AI use cases: 33 applications of artificial intelligence

Artificial intelligence is everywhere. It has become a valuable tool, especially in the modern business world. AI not only helps companies automate routine tasks, but also excels at optimising complex processes, recognising patterns and predicting trends. The use of AI promises significant efficiency and quality improvements in many industries and helps to open up completely new business opportunities.

Some companies are already reaping the benefits: according to a survey by the Federal Statistical Office from 2023, around one in eight companies in Germany uses artificial intelligence. It is striking that large companies (with 250 or more employees) use AI technologies significantly more often than small and medium-sized enterprises. The survey also provides insight into the most common areas of application for artificial intelligence in companies. This is illustrated in the following infographic:

The areas of application for AI are virtually limitless. Below, we present 33 current use cases for artificial intelligence from various industries and business areas.

Artificial intelligence is revolutionising sales and marketing by helping companies better understand and target customers. By using AI, you can increase your efficiency while optimising your customer approach. Specific examples of AI applications in sales and marketing include:

1. AI personalities (personas)

AI enables you to create detailed and realistic customer profiles, known as personas, giving you deeper insights into your target group. AI analyses large amounts of data to identify typical behaviours, preferences and demographic characteristics of your customers, and summarises the results in the form of a comprehensive user or buyer profile. These fictional personas serve as the basis for precisely targeting marketing strategies.

2. Hyper-personalisation

By using AI and real-time data, companies can accurately analyse the individual preferences and behaviours of their customers and create tailored offers, such as personalised newsletters, individual product recommendations and exclusive discounts. This not only creates a unique shopping experience, but also promotes customer loyalty and increases conversion rates.

3. Content production

There are now numerous AI-based tools and technologies that can be used to create content such as images, videos, audio files and text. In most cases, all you have to do is describe what you want the result to look like, and the AI creates the appropriate content based on these specifications. This automation saves time and resources and enables the production of appealing, high-quality content on a large scale.

By using specialised AI applications in customer service, companies can proactively respond to customer needs by offering both automated and personalised solutions. Areas of application for AI in customer service include:

4. Chatbots

AI-based chatbots are used to handle customer enquiries quickly and efficiently and are often found on websites, in mobile apps or via messaging platforms. Using natural language processing (NLP), they understand enquiries and can provide appropriate responses, guide customers through the ordering process or, in the case of more complex issues, forward them to human employees.

5. Digital assistants

Digital assistants support customer service representatives by providing real-time data and performing specific tasks. For example, they can automatically analyse customer profiles and provide employees with relevant information such as previous purchases or enquiries during customer conversations. They can also generate personalised product recommendations that employees can pass on to customers directly during the conversation.

6. Operations support bot

These AI-powered bots automate the management and processing of internal processes. They can perform real-time monitoring, detect anomalies, respond automatically to problems and take over repetitive tasks independently. For example, customer enquiries are automatically assigned to the right departments, the status of tickets is continuously monitored and enquiries are prioritised.

The use of AI in production and AI in industry offers enormous potential for manufacturing companies. The automotive industry is a major beneficiary. Here, AI systems optimise production processes, improve quality control and increase efficiency through predictive maintenance and automation.

7. Predictive maintenance

With the help of predictive maintenance, you can plan maintenance work in a targeted manner, avoid unplanned downtime and extend the service life of machines. In practice, sensors are attached to machines to record data such as vibrations, temperature or rotations. Predictive maintenance is already being used in tunnel construction, for example, to avoid any opportunity costs. Here, sensors are used to detect anomalies at an early stage that indicate the need for repairs or replacement of components such as drill heads.

Machine learning processes continuously improve predictive maintenance, enabling recurring patterns in machine data to be identified and the exact remaining service life of components to be calculated. To completely avoid downtime, spare parts orders are automatically triggered based on the calculated service life, ensuring that the spare parts arrive on site before the component is worn or worn out.

8. Predictive monitoring

Predictive monitoring enables proactive monitoring of production facilities. AI and machine learning are used to analyse data from IoT sensors anddetect even the smallest changes in production facilities at an early stage. Predictive monitoring can be used, for example, to provide timely warnings of unusual patterns in the CPU utilisation of servers and control systems.

These patterns can be an indicator of potential bottlenecks, malfunctions or inefficient processes. This helps to avoid breakdowns and increase the efficiency of production processes by enabling measures to be taken in good time before serious problems arise.

