Which tasks can be automated effectively – and where a person should make the decision.
In AI-driven processes, humans play a crucial role by monitoring, adapting, and optimizing the systems. Despite automation, human judgment remains indispensable, especially in complex or unpredictable situations. Humans...
Read answerIntegrating feedback into AI-based automation is achieved through continuous learning and adaptation of algorithms. Feedback can come from both users and system data. It's important to implement a system that collects, a...
Read answerArtificial Intelligence (AI) can significantly lower costs in companies through automation, efficiency improvement, and better decision-making. Automated processes reduce the need for manual labor, thereby lowering perso...
Read answerThe costs of an AI project can vary significantly and depend on several factors, including the complexity of the project, required resources, and implementation duration. Initial investments are typically high due to bot...
Read answerTasks that are suitable are those where an assessment is sufficient and a mistake can be corrected easily: sorting, summarizing, designing, extracting. Tasks that are unsuitable are those where accuracy is essential and...
Read answerFor most companies, the primary requirement is transparency: Starting from August 2, 2026, chatbots must identify themselves as machines, and artificially generated content must be labeled. The further obligations for hi...
Read answerIf the same questions are frequently asked and there are well-maintained answers: yes. A chatbot without a reliable knowledge base will provide incorrect information and create more work than before. The question is not...
Read answerMost reliably for transcription: converting meetings, dictations, and phone notes into text. This works well today, even in German. Automatic phone assistants are technically possible but often fail in practice due to ba...
Read answerMost reliably in clearly defined tasks under consistent conditions: counting, checking completeness, reading labels, detecting obvious deviations. The more variable the light, angle, and background, the more complex it b...
Read answerIn preparation and follow-up, not in selling. Specifically: summarizing meeting notes, drafting responses, compiling customer information before meetings, preparing offers from templates. The gain is time for conversatio...
Read answerWith great caution. Systems for selecting applicants or evaluating employees are considered high-risk applications under the EU AI Act. Additionally, Article 22 of the GDPR, the AGG, and co-determination rights apply. Su...
Read answerFour points: a data processing agreement according to Art. 28 GDPR, a commitment that your data will not be used for training, clarity about the processing location, and about subcontractors. The second point is the most...
Read answerArticle 4 of the EU AI Act requires providers and operators to ensure that their staff possess sufficient AI competence. This obligation has been in effect since February 2, 2025. It does not refer to a certification, bu...
Read answerIn general, yes. Technical systems suitable for monitoring behavior or performance are subject to co-determination according to § 87 Abs. 1 Nr. 6 BetrVG – it depends on suitability, not intent. Most AI systems that proce...
Read answerTo identify processes suitable for automation with Artificial Intelligence (AI), several criteria should be considered. First, repetitive and rule-based tasks are particularly suitable as they can be standardized well. A...
Read answerData quality is crucial for the success of an AI project. High-quality data should be accurate, complete, consistent, and up-to-date. Additionally, the relevance of the data to the specific application is of great import...
Read answerRule-based automation is sufficient when processes are clearly defined, stable, and predictable. It is particularly suitable for repetitive tasks with established rules and conditions, such as data processing or simple d...
Read answerThe review and approval of AI results should occur in critical areas such as medicine, law, and finance. In these sectors, erroneous decisions can have serious consequences. The responsibility for validating the results...
Read answerMeasurable quality criteria for AI responses can be defined through various dimensions, including accuracy, relevance, consistency, and understandability. These criteria should be specific and quantifiable to enable obje...
Read answerTesting an AI system before production launch involves several steps, including validating data quality, reviewing algorithms, and conducting functional tests. It is important to confront the system with realistic scenar...
Read answerThe quality of an AI after go-live is ensured through continuous monitoring, regular evaluations, and feedback loops. Important metrics such as accuracy, precision, and recall should be reviewed regularly. Additionally,...
Read answerPrompt Injection is a security vulnerability where malicious input is inserted into an AI application to produce unwanted or harmful outputs. To protect against this, inputs should be validated and filtered to identify p...
Read answerTransmitting confidential company data to external AI models involves legal and security risks. It is crucial to adhere to data protection regulations, particularly the GDPR. Companies should ensure that appropriate secu...
Read answerA Data Protection Impact Assessment (DPIA) is required when an AI system is likely to pose a high risk to the rights and freedoms of natural persons. This is particularly the case when extensive personal data is processe...
Read answerAI-generated content can pose copyright risks, as the question of authorship and the protectability of such works is unclear. It is often not clear whether the AI or the user is considered the author. Additionally, AI mo...
Read answerThe labeling of AI-generated content is an increasingly discussed topic. Currently, there are no uniform legal requirements in many countries that make such labeling mandatory. However, it is recommended to create transp...
Read answerThe traceability of automated AI decisions requires systematic documentation of the decision-making processes. This includes recording the data used, the algorithms, and the parameters that influence the decisions. Addit...
Read answerOrganizing approvals for new models and prompts requires a structured process that includes several steps. First, clear criteria for evaluation and approval should be established. Next, it is important to form an interdi...
Read answerTo prevent uncontrolled Shadow AI in the company, it is important to establish clear guidelines for the use of artificial intelligence. Training and awareness programs for employees help raise awareness of the risks and...
Read answerInvolving employees and business processes in AI introduction requires a strategic approach. First, training and workshops should be offered to promote understanding of AI technologies. Additionally, it is important to a...
Read answerIntegrating AI into legacy software without modern interfaces can be achieved through various approaches. One option is to develop middleware that acts as a bridge between the existing software and the AI solution. This...
Read answerA manual emergency operation requires careful planning and documentation. First, critical processes that must be maintained during the emergency operation should be identified. It is important to provide clear instructio...
Read answerThe choice between an open source model and a commercial AI API depends on various factors. Open source models offer flexibility and adaptability but often require more technical know-how and resources for implementation...
Read answerWhen using Artificial Intelligence (AI), companies must ensure that data processing complies with applicable data protection regulations. In particular, the General Data Protection Regulation (GDPR) sets requirements for...
Read answerHandling personal data in training and test data requires special care to protect the privacy of the individuals involved. It is important to anonymize or pseudonymize data to avoid conclusions about individuals. Additio...
Read answerTo prevent discriminatory results of an AI system, several measures are necessary. First, a careful data analysis should be conducted to identify and eliminate biases in the training data. Additionally, it is important t...
Read answerThe documentation of an AI system for audits and the EU AI Act should be comprehensive and structured. First, the system architecture, the data used, the algorithms, and the training methods should be documented. Additio...
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