AI Governance
AI Governance establishes the structures, processes, and responsibilities needed to develop and manage AI systems safely, compliantly, and responsibly.
AI Policy and Guideline Development
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Tailored guidelines
Policies and guidelines tailored to the organization’s AI ambitions, risks, and responsibilities.
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Consistent execution
Guidelines that ensure AI projects are designed and executed in a consistent and verifiable manner.
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Operational practice
Linked to workflows, decision-making processes, and control mechanisms.
Key results
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AI applications that consistently and demonstrably comply with both legal and internal frameworks.
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Less fragmentation between teams, with everyone working from the same foundational principles.
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Greater control over risks and governance, without unnecessarily hindering innovation.
What we deliver
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A tailored AI policy framework, including guidelines, standards, and application criteria.
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Practical templates for AI approval, risk assessment, and responsible deployment.
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Integration with existing processes such as IT governance, risk management, and innovation policy.
Establishing an AI Governance board
01
Structure and role distribution
A dedicated board with clear tasks, responsibilities, and mandates for decision-making on AI.
02
Decision-making and steering
Clear frameworks outlining how decisions are made, who is involved, and when escalation is required.
03
Management and execution
Ensures that AI strategies don’t remain on paper, but are brought to life through the right people and processes.
Key results
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Clear governance of AI with defined roles, responsibilities, and mandates.
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Fast and structured decision-making on AI risks, ethics, innovation, and compliance.
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Consistent adherence to agreements, as teams understand what’s expected and why.
What we deliver
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Establishment of an AI governance board with clear roles and responsibilities.
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A decision-making model and escalation paths aligned with existing business operations.
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Support for teams in understanding, applying, and adhering to governance agreements.
Integration with Existing Processes
01
Organizational integration
AI governance is integrated with existing processes such as risk management, quality assurance, and decision-making.
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Innovation with oversight
Compliance structures are designed not to hinder innovation, but to actively support it.
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Strengthened collaboration
Stronger connections between departments and disciplines lead to faster decisions and better alignment.
Key results
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Fewer fragmented governance processes, with AI embedded into existing risk and quality frameworks.
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Clear responsibilities and shorter decision-making lines for AI-related choices.
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Improved collaboration between innovation, compliance, and policy through a unified way of working.
What we deliver
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Integration of AI governance into existing processes such as risk management, procurement, and strategy.
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Practical governance structures with defined roles, decision points, and reporting responsibilities.
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Tools to enable collaboration between compliance, IT, policy, and innovation — without overlap or confusion.
Implementation of Monitoring and Reporting Tools
01
Real-time insight
Monitoring of AI system output, behavior, and anomalies — with a focus on reliability, ethics, and compliance.
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Proactive adjustment
Enables swift adjustments in response to incidents, changes, or new insights — without delays or confusion.
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Effective governance
Monitoring and reporting are embedded into existing decision-making structures and compliance cycles.
Key results
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Ongoing visibility into the performance, risks, and compliance of AI systems.
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Rapid adjustments in response to deviations or changes in regulations or application.
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Governance that operates based on data, not just policies.
What we deliver
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Guidance and support on the use of monitoring tools for AI systems.
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Linking monitoring to governance decision-making and incident response.
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Guidelines for analyzing and following up on AI performance and risks.
