The people most likely to find the flaws in an AI workflow are usually the people expected to use it.
They know which field is never current, which customer needs special handling, which policy changed but never made it into the documentation, and which part of the official process everyone quietly stopped following three years ago.
Most companies bring those employees in after the important decisions have already been made. The system has been selected. The workflow has been designed. Leadership has seen a clean demo. Training is on the calendar.
At that point, employees aren’t being asked whether the system understands the work. They’re being asked to adopt it.
I think there’s a better way to use their expertise: give them the prototype early and ask them to break it.
A clean demo proves very little
Prototype demos tend to use cooperative examples.
The information is complete. The request is recognizable. The data agrees across systems. The workflow follows the documented process. The system produces a useful answer in seconds, and everyone can picture how much time it might save.
Employees know the example is clean.
They’re thinking about the customer who sends three unrelated questions in one message. The account with an undocumented exception. The report that contains a number nobody trusts. The policy that changed last month but still appears in the official instructions.
Those are the conditions the system will face once people begin using it for real work.
When employees respond to a demo with skepticism, leaders may hear fear, resistance, or reluctance to change. Sometimes that’s part of it. But often, the employee is simply comparing the prototype with the work as it actually happens.
That comparison is valuable. It may be the first honest test the system has received.
The documented process is only part of the process
Almost every organization has some distance between the official workflow and the real one.
A process may have six documented steps, while employees perform four because two stopped adding value. One system may be called the source of truth even though everyone checks a spreadsheet before acting. A policy may apply in most cases, with a handful of customers, products, or locations following different rules.
Those workarounds and exceptions didn’t appear for no reason. Some are outdated habits, but others are how employees compensate for missing information, conflicting systems, unclear ownership, or policies that don’t fit every situation.
That distinction matters.
An AI system can work only with the context, rules, and permissions it has been given. It won’t automatically know that a field is unreliable, that an experienced employee ignores a particular instruction for good reason, or that a routine-looking decision becomes risky under one specific condition.
The people doing the work often know all of that.
If they’re involved only after implementation, each undocumented exception becomes a production problem. If they’re involved during testing, the same exception becomes design input.
Put the system under review
AI projects can make employees feel as though their work is being measured against a machine.
Can it complete the task faster? Can it produce the same output? How much of the workload can it absorb?
Asking employees to evaluate the system changes that relationship. The employee becomes the expert, and the prototype has to show that it understands the work.
Give it an incomplete request. Try the difficult customer. Use the unusual contract. Ask it to handle the case that causes the team to message the same person every month. See what happens when two systems disagree.
The point isn’t to stage a dramatic stress test. It’s to uncover the knowledge, judgment, and exceptions the implementation team hasn’t captured yet.
A failed test should leave the team knowing more about the work.
If an employee says, “That recommendation is wrong because this customer is handled differently,” the workflow may be missing account context.
If they say, “That field is never current,” the system may be relying on bad data.
If they say, “We don’t follow that process anymore,” the documentation may be stale.
If they say, “I would never allow that action without review,” the workflow may need a firm boundary around human judgment.
Sometimes an experienced employee will recognize that an answer is wrong before they can explain exactly why. I wouldn’t dismiss that reaction as intuition and move on. I’d slow down and help them make the reasoning visible.
What did they notice? What information were they using? What risk did they see that the system missed?
That conversation often reveals a rule the organization has never written down.
Make disagreement part of the workflow
Employee involvement shouldn’t end with a testing session.
Once the system is in use, people need a simple way to reject a recommendation, correct a classification, flag missing information, and explain why an action shouldn’t occur. Then someone needs to study those disagreements.
If employees override the same recommendation every week, the company may have an adoption problem. It may also have a bad rule, missing context, unreliable data, or a system that performs poorly under a common condition.
Those problems need different responses.
Treating every disagreement as resistance protects the system from the evidence that could improve it. The more useful question is why human and system judgment diverged.
Which one had the better information? What changed the decision? Does the workflow need better data, a clearer policy, a new exception, or a permanent role for human review?
Feedback matters only when it can change something. Otherwise, the company is asking employees to document problems it has already decided not to hear.
Be specific about what the saved time becomes
Employees understand that automation can change jobs.
Saying AI will remove tedious work may sound reassuring, but it leaves an obvious question unanswered: what happens to the time the company saves?
If ten hours of repetitive work disappear, what replaces them?
Employees might spend more time resolving difficult customer problems, improving the information the business relies on, doing proactive work, or supporting growth without adding staff at the same rate. A company might also choose to reduce roles or reorganize the work.
Those are operating decisions. They should be discussed plainly, not hidden inside vague promises about “higher-value work.”
Inviting employees to test a prototype won’t resolve every concern they have about job change. It does show that their expertise will help determine how the work changes, where the system can act safely, and where people still need to own the decision.
That is a more credible basis for trust than asking for it after the workflow is finished.
Skepticism is useful before launch
The skeptical employee may be the best tester on the team.
They know where the current process breaks. They’re motivated to find what the system missed. They can identify cases that would never occur to someone working from the official procedure.
Invite that scrutiny while the prototype is still easy to change.
Give employees permission to find the bad assumption, the missing context, the unsafe action, and the exception nobody documented. Let them stop a test when the system crosses a boundary they believe matters, then investigate what that reaction tells you about the work.
A prototype that succeeds only because nobody challenged it hasn’t earned much trust.
Let the people who know the work try to break it. What fails will show you what the system still needs to learn, what the workflow needs to account for, and what the organization itself has yet to decide.