AI is a worldwide megatrend. Importance, performance and complexity of AI solutions are growing rapidly. Therefore, the need to keep an overview of the relevant AI solutions and manage related priorities and risks is growing as well. The presented AI Methods, Capabilities and Criticality Grid (AI-MC2-Grid) represents a method and tool to gain this overview and manage the space of AI solutions. Users of the AI-MC2-Grid might be managers, developers and users of AI-solutions or investors, politicians and regulators who wants to understand market and control certain AI-solutions. The AI-MC2-Grid consists of three dimensions: AI methods, AI capabilities and the criticality of an AI solution. Each discussed AI solution can be placed in these three dimensions, as such, similar AI solutions can be compared. Alternatively, complex AI solutions can be analysed by its components. AI methods correspond to typical AI algorithms, while AI capabilities correspond to typical process steps to build intelligent workflows. Once the relevant AI methods and AI capabilities of a certain AI solution are located, performance, consequences and possible risks and alternatives can be discussed. Based on the classification, the damage potential of Artificial Intelligence is representing a certain level of criticality. In this course, a growing level of criticality increases the stringency of requirements concerning tests, calibration, inspection, control and certification. For this reason, the AI-MC2-Grid is a powerful method and tool to define and manage all kinds of upcoming norms and standards of AI solutions.With this good cause, the AI-MC2-Grid is at the heart of the German National Roadmap for Artificial Intelligence Norms, placed as a method and tool to support the development and management of future AI standards and norms in Germany. Other platforms, societies and programmes have the same need and could profit from the use of the AI-MC2-Grid. In particular, it is intended to facili