AI Management
For economically successful deployment of Artificial Intelligence, companies require an overarching control structure.
This requires targeted AI implementation, appropriate AI governance, and effective AI compliance.
The problem of isolated pilot projects
The company and its employees are starting motivated with the topic of AI: initial AI applications are being procured, and existing standard software is getting new AI functions.
In the specialist departments, there's experimentation with ChatGPT, Claude, or Copilot. This quickly turns into a „pilot project“ that everyone can get behind.
After 6 months of experimenting, the realisation dawned: the pilot projects showed no measurable impact on operating profit, and no one was taking responsibility.
Discontent is growing, trust in artificial intelligence is sinking. The licenses for the AI tools are being cancelled again.
Few employees are satisfied and no longer want to do without their AI tools. An uncontrollable shadow AI structure is emerging.
Technology is rarely the problem; it is rather missing AI management.
Management | Implementation | Governance | Compliance
AI Management
- Planning, organisation, control, monitoring, improvement of AI deployment
- The connection of economic, organisational, technical, and regulatory issues
AI Implementation
- Concrete introduction of an AI use case
- Project and implementation-oriented
AI Governance
- Framework for the selection, development, deployment, and monitoring of AI
- Determination of guardrails, responsibilities and controls for deployment
AI Compliance
- Compliance with all relevant specifications
- Obtaining comprehensible evidence
Cornerstones of AI Governance
A viable AI governance concept does not need to be overly complex. However, as a minimum requirement, it should cover the following five dimensions. In principle, there is no comprehensive legal obligation to implement such a concept.
The implementation of an effective and efficient AI governance concept also makes sense for economic reasons.
Vision + Strategy
At the start are the target image and the strategy to achieve the target image.
The company determines what role AI will play in the future, which processes or services will be improved, and by what key performance indicators success will be measured.
A clear target vision provides orientation for selecting use cases, prioritising investments, and designing the subsequent solution. Without this common direction, many individual initiatives will emerge, but no aligned, company-wide approach.
Resources + Skills
AI requires budget, data, technology, personnel capacity, skills and competencies.
This must include not only the costs of development or introduction.
Operations, monitoring, documentation, training and further development also incur ongoing costs.
In addition, internal dependencies, affected departments, required systems as well as external suppliers and consulting partners must be included in the resource planning.
Control
Every AI project requires clear responsibilities, binding decision-making processes, and a realistic timeline.
It's not only relevant to know when technical work packages will be completed. Equally important is when fundamental decisions, approvals, risk assessments, and investment decisions need to be made.
Control also includes a prioritised use-case portfolio, defined escalation paths, suitable controls and meaningful management reporting.
Law + Regulations
The legal classification must be part of project planning from the outset.
Particular relevance is attached to the AI Regulation, data protection, copyright, trade secret protection, contract and liability law, as well as employment law and co-determination requirements.
These framework conditions already influence the selection of use cases, the utilisation of data, the choice of providers and the design of processes.
Implementation + Application
A working solution is created from the target image.
Processes are adapted, systems selected or developed, interfaces created, tests carried out and users trained.
Technical implementation partners should be involved early on. In addition to the technical requirements, they need a clear understanding of the target business vision, the control requirements and the legal framework.
More on the fundamental knowledge for practical implementation can be found in my Udemy Course „From AI Experiment to Controllable AI Use“