Companies deploying AI agents must implement formal performance-management and accountability structures similar to those used for human employees, according to industry experts.
This shift is necessary because AI agents are moving beyond simple tasks to manage multistage workflows. Without oversight, businesses risk losing quality control and critical thinking in core operations.
Kate Smaje, McKinsey’s global leader of technology and AI and co-author of *Rewired*, said that the rise of AI does not mean humans suddenly devolve their responsibilities for quality control and critical thinking.
Recent data highlights the speed of this adoption. Approximately 57% of organizations are already using AI agents for multistage workflows [1]. Furthermore, 81% of organizations plan to expand their use of AI agents by the end of 2026 [1].
These agents are now being integrated into diverse business operations, including software development, cybersecurity, and customer service [2, 3]. As these tools take on tasks traditionally performed by people, the lack of a formal management framework creates a gap in responsibility.
Industry reports suggest that treating AI agents as digital employees requires a shift in corporate governance [1]. This includes establishing who is accountable when an agent makes an error, and how performance is measured over time.
Maintaining these standards is critical as the technology scales. The transition requires a balance between the efficiency of automation and the necessity of human oversight to ensure operational integrity [1, 4].
“The rise of AI doesn’t mean we all suddenly devolve our responsibilities for quality control and critical thinking.”
The transition toward 'agentic AI' represents a move from tools that provide answers to tools that execute actions. By advocating for performance management, experts are signaling that AI is no longer just a software utility but a functional component of the workforce. This necessitates a new legal and operational framework to determine liability and quality assurance when autonomous systems fail.
