One Year of Agentic AI: Six Lessons from The People Doing the Work – BRIAN HEGER

If you find value in content like this, sign up for my Talent Edge Weekly newsletter.  Agentic AI—AI agents built on generative AI foundation models that can act in the real world and carry out multistep processes—offers the potential for major productivity gains. Some organizations are starting to see benefits, but others are still struggling to capture value and, in some cases, have pulled back after unsuccessful deployments. To help reduce risks and increase success, McKinsey shares six lessons for implementing agentic AI. While all are useful, two that stand out are: focus on workflows, not just agents, since lasting value comes from redesigning end-to-end workflows (people, processes, and tools) rather than building agents that look impressive but don’t improve the work; and recognizing that agents aren’t always the answer, as simpler approaches like automation, rules, or analytics can sometimes be more effective, particularly in standardized, low-variance workflows (e.g., regulatory disclosures). The broader point is that deploying agentic AI is less about the technology itself and more about designing systems that enable effective collaboration between people and agents. 👉 In connection with this topic, Talent Edge Weekly, along with one of its sponsors, Draup, is conducting a short survey on the most
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