AI Governance & AI Transformation

Companies need clear rules for using AI safely and responsibly, and they need it anchored strategically in the organisation, in processes and in leadership structures.

Abstraktes blaues Dreiecksnetz aus Punkten und Linien, das wie eine Welle verläuft

Clear responsibilities, rules, and decision-making paths ensure that AI is not just used, but managed effectively and in compliance with regulations.

AI governance creates transparency, security, and compliance. AI transformation ensures that AI is used purposefully to increase productivity, decision quality, and corporate impact.

Artificial intelligence does not create added value through its use alone. What matters are clear guardrails, a strategic orientation, and anchoring it within processes, the organization, and leadership.

What are the foundations for the responsible use of AI?What are the foundations for the responsible use of AI?

This includes a viable AI strategy, clear responsibilities, and compliance with regulatory requirements such as the EU AI Act. This creates the foundation for the secure, responsible, and long-term effective use of AI within the company.

What is required for the sustainable implementation of AI solutions?What is required for the sustainable implementation of AI solutions?

AI must deliver impact in everyday business. That is why we identify concrete use cases, evaluate their benefits, and integrate them into existing business and support processes. We support the introduction of solutions like Microsoft Copilot with training, change management, and consistent implementation steering. This leads to better decisions, higher productivity, and AI usage that is permanently anchored within the company.

Are you looking to establish AI governance and initiate an AI transformation?

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AI Governance & AI Transformation

AI strategy and governance framework

An effective AI strategy connects the company goals with the potential of artificial intelligence and defines clear priorities for its use. An AI governance framework provides the roles, responsibilities and decision structures needed to steer AI safely, transparently and in line with the rules. This is the basis for encouraging innovation, minimising risk and anchoring AI in the company for the long term.

Compliance & AI risk

Implementing the EU AI Act requires assessing and classifying AI applications, establishing suitable governance and control mechanisms, and developing the necessary policies, processes and evidence.

Use case development

Developing AI use cases starts where concrete value can be created in everyday work. Together we identify and assess options along the business processes and concentrate on the applications that make economic sense, can actually be delivered and contribute measurably to the company's success.

Process optimisation with AI

AI cannot improve processes if the underlying workflows are already inefficient. That is why we analyse existing processes, identify potential for automation and bring AI in where it simplifies tasks, supports decisions and creates noticeable relief. The result is more efficient workflows, less manual effort and higher productivity day to day.

Technology and tool selection

Choosing the right AI technologies decides whether potential turns into concrete results. That is why we assess solutions against the company's requirements, the existing system landscape and the strategic goals. The result is technology that integrates sensibly, grows with the company and contributes measurably to value creation.

PROJECT EXAMPLES

AI Governance & AI Transformation

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AI Governance and Compliance in Human Resources

Industry: Mechanical Engineering

Our role: Consultant

Project objectives

  • Creating transparency regarding existing and planned AI applications in HR and assessing their relevance under the EU AI Act
  • Identifying, assessing and classifying compliance and AI risks across HR processes
  • Deriving the concrete action needed from the requirements of the EU AI Act for the AI systems in use
  • Developing robust governance with clear roles, responsibilities and decision paths for the use of AI in HR
  • Integrating requirements for risk management, transparency, documentation and human oversight into HR processes
  • Creating the conditions for using AI in HR safely, traceably and in line with the EU AI Act

Key results

  • Full transparency regarding the AI applications used in HR and their regulatory classification
  • Systematic assessment and prioritisation of compliance and AI risks by relevance
  • Clearly defined governance structures, roles and responsibilities for the use of AI in HR
  • Requirements for risk management, transparency, documentation and human oversight translated into a target vision
  • Concrete measures per AI application to meet the requirements of the EU AI Act
  • A prioritised roadmap for using AI in HR lawfully and sustainably

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Approach

  • Taking stock of all HR processes, systems and use cases involving AI
  • Analysing AI applications in use in terms of purpose, relevance to decisions and regulatory exposure
  • Classifying AI applications according to the requirements and risk classes of the EU AI Act
  • Identifying and assessing compliance, governance and AI risks in HR
  • Deriving concrete fields of action and prioritising the measures needed to ensure compliance
  • Developing a target vision for governance, responsibilities, human oversight, documentation and transparency requirements
  • Designing a risk management and control approach for the use of AI in HR
  • Creating a prioritised implementation roadmap for introducing the necessary processes, roles and compliance measures step by step

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Developing an AI use case strategy for the HR department

Industry: Mechanical Engineering

Our role: Consultant

Project objectives

  • Identifying and prioritising HR use cases that create value and have potential for AI and automation
  • Developing a structured target vision for the future use of AI across HR processes
  • Analyzing potential gains in efficiency, quality, and employee experience through the targeted use of data-driven solutions
  • Defining the functional and organizational requirements for delivering identified use cases
  • Assessing the benefits, feasibility, risks, and prerequisites of each use case
  • Deriving a prioritized roadmap to deliver HR use cases step by step and anchor them within the company

Key results

  • A transparent overview of all existing and potential AI use cases across HR processes
  • A prioritized use case catalog with a clear assessment of value potential, feasibility, and strategic relevance
  • Concrete scenarios for AI and automation in areas such as recruiting, employee development, and HR services
  • Functional requirements and prerequisites for delivering prioritized use cases
  • A target vision for the future use of AI in HR, including its organizational integration
  • A roadmap to deliver prioritized use cases step by step and realize the identified value

Approach

  • Analyzing existing HR processes and identifying areas with high potential for automation and AI
  • Conducting workshops with the HR department to collect and refine relevant HR use cases
  • Structuring and describing identified use cases using standardized profiles
  • Assessing use cases in terms of value potential, feasibility, data availability, and organizational prerequisites
  • Prioritizing use cases based on business value, strategic relevance, and implementation effort
  • Developing target visions and functional requirements for preferred use cases
  • Deriving the technical, organizational, and process-related prerequisites needed for delivery
  • Creating a roadmap to implement prioritized use cases step by step
  • Defining initial pilot applications to quickly validate value and acceptance
  • Establishing a workflow for the continuous identification and development of future HR use cases

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