The scenario clearly describes an early-stage AI adoption phase where experimentation and learning are prioritized over strict financial accountability. Leadership intentionally avoids introducing administrative complexity or cost attribution mechanisms that could hinder adoption and innovation.
The key indicators are:
Multiple pilots and early-stage use cases still being evaluated
Centralized financial monitoring rather than distributed accountability
No requirement for business units to track or justify their own usage
Focus on learning, experimentation, and identifying value
This aligns directly with the Centralized model , where costs are managed and absorbed centrally by a core team or budget. This approach is commonly used in early maturity stages to:
Encourage experimentation without financial barriers
Simplify governance and reduce overhead
Allow organizations to gather insights on usage and value before enforcing accountability
Other models are not appropriate at this stage:
Showback model introduces visibility of costs to business units but does not yet enforce billing
Chargeback model assigns actual costs to business units, which can discourage early experimentation
Team-based budgeting requires decentralized ownership, which is premature in early adoption
CAIPM emphasizes that organizations should begin with centralized cost management and gradually evolve toward showback and chargeback models as AI adoption matures and value becomes measurable.
Therefore, the correct answer is Centralized model , as it best supports early-stage experimentation and learning without introducing friction.
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