Artificial intelligence, particularly generative AI, LLMs, and autonomous AI agents, is transforming organizational processes, scientific infrastructures, and digital systems. However, successful adoption requires more than technological innovation; it depends on integrating AI into standards, governance frameworks, and interoperable infrastructures while preserving reliability, transparency, and rigor. This chapter examines how AI can be embedded into technical, organizational, and scientific standards without disrupting existing systems. It addresses challenges related to interoperability, data quality, traceability, governance, provenance, metadata consistency, and reproducibility. Special attention is given to integrating AI into Current Research Information Systems and scientific workflows while maintaining transparency, validation, and responsible decision-making. The chapter proposes a framework for standards-compliant AI adoption that balances innovation with interoperability and positions AI as an evolutionary extension of sustainable and trustworthy digital infrastructures.