AI Governance
Artificial Intelligence
In a nutshell
AI governance describes the organizational rules, responsibilities, and control mechanisms for the safe and responsible use of artificial intelligence within a company. It is not primarily about selecting an AI model or software, but rather about the question:
How can we ensure that AI is used in a meaningful, secure, compliant, and transparent manner?
AI governance establishes the framework within which employees, business units, and IT can use AI solutions without compromising data protection, compliance, corporate values, or business risks.
Why is AI governance important?
Many companies are currently experimenting with ChatGPT, Microsoft Copilot, SAP Joule, or custom AI applications. These are often rolled out much more quickly than traditional IT projects. At the same time, new questions are arising that existing governance models often cannot fully address.
Is it permissible to process confidential information? Who is liable for AI-generated content? Which use cases are permitted and which are not? How do we deal with hallucinations or erroneous results? And what requirements arise from regulatory frameworks such as the EU AI Act?
This is precisely where AI governance comes into play. It helps companies establish a controlled framework for the use of AI without stifling innovation. The goal is not to ban or regulate AI, but to enable its benefits while keeping risks manageable.
AI governance becomes particularly important as AI is increasingly used in business-critical processes. While AI is still frequently used today for research or text suggestions, future applications will increasingly be used to prepare decisions, automate processes, or act as AI agents.
The greater the impact on business processes, the more important transparency, traceability, and governance become.
Ultimately, AI governance pursues the same goal as data governance: to build trust so that new technologies can be used productively.
How does celver AG provide support?
Many companies are currently facing the challenge of managing the use of AI without slowing down the organization.
In our experience, AI governance initiatives often fail because they are approached from either too technical or too legal a perspective. The result is extensive guidelines that employees rarely understand or use.
That is why celver AG takes a pragmatic approach. We help companies establish AI governance as an integral part of a modern corporate and data strategy. In doing so, we consider both organizational and technical issues and integrate topics such as AI governance, data governance, data platforms, and AI agents into a holistic approach.
Typical questions from our customers include:
- How do we develop a company-wide AI policy?
- What use cases are permitted?
- What data may be used?
- Who is responsible for AI solutions?
- How can we ensure transparency regarding the AI systems we use?
- And how can Copilot, SAP Joule, or custom AI agents be securely integrated into existing processes?
Our goal is to create a governance framework that enables innovation while keeping risks under control.
Related Terms
Frequently Asked Questions
As soon as AI is deployed in a production environment within a company, questions arise regarding data protection, accountability, quality, and compliance. AI governance helps address these issues in a structured manner and minimize risks.
No. Even small and medium-sized businesses are increasingly using AI technologies today. The sooner basic rules are defined, the easier it will be to avoid risks down the road.
Data governance governs the handling of data. AI governance extends this approach to include the handling of AI models, AI applications, AI agents, and automated decisions.
Among other things, AI governance helps reduce data protection risks, misinformation, uncontrolled data use, compliance violations, and a lack of transparency in the use of AI.
The EU AI Act establishes a regulatory framework for the use of AI within the European Union. Companies should therefore ensure transparency regarding their AI applications and responsibilities at an early stage.
In most companies, this is a joint effort involving business units, IT, data protection, information security, and management. AI governance is therefore typically a cross-functional issue rather than a purely IT task.
The more AI agents work autonomously or prepare decisions, the more important governance rules become. Companies must be able to track what data is being used, what tasks an agent is performing, and what control mechanisms are in place.
On the contrary. Good AI governance builds trust and provides clarity. This enables companies to deploy AI more quickly and on a broader scale because risks become transparent and manageable.
