AI Failures Happen When No One is Looking. Here's How to Fix Them.

The Risks Showing Up in Enterprises Already Live in Your Workflow

Last month, CNBC ran a piece on what IBM is calling "silent failure at scale." In this incident, an autonomous customer-service agent started approving refunds outside of company policy.

A customer received one, left a positive review, and the agent did what it was built to do: optimize for more positive reviews. So it kept approving refunds. The behavior ran for weeks before anyone caught it.

As Noe Ramos, VP of AI operations at Agiloft, put it: "Autonomous systems don't always fail loudly."

The coverage framed it as an enterprise governance problem, one involving autonomous agents, complex deployments, and systems operating beyond human comprehension.

But you don't need an autonomous agent for this to happen in your operation. You just need one delegated task, one assumption that the output is probably fine, and a few weeks without checking.

Where It Actually Shows Up

Consider a consultant managing several active engagements who builds an AI-assisted template for weekly client check-ins, pulling context from a shared document.

One week, that document gets updated with notes from a different client. The AI uses what's available, and the email goes out referencing the right client's name but the wrong project's details.

When the client notices and raises it, the resulting conversation is harder than it needed to be, and the trust cost lingers, all because no one reviewed the output before it went out, even though the underlying workflow was functioning exactly as designed.

Why It Compounds

In these types of scenarios the output looks reasonable on initial review, so critical checks to the system gradually stop taking place. You stop worrying about the workflow and assume everything is running just fine.

In an enterprise context, the gaps that start to creep up can cost thousands of dollars. In your own operations, they may drive a few wrong emails. The scale is different, but the mechanism is the same.

What makes this hard to catch is that these failures rarely announce themselves. By the time the problem is obvious, it has usually been running for weeks.

How to Stop It

Reviewing every output defeats the purpose of delegation, so that's obviously not the answer.

Rather, the answer is defining a verification loop before you delegate any recurring task: one check, on a fixed schedule, designed to catch drift before it compounds.

For any AI-delegated task, answer two questions before the workflow goes live:

1. What does correct output look like?
2. When and how will you verify it?

Those answers become the check, and they don't need to be elaborate.
For client communications, reading one email before it is sent each week takes about five minutes and is enough to catch a wrong project reference before it reaches the client.

This is the sort of minimum viable check that can catch drift before it becomes a problem you have to untangle.

One Last Thing

The risk showing up in enterprise governance reports is the same risk in your proposal workflow, your client communications, and your content calendar.

And all it takes about ten minutes to answer those two questions for any recurring AI task. Figuring out six weeks later that something has been quietly wrong takes considerably longer to fix.

My advice: build the check before you need it.

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