 4.0 industrial revolution - 4IR - a world which is more interconnected and affected by technological innovation than ever before in history.

Organizations are facing an unprecedented convergence of technological, social, and regulatory forces. As artificial intelligence, automation, Internet of Things, blockchain, and 5G become pervasive, their combined impact will reshape standard business architectures. The "outside-in" digital transformation of the past decade is giving way to the "inside-out" potential of data exploited with these exponential technologies. We call this next-generation business model the Cognitive Enterprise. And our thought leaders have outlined seven steps toward getting there.

A new era of business reinvention is dawning. Organizations are facing an unprecedented convergence of technological, social and regulatory forces. As artificial intelligence, automation, Internet of Things, blockchain and 5G become pervasive, their combined impact will reshape standard business architectures. The "outside-in" digital transformation of the past decade is giving way to the "inside-out" potential of data exploited with these exponential technologies. We call this next-generation business model the Cognitive Enterprise.

For example, Netflix engages its audience by making customized content recommendations to each user. The company reported in 2012 that three-quarters of viewings originated from such suggestions. To make personalized recommendations at such scale, the company leverages an autonomous, integrated learning system that receives feedback (in the form of user ratings or viewer behavior) and updates suggestions accordingly. And it deliberately introduces variation into its recommendations, allowing new behaviors to emerge and letting the data speak for itself. For example, Amazon's dozens of data science systems are closely integrated, which allows decision engines to react to new data immediately and consistently.

From emotion AI that can detect real-time changes in mood to self-driving cars, the world is drastically remoulded by digital and technological transformation. AI is even now capable of acting like a curious 3-year-old toddler who will knock the glass off the table "just to see what happens." Perhaps, one day dating apps can finally make better recommendations than the last guy you went out with. Tall, dark and handsome – doesn't the algorithm get it? The algorithm does get it and all too well, inadvertently displacing humans from blue and white-collar jobs at such an alarming rate that policy makers, economists and technocrats are sitting up. AI pundits, however, are optimistic and exhort us to focus on how we can collaborate with machines.

Getting a credit card has never been easier. Yet the rise in issued cards exacerbates the ever-present challenges that face issuers, banks, and above all else, consumers. User loyalty is at an all-time low. Banks and issuers struggle to retain existing cardholders due to the plethora of rewards programs incentivizing customers to apply for new cards. At the same time, consumers are having a difficult time paying off their balances, driving card debt and delinquencies to record levels.

Artificial Intelligence (AI) and Machine Learning (ML) have already started making inroads into various industries. Healthcare is emerging as one of the biggest beneficiaries of the AI revolution. The technology is capable of facilitating easy and secure access to patient medical data, understanding and analysing their conditions. This ultimately helps improve accuracy and efficiency in the diagnosis and modernisation of health care practices. An example of an elementary implementation of AI is the use of chatbots and virtual assistants that can take care of basic yet tedious tasks like registering medical records, clinical workflows and monitoring lab results – all in an automated and secure process.