One year of agentic AI: Six lessons from the people doing the work

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A year into the agentic AI revolution, one lesson is clear: It takes hard work to do it well.  An agentic enterprise transformation holds the promise of unmatched productivity. While some companies are enjoying early successes with such activities, many more are finding it challenging to see value from their investments. In some cases, they are even retrenching—rehiring people where agents have failed.

These stumbles are a natural evolution of any new technology, and we’ve seen this pattern before with other innovations. To understand the early lessons, the authors recently dug into more than 50 agentic AI builds they have led at McKinsey, as well as dozens of others in the marketplace. They have boiled down their analysis results to six lessons to help leaders successfully capture value from agentic AI.

1. It’s not about the agent; it’s about the workflow.  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.
2. Agents aren’t always the answer.  AI agents can do a lot, but they shouldn’t necessarily be used for everything. Too often, leaders don’t look closely enough at the work that needs to be done or ask whether an agent would be the best choice to perform that work.  To help avoid wasted investments or unwanted complexity, business leaders can approach the role of agents much like they do when evaluating people for a high-performing team. The key question to ask is, “What is the work to be done and what are the relative talents of each potential team member—or agent—to work together to achieve those goals?”
3. Stop ‘AI slop’: Invest in evaluations and build trust with users.  One of the most common pitfalls teams encounter when deploying AI agents is agentic systems that seem impressive in demos but frustrate users who are actually responsible for the work. It’s common to hear users complain about “AI slop” or low-quality outputs. Users quickly lose trust in the agents, and adoption levels are poor. Any efficiency gains achieved through automation can easily be offset by a loss in trust or a decline in quality.  A hard-won lesson of this recurring problem is that companies should invest heavily in agent development, just like they do for employee development.
4. Make it easy to track and verify every step.  When working with only a few AI agents, reviewing their work and spotting errors can be mostly straightforward. But as companies roll out hundreds, or even thousands, of agents, the task becomes challenging.  Agent performance should be verified at each step of the workflow. Building monitoring and evaluation into the workflow can enable teams to catch mistakes early, refine the logic, and continually improve performance, even after the agents are deployed.
5. The best use case is the reuse case.  In the rush to make progress with agentic AI, companies often create a unique agent for each identified task. This can lead to significant redundancy and waste because the same agent can often accomplish different tasks that share many of the same actions (such as ingesting, extracting, searching, and analyzing).  Deciding how much to invest in building reusable agents (versus an agent that executes one specific task) is analogous to the classic IT architecture problem where companies need to build fast but not lock in choices that constrain future capabilities. How to strike that balance often requires a lot of judgment and analysis.  Identifying recurring tasks is a good starting point. Companies can develop agents and agent components that can easily be reused across different workflows, and make it simple for developers to access them.
6. Humans remain essential, but their roles and numbers will change.  As AI agents continue to proliferate, the question of what role humans will play has generated much anxiety—about job security, on the one hand, and about high expectations for productivity increases, on the other. This has led to wildly diverging views on the role of humans in many present-day jobs.  To be clear: Agents will be able to accomplish a lot, but humans will remain an essential part of the workforce equation even as the type of work that both agents and humans do changes over time. People will need to oversee model accuracy, ensure compliance, use judgment, and handle edge cases, for example.