AI governance
Support in creating a controllable and auditable framework for the use of AI
How I support you in your AI governance
d+Ai Approach
Development of an individual structured path for building your individual AI governance or for aligning the current status quo
Selection of application cases
Identify, compare, and prioritize potential AI use cases
AI-related documentation
Document the use cases, responsibilities, decisions, risks, and controls in a comprehensible manner
Risk analysis and control design
Recognize and effectively limit significant AI-related risks
Cornerstones of AI Governance
Five dimensions form the foundation for economic, controllable, and responsible AI deployment.
1st goal image + strategy
What role should AI play in the future, which processes should be adapted or improved, and how is success measured?
Clear goals provide guidance for the selection and prioritization of AI application cases.
2. Resources + Competencies
AI requires appropriate data, technologies, budgets, and expertise.
The costs include not only the initial investment but also ongoing expenses for operation, monitoring, training, and development.
3. Control
Clear responsibilities, decision-making paths, and criteria for approvals create bindingness.
The stakeholders expect a comprehensible prioritization of AI projects, appropriate and effective risk management and controls, and meaningful reporting.
4. Law + Regulations
Legal and internal requirements and constraints must be identified and taken into account early on.
In particular, on AI regulations, data protection (e.g., data use for training AI models), copyright, safeguarding business secrets, and labor law.
They influence the selection of providers, data, and applications.
5. Implementation + Application
Governance does not end with the approval of an AI application. Responsibilities, controls, and monitoring must also function in operational operations.
Substantial changes to AI systems, models, data, interfaces, or usage capabilities must be identified and evaluated. Controls must be implemented and documented, deviations must be classified, and relevant decisions must be documented in a comprehensible manner.
This ensures that the AI deployment can be controlled and verified even after implementation.
The problem of isolated pilot projects
Interested employees, initial licenses, and new AI features in the current software environment: AI projects are emerging quickly.
Uncertainty comes quickly too: What is allowed, and what isn’t? How much of my tasks is handled by AI, and what remains for me to do myself?
Uncertainty and therefore dissatisfaction are growing; attention to the use of AI applications is decreasing. Monitoring of AI use is fading away.
Only a few employees are satisfied and do not want to do without their AI tool anymore. There is uncontrolled or insufficient use of AI in the shadows.
Technology is rarely the problem. Often, it is unclear goals, unachievable expectations, inadequately regulated responsibilities, or unfulfilled legal requirements. In short: a question of AI management and governance.