Jul 2026· International Journal of Innovations in Science, Engineering And Management· pp. 104-112· 0 citations· 13 references
TL;DR
A comprehensive governance blueprint for the safe deployment of agentic Salesforce Healthcare CRM systems through a Human-in-the-Loop (HITL) framework is proposed, which incorporates governance-by-design principles, zero-trust security, consent-aware data management, and continuous feedback loops to strengthen operational resilience.
Abstract
Agentic artificial intelligence is transforming Salesforce Healthcare Customer Relationship Management (CRM) platforms by enabling intelligent decision-making, workflow orchestration, and personalized care operations. However, the increasing autonomy of AI systems in regulated healthcare environments introduces significant challenges related to explainability, accountability, privacy, auditability, and compliance. This paper proposes a comprehensive governance blueprint for the safe deployment of agentic Salesforce Healthcare CRM systems through a Human-in-the-Loop (HITL) framework. The proposed architecture integrates four core layers—data integrity, intelligence and orchestration, governance and observability, and human oversight—to ensure responsible AI adoption across healthcare operations. A risk-based operating model categorizes AI-driven actions into low-, medium-, and high-risk tiers, aligning each with appropriate monitoring, approval, and escalation mechanisms. The framework further incorporates governance-by-design principles, zero-trust security, consent-aware data management, and continuous feedback loops to strengthen operational resilience. The proposed blueprint provides healthcare organizations with a practical roadmap for achieving trustworthy, explainable, compliant, and scalable agentic CRM implementations while maintaining human accountability and improving governance maturity.
The findings show that meaningful oversight is not a single human approval step and is a lifecycle capability that combines bounded autonomy, evidence-based escalation, stop authority, continuous validation, audit records, and institutional learning.
Aaron K Montgomery, Hannah E Gallagher, Derrick L Mercer· International Journal of Eng...· 0 citations
An Autonomous Decision Assurance Layer (ADAL) is proposed for AI-driven enterprise analytics environments that bridges data governance, multi-agent AI, human-in-the-loop oversight, responsible AI controls, and executive decision intelligence.
Choudhry Bilal Mazhar· International Journal for Re...· 0 citations
A three-parameter cost framework is introduced that shows that alignment in adaptive SoS depends not only on the intensity of interoperability pressure but on its interaction with institutional transition burdens, suggesting that governance effectiveness is horizon-dependent rather than a simple linear function of regu...
Arash Vesaghi, Mohamed Mogahed, Mo Mansouri· Systems· 0 citations
Abstract—Federal and regulated organizations continue to rely on document-centric Authorization to Operate (ATO) processes even as the NIST Risk Management Framework (RMF), continuous monitoring guidance, Zero Trust Architecture (ZTA), and continuous authorization initiatives require more continuous, evidence-driven ri...
Anand Janjal· Journal of Cybersecurity Edu...· 0 citations
Artificial intelligence (AI) is increasingly embedded in healthcare delivery, and when systems are scaled across clinical workflows, errors may have system-wide consequences. The Artificial Intelligence Act (AIA) and the Medical Device Regulation (MDR) establish important safety requirements for AI-based medical device...
Jennifer Viberg Johansson, S. Slokenberga· Health Care Analysis· 2 citations
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