What is AI (artificial intelligence)?
Artificial intelligence (AI) refers to the ability of machines to perform tasks that typically require human intelligence. AI systems emulate cognitive capabilities such as learning, problem-solving, planning, and language understanding.
| Feature | Details |
| Category | Technology, computer science, data analysis |
| Areas of application | Business management, business intelligence, natural language processing, automation |
| Typical use cases | Data analysis, forecasting, speech recognition, image recognition, chatbots, planning, reporting |
| Related terms | Machine learning, deep learning, generative AI, explainable AI, natural language processing, predictive analytics |
| Benefits | Increased efficiency, faster decision-making, pattern recognition in large datasets, automation of repetitive tasks |
At a glance
AI applications are widespread in everyday life, from voice assistants to data analysis.
AI works by collecting data, recognizing patterns, and learning from data.
Types of AI range from reactive systems to generative AI.
In business, AI supports areas such as controlling, planning, and business intelligence.
Responsible use of AI requires transparency, explainability, and human oversight.
AI definition
Artificial intelligence (AI) describes the ability of computers, machines, or software systems to perform tasks that would normally require human intelligence. Specifically, AI encompasses technologies designed to emulate human cognitive capabilities such as learning, understanding, problem-solving, language processing, decision-making, and creativity. AI is therefore a machine-based system that can learn from input data and, based on that data, generate predictions, content, or decisions that affect physical or digital environments.
It is important to distinguish AI from conventional systems that do not have learning capabilities. In the context of modern, learning-based systems, technologies that can adapt to new information and improve their performance are generally considered AI. Methods such as machine learning and deep learning form the basis of modern AI. Systems that merely execute fixed rules without the ability to learn flexibly are generally not classified as AI in the sense of modern learning-based systems.
How does AI work?
Artificial intelligence describes systems that capture and analyze information from their environment or from provided data sources and independently derive conclusions from it. Instead of human senses, AI technologies use structured and unstructured data, which may come from sensors, ERP and CRM systems, or online sources.
Using complex algorithms and models such as machine learning and deep learning, AI systems process these inputs, identify patterns and relationships, and generate predictions, recommendations, or decisions based on them without requiring human intervention. Artificial neural networks are particularly powerful in this context. Inspired by the human brain, they can identify complex structures in large datasets.
Modern AI systems can increasingly adapt to changing environments and requirements through learning. This learning process takes place in two central phases: During the training phase, models are provided with large datasets to identify patterns and learn statistical relationships. In the subsequent inference phase, the trained models apply this knowledge to new, previously unseen data in order to make predictions or perform tasks. The input data is processed layer by layer, with each “neuron” performing mathematical operations. Through repeated training, the network can continuously improve the accuracy of its predictions.
What types of AI are there?
Artificial intelligence can be classified into different types, including according to their scope of capabilities and ability to adapt or optimize themselves:
Reactive machines respond exclusively to current inputs and have no memory. They make decisions based on predefined rules and cannot retain or use past experiences.
Limited-memory AI uses current and past data, but stores this information only temporarily. Autonomous vehicles are an example of this type of AI.
Symbolic AI operates on a rule-based principle by combining symbols such as words or numbers according to predefined logical rules in order to arrive at an explainable result. A chess computer, for example, falls into this category.
Subsymbolic AI processes information mathematically without the reasoning process being directly interpretable. Results are generated based on probabilities rather than logical deductions. Machine learning and neural networks are examples of this approach.
Self-aware AI would possess consciousness, self-awareness, and an independent understanding of its environment. This remains a theoretical concept and does not currently exist.
Generative AI (GenAI) is designed to create new content. AI models such as GPT-4 or Google Gemini Ultra fall into this category. Generative AI is an active area of research and forms the basis for innovative applications such as text and image generation.
How can AI be used? – AI examples in everyday life and business
Artificial intelligence has long been an integral part of everyday life and supports people in many areas. Private and business users can employ the technology in a wide range of ways to simplify tasks and make processes more efficient. Examples of AI applications include:
AI-assisted content creation: In addition to generating text, generative AI can be used to create content such as AI-generated images, videos, and voices.
AI-assisted recruitment: AI tools support recruiting by analyzing résumés, matching candidates to job profiles, and, in some cases, analyzing video interviews.
Business intelligence: Business intelligence solutions such as DeltaMaster provide AI capabilities for data-driven business management, helping organizations turn data efficiently into decision-relevant insights.