9. Predictive quality

Predictive quality uses data-driven forecasts and artificial intelligence to continuously improve the quality of processes and products in production. By analysing production data, unknown patterns can be uncovered and predictions about future quality can be made. This enables you to take early action to reducewaste and increase product quality.

Predictive quality is used in the automotive industry, for example. Here, data from production processes, such as welding parameters and material properties, are analysed and used to make predictions about the quality of the end product. This allows quality fluctuations to be identified at an early stage and appropriate adjustments to be made in the production process.

10. Demand Forecasting

Demand forecasting is an essential method in supply chain management that helps you accurately predict future customer demand and identify market trends at an early stage. In the textile industry, fashion houses analyse historical sales data, market and price developments, and seasonal trends to forecast demand for upcoming collections. Machine learning algorithms are used to process this data to make accurate predictions about future demand. This enables purchasing departments to optimally adjust order quantities and optimise inventory levels.

The following use cases show how AI technologies optimise logistics and make companies more competitive:

11. Intelligent route planning

AI systems optimise delivery routes by using a variety of data sources, including information about the goods to be transported, the available vehicles and drivers, and real-time traffic data.

They analyse historical data and past routes to minimise resource consumption, emissions and delivery times for future trips and adjust routes in real time to accommodate unforeseen events. This type of planning is particularly beneficial for fleets with electric vehicles, as it takes charging times and ranges into account, making route planning not only more economical but also more environmentally friendly.

12. Efficient inventory management

Artificial intelligence optimises inventory management by monitoring inventory in real time and analysing a wide range of data sources such as sales figures, supply chain information, and seasonal and regional developments. Market fluctuations and trends on social media platforms or in Google searches are also taken into account.

Generative AI models process this unstructured data to identify patterns andcreate accurate demand forecasts. This enables companies to identify sudden peaks in demand at an early stage and generate automated order suggestions, therebyavoiding bottlenecks.

AI also improves warehouse space allocation by analysing data on demand frequency and stock movements. Based on this information, it positions frequently requested items in easily accessible locations. This shortens picking times and increases efficiency.

13. Autonomous driving and robotics

Artificial intelligence enables the use of autonomous vehicles and robots in logistics. Robots equipped with deep learning can independently identify, analyse and count goods, as well as transport them. Driverless transport systems (AGVs) also navigate automatically through warehouses and company premises to minimise empty and incorrect trips and improve workflows.

In finance and accounting, artificial intelligence (AI) opens up new opportunities to increase efficiency and precision. These technologies help to better understand complex data structures and offer innovative solutions to the challenges in these areas.

14. Risk analysis

Artificial intelligence can be used to assess risks associated with financial transactions such as loans and investments. By analysing large amounts of data, AI recognises patterns and trends that indicate the risk of default. This enables financial institutions to accurately assess the creditworthiness of applicants and make more informed decisions.

15. Fraud detection

By analysing large data sets, AI can identify unusual patterns in financial transactions that indicate fraud. Machine learning algorithms learn from previous cases of fraud and are thus able to recognise new and previously unknown fraud patterns. This enables financial institutions to respond in real time to suspicious activity, whether it be credit card transactions, duplicate payments or money laundering.

16. Automated reporting

AI-powered systems simplify the creation of financial reports by automatically processing and categorising documents.

Using technologies such as optical character recognition (OCR) and natural language processing (NLP), data such as invoice numbers, suppliers and amounts are accurately extracted, quickly and error-free recorded and assigned to the correct categories. This allows you to generate comprehensive reports on expenses, tax deductions and budget overruns in no time at all.

The use cases for AI in healthcare and insurance demonstrate how technology-driven innovations can improve the quality of patient care and increase the efficiency of insurance processes. However, in sensitive areas such as medicine, you should not (yet) rely entirely on AI's judgement, but rather view artificial intelligence as a supporting tool.

17. Diagnostics and prevention

AI algorithms are used in radiology, dermatology and other fields to analyse medical images and detect diseases such as cancer or heart disease at an early stage. AI can also be used to analyse genetic markers and other health data to predict the risk of future diseases such as diabetes, heart disease or dementia.

18. Personalised therapy selection

Artificial intelligence is revolutionising precision medicine by analysing medical data such as biological markers and using this information to help develop personalised treatment plans for patients. This is particularly useful in oncology, where different genetic mutations require different treatment approaches.

