AI Governance

AI governance refers to the systematic framework of policies, processes, roles, and control mechanisms that ensures the responsible use of artificial intelligence within organizations. It is the steering instrument that ensures AI systems not only function technically, but are also deployed in an ethically sound, legally compliant, and commercially sensible manner.The necessity of AI governance has been fundamentally changed by the EU AI Act. What was previously considered best practice is now becoming a regulatory obligation. Companies that develop or deploy AI systems must demonstrate that they have established adequate governance structures. Violations can result in fines of up to

35 million euros or

7 percent of global annual turnover.However, AI governance is far more than compliance. It creates the foundation for scalable AI use within the organization. Without clear policies, shadow AI, inconsistent quality standards, and incalculable risks emerge. With a well-conceived AI governance framework, on the other hand, companies can roll out AI initiatives more quickly — because approval processes are clearly defined, risks are systematically assessed, and all stakeholders know which standards apply.Advisori knows this challenge firsthand: as the operator of its own multi-agent AI platform with over 1,

500 interfaces, we have not only developed AI governance for clients, but implemented it for our own operations. We know that a framework only has impact when it is practical — and does not end up as a paper tiger gathering dust in a drawer.An effective AI governance framework typically includes: an AI register of all systems in use, a risk classification according to the EU AI Act, clear roles and responsibilities, approval processes for new AI applications, monitoring and audit mechanisms, as well as policies for data protection, transparency, and fairness.

The EU AI Act is the world's first comprehensive AI regulation and affects virtually every company that uses or develops AI systems — regardless of whether the provider is based in the EU. The regulation follows a risk-based approach and divides AI systems into four categories: prohibited practices, high-risk systems, limited-risk systems, and systems with minimal risk.For most companies, high-risk AI systems are of primary relevance. These include AI applications in areas such as human resources (applicant screening, performance evaluation), credit lending, insurance, critical infrastructure, and law enforcement. For these systems, the EU AI Act prescribes extensive requirements: risk management systems, data quality standards, technical documentation, transparency toward users, human oversight, and robustness requirements.The timeline is tight: the prohibitions on certain AI practices have already been in effect since February 2025. From August 2025, transparency obligations for general-purpose AI models will apply. From August 2026, all high-risk requirements must be fully met. Companies that do not act now will barely be able to meet these deadlines.The specific implications depend on your AI landscape. Advisori conducts a structured EU AI Act gap analysis for this purpose: we inventory all your AI systems, classify them according to the risk categories of the AI Act, identify compliance gaps, and produce a prioritized action plan. You benefit from our experience with regulatory compliance — as an ISO 27001-certified company with longstanding expertise in DORA, NIS2, and GDPR, we understand the mechanics of regulatory requirements.The key point is: the EU AI Act requires not only technical measures, but an organizational governance system. Documentation alone is not sufficient — you must demonstrate that governance is practiced.

The fundamental difference lies in our dual role: Advisori is not only a consultancy, but also the operator of its own multi-agent AI platform. While other consultancies approach AI governance exclusively from a theoretical perspective, we have developed, implemented, and optimized governance processes for our own platform with over 1,

500 interfaces. Every recommendation we make is one we have tested ourselves.This practical experience translates into concrete advantages: we know which governance processes work in day-to-day operations and which lead to bureaucratic bottlenecks. We are familiar with the typical resistance encountered during implementation and have proven change management approaches. We understand the technical realities of AI systems and can translate governance requirements in a way that development teams accept and implement.Our second differentiating factor is regulatory depth. Advisori has been advising in the areas of information security, risk management, and compliance for years. We have accompanied DORA implementations, established NIS 2 programs, and led GDPR projects. This experience flows directly into our AI governance consulting — because the EU AI Act does not stand in isolation, but must be harmonized with existing regulations.Thirdly: we do not build ivory towers. Our approach is pragmatic and designed for effectiveness. An AI governance framework from Advisori is not a 200-page document that no one reads, but an operational steering instrument with clear processes, measurable KPIs, and practical templates. We deliver governance that your organization can actually use.With approximately

150 employees and certifications to ISO 27001, 9001, and 14001, we also bring the scalability and quality assurance that enterprise projects require. Our consultants speak both the language of IT and that of business and regulation — a bridging function that is critical in AI governance.

