Governance that makes AI scalable.
We help you establish responsibilities, approvals, quality standards, and guardrails so that AI can be used reliably, securely, and transparently across the organization—now and at scale.
Rethink governance
Governance creates clarity for scaling
Governance provides direction when initial AI initiatives are to become a robust operating model. When set up properly, it ensures that responsibilities, approvals, and quality requirements are clarified early so teams can make decisions with greater confidence.
Together, we define role models, approval processes, quality requirements, and how to handle sensitive data—so that AI initiatives can be prioritized transparently and transferred cleanly into the organization.
Clarity
when roles, responsibilities, and approvals are clearly defined early on
Speed
when use cases can be prioritized faster with transparent guardrails
Direction
when regulatory requirements are translated early into a practical governance model
AI Governance Framework – Overview
APPROVALS
DATA PROTECTION
DATA PROTECTION
Governance building blocks
What we build together
The governance model consists of defined building blocks that can be introduced modularly and expanded step by step.
Building block 01
Role model & responsibilities
- Definition of the AI Owner role and its tasks per use case
- Clear delineation between the business unit, IT, data protection, and compliance
- Escalation paths and decision-making authority
- Integration into existing organizational structures
Building block 02
Approval processes & policy framework
- Standardized use case review process (risk class, data basis, area of use)
- Approval stages for pilot, rollout, and expansion
- Prohibited application areas and grey zones
- Usage guidelines for AI tools in day-to-day business
Building block 03
Quality standards & Human-in-the-Loop
- Definitions of acceptable output quality by area of use
- Human-in-the-Loop design for critical decisions
- Logging and traceability of AI outputs
- Procedures for incorrect or harmful outputs
Building block 04
Data protection & RBAC concept
- Categorization of data by sensitivity and intended use
- Role-based access model (RBAC) for AI systems and data
- GDPR mapping and data protection risk assessment
- Retention policy for AI-generated data and logs
Building block 05
EU AI Act – preparation & compliance
- Classification of the AI systems in use by risk class
- Documentation requirements for high-risk systems
- Gap analysis between the current governance maturity and legal requirements
- Roadmap for step-by-step compliance implementation
Regulatory context
Requirements companies should be aware of today
AI governance is not only an internal management issue—it is increasingly a legal requirement.
EU AI Act
The EU AI Act came into force in 2024 and will be applicable in stages. Companies should check early on which AI systems are affected, which risk classes apply, and what documentation, transparency, and control obligations arise from this.
GDPR & AI-specific obligations
Automated decisions with significant impact on individuals require special care. Under the GDPR, this includes rights to information, access, and objection, as well as increased requirements for transparency, human review, and clear responsibilities—especially for AI in HR, sales, and customer service.
Industry-specific requirements
In regulated industries (financial services, healthcare, critical infrastructure), additional sector-specific requirements apply—from BaFin guidelines to ISO standards. Robust AI governance provides the foundation for auditability and certifications.
Frequently asked questions
What decision-makers ask about AI governance
Governance projects often fail not due to a lack of intent, but due to missing answers to specific entry questions. Here are the questions we hear most often.
When is the right time for AI governance?
Ideally before the first productive use of AI—but establishing governance retroactively also makes sense in ongoing operations. The more applications are active, the more important clear rules become. We recommend introducing governance in parallel with pilot projects.
How much effort is required to build a governance model?
That depends on your starting point. For companies with initial AI initiatives, key building blocks (role model, use case approval, usage policy) can be introduced in 4–8 weeks. A full enterprise framework for multiple areas of application takes more time—but this, too, is implemented step by step.
Will governance block our AI rollout?
No—quite the opposite. A well-designed governance model reduces management hesitation, accelerates approvals, and creates clarity about which use cases can be advanced quickly. Most delays in AI rollouts are caused by missing governance, not by having it.
Do we already need to be EU AI Act compliant?
EU AI Act requirements take effect in stages. For most corporate AI applications (medium- or low-risk class), initial transparency obligations will apply from 2025/2026. High-risk systems must be documented more extensively. We help you assess your current status and create a pragmatic compliance roadmap.
How are governance and Sovereign AI connected?
Sovereign AI addresses technical control over data, models, and infrastructure. Governance complements this at the organizational and process level. Together, they form a sovereign, controllable AI platform—technically and organizationally. We recommend developing both dimensions together.
Next step
Governance as a stable foundation
for productive AI
Talk to us about your current governance maturity, your AI ambitions, and which building blocks will provide you with the most direction next.
Competent Advice at Your Side
Our Expert for Your Concerns
Thomas Pietrzykowski supports organizations in not only positioning AI strategically, but also making it productively usable. His focus is on developing pragmatic AI architectures, evaluating relevant use cases, and implementing secure, scalable solutions across existing business processes.
With 25 years of experience in software engineering, enterprise architecture, cloud, DevOps, and digital transformation, he combines technological depth with operational implementation experience. He is familiar with modern AI platforms, automation tools, and integration approaches not just from consulting, but from direct practical application—from prototyping and system integration to governance, operations, and scaling.
His strength lies in translating business requirements into actionable technical solutions. In doing so, he brings international leadership experience, experience in regulated environments, and a deep understanding of data, interfaces, security, and operating models.
