Productivity Miracle for SMEs Efficient Software and Systems Engineering With AI

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In light of the current crises, small and medium-sized enterprises (SMEs) in particular have a great need to catch up in terms of innovative strength. Do AI technologies in software and systems engineering offer an effective lever for innovations and increasing productivity?

A recent study by the German Economic Institute in Cologne concludes that artificial intelligence will not trigger a productivity miracle in Germany for the time being. However, this is not due to the potential of the technology itself, but because it is not yet being used by companies in Germany to the extent that it could be. Software systems, in particular, can be made significantly more efficient with the help of AI.

Efficiency in software and systems engineering primarily means reducing the workload in the previously heavily human-centric development. If AI technologies replace manual tasks, it relieves specialists, shortens development times, and ultimately reduces costs. A productivity increase of up to 20 percent seems quite realistic here.

Not only the technology sector can benefit from this potential. Research-related areas such as medicine or data-driven activities in the finance and e-commerce sectors also benefit from the efficiency gains enabled by AI. In industry, it is particularly the automotive suppliers and manufacturers, important for Germany, who should view AI as an opportunity. Many sectors are now heavily influenced by software. Therefore, technical solutions are needed that allow companies to specifically optimize their existing business as well as bring about fundamental innovations.

Efficiency Through Automated Data Processing, Code Generation, and Security Analyses

Specifically, large language models (LLMs) can contribute to increased efficiency. The prerequisite for this is the targeted integration of such models into the company's own software systems. The use of language models then opens up a variety of applications. For example, documents can be accessed and evaluated much faster, structured data can be extracted, and processes can be accelerated—all while reducing susceptibility to errors. Examples include the analysis of tender texts or specifications and support in submitting offers. Generating or cleaning code can also be done very efficiently with the help of language models, fundamentally changing software and systems engineering.

In addition, the use of AI offers great advantages for safety analyses. In the areas of safety and cybersecurity, AI technologies can help develop simulations, conduct targeted tests, or identify vulnerabilities. Since large amounts of data can be processed quickly and in a structured manner, efficiency increases significantly. However, particularly in this area, it is essential that humans can comprehend and verify the AI's results. The work of professionals is thus not replaced but specifically complemented —a crucial aspect given the ongoing shortage of skilled workers.

AI as a Long-Term Investment in Competitiveness

Given the current tense market situation, companies in Germany find it difficult to invest in innovations. They are already heavily burdened by high costs, a shortage of skilled workers, bureaucratic hurdles, and political uncertainties. However, the gap with international competitors, particularly in the area of digitalization, continues to grow. Companies that do not act now risk permanently losing their competitiveness.

It is crucial to keep the timeline in mind that is required to harness the potential of AI. The meaningful deployment of AI technology in software systems is not comparable to buying a tool at a hardware store. There are several intermediate steps between acquisition and productive use: companies need to establish suitable processes, potentially adapt their own products, and qualify their personnel. This doesn't happen overnight. Rather, companies should anticipate a lead time of one to two years before an AI technology can be used efficiently in regular operations.

Those who set the course for the use of AI now can benefit in the long term. However, this requires that leaders realign strategically. If the necessary expertise is lacking in-house, external support should be sought. This approach offers the advantage, especially for SMEs, of being able to start implementation immediately. Together, it can be determined where and how AI technologies can be meaningfully applied in the company—and the hoped-for productivity miracle could still become a reality.

*Ralf Kalmar is Head of Business Development at the Fraunhofer Institute for Experimental Software Engineering IESE in Kaiserslautern.

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