“How can AI be profitably deployed in an enterprise without changing business processes?”

AI is and remains Hot Topic

Since the launch of ChatGPT at the latest, artificial intelligence has been on everyone’s lips in the business world, and in Germany too, more and more executives are asking themselves how AI can be used profitably in their own companies. Unfortunately, the two concerns that day-to-day business could be neglected during an AI implementation and that an AI project is far too large are still particularly prominent here. In this Insight, we explain how these concerns don’t stand in the way of an initial enterprise AI solution. Persistent AI procrastination in Germany is slowly dissolving More and more small and medium-sized companies are looking into the topic of artificial intelligence, but are still procrastinating on a first concrete project that goes beyond the use of ChatGPT. The reasons for such postponement, even though it has long been known how valuable the technology is, are far-reaching. There are no capacities, the employees have not yet been trained, the IT infrastructure does not allow for such complex processes or, most excitingly, AI is too specific for the company’s own business and is not needed. Most of the time, these reasons are just excuses based on lack of knowledge. After a short dialog, it quickly becomes clear how uncomplicated an AI implementation can be in one’s own company. It doesn’t always have to be the full automation of entire process chains or setting up your own cloud to provide immense computing capacity on demand.

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Every company of a certain size that does not operate offline without restrictions has some form of IT system as well as database. The foundation for AI solutions is data, and especially from a company’s own data, valuable applications can be developed that are not accessible to any outsider and are just waiting to be unleashed. This data-based foundation can be used to develop new products or optimize existing processes. Especially in process optimization, an initial AI implementation does not have to be a mammoth project. Not every step of the process chain can be easily improved by applying AI, but not every step adds huge value either. The first step is to assess the process chain and define an AI strategy that first optimizes the part of the process chain where the impact and effort are in the best ratio. In this way, it is possible to proceed step-by-step without having to plan a new fully automated system with a great deal of engineering effort. The optimization of one part of the process chain could, for example, already be a daily circular e-mail to the colleagues concerned, which provides short-term decision support in the form of a forecast based on the data of the previous business day. In this way, the company’s own data can be used to support decisions that until now have been made primarily on the basis of gut instinct. AI doesn’t have to replace employees, but it can already process large amounts of data with ease in short order and recognize structures that humans overlook. Even such a simple process can have great added value on business figures, depending on the quality and history of the data. It is absolutely worthwhile not to put the topic of artificial intelligence on the back burner.

Author

Dr. Kay Stankov
Head of Data Science, Ainovate GmbH