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McKinsey’s review of more than 50 agentic AI deployments shows that capturing real value from agentic AI requires more than impressive technology. After a year of experimentation, six key lessons stand out for organizations aiming to succeed. First, focus on redesigning workflows rather than the agents themselves.
Value comes when workflows integrate people, processes, and tools, using agents where they fit naturally. Not every task needs an agent. In many cases, simpler automation, rules-based systems, or predictive analytics are more effective than an autonomous agent. Trust and quality matter. Early AI systems often create “AI slop” that frustrates users and blocks adoption, so ongoing evaluation and feedback loops are essential to improve performance and build confidence.
Observability and verification at every step of a workflow help teams catch errors and refine systems. Reusability of agentic components prevents redundant work and speeds scaling. Finally, humans remain critical. While roles evolve, human judgment, oversight, compliance checks, and handling edge cases are still essential for success. Thoughtful workflow design and collaboration between humans and agents are the foundation for sustained impact with agentic AI.
