Last Saturday, I wrote about what work should never be handed to an AI agent — even if the tech can do it.

This week, let’s talk about what actually goes wrong when you try to implement AI agents too early. These are not theory problems. I’ve run into every one of these so far.

Real issues that stop AI agents from working:

• Teams ask, “What agent should we use?” before agreeing on the problem
• Data exists, but no one trusts it
• Processes change weekly, so agents automate chaos
• No one owns decisions the agent is supposed to support
• Security and governance are “we’ll handle it later” items
• Everyone assumes agents reduce work, but no one removes work

Here’s the uncomfortable truth: AI agents identify organizational gaps more quickly than any workshop ever could. That’s not a failure of the agent. That’s a readiness issue.

Practical guidance if you’re experimenting right now:

• Freeze the process before automating it
• Name a human decision owner before naming an agent
• Define one outcome the agent supports — not ten
• Assume governance work takes longer than the build
• Expect pushback from people whose work becomes visible

If this sounds familiar, you’re not behind. You’re early — and you still have time to do this right.

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