AI In Medium-Sized Companies

AI In Medium-Sized Companies

AI In Medium-Sized Companies

AI In Medium-Sized Companies is a crucial technology for the competitiveness of companies of all sizes. With AI, processes and products can be improved, and new business models can be developed.

So far, however, many medium-sized companies have been reluctant about AI: There is often a lack of well-processed data, specialists and investment leeway. Ralf Klingenberg, founder of the software provider Rapid Miner and member of the steering committee of the Learning Systems platform, explains how medium-sized companies can use the potential of AI and overcome stumbling blocks when introducing AI.

Whether car repair shop, shelf manufacturer or delicatessen business: Not only large corporations can set up their business with AI systems for the future. The central potential benefits include efficiency advantages (e.g. production/resources), customer-specific offers, business model innovation and profit increases for small and medium-sized enterprises (SMEs). The fields of application range from demand forecasting for purchasing and production planning or intelligent chatbots in customer management to AI-assisted quality control and smart plant maintenance. According to current studies, only six percent of all SMEs consistently use AI technologies in all business areas. On the other hand, most medium-sized companies only use AI in individual projects, are planning their first pilot projects, or are not yet using AI at all. Small and medium-sized companies in particular, which are often still owner- or family-run, with their short decision-making processes, have good prerequisites for quickly introducing technical innovations such as artificial intelligence.

Recognizing The Potential And Designing An AI Strategy

Nevertheless, medium-sized companies also face unique challenges when introducing AI technologies – especially if they are still at the beginning of digitization. Often there is a lack of the necessary know-how or the corresponding specialists or investment leeway. In some cases, existing data sources are scattered throughout the company and cannot be linked to one another automatically. Technical competencies, the existing infrastructure, and the company’s strategic orientation determine how well prepared a company is for the use of technologies.

In many cases, the data of medium-sized companies hide untapped potential that can be leveraged with AI. But artificial intelligence is not an end in itself. Not every problem can and should be solved with AI methods.

How can products or processes can optimize with the help of AI, sales or profits increase or innovative business models developing? At the beginning of the first AI project, companies should define precisely what they want to achieve with AI and in which areas is worthwhile.

Cooperate With Partners And Involve Employees

Ultimately digitized company areas or large amounts of data are not require for the first implementation projects. The first projects in relevant departments also make it possible to start. A larger strategy can build for the entire company. Companies do not necessarily have to develop the necessary know-how but can obtain the required AI knowledge from outside. In addition to advisory services, cooperation with research institutions is also possible.

Suppose the technical infrastructure such as computing capacity or investment scope for developing your own AI systems is missing. In that case, tailor solutions can also purchase as AI-as-a-Service offers. For the intelligent maintenance of systems, companies can, for example, fall back on services from machine manufacturers. AI-as a Service offers can make it easier to get start with AI. But only your solutions will bring a real competitive advantage in the long term.

A company often does not have the necessary data for innovative AI applications. This is where networking with competitors or suppliers in ecosystems comes in handy. Cooperation with providers of data, technologies. And digital platforms can also help build up the required knowledge within digital value-added networks. In this way, data, know-how and infrastructure will use together, and risks will share.

The involvement of employees in AI change management is also central to the sustainable introduction of AI in SMEs. Continuous employee participation is an essential building block for reducing worries and reservations and motivating employees for the AI ​​transformation.

Outlook: Expand Data Infrastructure And Transfer

Business, science and politics must work together to improve central framework conditions to promote AI and the ​​transformation in SMEs. For SMEs, the expansion of high-performance networks is also crucial over a large area. Since many SMEs are not located in metropolitan areas. To show the range of applications of AI in SMEs. Technology transfer in SMEs should also expand & targeted support measures expand accordingly.

Also Read: The Internet Of Things: Into The Age Of Networked Things

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