By Michael J Padilla-Pagan Payano
Early in my military career, I learned a simple lesson: when you give people a task, you also have to give them room to think.
Related: AI agents take initiative, unchecked
As a young “door kicker,” I made a point of telling my teams to ask why. Not because every decision needed a debate, but because the person closest to the work often sees something the person giving the order does not.
During a deployment in Afghanistan, after operating in the same area for three weeks, we received intelligence about a location that did not match what the team had seen on the ground. One of my team members asked, “Why are we accepting this intelligence as current? Where did it come from, how old was it, and who had vetted it?”
His question was valid. We had been moving so quickly from one operation to another that we had not stopped to verify the intelligence, revalidate the source, or revise the plan before moving. It reinforced a lesson I carried into business: rank carries responsibility, but it does not give a leader a monopoly on judgment.
Under pressure, questions can look like delay. Leaders want movement. Teams want to show progress. Everyone wants to reach the result.
But moving quickly in the wrong direction is not progress. That lesson matters as businesses bring artificial intelligence into everyday decisions.
AI can recommend a staffing change, flag a customer transaction or rank job applicants. It can process more information than any individual manager and identify patterns people might miss.
But AI cannot own the consequences. A named person still must do that.
Make “why” part of the process
When an AI system recommends an action, the first question should not be, “How quickly can we implement it?”
The first questions should be:
•Why is the system recommending this?
•What problem are we trying to solve?
•What assumptions is the recommendation based on?
•What information could change the decision?
•Who has the authority to challenge it?
Imagine a manager receives a recommendation to reduce staffing because recent demand has fallen.
The recommendation may be based on accurate historical data. But an employee may know that a major customer is preparing a launch that will increase demand next month. That information may not exist in the system yet.
If the employee cannot raise that concern, the company has created a process that looks efficient but is not intelligent.
Data deserves attention, but it does not remove the need for judgment.
Put a person in charge
Telling a team to “review the recommendation” is not enough.
Who owns the decision? Who resolves disagreements? Who has the authority to stop the process and change course?
If the answer is “the team,” responsibility becomes unclear. Each person may assume someone else has checked the recommendation. When something goes wrong, everyone can point to the system or to one another.
A named decision-maker changes that.
Employees know where to take a concern. Leaders know who must explain the decision. The person approving the action understands that approval carries an obligation to examine the recommendation, not simply accept it.
AI does not change the basic standard of accountability.
Define when the process stops
Employees need clear instructions about when human judgment must take over.
A process should pause when the information conflicts, the situation is unfamiliar or the decision could seriously affect someone’s employment, safety or access to an important service.
The response does not need to be complicated:
•Pause the action.
•Explain the concern.
•Bring it to the person responsible for the decision.
That process also needs leadership support. If a manager tells employees to question the system but treats every delay as failure, employees will quickly learn which instruction matters more.
They will stop raising concerns.
Test accountability in one workflow
Businesses do not need to solve every AI governance question at once. Start with one process where AI already influences a decision.
Ask the people using it three questions:
•Who owns the decision?
•What would cause you to challenge the recommendation?
•What happens when you do?
The answers will show whether accountability exists in daily practice or only in a policy document.
My military experience taught me that giving people responsibility means giving them room to use their judgment. That is part of good leadership, whether the decision involves a field operation, a workforce or an AI system.
Use the technology. Examine the recommendation. Challenge it when the facts do not fit. And make sure a real person remains responsible for what happens next.
About the essayist: Michael J. Padilla is a retired U.S. Army colonel and global business leader with more than three decades of military and international leadership experience. He is founder and CEO of Al Thuraya Holdings, a global risk, security and business services group.
