What is the AI maturity level?
The term “AI maturity” refers to the development and progress of AI in very different areas of application. It is often used to describe the state of AI technology in terms of their respective capabilities, applicability, and sustainable integration into business processes. It is always possible to understand the AI maturity level in various ways, i.e. there is no generally accepted definition or scale for it. Roughly speaking, however, the AI maturity level can be broken down to the following hierarchical development stages:
- Experimental initial phase
- prototype
- pilot project
- Operational embedding
- Successive scaling
- Transformative final phase
Each of these sections is characterized by highly specific learning, which, in a sense, seamlessly transfers to the next higher level. The AI maturity level is designed in such a way that it shows in a relational manner how AI helps to outperform the competition; there can therefore be various parallel, industry-specific forms of an AI maturity level. From an experiment, which is highly susceptible to errors, you go through various stages to the transformative target corridor, which begins to indicate a general change in the business environment.
Further information on AI maturity
But why is the objective framing of the topic of AI so appropriate to promise sustainable success? Well, first of all, it remains to be stated that AI is a phenomenon which, due to its sheer novelty, entails a lot of uncertainty: which areas of application are particularly predestined to sustainably integrate AI use into one's own business practice can be worked out in a particularly clear and easier to understand way with the help of a framework such as the AI maturity level. Great players, such as Accenture have published extensive documentation on the maturity level of AI. We are also aware of the importance of a comprehensive scale, so that dealing with issues related to discourse appears particularly important. This requires both cooperation and competition: Cooperation is necessary to create an environment within which AI can really be effective. Competition is essential to find fine-grained niches in the roughly defined environment, to fill them and thus to ensure the necessary sharpening of AI — in the present and in the future.
Dealing with disruptive qualities of AI
If you look at the general transformation potential that AI brings with it, it quickly becomes clear why it is so profitable to use a general level of AI maturity as a yardstick: if your own company is not to lose touch with the competition and a timely initiative in terms of AI integration may be required. Since AI development describes a completely volatile process that can result in extremely disruptive procedures, a stabilizing measure of how the AI maturity level is able to represent helps you navigate through the metaphorical thicket without ever losing the necessary orientation. Where is your own organization located with regard to the state of development of AI? What blessings can be received? Which hurdles need to be considered? Answers to these questions can be found with reference to the AI maturity level.
The AI maturity model enables a structured and holistic examination of the 7 resulting design dimensions data, culture, competency, strategy, regulation, customer behavior as well as general compatibility. This allows situational identification of measures with which AI mechanisms can be successfully integrated into existing corporate structures.
Conclusion
In order to be able to do business safely in a fast-moving world, it is just as important to focus on the future in a visionary way as to focus on one's own strengths at regular intervals. Nothing is worse than getting lost in the face of your own noble goals.
