How many mechanical engineering companies know ad hoc how their liquidity, margins or order profitability are doing? Small and medium-sized enterprises (SMEs) in particular are stuck in an information crisis. They do not understand their business figures in detail - and make decisions without a clear data basis or too late. AI tools help machine builders to avoid having to rely solely on gut feeling in times of crisis.

The economic situation in the German mechanical engineering sector remains tense. Although incoming orders are stabilizing at a low level, the number of insolvencies continues to rise. According to the Federal Statistical Office, the number of company insolvencies in 2025 increased by around twelve percent compared to the previous year. Many companies are struggling with high energy costs, supply bottlenecks and a declining willingness to invest. This situation determines who really understands their figures - and who only reacts when it is already too late.

But how many mechanical engineering companies actually know what their liquidity, margin or order profitability is like? Could they answer ad hoc whether an investment of 120,000 euros in a new milling machine could be financed? Or would they first have to ask their tax advisor and wait several days for a response? This delay not only costs time, but often money too. Because if you don't know your financial situation, you are just as unable to take advantage of opportunities as you are to ward off risks.

Paper BWA instead of current data

The reality in many medium-sized companies is sobering. Controlling is still heavily dependent on tax consultants and many processes are manual or only partially digitalized. According to IfM Bonn (2023), around 42% of small and medium-sized enterprises in Germany do not have a largely digitalized controlling system. A KPMG study from 2024 confirms that only 28% of German SMEs use learning (AI) systems in the area of finance.

As a result, although entrepreneurs receive their business analysis (BWA) every month, they only receive their P&L and balance sheet once a year, sometimes months late. A snapshot with no relevance for action. The BWA alone does not provide enough data to serve as a control element. Decisions are therefore based more on a gut feeling than on meaningful data. In mechanical engineering, where material prices, personnel costs and delivery times fluctuate greatly, this is risky.

Financial intelligence as a competitive factor

In the manufacturing industry in particular, competitiveness increasingly depends on how quickly decisions can be made. Whether it's about expanding production, a new machine or adjusting prices: those who understand current financial data can react at an early stage.

Today, digital systems in controlling go far beyond pure accounting. They read financial data, link it to historical developments and provide understandable answers to business questions: How is my cash flow developing? Which product line is the most profitable? When is a liquidity gap imminent? Artificial intelligence can analyze these correlations within seconds - based on valid, tested logic. Instead of just collecting data, the result is a tool with financial intelligence.

From gut feeling to data-based decision

Many medium-sized companies rely on experience, which is no longer enough in uncertain times. Smart systems bring controlling back into everyday life. They translate figures into language and make business contexts accessible to non-controllers. Entrepreneurs can ask questions such as: How will the planned purchase of machinery affect profitability or how will my operating result develop compared to the previous year?

Answers come in seconds, not days. This creates room for maneuver. Especially when market changes require short-term decisions.
One example of this is smart tools such as CANEI.luna, which combine verified financial logic with natural language interaction. Any apps provide answers in understandable language, are GDPR-compliant and can automatically recognize trends and deviations. This enables managing directors, controllers and CFOs to act without detours, independently of tax advisors and paper-based evaluations.

Financial knowledge as a future competence

The current wave of insolvencies shows: It is not just a lack of demand, but a lack of transparency about their own profitability that is getting many companies into trouble. AI cannot save a company, but it can ensure that problems are identified at an early stage. SMEs must learn to understand financial data as a continuous dialog. AI-supported tools offer the opportunity to establish a new culture of financial communication: away from the fear of figures and towards genuine understanding. The numbers crisis in many companies is actually an information crisis. AI creates clarity and gives entrepreneurs back their control.

Outlook: The digital controller in mechanical engineering

Mechanical engineering companies that actively use financial data make better decisions and act more resiliently. AI does not replace controllers, it strengthens their role. The digital controller of tomorrow combines empirical knowledge with data-based analysis and can make reliable decisions on this basis. While large corporations have long relied on real-time reports, the use of AI in controlling also opens up new opportunities for smaller companies. Financial intelligence is becoming a prerequisite for sustainable success - not as a technical add-on, but as a central component of corporate management. After all, the future does not belong to the largest companies, but to the best-informed ones.