What is generative AI?
Generative AI refers to AI systems that independently generate new content based on large training datasets – including text, images, code, or structured data. Unlike traditional AI, which analyzes and classifies existing information, Generative AI synthesizes original outputs: it writes, formulates, reasons, and communicates based on learned patterns. Bissantz uses generative AI to facilitate access to data and help users analyze and interpret business information.
| Feature | Details |
| Category | Artificial intelligence / business intelligence / digital transformation |
| Application | Automated generation of text, code, images, and structured data |
| Typical areas of use | Controlling, reporting, customer service, software development, knowledge management |
| Related terms | Large language models, agentic AI, explainable AI, predictive analytics, natural language processing |
| Benefits | Efficiency gains, democratization of knowledge, automated communication, decision support |
At a glance
Generative AI produces new outputs and therefore goes beyond traditional, purely analytical AI.
The foundation is formed by Large Language Models (LLMs): systems trained on enormous text corpora that can formulate, explain, and reason in context.
Generative AI supports but does not replace business judgment. Responsibility for decisions remains with humans.
There is significant potential in BI and controlling: automatic commentary on deviations, summarization of planning scenarios, and answering questions about data in natural language are typical use cases.
Generative AI – definition
Generative AI (also: generative artificial intelligence or GenAI) refers to a class of AI that independently generates new content based on large training datasets – including text, images, audio, code, or structured data. Unlike traditional AI methods, which analyze, classify, or predict based on existing data, generative models synthesize original outputs: they write, formulate, design, and reason based on learned patterns.
The underlying technology is primarily based on Large Language Models (LLMs), which are trained on enormous text corpora and respond contextually to inputs – known as prompts.
What are examples of generative AI? – Use cases in companies
GenAI is no longer a future topic – companies across a wide range of industries are already using it productively. The most important areas of application include:
Text generation and document creation: Marketing texts, product descriptions, job advertisements, emails, or internal guidelines can be automatically created or revised based on specifications.
Code generation and software development: Developers use generative AI assistants to write, debug, or document code.
Customer service and chatbots: Generative models enable chatbots to respond to customer inquiries naturally and context-sensitively rather than following rigid decision trees.
Knowledge management and internal search: Companies use generative AI to make internal documents, manuals, or knowledge databases searchable and accessible through dialogue.
Financial controlling: Variance analyses, management commentary, and summaries of financial reports can be generated automatically and prepared for different target audiences.
How does generative AI work?
Generative AI systems are predominantly based on so-called transformer architectures. During training, these models learn statistical patterns from enormous amounts of data – typically billions of text documents in the case of language models. The result is a model that calculates a probability-based continuation or response for a given input (the so-called prompt).
The key difference from rule-based AI systems is that generative models do not merely classify or predict; they independently synthesize new content. They can summarize, rephrase, translate, write code, and also generate images and graphics.
For business applications, foundation models are often adapted to specific data sources and specialist domains through fine-tuning or Retrieval-Augmented Generation (RAG). In this process, the model does not rely solely on its trained knowledge but also incorporates current internal company documents and database queries into its responses.
Generative AI vs. Traditional AI: What is the difference between AI and generative AI?
Traditional AI methods – such as statistical forecasting models, clustering algorithms, or rule-based systems – are designed to identify patterns in structured data or make decisions according to defined rules. Their output may be a number, a category, or a yes/no answer.
Generative AI, on the other hand, produces open-ended, context-sensitive outputs. It can not only calculate a variance analysis but also explain it in understandable language. It can not only compare several scenarios but also formulate their implications. In this way, generative AI creates a bridge between quantitative analysis and human-readable communication.
| Feature | Traditional AI | Generative AI |
| Output type | Structured (number, class) | Open-ended (text, code, image) |
| Training basis | Structured datasets | Many, heterogeneous information sources |
| Adaptability | Model retraining required | Prompt engineering, RAG, fine-tuning |
| Strength | Precise prediction, classification | Synthesis, explanation, communication |
| Risk | Overfitting, data bias | Hallucinations, loss of control |
| Strength | Precise prediction, classification | Synthesis, explanation, communication |
| Risk | Overfitting, data bias | Hallucinations, loss of control |
What are the opportunities and risks of generative AI for companies?
Opportunities
Efficiency gains: It accelerates repetitive tasks such as automatically summarizing meetings and documents, creating presentations, or generating code in software development. Employees gain time for other tasks.
