AI Governance

AI Governance refers to the totality of all structures, processes, policies, and control mechanisms that govern the responsible use of artificial intelligence within an organization. An AI Governance Framework provides the systematic structure within which AI systems are developed, deployed, and monitored.The need for such a framework arises from several factors: First, the EU AI Act has imposed binding regulatory requirements since

2024 on companies that develop or deploy AI systems. Violations can be penalized with fines of up to €

35 million or

7 percent of global annual turnover. Second, AI systems carry specific risks such as algorithmic discrimination, lack of transparency in decision-making, and data protection violations, which remain uncontrolled without structured governance.Third, stakeholders — from customers to investors to regulatory authorities — increasingly expect evidence of responsible AI use. An AI Governance Framework delivers this evidence systematically. Fourth, good governance paradoxically enables faster innovation: when clear guardrails exist, teams can implement AI projects with less uncertainty and shorter approval cycles.For companies in the financial sector, there is the additional consideration that supervisory authorities such as BaFin and EBA have already formulated specific expectations regarding the use of AI — for example, in the context of credit decisions, anti-money laundering prevention, and algorithmic trading. An AI Governance Framework is therefore not an optional best practice, but a business-critical necessity for any organization that productively uses or plans to use AI.

AI Governance builds on the foundations of IT governance and data governance, but addresses specific challenges that arise from the use of artificial intelligence and are not covered by traditional governance frameworks.IT governance focuses on managing the entire IT landscape: infrastructure, applications, projects, and services. It governs topics such as IT strategy, investment decisions, service level management, and IT security. Data governance, in turn, concentrates on data quality, data catalogs, data ownership, and data protection. Both are necessary prerequisites for AI Governance, but neither is sufficient on its own.AI Governance additionally addresses AI-specific aspects: model risk management encompasses the validation, monitoring, and versioning of machine learning models — including the detection of model drift and performance degradation in production. Algorithmic fairness requires specific metrics and tests to identify and mitigate discrimination by AI systems. Explainability and transparency are regulatory requirements of the EU AI Act that demand technical measures such as SHAP values, LIME, or attention visualizations.AI Governance also introduces its own roles that do not exist in classical governance structures: AI Ethics Officers, Model Validators, AI Product Owners, and AI Governance Boards. Decision-making processes also differ — for example, when determining whether an AI system qualifies as a high-risk system under the EU AI Act, or what transparency obligations apply to generative AI.In practice, we recommend implementing AI Governance as an extension of existing governance structures rather than as a parallel silo. This allows you to leverage existing processes and responsibilities and supplement them in a targeted manner with AI-specific elements.

The landscape of international AI Governance frameworks has evolved considerably in recent years. It is essential for companies to be aware of the relevant standards and to integrate them strategically into their governance structures.The NIST AI Risk Management Framework (AI RMF) from the US standards body is one of the most comprehensive frameworks. It structures AI risk management into four core functions: Govern, Map, Measure, and Manage. It is voluntary but has established itself as a de facto standard for many international companies. ISO/IEC

42001 is the first international management system standard for artificial intelligence. Analogous to ISO 27001 for information security, it defines requirements for an AI management system and enables certification.The OECD AI Principles, adopted by more than

40 countries, define five core principles for trustworthy AI: inclusive growth, human-centered values, transparency, robustness, and accountability. The EU AI Act is the world's first binding AI regulation and classifies AI systems by risk level with corresponding obligations.In addition, sector-specific frameworks exist: in the financial sector, the Bank for International Settlements has formulated principles for the responsible use of AI. The EBA and ECB have published their own expectations regarding AI in banks. Singapore's MAS has created FEAT, a framework for Fairness, Ethics, Accountability, and Transparency in the financial sector.At ADVISORI, we integrate the relevant frameworks into a coherent AI Governance Framework tailored to your specific industry, jurisdiction, and company size. In doing so, we prioritize regulatory binding requirements and supplement them with best practices from voluntary standards.

