Targeted AI management: Why businesses need an AI Management System (AIMS) now

Key Takeaways

• Transparency is the foundation of effective AI governance.
• An AIMS links strategy, processes and responsibilities.
• Governance and business value go hand in hand.
• Tools support AI governance, but they are no substitute for a management system.

The real challenge: a lack of understanding of AI

Many companies are currently grappling with the question of how to successfully implement AI. And with good reason: in times of skills shortages, the potential for greater efficiency and productivity whilst maintaining the same staffing levels is enormous. 

Nevertheless, AI rarely emerges as a major strategic project. It develops wherever people are looking for simpler ways to get their work done. And this usually happens faster than the rules governing AI can be defined. 

The moment of truth usually only arrives when senior management asks about the benefits, when approval is required due to data protection requirements, or when a customer wants to know which AI systems are being used for their project. 

Do we know which AI is being used in our company?

Many companies cannot answer this question. Yet the pressure to act is mounting due to regulatory requirements under the EU AI Act, and this is finally shifting responsibility for AI from the ‘playground’ to senior management.

A key risk therefore lies not solely in the technology itself, but in the lack of transparency. AI can be managed – shadow AI cannot.

AI governance requires more than just a tool

Many organisations react to this situation instinctively with an obvious idea: ‘We need a tool.’ Of course, a tool can help in the short term. Dashboards, workflows, audit trails and reports create transparency, an overview and a sense of regulatory security. But a tool is not a management system in itself.

Even a train does not reach its destination solely through its technical capabilities. What matters are clear timetables, defined responsibilities and well-functioning processes. It is no different with AI. Software can support governance, but it cannot replace clear decisions, roles, processes and responsibilities.

Why an AI Management System (AIMS) addresses precisely this issue

It does not answer the question of which AI tool should be purchased next. It answers the question of how AI can be managed in such a way that value is created, risks remain manageable and accountability is transparent.

This is not just about governance and compliance. An AIMS also lays the foundation for managing AI investments in a more targeted way: Which applications actually contribute to the organisation’s objectives? Where is measurable added value being created? And which solutions should be scaled up, adapted or, where necessary, discontinued? Governance and business value therefore go hand in hand.

After all, AI rarely fails because of the technology itself these days. Powerful models, platforms and agents have long been available. The greater challenge lies within the organisation. 

Try answering these questions for your organisation: 

• Do we know where AI is already being used today? 
• Do we understand the business value it generates? 
• Are we aware of the associated risks and obligations? 
• Can we trust our data? 
• Is it clearly defined who bears responsibility? 

If you cannot answer several of these questions with certainty, it is often not the technology that is lacking, but a structured framework for managing AI. 

Anyone who now establishes an effective AI management system based on, or taking into account, ISO/IEC 42001 will not only create transparency, but also clear lines of responsibility and decision-making, a systematic approach to managing risks and regulatory requirements, and a robust foundation for investment and scaling decisions.

In this way, AI governance does not become a stumbling block to innovation, but rather a prerequisite for rolling out successful AI applications safely and in a controlled manner. The future belongs not to the companies that are the first to adopt AI, but to those that understand what they are already using. If your AI landscape is growing faster than your ability to keep track of it, it is time to get back in the driver’s seat and actively steer the journey.