Process optimization and productivity increase through AI

So what is holding back the medium-sized businesses from embracing the new technology more readily? The answer, if you want to reduce it to one word, is "implementation." If you want to elaborate a bit more, terms like "challenges," "expertise," or even "infrastructure" come into play. When it comes to AI implementation, the challenges can also be aptly described as thorny opportunities; because any effort made in this area by medium-sized businesses has the potential to pay off many times over the investments made.

The productivity gains that can be achieved through AI-supported automation of processes are indeed enormous.

According to McKinsey, generative AI has the potential to realize an annual global productivity increase of $2.6 to $4.4 trillion. This is probably why, according to the IDC study, 44 percent of medium-sized companies plan to use automation and AI in the next 12 to 24 months to further increase employee productivity. In particular, digital champions are increasingly focusing on process automation, IT/OT convergence, and productivity improvements, with 60 percent of them seeing these areas as a priority.

The mid-sized sector is not on its own - what added value does AI offer and what is needed for it?

To facilitate the entry into AI implementation, it helps to consider the available use cases. Once the right solutions for your own company have been identified in the first step, the question then arises: what now?

The answer to this simple yet pressing question lies in a tailored infrastructure. There is certainly no shortage of offerings in the field of AI solutions. However, not every provider has a well-thought-out, tested, and, last but not least, powerful infrastructure that is indispensable for smooth and reliable operation. So, anyone who wants to save themselves potential trouble during the initial AI implementation in their own company should first ensure that the chosen solution remains flexible and resilient even in tough situations.

What can AI achieve and what is needed for it?

An effective digitalization project in medium-sized businesses includes the following steps:

Digitally capture and collect data, digitize existing analog data

The first step in implementing AI is the digitization of existing analog data and the continuous collection of new digital data. This includes the installation of sensors, the integration of IoT devices, and the digitization of existing documents and processes. This digital data collection forms the foundation for all further steps and enables detailed analysis and use of the data through AI technologies.

By using modern cloud technologies, companies can capture and store data in real-time. These platforms offer scalable and secure storage solutions that efficiently manage large amounts of data. Additionally, they support the integration of various data sources and formats, significantly facilitating data collection and its subsequent processing.

Structure collected data, centralize and make it usable

The collected data must be structured and consolidated in a central database. This central data storage allows the data to be used efficiently and made accessible to AI algorithms.

A comprehensive cloud platform provides the necessary end-to-end stack for AI development through to implementation. This not only enables the central storage and management of data but also its rapid processing and analysis. Mature platforms have integrated tools for data management, data analysis, and machine learning (ML), enabling companies to effectively structure and utilize their data.

Analyze data, identify new correlations and insights

With the help of AI technologies such as ML and predictive analytics, valuable insights can be gained from the collected data. AI-supported analyses help to identify patterns and correlations that would remain hidden to humans. For example, predictive maintenance can increase the availability of equipment by early anomaly detection, thus reducing downtime. In the manufacturing sector, this can lead to significant efficiency gains. By collecting, processing, and evaluating machine data, predictions can be made about when a machine might fail (keyword: predictive maintenance).

An example of this is the case of König and Bauer, where the use of ML technologies reduced machine downtimes and increased production efficiency. The main goal is to increase the productivity of as many machines as possible to nearly 100 percent. Central to this endeavor is the Manufacturing Data Engine (MDE) from Google Cloud. This technology enables fragmented data sets to be consolidated and valuable insights to be gained from manufacturing data. This allows efficiency and productivity in production to be optimized.

In the retail sector, AI analyses can help predict customer behavior, optimize inventory, and develop personalized marketing strategies. For example, OTTO uses an AI assistant to quickly and accurately answer customer inquiries about products by evaluating all previous customer reviews and summarizing relevant information. This leads to an improved user experience and potentially higher sales figures. Modern cloud platforms offer powerful AI tools for complex data analyses. These tools use advanced algorithms and ML to gain deeper insights into the data and make precise predictions. They also support the automation of analysis processes, which increases the efficiency and accuracy of the results.

In all these cases, the use of a cloud platform significantly facilitates the implementation of such improvements. What is important are flexible and scalable solutions that allow companies to quickly adapt and expand their infrastructure. Established providers also support the integration of AI models into their customers' existing systems, which makes the implementation and operation of AI solutions easier.

A plan designed in this way describes a goal-oriented and efficiency-optimized feedback loop. The better the data, the better the insights, the greater the improvements to the entire system and all the processes linked within it.

A holistic approach is crucial

For successful AI implementation, medium-sized companies should pursue a holistic approach that considers both technological and organizational aspects. A well-founded digitization strategy sustainably strengthens the innovative power and competitiveness of medium-sized businesses by putting decision-makers in a position to fully exploit the potential of AI technologies and continuously optimize their processes. Support from a comprehensive cloud platform that offers a well-thought-out end-to-end stack for AI development and implementation can help medium-sized businesses successfully meet the challenges of digital transformation and realize the enormous efficiency gains within their own company.