Two of the three building blocks differ in one respect: one improves the goal, the other the path to it.
SAP is investing heavily, and major consultancy firms are reporting impressive figures. On closer inspection, however, it becomes clear that the promised pace is rarely achieved in practice.
The market promises that AI will automatically make SAP transformations faster and more efficient. So far, however, the actual benefits have often fallen short of these promises. A “30 per cent increase in speed” is not achieved by a single AI tool, but by the ability to actually implement it with the right partner and a consistently tailored delivery model.
Without a systematic approach, it is not possible to fully realise the potential of AI in the transformation process. The acceleration achieved does not stem from a single tool, but is the result of four factors that must work together.
Proven programme management capabilities: process and architectural expertise, project management, testing and quality assurance, as well as genuine project reviews.
Without capability, AI merely scales up the chaos.
It is the end-to-end transformation context – from the project context, through the validated scope map and process map, to the delivery backlog – that makes agents valuable in the first place.
Without context, AI produces nothing but noise.
Industrialised methodology combined with our Agentic M-cbs agent portfolio across the entire lifecycle – from Analyse to Operate. What one agent releases serves as the input for the next.
Without a method, AI remains a tool rather than a chain.
Adoption is something you do, not something you accept: one AI facilitator per project, everyday AI enablement, and AI as a fixed agenda item in steering group meetings and reviews.
Without adoption, any acceleration remains purely theoretical.
The cbs DNA for company-wide change projects — from defining objectives right through to embedding the changes.
Quickly measurable results and industrialised transformation rather than project entropy.
A chain of specialised agents throughout the entire lifecycle — context as a data dependency, not as a convention.
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