An artificial intelligence life cycle: From conception to production.

Existing frameworks often fail to provide a holistic roadmap for managing complex machine learning initiatives from start to finish. Many current methodologies prioritize narrow technical tasks while neglecting the broader organizational requirements for success. That uncertainty drove the need for a more inclusive approach that bridges the gap between engineering and business operations. Prior research has shown that deploying automated systems involves significant risks that standard software development models do not adequately capture. No prior work had resolved the tension between rapid innovation and the necessity for rigorous ethical oversight during model implementation. This gap motivated the development of a comprehensive strategy that accounts for team composition and governance. Previous studies have frequently adapted generic project management tools rather than creating specialized structures for modern computational solutions. Consequently, practitioners often struggle to navigate the transition from experimental prototypes to reliable, production-ready systems.

The aim of this article is to present the CDAC AI life cycle as a comprehensive framework for the design, development, and deployment of computational systems. This study addresses the lack of practical, inclusive approaches that extend beyond basic technical requirements. The researchers seek to provide a roadmap that incorporates risk analysis and ethical governance into the standard project workflow. They intend to resolve the ambiguity surrounding team composition and the specific skills needed for successful project execution. By detailing nineteen distinct stages, the authors provide a clear path from initial conception to final production. The work also addresses the challenges of model transferability, which often hinder the adoption of prebuilt solutions. Furthermore, the authors aim to contribute a technical and organizational taxonomy that clarifies the functional value of these systems. This research is motivated by the need to align modern technological development with broader organizational and ethical standards.

The review approach involves synthesizing existing literature to identify shortcomings in current project management methodologies for computational systems. Researchers examined the progression of various initiatives to map out the necessary phases for successful deployment. This analysis focused on identifying nineteen constituent stages that span the entire duration of a project. The team evaluated organizational requirements, including necessary skills and knowledge, to build a complete profile for project success. They also investigated the challenges associated with model transferability and risk assessment during the adoption process. By comparing these findings against traditional software development models, the authors highlighted the specific needs of modern machine learning projects. The study integrates these diverse elements into a unified, practical framework for practitioners. This systematic review ensures that the proposed model addresses both technical constructs and broader governance concerns.

Key findings from the literature indicate that the CDAC AI life cycle provides a structured progression through three main phases and nineteen specific stages. The authors demonstrate that this model effectively addresses the void of inclusive approaches for managing complex systems. Their results show that focusing on risk analysis and ethical governance significantly improves the viability of production-ready solutions. The study identifies that team composition, including specific skill sets and knowledge, is a critical factor for project completion. Furthermore, the research highlights that prebuilt model transferability remains a major challenge that requires dedicated management within the lifecycle. The taxonomy developed by the authors successfully synthesizes the functional value of these systems for organizational stakeholders. These findings suggest that moving beyond purely technical constructs is essential for the sustainable adoption of automated technologies. The data confirms that a comprehensive framework can bridge the gap between initial conception and successful real-world implementation.

The authors propose the CDAC AI life cycle as a robust standard for managing sophisticated computational projects. This framework synthesizes multiple dimensions of project success, including team expertise and risk mitigation strategies. By organizing the workflow into nineteen distinct stages, the model offers a clear path for organizations to follow. The researchers emphasize that technical excellence alone is insufficient for long-term viability in real-world environments. Governance and ethical considerations are presented as integrated components rather than secondary concerns for development teams. The taxonomy provided serves to clarify the functional value of various system architectures within an enterprise context. This synthesis implies that successful deployment requires a balanced focus on both human skills and algorithmic integrity. Future initiatives should utilize this structured progression to ensure that all aspects of the system lifecycle are addressed systematically.