Automating processes is an enormous lever that has been creating competitive advantages for many years by using human resources more efficiently. By automating manual and dull tasks, more budget can be allocated to other areas and human resources can be used for more complex activities.
Processes have been automated for many decades, first by machines and now by software. In the meantime, however, there are new and modern automation possibilities which are hardly used by many companies.
Data science is a broad field that encompasses many areas. Basically, with data science, we focus on using data to process information. For example, a company can use data science to set up detailed analysis and monitoring. The automated evaluation of business data combined with monitoring mechanisms eliminates the need for manual evaluation of business reports and operational activities, for example. Based on algorithms, most business processes can be monitored and analyzed with hardly any effort. At the same time, a data scientist can use algorithms to automatically evaluate existing data in order to make predictions for the future, such as in predictive maintenance. Here, algorithms predict when a machine needs to be serviced again. There is no need for superfluous maintenance and control cycles. Data science can be applied in every area of a company and holds extensive potential for automation and optimization. To identify individual examples and opportunities for using data science in your company you should contact an expert. Usually, the most promising opportunities are apparent after a half-day workshop.
Low-code and no-code applications enable business processes to be automated and digitized without the effort of traditional software developers. The idea behind this is that normal business users from the specialist departments can digitize complete processes by simply dragging and dropping and clicking them together in software designed for this purpose. In 2022, low-code applications are one of the most important trends – but they also harbor risks. Many companies imagine the implementation to be easy and underestimate that software development is not only about programming, but also about the conceptual elaboration of processes and dependencies. Developing and implementing the architecture is often more work and requires experience in the field that normal business users do not have. Accordingly, users of low-code development should be trained extensively and provided with experts in software development and architecture to ensure successful integration.
Many entrepreneurs think of AI primarily in terms of automated driving, budgets in the millions, Google, Apple and highly complex scientific projects. AI projects can often achieve first usable results within 20-60 project days without any problems and often reach their goals within a 5-digit budget. Experience shows that well-designed AI projects often pay for themselves after only a few months and that the data of a normal medium-sized company is quite sufficient. In an initial workshop, a professional service provider will determine which processes can be automated through AI and develop an initial proof of concept for a small budget. This ensures that the implementation is actually realistic and that the data is sufficient. After that, concrete statements can also be made about the scope and costs of the overall project. AI makes it possible to automate processes that could previously only be carried out by humans. It paves the way for new opportunities and competitive advantages for the middle class that are currently hardly used or not used at all.
Ailio GmbH is a Bielefeld-based service provider specializing in data science and artificial intelligence. We advise in both areas and unleash the potential of data that is currently lying fallow in German SMEs. In doing so, we take a cost-optimizing and risk-minimizing approach. If you are interested, please contact us directly!
Do you want to work with us to bring the potential in your data to life? Then we should talk!
Concrete figures for costs , ROI, scope and duration for the use of AI and data science.
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