An effective AI governance framework consists of several interlocking components that together form a consistent steering system. Based on our experience operating our own AI platform and numerous client projects, Advisori has developed a proven framework.The first component is the governance organization. This defines roles and responsibilities: who decides on the deployment of new AI systems? Who monitors compliance with policies? Who is the point of contact for AI-related incidents? Typical roles include the AI Officer, AI responsible persons in the business units, an AI Ethics Board, and Data Stewards. The precise design depends on your organization's size and AI maturity level.The second component is the AI register — a complete inventory of all AI systems within the organization. This sounds straightforward, but in practice it is one of the greatest challenges. Many companies do not know where AI is being used — from employees using ChatGPT to embedded ML models in standard software. Without a complete AI register, no governance is possible.The third component encompasses AI policies: an acceptable use policy for AI tools, data protection and data quality standards, transparency requirements, fairness criteria, and vendor management policies for third-party AI providers. These policies must be understandable, practical, and enforceable.The fourth component is AI risk management. Each AI system is assessed according to a defined procedure — by risk category under the EU AI Act, but also according to internal criteria such as business criticality, data sensitivity, and impact on end customers. The assessment leads to concrete measures and controls.The fifth component consists of approval and lifecycle processes: stage-gate procedures for new AI applications, regular re-validations of existing systems, monitoring mechanisms, and defined processes for changes and decommissioning.The sixth component is continuous governance: KPIs and dashboards for measuring governance effectiveness, regular audits, training programs, and a continuous improvement process. Governance is not a project with an end date, but a permanent process.

Introducing an AI governance framework is a structured program whose duration and effort depend on several factors: the number of your AI systems, the current governance maturity level, the size of the organization, and the complexity of your regulatory requirements.As a benchmark from our project experience, you can expect the following timeframes: an initial gap analysis and inventory typically takes

4 to

6 weeks. During this phase, we inventory your AI landscape, assess the current state against EU AI Act requirements, and produce a prioritized roadmap.Framework design — that is, the development of governance structure, roles, processes, and policies — requires a further

6 to

8 weeks. Here we work closely with your business units to develop a framework that fits your corporate culture and existing governance structures.The implementation phase — rollout of processes, establishment of the organization, employee training, introduction of tools — takes

2 to

4 months depending on scope. During this phase, the concept becomes lived practice.Overall, you should plan for

3 to

6 months for a complete AI governance program. We recommend an agile approach: start with the most critical AI systems and the most urgent regulatory requirements, and expand the framework incrementally.Regarding investment: AI governance is not a one-time expenditure, but a strategic investment. The costs of a governance program are not proportionate to the potential fines under the EU AI Act (up to

35 million euros), the costs of AI-related incidents, or the reputational damage caused by irresponsible AI use.Advisori offers flexible engagement models: from a focused gap assessment to full framework development to long-term governance support. We tailor the scope to your needs — whether a mid-sized company with initial AI applications or a large enterprise with a complex AI landscape.What is critical for success is not the budget, but the commitment of senior management. AI governance only works as a top-down initiative with a clear mandate from the executive level.

AI governance must not be an isolated silo — it must be integrated into existing governance, risk management, and compliance (GRC) structures. Advisori has demonstrated in numerous projects that this integration is not only possible, but is the key to efficient and accepted AI governance.The EU AI Act explicitly requires a risk management system for AI. If your organization already operates an enterprise risk management system — for example, in accordance with ISO

31000 or as part of your internal control system — it makes sense to integrate AI risks into this existing structure rather than building a parallel system. We extend your existing risk taxonomy with AI-specific risk categories and integrate AI risk assessments into your established evaluation processes.The same applies to compliance: the EU AI Act does not stand alone, but interacts with GDPR, sector-specific regulations such as DORA or MaRisk, and internal compliance policies. Advisori has deep expertise in all of these regulatory frameworks and ensures that your AI governance framework defines consistent requirements rather than creating contradictory parallel worlds.Information security is another critical integration point. AI systems often process sensitive data and make business-relevant decisions — they must therefore be incorporated into your ISMS (Information Security Management System). As an ISO 27001-certified company, Advisori understands the interfaces between AI governance and information security and implements governance controls that satisfy both sets of requirements.At the organizational level, we recommend linking with existing committees: the AI Ethics Board can be established as a subcommittee of an existing risk committee. AI approval processes can be integrated into existing change management processes. AI audits can be incorporated into the annual audit plan.The benefits of this integration are considerable: lower overhead, better acceptance among employees, consistent reporting lines, and more efficient use of resources. In our experience, companies that introduce AI governance as a complement to existing structures rather than as a parallel system achieve operational effectiveness significantly more quickly.Advisori brings a unique advantage here: we have been advising at the intersection of IT, risk management, and regulation for years. We are familiar with your existing structures — whether DORA compliance, NIS 2 implementation, or ISO 27001 certification — and can integrate AI governance in a way that creates efficiencies rather than redundancies.