The EU AI Act has been in force since August

2024 and is being applied in stages. For your AI Governance Framework, it has far-reaching and very concrete implications that go well beyond general principles.First, all AI systems in your organization must be classified. The EU AI Act distinguishes four risk categories: prohibited practices (e.g., social scoring, manipulative AI), high-risk systems (e.g., AI in credit decisions, HR processes, critical infrastructure), systems subject to transparency obligations (e.g., chatbots, deepfakes), and systems with minimal risk. This classification must be embedded in your AI Governance Framework as a systematic process.For high-risk systems, the EU AI Act requires a comprehensive compliance program: a risk management system covering the entire lifecycle, requirements for data quality and data governance, technical documentation and record-keeping obligations, transparency and information obligations toward users, human oversight, as well as accuracy, robustness, and cybersecurity.Your AI Governance Framework must operationalize these requirements. Concretely, this means: processes for risk classification of new AI projects, templates for conformity assessments, a central AI system register, defined responsibilities for fulfilling obligations, and regular review cycles.For providers of General Purpose AI Models (such as companies fine-tuning foundation models), additional obligations apply regarding technical documentation and transparency. ADVISORI supports you in fully integrating the EU AI Act requirements into your existing or newly developed AI Governance Framework — practically and with a view to implementation deadlines.

The implementation timeline for an AI Governance Framework depends on several factors: the size and complexity of your organization, the maturity of existing governance structures, the number and criticality of your AI applications, and the regulatory requirements of your industry.As a guide, the following timeline has proven effective in our project experience: The discovery phase — comprising inventory, stakeholder interviews, and gap analysis — typically takes four to six weeks. During this phase, we identify all relevant AI systems, assess governance maturity, and analyze regulatory requirements.Framework design — i.e., the development of governance structures, policies, processes, and roles — requires a further six to eight weeks. This phase produces the AI Governance Policy, the risk assessment framework, role and responsibility models, and process descriptions. Piloting on selected use cases spans four to six weeks, during which the framework is tested in practice and iteratively improved.The company-wide rollout, including training and tool integration, extends over eight to twelve weeks depending on organizational size. Overall, for a mid-sized company, you should plan for a timeframe of six to nine months from project start to full operationalization.Importantly: AI Governance is not a one-time project but a continuous process. Following the initial implementation comes the phase of continuous improvement, with regular reviews, adaptation to new regulations, and scaling to new AI applications. ADVISORI also offers long-term support for this, for example through quarterly governance reviews or by assuming the role of an external AI Governance Officer.

ADVISORI brings a unique combination of regulatory depth, technical implementation expertise, and practical AI experience that differentiates us from purely strategic consultancies and pure technology providers.First, we operate our own multi-agent AI platform with more than 1,

500 interfaces. This means: we do not merely advise on AI Governance in theory — we apply the principles daily to our own platform. This practical experience flows directly into our consulting work; we know the real challenges of implementing fairness checks, model monitoring, and explainability from our own development.Second, we are certified to ISO 27001, ISO 9001, and ISO 14001. These certifications not only demonstrate our own governance standards but also give us deep understanding of how to integrate AI Governance into existing management systems. If you have already implemented ISO 27001, we can connect AI Governance to it directly.Third, we have a strong focus on the financial sector and regulated industries. We know the requirements of BaFin, EBA, and ECB, and understand how AI Governance interacts with DORA, MaRisk, and sector-specific regulation. This industry expertise is critical, as generic frameworks often do not adequately address the specific requirements of regulated industries.Fourth, we work vendor-independently. As a partner of Microsoft Azure, AWS, and Google Cloud, we advise in a technology-neutral manner and optimize your AI Governance Framework for your specific technology landscape — whether cloud, on-premise, or hybrid. And fifth, with approximately

150 consultants, we offer the capacity to implement AI Governance comprehensively even in large, complex organizations.