Agentic AI is rapidly transitioning from a concept to a tangible reality within enterprise settings, evolving from mere automation to systems capable of taking proactive and autonomous actions across various business functions. As a consultant, I've observed organizations unlock considerable value by allowing AI agents to address customer concerns in real time or to dynamically adjust business processes. Nevertheless, this increase in autonomy brings about unprecedented risks. Many companies are unprepared for the complexity and the internal and external threats posed by these agents, which range from security vulnerabilities to deviations from their intended purpose and compliance failures.
A key element in successful implementations is strong governance: establishing dedicated councils, incorporating security into the design phase, enforcing strict privilege controls, and consistently monitoring agent activities. Low-code platforms are becoming increasingly popular as a means to make these safeguards more accessible, enabling organizations to integrate governance and compliance measures directly into agent workflows without the need for extensive custom development.
Based on your experience, how are organizations managing the balance between granting AI agents significant autonomy and ensuring accountability? Where have you witnessed governance or oversight either enhance or undermine an agentic AI initiative?
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