July, 20208 IN MYV EWArtificial intelligence, a buzzword that is currently of great concern to the economy in the course of digitization, is on the advance and various technological hurdles seem to have gradually disappeared. This is one reason why companies are increasingly asking themselves: "How can I use this technology profitably for my business?" In order to prevent disappointment and frustration during the implementation phase, it is imperative that the business perspective, customer perspective and ethical issues are considered in the overall view, in addition to the technological possibilities. The use case must be clearly outlined, and an economic assessment must be done. An over-arching strategy helps to classify the technology and can lead to further synergy effects. We now know far over 50 percent of data, AI and IoT projects fail because at least one dimen-sion has not been considered deeply in advance.However, the central question that companies should always ask themselves before investing money in expensive data specialists and infrastructure is still too rarely asked at the be-ginning: "What should the collected data be used for and how should it be prepared for this purpose?" Answers to these central questions are the way to put quality before quantity. A comprehensive consideration of the individual relationships with each other, from end to end, is essential for the following steps. In an economy that is growing increasingly data-driven, it is important to face the transfor-mation to a data-driven enterprise and clearly evaluate the benefits and added value of da-ta. It is important to analyze and evaluate the data processes within the enterprise at an early stage by defining which data is needed, which data is available internally, or where it is necessary to access external data. With Data Thinking, Detecon has created an overarching framework that covers all relevant aspects. From observation of the market environment, analysis within the company in order to develop new, data-driven applications to prototype development, test phase, and intro-duction. The needs of customers and users are identified and creative solutions for data-driven challenges are modulated. The early integration of data and AI experts and the correspondingly developed data-based methods Canvas, and checklist-templates ensure that there are no unnecessary breaks and that nothing is forgotten. The continuous data reference ensured by the technical expertise enables relevant trends to be identified at an early stage in the conception of solutions and thus the latest technological approaches and standards to be incorporated. The participants benefit from new perspectives, which create out-of-the-box ideas that give the company innovative access to data-oriented solutions.The technological feasibility is validated by proof-of-concept, which show the business im-pact of the developed solutions and thus prevent misguided investments at an early stage. Prototypes are the focus here, as they illustrate the usability of the solution approaches to complex problems. With the help of the data-thinking methodology, this process is no longer a black box! By integrating different departments, continuous proof-of-concept, and an exact reflection of data relevance, a very accurate assessment of the ultimate success of a data-oriented digitization strategy can be made.In addition to the technological and economic aspects, it is also important to take ethical aspects into account. They usually address the unspoken core of customer requirements. Digital ethics is not a mainstream or marketing aspect. Only if customers have confidence in the products and services, they will buy them and recommend them to others. ELIMINATING THE DISAPPOINTMENT OF ARTIFICIAL INTELLIGENCE BY STEFFEN KUHN, GLOBAL PRACTICE LEAD INNOVATION & SPECIAL ASSETS /HEAD OF DIGITAL ENGI-NEERING CENTER/MANAGING PARTNER AND MANUELA MACKERT, CHIEF COMPLIANCE OFFICERS, DEUTSCHE TELEKOM [ETR: DTE]Steffen Kuhn
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