AI can identify patterns that a human manager may never see. It can also make a flawed decision at a speed and scale no human manager could ever match. That creates a question every founder, CEO, and business leader will eventually need to answer:
Should AI be allowed to manage people?
My honest answer is that I am still on the fence.
I do not believe the right answer is to keep AI completely outside the management process. The technology can analyze enormous amounts of information, identify inconsistencies, detect emerging problems, and surface insights that even strong leaders can miss.
But I am equally uncomfortable with a future where an algorithm quietly determines:
- Who receives the best assignments
- Who is considered a high performer
- Who receives a raise or promotion
- Who is placed on a performance plan
- Who is identified as a retention risk
- Who is ultimately shown the door
AI can make management more intelligent. That does not mean we should allow it to become the manager.
This Is Not a Future Problem
“Algorithmic management” sounds like something that belongs in a future-of-work presentation. It is already here.
The OECD defines algorithmic management as technology that partially or fully automates work traditionally performed by managers. That can include assigning schedules and activities, monitoring work time and speed, tracking communications, setting targets, rewarding performance, and sanctioning poor performance.
In a survey of more than 6,000 mid-level managers across six countries, the OECD found that 90% of U.S. firms surveyed used at least one form of algorithmic management. More than three-quarters of U.S. managers said their organizations used at least 10 of the 15 categories included in the study.[1]
There is an important qualification: not every one of these systems uses artificial intelligence. Some are conventional rules-based or workflow technologies.
But the direction is unmistakable. Software is no longer just helping employees complete their work. It is increasingly helping organizations decide how work is assigned, monitored, evaluated, and rewarded. That is management.
The Business Case Is Real
It would be easy to turn this into another alarmist argument about machines taking control of the workplace. That would ignore the other side of the evidence.
We found in the OECD research, 60% of managers using algorithmic management tools said the technology improved the quality of their decisions. More than half reported improved job satisfaction, often because the tools reduced repetitive work and stress.
There are also compelling examples of AI augmenting performance rather than simply monitoring it. A large-scale study involving approximately 5,000 customer-support agents found that an AI assistant increased the number of customer issues resolved per hour by nearly 14%. The gains were even greater for less-experienced and lower-performing employees, who improved by roughly 35%. The technology helped newer employees access some of the knowledge and communication patterns normally accumulated through experience.
That is not a small efficiency gain. It suggests AI can potentially:
- Detect performance barriers earlier
- Give employees more immediate coaching
- Reduce inconsistencies between managers
- Identify overlooked skills and potential
- Help new employees develop faster
- Surface workforce patterns that leaders cannot easily see
- Improve decisions that have historically depended on incomplete information and individual judgment
Human managers miss things. We rely on recency bias. We overweight visible contributions. We can favor people whose communication styles resemble our own. We make different decisions depending on our workload, energy, experience, and assumptions.
AI may help challenge some of that inconsistency. Ignoring that opportunity would be shortsighted.
But Efficiency Is Not the Same as Leadership
Here is where I draw a distinction. AI can evaluate recorded information. It cannot fully understand a person’s circumstances.
A system may detect that an employee’s productivity declined. It may not understand that the employee was carrying an understaffed team, training three new hires, resolving an invisible client crisis, or being measured against a target that no longer reflected the work.
It may identify someone as a flight risk. It may not understand that the person is waiting for a manager to have a career conversation that should have happened six months ago. It may determine that another employee is outperforming the team. It may not recognize that the employee is hitting individual metrics while creating problems for everyone around them.
Data can reveal the pattern. Management must interpret the pattern. Leadership must accept responsibility for what happens next.
We Already Know What Happens When the System Becomes the Boss
Amazon’s warehouse-management systems provide one of the most visible examples of how far algorithmic oversight can extend. Publicly reported company documents showed systems tracking individual productivity and automatically generating warnings or termination notices when performance fell below established thresholds. Amazon stated that supervisors could override the system. It is important to understand the business context rather than simply criticizing the company.
Organizations operating at enormous scale need consistent workflows, accurate capacity planning, measurable standards, and fast decisions. Human managers alone cannot manually process every operational data point across a global workforce. The pressure to use technology is understandable.
But the example exposes a much broader governance question - When a system generates the decision and a manager can technically override it, who is actually making the decision?
A human being at the end of an automated process does not automatically create meaningful human oversight. A manager who lacks the information, authority, time, or confidence to challenge the recommendation may become little more than the final approval button. That is not human-in-the-loop management. That is algorithmic management with human cover.
The Same Technology Can Produce Very Different Workplaces
Research conducted by the European Commission’s Joint Research Centre and the International Labour Organization examined algorithmic management in logistics and healthcare across several countries.
The researchers found that these technologies could streamline processes, improve efficiency, and support service quality. They also found risks involving surveillance, work intensity, job quality, and employee autonomy.
Most importantly, similar technologies produced very different outcomes depending on how they were implemented, governed, and integrated into the workplace. That may be the most important lesson for executives.
The technology alone does not determine whether AI becomes:
- A coach or a surveillance system
- A source of insight or an unchallengeable authority
- A way to improve decisions or avoid accountability
- A tool that builds trust or quietly destroys it
The operating model determines that.
