A conceptual framework for AI Maturity Assessment in Technology Supply Chains

Abstract

Artificial Intelligence (AI) has become a fundamental part of technological supply chains which now use AI for better demand prediction, fraud prevention, risk assessment and efficiency improvement. Organizations face a problem with AI assessment because they currently operate in multiple supply chain environments yet need an assessment method which lacks standardization and specific industry guidelines. The current AI maturity frameworks focus only on business organizations which makes them unsuitable for measuring technology supply chain systems because they fail to account for dependencies and data exchange and system compatibility issues. The research introduces a new conceptual framework which enables organizations to assess their AI maturity through a dedicated framework for technology supply chains. The framework introduces a multi-dimensional structure which includes data readiness and model capability and infrastructure scalability and governance and ethics and interoperability and risk and resilience and organizational capability. The evaluation process assesses these dimensions through a five-level maturity continuum which starts from basic adoption and ends with complete automated AI implementation.

The study establishes a complete assessment system which includes measurable performance indicators and assessment scoring system to conduct structured evaluations and performance comparisons. The framework becomes more understandable through its demonstration in real-world financial technology applications and cloud ecosystem operations and healthcare data systems. The implementation guide provides organizations with a step-by-step process to develop their artificial intelligence capabilities while reducing operational and systemic dangers. The research introduces a scalable assessment tool which assesses AI maturity in technology supply chains to benefit researchers and industry professionals. The proposed framework addresses a critical gap in current literature and provides a foundation for future empirical validation and industry standardization