AI in Business

How companies use artificial intelligence

AI technologies such as machine learning, chatbots, generative AI and autonomous AI agents have established themselves as key tools in numerous areas of the business world. The development of machine learning algorithms has enabled the analysis of large amounts of data, while chatbots have automated interaction between companies and customers.

Recent advances in generative AI have opened up new possibilities in the creative field and in automated content creation. At the same time, autonomous AI systems are creating potential for making complex decisions and controlling processes. These advances have led to accelerated digitalization, which not only enables efficiency gains but also completely new business models.

The widespread introduction of artificial intelligence into the business world has taken place in several phases:

While data-driven models were initially developed for specific tasks such as predictions or classifications, the further development of deep learning enabled AI models to tackle more complex problems. This area includes natural language processing and image interpretation. Generative AI made it possible to create content such as text, images and music, opening up new horizons, particularly in the fields of marketing and entertainment. Thanks to the business field of autonomous AI agents, increasingly complex decision-making processes can now be automated, for example in logistics and finance.

AI applications in companies

The areas of application for artificial intelligence span virtually all industries and departments within a company. AI enables a wide range of applications that promote efficiency gains, cost reductions and better decision-making. While AI plays a supporting role in some areas of business, in others it is leading to the creation of new business areas.

• Production & manufacturing: In industry, AI systems optimize manufacturing processes through predictive maintenance and anomaly detection. Sensors and algorithms monitor machines in real time, detect potential failures, and can thus minimize expensive downtime. Robotics also benefits from this technology, for example through intelligent control systems that can adapt to variable production conditions.
• Logistics & supply chain: Artificial intelligence enables precise planning and control of supply chains in logistics. Predictive analytics allows companies to optimize inventory levels and shorten delivery times. AI systems use real-time traffic data to calculate optimal delivery routes, thereby reducing costs.
• Insurance & banking: In the financial sector, AI is used for numerous applications, including fraud prevention, credit risk assessment and the automation of compliance processes. AI-based systems analyze large amounts of data to identify anomalies and potential risks. Robo-advisors are another example where AI generates individual investment recommendations based on customer profiles. Attempts are also being made to use algorithms for market analysis to optimize investments in real time and improve trading strategies.
• Medicine & healthcare: In healthcare, AI improves diagnostics and treatment. Algorithms analyze medical images to detect signs of disease. AI can also help in the development of new drugs by simulating molecular reactions. Wearables that collect health data in real time use AI to provide personalized recommendations and improve health monitoring.
• Software development: AI-based tools support software developers in error analysis, code generation and project planning. For example, systems such as Copilot help with the automatic completion of program code, saving time and resources. AI models are also used to optimize development cycles by predicting bottlenecks and suggesting solutions.
• Human resources: In the field of human resources, tools are used to analyze application documents and optimize recruitment processes. Algorithms can help identify the most suitable candidates and promote high-quality decisions. AI-powered chatbots can also answer questions from employees, reducing the workload for HR teams.
• Retail & e-commerce: In online retail, AI plays a central role in improving the customer experience. Recommendation algorithms analyze customer purchasing behavior to generate personalized product recommendations. At the same time, AI models help with price optimization and predictive demand planning to optimize order quantities and inventory management. Chatbots and virtual assistants, in turn, support customer service and answer simple queries in real time. With advanced analytics tools, companies can also reduce shopping cart abandonment by offering targeted incentives or support.
• Marketing & sales: Marketing departments benefit from artificial intelligence through the automation and personalization of advertising campaigns. Data analysis tools identify target groups and optimize content based on customer preferences. Programmatic advertising, a business area in which online ads are automatically placed with the most relevant target groups, enables companies to advertise in a highly targeted manner. In addition, AI enables the analysis of sentiment data from social media to adapt brand strategies and identify trends at an early stage.

