Operationalizing Data and AI Capabilities in Modern Software Engineering Systems
DOI:
https://doi.org/10.22399/ijcesen.4958Keywords:
Data operationalization, AI lifecycle management, software engineering systems, MLOps, data governanceAbstract
The rapid integration of data and artificial intelligence (AI) into software-intensive applications has fundamentally transformed modern software engineering systems. While advances in machine learning have enabled intelligent functionality, realizing sustained value in production environments depends on the effective operationalization of data and AI capabilities. This study investigates how data engineering, AI model lifecycle management, software delivery processes, and governance mechanisms collectively shape the performance and reliability of AI-enabled software systems. Using a system-level analytical framework, the research evaluates key operational variables related to data quality, model stability, deployment efficiency, scalability, and observability. The findings reveal that stable and trustworthy AI operations are driven primarily by the maturity of data pipelines, automation in continuous integration and deployment, and comprehensive monitoring and governance practices, rather than by model accuracy in isolation. Temporal analysis further demonstrates that coordinated improvements across these dimensions lead to measurable gains in operational stability over time. The study underscores the necessity of a holistic, system-centric approach to operationalizing data and AI, offering insights that support the design of scalable, resilient, and accountable software engineering systems in data- and AI-driven environments.
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