10 Examples of Artificial Intelligence in action: practical applications across industries and departments

In the semiconductor industry, maintaining high quality is crucial, particularly in laser diode production. Traditionally, visual inspections are carried out by trained personnel, a process that can be monotonous and susceptible to human error. To enhance this process, organizations have developed AI-driven optical defect detection systems. For instance, a project at dida implemented machine learning models based on convolutional neural networks (CNNs) to automate the detection and classification of defects during various production stages. The system analyzes two types of images: facet images that showcase cross-sections of the diodes and p-side images revealing intricate etched structures. This approach not only increases inspection accuracy and operational efficiency but also reduces costs associated with manual inspections. Furthermore, it alleviates the burden on workers by removing the need for repetitive tasks. The output includes highlighted images of defects and detailed CSV files documenting their classes and locations, enabling real-time monitoring and improved quality control processes.

In the financial sector, a significant amount of time is spent manually transferring data from various documents, such as emails and PDFs, into databases or spreadsheets. This labor-intensive task is not only time-consuming but also prone to errors. AI-driven solutions like smartextract have emerged to address these challenges by automating the extraction, validation, and transfer of data. These tools employ advanced machine learning algorithms to quickly and accurately identify relevant data points across multiple document formats, even those with varying layouts. By automating these processes, financial institutions can streamline workflows, minimize the risk of errors, and allow professionals to focus on higher-value tasks like strategy and analysis. Integration with existing software systems is seamless, enabling smooth data transfer into platforms such as Excel or ERP solutions.

Knowledge management is essential for organizations aiming to enhance productivity and collaboration. AI applications can significantly streamline information retrieval. A project at dida developed a document similarity search and retrieval software prototype that assists employees in finding relevant information quickly. This system uses advanced algorithms to analyze existing documents and match them with current queries, allowing employees to access previously answered questions efficiently. During the proof of concept (PoC) phase, the prototype was tested to determine its effectiveness in improving the inquiry response process. Following successful testing, the solution was integrated into a broader knowledge management platform. By enabling more efficient document retrieval, this initiative helps employees access critical information faster, ultimately enhancing decision-making and overall productivity.

As public administrations across Europe adopt digital solutions, making services accessible and user-friendly for citizens is paramount. dida developed an AI algorithm designed to assist users in navigating complex bureaucratic processes, such as business registration. In Germany, individuals must select an industry code from a list of over 800 options, often described in technical language that can confuse the average citizen. The AI solution leverages Natural Language Processing (NLP) to convert everyday business descriptions into relevant industry codes, effectively bridging the gap between common language and bureaucratic terminology. This approach has been integrated into an online service portal for North Rhine-Westphalia, enhancing the functionality of existing chatbots and business registration assistants. By simplifying the registration process, the solution improves citizens’ access to essential services, thereby fostering greater engagement with public resources.

In the transportation sector, AI enhances safety and operational efficiency by identifying potential hazards along transport routes. dida collaborated with Deutsche Bahn on the “Digitale Schiene Deutschland” initiative to create an AI system capable of detecting anomalous objects in real-time as trains operate. Utilizing video feeds from onboard cameras, the system autonomously identifies and ranks objects that may pose hazards. It leverages a diverse dataset that includes both annotated and unannotated data from various sensors. The challenge lies in defining what constitutes an “anomalous object,” as not all items, such as pedestrians near platforms, are inherently dangerous. By processing video frames and generating depth maps, the AI system can discern objects that stand out from their surroundings. This capability significantly enhances the ability of trains to detect and respond to potential hazards, thereby improving overall safety in the transportation system.

AI is improving customer support by enabling more efficient and responsive service. Organizations are increasingly implementing AI-driven tools, such as chatbots and virtual assistants, to handle a wide range of customer inquiries. These systems are capable of providing 24/7 assistance, which not only improves customer satisfaction but also reduces the workload on human agents. For instance, chatbots can resolve common questions, allowing human representatives to focus on more complex issues. AI systems also analyze customer interaction data to identify preferences and tailor responses accordingly, which enhances the overall user experience. Additionally, AI can automate ticketing systems that categorize and prioritize customer requests based on urgency and complexity, leading to faster response times and improved resolution rates.

AI is transforming research by enabling more efficient data analysis and simplifying access to extensive databases. For instance, tools like Google Dataset Search or IBM Watson Discovery help researchers explore and analyze massive datasets, such as public health records, environmental data, or government statistics, with greater precision and speed. These platforms use advanced algorithms to filter, categorize, and organize information, allowing users to quickly pinpoint relevant insights. For example, in environmental studies, AI can be used to track and predict climate patterns by analyzing years of meteorological and satellite data. Such applications free up researchers to focus on interpretation and decision-making, rather than manual data sorting.

Artificial intelligence is making significant strides in agriculture through precision farming techniques. Farmers are leveraging AI-powered systems like Climate FieldView and John Deere’s Operations Center to analyze vast amounts of data collected from various sources, including satellite imagery, weather forecasts, and soil sensors. For instance, AI algorithms can predict crop yields based on historical data and current conditions, allowing farmers to make informed decisions about planting and resource allocation, like dida's crop type classification solution. Additionally, AI-driven drones, such as those from DJI, and autonomous tractors are being used to monitor crops and optimize field operations. These technologies can identify areas that require attention, such as pest infestations or nutrient deficiencies, enabling targeted interventions that enhance productivity while minimizing resource usage. By adopting AI in agriculture, farmers can increase efficiency, reduce costs, and promote sustainable farming practices.

In the marketing realm, AI applications are revolutionizing how businesses engage with consumers. Solutions like Google Ads and Facebook Ads utilize AI-driven tools to analyze data on consumer behavior and preferences, allowing for the creation of more effective targeted advertising campaigns. For example, machine learning algorithms can segment customers based on demographics, purchase history, and online interactions, enabling marketers to tailor messages that resonate with specific audiences. Moreover, AI technologies such as dida's AI project for Ströer about ad campaign reach predictions, enabling marketers to adjust strategies dynamically based on consumer responses. This data-driven approach enhances customer engagement and maximizes the return on investment for marketing initiatives.

In human resources, AI technologies streamline the recruitment process, improving the efficiency and effectiveness of talent acquisition. Platforms like HireVue and Pymetrics utilize AI to analyze resumes and candidate profiles, identifying the best matches for job openings based on specific criteria. This reduces the time spent on manual screening and helps HR professionals focus on strategic activities such as employee engagement and retention. Additionally, AI tools such as LinkedIn Talent Solutions can assess candidates' skills and cultural fit through predictive analytics, enabling organizations to make data-driven hiring decisions. Chatbots like Olivia and Mya further enhance the recruitment experience by engaging with candidates, answering queries, and providing updates throughout the hiring process.

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