According to the CPMAI framework, the first phase is ‘Problem Definition,’ which includes a critical step of Data Assessment. Before any cleaning or modeling, you must systematically evaluate the data’s fitness for purpose—its availability, relevance, quality, and structure—to validate the project’s feasibility and inform the plan. Jumping to cleaning or modeling without this assessment risks building on a flawed foundation.

The ‘Analysis’ phase in CPMAI is where potential solutions are explored and validated before full-scale development. The recommended activity is to develop a Proof of Concept (PoC). A PoC is a small-scale, rapid test of a proposed solution to verify its technical feasibility and its potential to deliver business value, providing the evidence needed to make an informed go/no-go decision on a specific approach.

Model Optimization is the process of improving a model’s efficiency—often in terms of inference speed, size, or resource consumption—without significantly sacrificing its accuracy. This directly addresses the trade-off between performance and cost. While deployment and operationalization are broader lifecycle stages, optimization is the specific practice for tuning the model to be more cost-effective in production.

CPMAI emphasizes disciplined, value-driven project management. The framework advocates for a structured change control process. The first step is always to evaluate the impact of the proposed change. This evaluation must consider alignment with core business objectives, effect on defined success metrics, and implications for cost, schedule, and resources, not just technical effort or stakeholder pressure.

Model Interpretability and Explainability refers to the ability to understand and articulate how an AI model makes its decisions. This is a non-functional requirement that becomes critical in regulated industries, for ethical AI practices, and for building stakeholder trust. CPMAI highlights the need to address this during development, as a model that cannot be explained may face rejection or legal challenges, regardless of its accuracy.

CPMAI’s ‘Deployment’ phase extends beyond technical go-live. It stresses the importance of operationalization—preparing the organization and systems for the ongoing use of the AI solution. This includes defining how the model will be monitored for performance decay, who will support end-users, and what training is required. A successful deployment ensures the solution can deliver sustained value, not just that it is technically functional.

CPMAI treats AI projects as part of a continuous lifecycle, not a one-time build. Model drift (concept drift or data drift) is a common phenomenon where the statistical properties of the live data change over time, causing model performance to degrade. The framework’s ‘Operations & Maintenance’ phase is designed to address this through continuous monitoring and planned model refresh cycles, making this the correct diagnosis and response.

Responsible AI is a core principle of the CPMAI framework. It requires that AI projects are conducted ethically, fairly, and transparently. This includes actively working to identify and mitigate bias in data and algorithms, ensuring fairness, and considering the societal impact of the AI solution. Addressing potential bias in a hiring tool is a direct application of the Responsible AI principle.

Phase-gate reviews in CPMAI are key governance checkpoints. The gate between Analysis and Development is critical. Its purpose is to review the outcomes of the Analysis phase—particularly the Proof of Concept results and updated business case—and make a conscious business decision: to proceed with development, to iterate further in analysis, or to cancel the project. It’s a go/no-go decision based on validated evidence of value and feasibility.

According to the CPMAI framework, the first phase is ‘Problem Definition,’ which includes a critical step of Data Assessment. Before any cleaning or modeling, you must systematically evaluate the data’s fitness for purpose—its availability, relevance, quality, and structure—to validate the project’s feasibility and inform the plan. Jumping to cleaning or modeling without this assessment risks building on a flawed foundation.0