How should CPMAI manage the total cost of ownership for AI initiatives?

• A. Forecast data and compute costs, and ignore governance and ROI.
• B. Forecast data and compute costs, model training/inference, and governance overhead.
• C. Forecast data, compute costs, model training/inference, governance overhead, and ROI with ongoing optimization.
• D. Focus exclusively on hardware procurement.

Answer: (C)

Managing the total cost of ownership for AI initiatives requires a holistic view across data, compute, model development and deployment, governance, and the ongoing realization of value. Forecasting data and compute costs sets the base, while including training and inference captures the model lifecycle. Governance overhead covers data management, security, regulatory compliance, risk controls, and oversight, which are essential as AI systems scale and operate in real-world environments. Including ROI with ongoing optimization ensures the initiative keeps delivering value as models drift, workloads evolve, and cost-efficiencies are discovered over time.

This is the strongest approach because it recognizes every major cost area and the need to drive continuous value. The other options leave out important elements: one ignores governance and ROI; another includes governance but misses ROI and ongoing optimization; focusing only on hardware procurement neglects data, software, governance, and the opportunity to optimize value over the life of the project.