Digital assistants and AI chatbots: Applications such as Siri, Alexa, and Google Assistant answer questions, control smart-home devices, and help with scheduling. In business, AI-powered chatbots handle routine customer-service inquiries around the clock.
Navigation and transportation: Navigation systems and autonomous vehicles use AI to analyze traffic data, calculate optimal routes, and make decisions in road traffic.
Personalized recommendations: Platforms such as Amazon, Netflix, and Spotify use AI to suggest individual products or relevant content based on user behavior.
Smartphones: Features such as facial recognition, automatic photo enhancement, and intelligent text recognition are based on AI technologies.
Translation and text processing: AI tools such as DeepL and ChatGPT translate texts, perform spelling and grammar checks, and can create or revise entire texts based on short prompts, including emails, marketing copy, and summaries.
What does AI mean for controlling and business intelligence?
AI offers significant potential in controlling and business intelligence: large datasets can be analyzed automatically, anomalies can be detected, and forecasts can be generated faster and more consistently than through manual analysis.
Typical AI applications in controlling include:
Automated variance analysis: AI identifies where and why KPIs deviate from plan.
Predictive analytics: Forecasting models calculate future developments based on historical data.
AI-assisted planning proposals: AI generates proposed planning values based on internal and external data.
Natural-language report navigation: Users can ask questions in natural language and receive substantiated answers.
Automated commentary: AI generates explanations of KPIs and variances.
The key requirement is that AI in controlling must be transparent, explainable, and traceable – supporting human decision-making, not replacing it.
Bissantz and AI
Bissantz integrates artificial intelligence into its business intelligence solutions to make analysis more intelligent, faster, and easier to understand – with a consistent focus on transparency rather than black-box behavior. AI at Bissantz means decision support rather than decision replacement, explanation rather than speculation, and systematic assistance rather than automated action for its own sake.
The focus is on intelligent functions in DeltaMaster and DeltaApp that are based on machine learning and natural language processing. These include:
AI-assisted analysis: DeltaMaster automatically identifies anomalies, outliers, and drivers in complex datasets and presents the findings in an understandable way. Its integrated variance analysis explains not only what happened, but also why it happened.
AI-assisted planning: In DeltaMaster, AI can automatically generate proposed values and enable driver-based planning with AI-supported simulations. Both internal historical data and external, non-tabular information such as market data can be taken into account.
Natural-language dialogue and search: Generative AI functions allow users to query information using natural language, either by voice or text, similar to a chatbot – including on mobile devices. The aim is not to provide a gimmick, but targeted navigation and substantiated answers based on the company’s data.
DeltaApp Insights: Users receive automated explanations and notifications about anomalies in their dashboards directly in the app. The information is contextual and concise, making the app a conversational companion for reporting and business management.
Multilingual reporting with AI: DeltaMaster can provide reports automatically in multiple languages, including commentary. This supports international organizations in maintaining consistent communication and management processes.
Governance and traceability: All AI functions follow the principles of “Explainable AI” and “Human-in-the-Loop”. The origin of the data, the method applied, and the reasoning behind the result remain transparent, traceable, and verifiable at all times.
Bissantz demonstrates with this approach that AI is particularly effective in business management when it emphasizes relevance rather than masking complexity – and when it strengthens human judgment instead of displacing it.
FAQ – frequently asked questions
Artificial intelligence is the ability of computers to perform tasks that normally require human thinking – for example, understanding texts, recognizing patterns, or making predictions. Instead of relying solely on fixed rules, many AI systems learn from data and improve their performance over time.
AI is the umbrella term for systems designed to emulate aspects of human intelligence. Machine learning is a method within AI in which systems learn from data rather than being explicitly programmed for every individual task. Deep learning, in turn, is a subset of machine learning based on artificial neural networks.
AI can pose risks when it is used without adequate controls, transparency, or reliable data. Regulatory frameworks such as the EU AI Act and principles such as explainable AI and human-in-the-loop can help mitigate these risks and support responsible AI use.
Bissantz provides BI software with AI-supported data integration, analysis, planning, and reporting. This helps turn data efficiently and effectively into actionable recommendations. In addition, Bissantz advises companies on implementing AI in BI and controlling processes.
Summary
Artificial intelligence is one of the defining technologies of our time and is changing the way companies analyze data, prepare decisions, and automate processes. In controlling and business intelligence, AI delivers particular value when it is used transparently, explainably, and under human oversight. Bissantz applies this principle consistently across DeltaMaster, DeltaApp, and its consulting approach – with the aim of strengthening human judgment through AI rather than replacing it.
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