19. Automated damage assessment

AI can be used in insurance to assess claims more quickly and accurately. By analysing photos and reports using image forensics and NLP techniques, AI can determine the extent of damage and identify possible fraud attempts.

20. Personalised risk assessment

Insurance companies use AI to offer personalised contracts and premiums based on a variety of factors such as health data, lifestyle and creditworthiness. Predictive models help reduce insurance costs for cautious customers and minimise risk for insurers.

In human resources, the use of AI opens up exciting new possibilities and brings a breath of fresh air to talent management and assessment. Possible areas of application for artificial intelligence include:

21. Text generation

AI-based large language models (LLMs) such as ChatGPT can be used in human resources to create and optimise job advertisements, emails and other HR documents. Existing texts can also be edited quickly and easily thanks to AI, e.g. if you want to change the form of address from ‘you’ to ‘we’.

22. Applicant screening

The classification of application documents plays an important role in the recruiting process. AI-supported systems analyse CVs and cover letters to identify relevant qualifications and experience and classify candidates accordingly. These systems can automatically group similar applicant profiles or filter out unsuitable candidates, making the selection process more efficient.

23. Employee evaluation

AI helps to create objective and data-based evaluations by analysing various metrics such as project completions, team collaboration and goal achievement. The insights gained from this serve as a basis for feedback meetings, appraisals and, for example, salary negotiations.

24. Optimisation of smart grids

AI-based large language models (LLMs) such as ChatGPT can be used in human resources to create and optimise job advertisements, emails and other HR documents. Existing texts can also be edited quickly and easily thanks to AI, e.g. if you want to change the form of address from ‘you’ to ‘we’.

There are also numerous promising areas of application for AI in the energy industry.

25. Intelligent energy management

AI optimises energy consumption in households and commercial buildings through smart home technologies and intelligent thermostats. These systems analyse energy consumption based on data such as temperature, occupancy times and user preferences. Based on this data, heating and cooling systems can be intelligently controlled, for example, which reduces energy consumption and saves costs. In addition, AI enables detailed monitoring of energy consumption. This allows companies to identify inefficient consumption patterns and take targeted measures to improve energy efficiency.

26. Cybersecurity

AI technologies are used to detect and defend against cyberattacks on critical infrastructure such as power grids. These systems continuously analyse traffic patterns, access logs and system diagnostics to detect unusual activity that could indicate potential security threats. With the help of machine learning, these systems learn from historical data to identify recurring patterns and improve detection accuracy. Once the AI detects threats, it can respond independently, for example by automatically isolating affected systems or applying security patches. This reduces the impact of attacks and ensures network security.

In IT and information management, AI can primarily take on tasks that are not trivial but are very repetitive. Good examples of AI applications for this include code migration and code documentation.

27. Code migration

Artificial intelligence can help to create code documentation quickly and efficiently by automatically generating comments on code sections and updating existing documentation. This saves time and ensures that the documentation is complete and accurate without disrupting the workflow of developers.

28. Code documentation

Artificial intelligence can help create code documentation quickly and efficiently by automatically generating comments on code sections and updating existing documentation. This saves time and ensures that documentation is complete and accurate without disrupting the workflow of developers.

29. Chat with your data

The ‘chat with your data’ approach enables users to gain insights into complex data sets through natural language interactions. With the help of AI-based chatbots, users can ask questions about their data and receive immediate answers in understandable language.

30. Process automation

Artificial intelligence automates IT processes by taking over routine tasks such as data processing and system monitoring. It can also proactively identify and resolve problems by using self-learning algorithms that continuously learn from new data and patterns.

AI is increasingly becoming an indispensable tool in research and development. It helps to analyse data efficiently and supports researchers in developing new products and technologies more quickly. Specific areas of application for AI include:

31. Data analysis

AI-supported data analysis enables companies to efficiently process large amounts of research data and gain valuable insights. Machine learning algorithms can recognise patterns in the data that human analysts might overlook.

32. Product development

Artificial intelligence, especially generative AI, is revolutionising product development by generating innovative ideas and designs quickly and efficiently. For example, AI can perform complex virtual simulations, plan physical product tests and identify potential errors or weaknesses in product designs.

33. Innovation

AI supports innovation processes by promoting idea generation and creative problem solving. By analysing trends and existing patents, AI can generate suggestions for new technologies or business models. It also helps identify innovation gaps and thus steer research in promising directions.

Have you recognised the potential of artificial intelligence and would now like to identify possible areas of application for AI in your company? We reveal the challenges you will face and which AI use cases you should steer clear of for now.

