When AI Becomes an Employee, Who Is in Control?
Executive Overview
AI agents are beginning to change the way organisations think about artificial intelligence.
Until recently, most business AI acted primarily as an assistant. An employee asked a question, AI generated a response, and the employee decided what to do next.
AI agents are different.
They can potentially be given access to business systems, retrieve information, communicate with customers, update records, initiate workflows and perform sequences of tasks with limited human involvement.
That creates enormous opportunities for productivity.
It also creates an important new executive question:
If an AI agent is acting on behalf of your organisation, who controls what it is allowed to do?
From AI Assistant to AI Worker
Consider the difference.
An AI assistant might draft an email for an employee.
An AI agent could potentially:
Identify the customer → retrieve their account → decide the appropriate response → send the email → update the CRM → schedule a follow-up.
The employee has moved from performing each step to supervising the overall process.
This is where AI starts to resemble a digital worker rather than simply a productivity tool.
And that means organisations need to think differently about governance.
We wouldn’t give a new employee unrestricted access to every company system simply because they were capable of using it.
We define their role.
We determine their authority.
We control their system access.
We supervise their work.
And we have procedures for intervening when something goes wrong.
AI agents require the same management discipline.
The Five Questions Executives Should Ask
Before deploying an AI agent, leadership should be able to answer five fundamental questions.
1. Identity — Who is performing the action?
An AI agent should be identifiable.
Organisations need to know whether an action was performed by an employee, an application or an autonomous agent.
Where practical, agents should therefore operate through identifiable accounts and credentials rather than invisibly through an employee’s identity.
This creates an audit trail:
Who initiated the activity? → What did the agent do? → When did it happen? → What systems did it access?
Without that visibility, accountability becomes difficult.
2. Authority — What is the agent allowed to do?
This may be the most important question.
An agent helping prepare an insurance claim might be permitted to:
✓ Retrieve claim information
✓ Summarise documents
✓ Recommend the next action
But perhaps not:
✗ Approve the claim
✗ Authorise payment
✗ Change financial information
Those boundaries need to be deliberately designed.
A useful principle is:
Give an AI agent the minimum authority necessary to perform its role successfully.
3. Access – What can the agent reach?
An agent’s capability is determined partly by the systems and information available to it.
An AI agent connected to email, CRM, financial systems, customer records and internal databases potentially has considerably more organisational power than a standalone chatbot.
Executives should therefore ask:
What data does it need?
Which systems does it need?
What doesn’t it need?
This is essentially the familiar security principle of least privilege, applied to AI.
4. Oversight — When must a human intervene?
Not every AI action requires human approval.
If every minor activity needs manual authorisation, much of the benefit of automation disappears.
But the opposite extreme — allowing agents unlimited autonomy — creates obvious risks.
The solution is risk-based human oversight.
For example:
Low risk: AI can act automatically.
Medium risk: AI acts within defined limits and exceptions are reviewed.
High risk: AI recommends, but a human approves.
The greater the potential financial, customer, regulatory or reputational consequence, the stronger the case for human involvement.Perplexity is an outstanding research assistant, but executive judgement remains essential.
5. Intervention — Can We Stop It?
This is the question organisations may overlook until something goes wrong.
What happens if an AI agent begins behaving unexpectedly?
There should be a clearly understood mechanism to:
Detect → Suspend → Revoke Access → Investigate → Recover
Recent concerns around autonomous AI have demonstrated why this matters.
The organisation should not have to work out how to disable an AI agent during an incident.
The intervention process should be designed and tested beforehand.
Think of an AI Agent as a New Employee
A useful way for executives to approach agent governance is to imagine onboarding a new employee.
The comparison isn’t perfect, but it provides executives with a familiar management framework.
The important principle is:
Capability should never automatically equal authority.
Just because an AI agent can perform an action doesn’t mean the organisation should permit it to do so.
The New Governance Challenge
Traditional IT governance primarily controls systems.
AI governance increasingly needs to control behaviour.
An AI agent may encounter situations its designers never explicitly anticipated.
That makes monitoring particularly important.
Executives should therefore expect agent governance to include:
Identity + Access + Authority + Monitoring + Human Oversight + Incident Response
And these controls should follow the agent throughout its lifecycle:
Approve → Test → Deploy → Monitor → Review → Change → Retire
What Should Executives Do Now?
Most organisations do not need a complicated new bureaucracy for AI agents.
They do need clear rules.
For every agent being considered, ask:
What is its job?
Who owns it?
What can it access?
What can it do without permission?
Which decisions remain human?
How will we know if it behaves unexpectedly?
Who can stop it?
If those questions cannot be answered, the agent probably isn’t ready for production.
Executive Takeaway
AI agents could become one of the most significant developments in enterprise technology.
They offer the possibility of moving beyond AI that merely advises employees towards AI that can perform work on their behalf.
But that shift also changes the governance equation.
The question is no longer simply:
“What can our AI do?”
Leadership increasingly needs to ask:
“What should we allow it to do?”
And perhaps most importantly:
“When AI becomes a digital worker, who remains accountable?”
The answer should always be clear:
The organisation does.