<p>Artificial intelligence (AI) governance has become a strategic priority for organisations seeking to balance innovation with accountability, compliance and trust. Prevailing governance frameworks emphasise ethics, transparency, explainability, risk management, human oversight and regulatory compliance. While these principles are essential, they tend to assume that the human processes, expertise and organisational knowledge being embedded into AI-enabled environments have already been accurately captured and represented. This paper argues that AI adoption should be understood not merely as automation but as a technology-transfer problem. Drawing on principles established in regulated industries; pharmaceuticals, biotechnology, aerospace and advanced manufacturing; it proposes that successful AI implementation depends on the same characteristics that define successful technology transfer: reproducibility, robustness and validation; traceability and knowledge preservation; and process control and continuous verification. To operationalise this view, the paper introduces the AI Technology Transfer Framework (AITTF), a seven-stage governance model designed to help organisations transfer workflows, expertise and decision-making into AI-enabled systems while maintaining accountability, operational integrity and organisational memory. The argument is situated within an evidence base of peer-reviewed research, preprints and sector reports, alongside emerging standards and regulatory guidance (ISO/IEC 42001, ISO 9001, the NIST AI Risk Management Framework, and guidance from the FDA, EMA, MHRA, OECD, the Council of Europe and the WHO). Each strand maps onto one or more components of the framework. Two anonymised case studies illustrate the framework applied in professional-services settings.</p>