Trust Is Not a Soft Issue
Some leaders will view employee discomfort as resistance to change. That is too simplistic.
The American Psychological Association found that workers who experienced electronic monitoring were more likely to report feeling tense or stressed at work than employees who were not monitored. Pew Research Center has also found significant discomfort with employers using AI to influence promotions and terminations. A majority of Americans opposed allowing AI to make final hiring decisions.
Employees are not necessarily rejecting the use of data. They are reacting to the possibility of being judged by systems they do not understand, using information they cannot see, through decisions they may have no meaningful way to challenge.
That is not merely an employee-relations problem. It creates operational risk. When people do not trust the management system, they begin managing the metric instead of performing the work. They avoid experimentation. They conceal mistakes. They spend energy appearing productive instead of creating value. They become less willing to question flawed recommendations. And strong performers who have options leave.
Trust is not the alternative to performance. Trust is part of the infrastructure that makes sustained performance possible.
Human Oversight Can't Be a Checkbox
“Human in the loop” has become one of the most repeated phrases in responsible AI. But the phrase can create false confidence. Meaningful human oversight requires more than placing a manager somewhere in the workflow.
The person reviewing the recommendation must have:
- Access to the evidence behind it
- Enough context to evaluate its limitations
- Training to understand how the system works
- Authority to disagree with the output
- Time to conduct an actual review
- Accountability for the final decision
The National Institute of Standards and Technology recommends clearly defined accountability structures, documented responsibilities, ongoing monitoring, executive ownership, and explicit roles for human-AI oversight. That standard matters. If everyone can blame the algorithm, no one is accountable.
The Line Should Move With the Consequence
I do not believe every use of AI in management requires the same level of intervention. The level of human control should increase as the consequence to the employee increases.
Lower-consequence decisions - AI may be given greater autonomy in areas such as:
- Recommending training content
- Summarizing employee feedback
- Identifying scheduling conflicts
- Suggesting coaching questions
- Highlighting workflow bottlenecks
- Organizing existing performance information
Moderate-consequence decisions - AI can provide analysis, but a qualified person should confirm decisions involving:
- Workload and project assignments
- Performance flags
- Development recommendations
- Succession indicators
- Retention-risk predictions
- Changes to employee goals or targets
High-consequence decisions - AI should remain an input—not the final authority—when a decision affects:
- Compensation
- Promotion
- Formal discipline
- Performance-improvement plans
- Layoffs
- Termination
- Access to employment
The higher the consequence, the stronger the standard for evidence, explainability, review, documentation, and appeal should become.
Regulators are already moving in this direction. Existing federal employment protections still apply when AI is involved. New York City requires certain automated employment tools to undergo bias audits and requires notice to affected candidates or employees. The European Union classifies several employment and worker-management applications as high-risk uses of AI.
The strategic message is straightforward: Responsible AI governance is becoming part of the management operating system - not a policy document that can be forgotten.
Five Guardrails I Would Put in Place Now
1. Separate insight from authority
- Define what the system may recommend, what it may initiate, and what it may never decide on it's own.
- Never leave those boundaries to the software vendor or the individual manager.
2. Name the accountable human
- Every AI-supported decision should have a clearly identified person who owns the outcome.
- “The system recommended it” is not an explanation that you tolerate.
3. Give people a way to challenge the data
- Employees should be able to understand what information was considered, correct wrong information, and raise legitimate contextual arguments and iterations.
- An appeal process should not be thought of as friction - it's quality-control.
4. Audit business and human outcomes
- Don't limit the review to whether the technology is functioning as designed.
- Examine whether its recommendations produce better decisions.
- Track differences across roles, locations, demographic groups, managers, performance levels, and success outcomes.
- Also measure unintended insights: turnover, burnout, employee behavior, customer outcomes, and manager dependence on the system (hard to do).
5. Involve the people affected
- The employees closest to the work often understand the limitations of the data better than the executives buying the platform.
- ILO research suggests that employee participation can lead to more effective and more accepted AI implementation by complementing skills instead of merely increasing control.
- Worker input is not always an obstacle to adoption.
My Position - For Now :)
I am not convinced AI should never manage people. I am even less convinced that it should be allowed to manage them invisibly. There are decisions where AI may ultimately prove more consistent, objective, and accurate than an individual manager. There are also decisions where context, empathy, judgment, dialogue, and personal accountability are inseparable from the act of leadership.
The challenge is not choosing between humans and AI. It is determining where each creates the most value—and ensuring the organization never confuses computational confidence with managerial wisdom. AI should help leaders see what they are missing. It should challenge inconsistent decisions. It should surface evidence, patterns, risks, and opportunities.
But when someone’s compensation, career, reputation, or continued employment is at stake, a leader must still be willing to understand the decision, explain it, defend it, and own it. Because the moment no human being is truly accountable for a people decision, the technology is no longer supporting the management system. It has become the management system. And before we allow that to happen, we should be very clear about what we are giving up.
Where would you draw the line?
Should AI be allowed to recommend, evaluate, promote, discipline, or terminate?
I am actively exploring how leaders can build AI into workforce and operating decisions without surrendering judgment or accountability. If your organization is working through the same questions, send me a message, or share your perspective in the comments.
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