The appetite for investment is also strong: according to a BCG survey from January 2025, 65 per cent of German executives plan to increase their AI budgets in the near future. Statista forecasts that a total of ten billion euros will be invested in AI technologies by the end of 2025. At the same time, EU regulation is tightening the framework – and doing so at a rapid pace. Those who understand now where AI is making an impact, which hurdles are real and which tools have become established will make better decisions – whether they are a large corporation or a medium-sized family business.
Who’s in the lead – and who’s still catching up
Sectors where data volumes are high and processes are highly repetitive are particularly active in the use of AI: IT and communications, management consultancy and marketing, as well as manufacturing and financial control. In these sectors, automation pays off quickly because it can be applied to existing data sets and clearly definable processes. Large companies have structural advantages in this regard – they have the IT infrastructure, data sets and specialised teams that smaller businesses first need to build up.
Small and medium-sized enterprises (SMEs) face a different set of challenges. The main problem: a lack of expertise and too few trained specialists. According to a Deloitte study from 2025, targeted in-house training and open communication about fears surrounding automation are needed before AI projects can gain momentum. The technical transition is often easier than the cultural one.
Regulation and investment: What will apply in 2025
The EU AI Act has been in force since February 2025, with its first prohibitions coming into effect. Since then, high-risk applications have been subject to strict requirements regarding transparency, human oversight and comprehensive documentation. Added to this is the GDPR, which provides a framework for all AI-supported data processing and requires businesses to verify that their systems operate in a non-discriminatory and traceable manner. Compliance is therefore no longer an optional component; it must be integrated into every AI project from the outset.
Ethical requirements further complicate the picture. Fairness, non-discrimination and transparency in decision-making logic are requirements being imposed by regulatory bodies, partners and, increasingly, customers too. Businesses that address these issues at an early stage avoid costly rework – and ensure more reliable operation of their AI systems.
AI in practice: use cases that are already working
Three real-world examples from Germany illustrate the diverse ways in which AI is already being used. Deutsche Telekom utilises AI-powered personalisation to improve customer engagement across digital channels. Allianz relies on automated customer service using chatbot technology to handle enquiries more quickly and consistently. BMW uses predictive maintenance – that is, algorithm-based maintenance planning – to reduce unplanned machine downtime.
These examples illustrate a broader pattern. AI delivers its greatest benefits wherever processes are predictable, data-rich and high-volume:
- Marketing: Content personalisation, social listening, performance analysis
- Sales: Lead scoring, upselling and cross-selling predictions
- Customer service: chatbot automation, sentiment analysis, support ticket routing
- Operations: Predictive analytics, automated reporting
- Accounting: Fraud monitoring, compliance audits, invoice processing
- HR/Recruitment: Automated candidate screening, bias reduction
- Supply Chain: Inventory Management, Risk Analysis, Logistics Optimisation
That’s a staggering number of areas of application – and the market for available solutions is growing accordingly.
AI tools: What businesses are actually using
The tools market has become increasingly diverse. Alongside general-purpose tools such as ChatGPT and IBM Watson, there are specialised solutions for almost every function. It is crucial to define the use case before selecting the tool – anyone who starts with the tool and then looks for a task to suit it will quickly find themselves in a proof-of-concept limbo.
An overview of common solutions by area of application:
- General applications: ChatGPT, IBM Watson, amberSearch
- Marketing & Content: Jasper AI, Synthesia, Lumen5
- Data analysis: DataRobot, H2O.ai
- Customer service: Tidio AI, Observe.AI
- Images & Video: MidJourney, DALL·E 3, Runway
- Industry-specific: Infosys XtractEdge (document processing), amberSearch (skilled trades and SMEs)
If you want to work out from this list what suits your own business: first the process, then the tool. And if you’re looking for help in making sense of it all, you should speak to someone who knows your industry.
FAQs on AI in German businesses
How many German businesses use AI?
According to Destatis (November 2024), around 20 per cent of German businesses use artificial intelligence – an increase of eight percentage points compared with the previous year. Among large companies with 250 or more employees, the figure stands at 48 per cent; for medium-sized companies, it is 28 per cent; and for small businesses, 15 per cent.
How much are German businesses investing in AI?
According to Statista, a total of ten billion euros is set to be invested in AI technologies by the end of 2025. According to BCG (January 2025), 65 per cent of German executives plan to increase their AI investments in the coming months.
What does the EU AI Act stipulate for businesses?
The EU AI Act has been in force since February 2025, with its prohibitions now applicable. It requires businesses to ensure transparency, human oversight and comprehensive documentation for high-risk AI systems – in addition to the existing GDPR requirements.
Which sectors in Germany are making particularly extensive use of AI?
AI is particularly widespread in IT and communications, management consultancy and marketing, as well as in production and financial control – wherever data volumes are large and processes can be easily structured.
Which AI tools are commonly used in German businesses?
Among the most widely used tools are ChatGPT and IBM Watson for general applications, Jasper AI and Synthesia in the content and marketing sectors, and DataRobot and H2O.ai for data analysis. In customer service, Tidio AI and Observe.AI are widely used.