Examples of AI in business

The following overview shows various relevant examples of AI applications within a company, which can ideally be linked together to create further added value:

Process automation

Robotic Process Automation (RPA) supported by AI is the accelerator for all business processes. Repetitive processes and tasks are a necessity in many areas of a company. RPA allows these to be automated using ‘software robots,’ leaving more time for the really important tasks. Intelligent process automation thus opens up new business and revenue opportunities and drives process excellence.

Predictive analytics

By analysing historical data, companies can predict future trends and make data-driven strategic decisions. This technology can help identify seasonal trends, make sales forecasts and manage inventory efficiently.

Quality control in production

Image processing algorithms identify errors in the manufacture of products. This reduces the scrap rate and improves product quality. Another problem is slowly changing production processes. Unlike humans, AI does not ‘get used’ to such slow changes, but can reliably detect them.

Intelligent logistics systems

Supply chain optimisation is made possible by real-time data analysis and precise route planning. AI systems analyse traffic data, weather conditions and inventory levels to calculate optimal routes and delivery times. By linking warehouse, supplier and production data, an AI-monitored supply chain can meet shorter delivery times, avoid contractual penalties and optimise efficiency.

Financing and forecasting

Data-driven and automated planning of future income, expenses and other business parameters is now standard practice for companies. Artificial intelligence enables KPIs to be predicted and analysed more accurately. AI-based software tools can detect compliance violations in real time, perform market analyses and calculate and display key performance indicators.

Automated accounting

Finance departments benefit from AI through the automation of routine tasks such as categorising transactions, generating reports and monitoring budget deviations. These systems can detect anomalies and potential fraud at an early stage, which increases both efficiency and security.

Fraud detection

Fraud is a major problem in all financial services transactions. In 2020 alone, payment fraud, including wire fraud, card fraud and credit fraud, resulted in a total loss of $32.39 billion globally – and the trend is rising. AI models can detect and block fraudulent transactions in real time.

Automated customer service solutions

AI-powered chatbots and virtual assistants provide customers with round-the-clock support. They can answer frequently asked questions, solve simple problems and direct customers to specific resources. They can also assist with multilingual communication.

Personalisation of customer communications

Helping customers make purchasing decisions by offering them a product tailored to their needs is a top priority for every company. According to a McKinsey report, 75 percent of the content consumed by Netflix customers and 35 percent of the products purchased on Amazon are suggested content or products. AI offers opportunities to create a dynamic customer experience.

Content creation

Generative AI generates text, images and videos for marketing and internal purposes. A popular example is the automated creation of product descriptions for online shops.

Current limitations in the use of AI

Despite its versatility and advances, artificial intelligence faces technical and organizational limitations in practice. Many AI systems rely on large amounts of data, the collection, storage and processing of which entails considerable infrastructure costs.

Furthermore, the results of AI models are often difficult to understand, which can impair acceptance of and trust in the technology. This barrier of traceability is often referred to as the ‘black box’ problem.

In addition, AI systems are not error-free, meaning they can adopt biases from training data and reinforce unintended discrimination. To prevent this, the role of skilled workers remains crucial. Specialists must ensure that data is cleanly prepared, models are correctly trained and results are continuously reviewed. AI also has its limits in creative and emotionally intelligent activities. The human ability to recognize complex relationships, maintain interpersonal relationships and make ethical decisions cannot be replaced by AI.

Another challenge lies in the legal and ethical regulation of AI applications. Companies must ensure that their AI systems are used in accordance with data protection regulations and ethical guidelines. Infrastructure also represents a limitation. Computing power, energy consumption and network capacities must be able to withstand growing demands. At the same time, there is currently a shortage of sufficiently qualified specialists in many areas to develop and operate AI systems effectively.

The future of AI use will be characterized by increased collaboration between humans and machines. Companies must ensure that AI systems are not used as a substitute for employees, but as tools to improve productivity and the quality of work. Challenges such as integration into existing work processes and building trust in the technology remain key issues.