Implementing AI use cases in companies can involve various hurdles that slow down or even prevent the process. Below is a brief overview of five key challenges that frequently arise when developing AI use cases:

1. Lack of knowledge Scepticism
2. Insufficient (high-quality) data
3. Data protection (and other ethical and legal issues)
4. High costs
5. Lack of scalability

The fields of application for AI are diverse and promising. Nevertheless, there are areas in which generative AI in particular should not (yet) be used.

Imagine a bot that has read all your private messages, seen all your photos, knows when you get up in the morning, where you eat and who you spend time with. And then imagine that bot suggesting personalised advertising to you. Not a particularly pleasant thought, is it? The technical capabilities to do this already exist, but from an ethical point of view, this application of AI is more than questionable.

Generative AI is also widely used for content production. Depending on the area of application and quantity, this can also be unethical. In the 2024 election campaign, for example, AI-generated images were used to sway public opinion without being transparently labelled as such. Such practices can contribute to opinion manipulation in election campaigns and should be critically examined.

Generative AI can already be used very effectively for semantic text processing, for example for proofreading, summarising, automatically editing texts and creating drafts. The analysis of large amounts of data is currently still associated with relatively high costs. However, with the support of experienced data engineers and effective data science consulting, this challenge can be overcome. It is also essential to weigh up the costs and benefits carefully to decide whether this area of application for artificial intelligence is already suitable for your specific case.

Artificial intelligence is currently reaching its limits when it comes to so-called ‘long horizon tasks.’ These long-term tasks require the ability to recognise and correct past mistakes. Unlike humans, who can go back and revise parts of their work when faced with complex tasks, AI often struggles to revise previous decisions and correct mistakes made earlier. Solutions to this weakness of AI are currently being researched. In the future, it is therefore quite possible that AI will also be used for such long-term tasks.

Artificial intelligence, especially large language models (LLMs), are trained with large amounts of text and image data, but they do not have direct access to data about the physical world. This means that they often lack the understanding and experience to efficiently perform physical tasks such as controlling robots.

Since LLMs are still very new, we should not rely entirely on their judgement, especially in high-risk environments such as medical diagnoses, psychological therapy or financial decisions. However, this may change in the near future as the models gain more experience and evolve.

Identifying useful areas of application for artificial intelligence within your own company is the first step, followed by the actual work of implementing AI. However, companies often face a variety of challenges in doing so.

Challenge: A machine manufacturer wants to use AI to predict the maintenance requirements of its production machines and minimise unplanned downtime. However, the AI models have not been sufficiently trained for the specific operating conditions of the machines, resulting in inaccurate predictions and missed maintenance intervals.

Solution: The company recognises the need for more specific modelling and collects additional data that comprehensively maps the operating conditions of the machines. It integrates sensor data from the machines to obtain real-time information about their condition and performance. In addition, algorithms are implemented to continuously adapt the models to take changing operating conditions into account. By adapting the models to the specific operating conditions of the machines and continuously improving the predictions, it is possible to predict maintenance requirements more accurately and minimise unplanned downtime.

Challenge: A manufacturing company plans to implement AI-based production planning to increase efficiency and reduce delivery times. However, the biggest challenge is ensuring employee acceptance and cooperation during the introduction of this new technology. Some employees are sceptical about the use of AI and fear that automation will put their jobs at risk.

Solution: The company recognises the importance of change management and is taking appropriate measures to ensure the successful introduction of AI-based production planning. It is implementing a comprehensive communication and training plan to educate employees about the benefits of the new technology and address their concerns. The company organises training courses and workshops to provide employees with the understanding and skills they need to work effectively with AI-based production planning. Employees are also actively involved in the implementation process by having their opinions and suggestions taken into account.

The company also creates a positive corporate culture that supports change and innovation. It emphasises the opportunity for employee development and shows how AI technology can make their work easier rather than replace them. Through these targeted change management measures, the company is able to gain employee acceptance and cooperation. AI-based production planning is successfully implemented, leading to improved efficiency, shorter delivery times and a positive change in corporate culture.

The fact is that there are now applications for artificial intelligence in almost all industries and companies. It is therefore not unlikely that you too can benefit from the use of AI. The most important thing is that the AI applications are specifically tailored to the needs and requirements of your company. So don't use AI indiscriminately; instead, identify clear AI use cases in advance that will make your daily work easier and help you achieve your individual business goals.