To understand the early lessons, we recently dug into more than 50 agentic AI builds we’ve led at McKinsey, as well as dozens of others in the marketplace. We’ve boiled down our analysis results to six lessons to help leaders successfully capture value from agentic AI (see sidebar “What is agentic AI?”).
Achieving business value with agentic AI requires changing workflows. Often, however, organizations focus too much on the agent or the agentic tool. This inevitably leads to great-looking agents that don’t actually end up improving the overall workflow, resulting in underwhelming value.
Agentic AI efforts that focus on fundamentally reimagining entire workflows—that is, the steps that involve people, processes, and technology—are more likely to deliver a positive outcome. Understanding how agents can help with each of these steps is the path to value. People will still be central to getting the work done, but now with different agents, tools, and automations to support them.
An important starting point in redesigning workflows is mapping processes and identifying key user pain points. This step is critical in designing agentic systems that reduce unnecessary work and allow agents and people to collaborate and accomplish business goals more efficiently and effectively. That collaboration can happen through learning loops and feedback mechanisms, creating a self-reinforcing system. The more frequently agents are used, the smarter and more aligned they become.
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