Lower barriers to accessing data: Instead of complex tools or specialist knowledge, a question in natural language is enough to access data, documents, or systems.
Democratization of knowledge: Expertise that was previously concentrated among individual specialists can be encoded in models and made accessible to broader user groups.
Creativity and idea generation: It can serve as a sparring partner during early stages of work – for brainstorming, drafting texts or concepts, or developing product ideas. It quickly provides an initial draft that can then be further developed.
Risks
Hallucinations: Generative models can produce outputs that are grammatically correct and factually plausible but nevertheless incorrect.
Data security and compliance: If internal financial data is transferred to external cloud models, potential risks arise for trade secrets and personal data. Companies must clarify which data may be made available to which models.
Uncritical adoption of outputs: When texts are generated automatically, there is a risk that they will be passed on without sufficient reflection – resulting in a loss of the professional responsibility that previously rested with the user.
What does generative AI mean for controlling?
Generative AI is structurally changing the role of the controller within a company. Work is shifting away from manual data aggregation and report production toward the interpretation, validation, and strategic contextualization of AI-generated results. Controllers are increasingly becoming quality assurance specialists and sparring partners for management. This is a development that many experts have been calling for years, but which is now becoming increasingly feasible through technological support.
At the same time, requirements for data literacy and critical thinking are increasing: Anyone evaluating AI outputs needs to understand how they are generated, which data they are based on, and where models typically reach their limits. Controlling expertise and AI competence are therefore becoming inseparably connected.
Practical example: generative AI in controlling with DeltaMaster
A controller opens the monthly report in DeltaMaster. Instead of manually commenting on deviations, DeltaMaster automatically generates a context-specific management commentary: “Contribution margin in Segment B is 11% below the previous year. The main driver is a 7% decline in prices due to increased competitive pressure; the volume effect of -4% is attributable to a postponed major order.”
The controller reviews, supplements, and approves the commentary – in minutes instead of hours. Management receives a concise, audience-specific summary that shows not only what happened, but also what needs to be done. Generative AI takes care of the wording – human judgment remains with people.
Bissantz and generative AI
Bissantz has long been concerned with the question of how numbers can not only be calculated correctly but also communicated clearly. Generative AI is a natural next step in this development: With DeltaMaster, Bissantz integrates GenAI functionalities directly into the analytical workflow – for example, for automatically commenting on deviations or preparing reports in natural language. In this way, the technology serves as a tool for decision support, including the following functionalities:
Interpretation of anomalies: Deviations between actual and planned values can be automatically contextualized and interpreted using generative models.
Natural language querying: Users ask questions in natural language – “Which cost centers exceeded their Q3 budget by more than 15%?” – and receive a structured answer directly, without SQL knowledge or the need to navigate through the BI system.
Simulation and planning support: Generative models can not only calculate scenarios but also describe their effects in prose, identify risks, and formulate possible courses of action.
Management Summaries: The most important insights are automatically condensed into a concise, audience-specific summary – providing a reliable basis for management discussions and decisions.
In addition, Bissantz offers comprehensive consulting services for the use of generative AI in controlling.
FAQ – frequently asked questions
It is a form of AI that does not merely analyze information but also creates new content. It reads a prompt – an input or question – and then independently generates new content: text, an image, code, or an analysis. Think of it as a highly knowledgeable assistant that formulates an appropriate response based on everything it has learned.
A hallucination refers to outputs that sound grammatically correct and plausible but are factually incorrect. Addressing them requires human review of outputs, the use of RAG systems that restrict models to reliable data sources, and clear governance defining which outputs may be shared without additional review.
If sensitive data is transferred to external cloud models, risks arise concerning trade secrets, personal data, and regulatory compliance. Companies must clarify which data may be made available to which models – and whether on-premises solutions or strict data isolation are required.
DeltaMaster integrates generative AI directly into the analysis and reporting process: deviations are automatically commented on, questions in natural language are answered, scenarios are prepared in natural language, and management summaries are generated automatically. Bissantz also supports companies with governance concepts and the productive integration of generative AI into controlling.
Summary
Generative AI marks a paradigm shift in how companies work with data and knowledge: from pure analysis to active communication, and from manual report production to automated preparation of insights. For controlling and Business Intelligence, this means more time for interpretation and decision-making and less time spent on data maintenance and formatting. With DeltaMaster, Bissantz integrates generative AI as a natural part of the analytical workflow.
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How AI takes over the work of interpretation—and helps companies move more quickly from analysis to action
