Study Artificial intelligence ensures greater profitability in mechanical engineering

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At the Hannover Messe (Germany) 2025, the VDMA Software and Digitalization Association and Strategy& presented the results of a joint study: Accordingly, the targeted use of GenAI can increase the profit margin in mechanical and plant engineering by up to 10.7 percent.

Generative artificial intelligence (GenAI) has established itself as an important catalyst for digital transformation and is increasingly shaping the machinery and plant engineering industry. "Already ten of the total 45 use cases considered can exploit 58 percent of the potential for margin improvement, thereby achieving a significant increase in efficiency in the industry," confirms Guido Reimann, Deputy Managing Director of VDMA Software and Digitization.

These are the key findings of the new study "GenAI in Machinery and Plant Engineering—From Promise to Profitability," conducted by the VDMA Association of Software and Digitization together with Strategy&, PwC's global strategy consulting service. The study is based on a survey of 247 industrial companies in Germany, Austria, and Switzerland, as well as an analysis of 45 possible GenAI use cases and their potential for increased productivity and profitability.

Additional profit of 28 billion euros possible

The study highlights that GenAI has a significant impact on revenue growth, cost optimization, and efficiency increases in companies. Overall, the targeted implementation of GenAI could increase the industry's operating margin by up to 10.7 percentage points, equivalent to an additional profit of 28 billion euros (30.2 billion US dollars) for the machinery and plant engineering industry in Germany. Against this backdrop, the rising expectations for the use of artificial intelligence (AI) are understandable: 52 percent of executives see AI as a potential "game changer" for the industry.

Only those who make GenAI a top priority, embed it deeply into core processes, and develop differentiating new capabilities from it can strengthen their competitiveness and increase their profitability in the long term.

Bernd Jung, Senior Partner at Strategy & Deutschland

Promising application of GenAI in research & development

The most promising GenAI applications are found in the core processes of companies. Among the respondents, sales and marketing (53 percent) as well as research and development (43 percent) are particularly mentioned, but higher potential is also seen in production and after-sales. These core functions influence both revenue increases and efficiency gains in manufacturing costs and operating expenses.

Challenges and strategic recommendations

Although interest in GenAI is steadily growing, its use is often limited to experimental or "proof-of-concept" projects.

• The central challenge is the lack of data quality. 25 percent of the companies surveyed see this as the biggest hurdle.

• Almost close behind, at 24 percent, is the lack of GenAI specialists.

• Similarly, 24 percent of the companies cite technical challenges, particularly with regard to IT infrastructure.

"Rising costs, stagnant productivity, and declining margins have been putting increasing pressure on the German machinery and plant engineering industry for years. Many companies in the industry therefore have high expectations for the possibilities of GenAI to improve productivity and profitability. At the same time, however, they remain stuck in a situation we call the 'Pilot Trap': they test ideas, initiate projects, and evaluate results. However, they have not yet dared to move toward broad implementation and the development of new business models. Although 91 percent of machinery and plant engineering companies plan to invest in GenAI by 2025, more than half will invest less than 100,000 euros (108,000 US dollars). Only those who make GenAI a priority, deeply embed it in core processes, and develop differentiating new capabilities from it can strengthen their competitiveness and increase their profitability in the long term," explains Bernd Jung, Senior Partner at Strategy& Germany.

The study recommends that companies specifically categorize GenAI use cases:

• Game changer with direct impact on the profit and loss account,

• Must-haves for sustainable profitability increase,

• Hyped use cases that have only limited impact on supporting processes.

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