Why the transformation model must evolve

Over three decades in SAP, I have watched tools and methodologies improve significantly. The underlying coordination model has changed much less.

Strong specialists still work inside functional and organizational boundaries. Critical context is distributed across workshops, presentations, specifications, ticketing systems, emails and individual experience. Senior people spend considerable time reconstructing what has already been discussed, decided or built.

The recurring issues are familiar:

• requirements lose their link to business outcomes;
• local preferences are elevated into design requirements;
• decisions are documented inconsistently;
• dependencies surface late;
• tests are derived after design rather than with it;
• change impacts lag behind solution decisions;
• quality reviews rely on manually assembled evidence; and
• go-live creates another major loss of project knowledge.

These are not simply productivity problems. They are failures of continuity.

Why faster documents are not enough

AI can summarize workshops and generate specifications. This reduces effort, but it can also produce more artifacts without improving the underlying decisions.

The larger opportunity is to maintain context continuously:

1. capture evidence;
2. distinguish observations, assumptions, proposals and decisions;
3. connect each decision to its consequences;
4. coordinate specialist work;
5. challenge inconsistencies early; and
6. preserve knowledge into operations.

That requires agents designed as participants in a governed transformation system.