Human–AI Collaboration as Critical Digital Infrastructure: Hybrid Impact on Enterprise Operations and Quality Engineering - Shreelekha Ramabadran (International Journal of Intelligent Systems and Applications in Engineering, 2026)
Collaboration with AI has crossed the divide from curiosity in the lab to a core expectation of enterprise automation, especially in regulated and high-reliability systems. AI assistants based on LLMs, RAG, and agentic AIOps are all indispensable tools for engineers and support professionals when it comes to knowledge retrieval, drafting, incident triage, root cause analysis, and workflow automation. However, quality engineering teams can rely upon human-in-the-loop (HITL) collaboration to accelerate test design, understand defects, and assess safety for release. This can lead to operational hazards, including hallucinations (confabulation), automation bias, model drift, exposure of private training data, and governance issues, which can erode trust in AI outputs when they’re assumed to be correct. This summarizes the state of the art in human-AI interaction models, including the HITL pipeline, mixed-initiative control sharing, and symbiotic teaming, and their supporting toolchains, benefits, and challenges. To inform future governance directions, moving on to discussing NIST AI RMF 1.0 and Generative AI Profile (NIST AI 600-1), ISO/IEC 23894, and ISO/IEC 42001. A hybrid governance approach is proposed. It introduces principles for evidence-based grounding, risk-based autonomy, and traceable decision-making. Lastly, it introduces a vision of collaborative adaptation where AI initiatives based on confidence, impact, and policy constraints maintain human accountability while achieving scalability of productivity and reliability. DOI: https://doi.org/10.17762/ijisae.v14i1s.8225