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Thursday, October 29, 2026

PO Prompts #006 — What If AI Agents Became the Managers?

What If AI Agents Became the Managers?

One Strange Question. A New Possibility.

PO–AI Thinking Lab | AI Agents • Leadership • Future of Work • Human–AI Collaboration

What if AI agents became the managers—and humans decided what work was worth doing?

It sounds like an upside-down organization.

But that is precisely why it is worth exploring.

As AI agents become capable of planning tasks, using digital tools, coordinating workflows and monitoring progress, organizations are beginning to reconsider how work is organized.

Much of the conversation asks whether AI will replace employees or managers.

PO thinking invites us to ask a different question:

What if AI did much of the coordination traditionally associated with management, allowing humans to concentrate on judgment, relationships, creativity and responsibility?

This is not a prediction that AI agents should become people's bosses. It is a deliberate lateral-thinking provocation designed to reveal possibilities hidden inside familiar organizational structures.

Welcome to PO Prompts #006, where we turn a contemporary development into a practical experiment in thinking differently.

1. The Contemporary Trigger: AI Agents and the Changing Workplace

The workplace is moving beyond AI systems that simply answer questions.

Increasingly, organizations are exploring AI agents that can pursue defined goals, plan sequences of tasks, interact with authorized software tools and assist with multi-step workflows.

Microsoft's 2025 Work Trend Index introduced the concept of the agent boss: a human who builds, delegates to and manages AI agents.

Its 2026 Work Trend Index reported significant growth in agent activity across its Microsoft 365 ecosystem, while emphasizing the continuing importance of human judgment and organizational readiness.

These developments raise questions about the future of management.

If an AI agent can prepare a project schedule, identify delays, consolidate reports and recommend priorities, how much managerial time must still be devoted to routine coordination?

And if those activities change, what becomes more important in the human manager's role?

Rather than predict the answer, let us provoke a different way of thinking.

PO: AI agents become the managers. Humans decide what work deserves to be done.

PO is a deliberate provocation, not a statement of fact or a recommended transfer of managerial authority.

2. The Story: Priya and Her New Manager

Priya is a project manager at a technology company in Bengaluru.

She manages a team of twelve professionals working on software development, customer support and product improvements.

Her Mondays follow a familiar pattern.

She checks project updates, reviews unfinished tasks, distributes new assignments, prepares schedules, follows up with colleagues and attends coordination meetings.

By lunchtime, she has already spent several hours managing the work.

But she has had almost no time to think about improving the product, developing her team or understanding emerging customer needs.

One day, the company introduces an AI coordination agent.

The agent can summarize project information, prepare draft task lists, identify potential bottlenecks and suggest schedules.

Initially, Priya uses it as an assistant.

During a team discussion, one colleague asks:

“Why don't we let the agent coordinate the entire project?”

Another jokes:

“Then Priya will have an AI manager!”

Everyone laughs.

But Priya becomes curious.

What if I stopped being the person who coordinates everything?

She begins imagining a different working day.

Instead of spending the morning asking people for status updates, she could help a junior engineer solve a difficult problem.

Instead of preparing routine reports, she could speak to customers about unmet needs.

Instead of chasing deadlines, she could identify why the team repeatedly struggles with particular tasks.

Instead of merely distributing work, she could help people grow.

The AI agent would not decide what matters or exercise authority over employees.

It would help organize information and recommend coordination actions, while Priya and her team retained control.

The thought experiment leads to an unexpected realization:

Perhaps the future of management is not simply managing more work. Perhaps it is creating more value from human capabilities.

Priya and her workplace are fictional. The scenario illustrates a possible direction for human–AI collaboration.

3. The Hidden Assumption

Traditional management often combines several responsibilities:

Deciding what the organization should accomplish.
Assigning and coordinating tasks.
Monitoring progress.
Communicating priorities.
Developing employees.
Resolving disagreements.
Making difficult decisions.
Accepting responsibility for outcomes.

Because these responsibilities are commonly performed together, we may unconsciously assume they must remain together.

Hidden assumption: Effective leadership requires the manager to personally coordinate and monitor the work.

But is coordination the same as leadership?

Is preparing a schedule the same as making a wise decision?

Is collecting status reports the same as helping people succeed?

These distinctions create an opening for lateral thinking.

4. The PO–AI Thinking Lab

Our six-stage AIYING extension combines Edward de Bono's PO provocation and movement methods with AI-assisted exploration, critical judgment and practical experimentation.

The stages are:

FOCUS → PROVOKE → MOVE → GENERATE → JUDGE → ACCOMPLISH

Stage 1 — FOCUS: What Problem Are We Really Solving?

The problem is not necessarily that organizations need fewer managers.

A more useful formulation might be:

How can organizations reduce the burden of routine coordination while improving leadership, teamwork and outcomes?

We can begin by identifying activities that consume managerial time.

Some involve information processing and routine administration.

Others require empathy, contextual judgment, trust, ethical reasoning and accountability.

The opportunity is to examine these responsibilities separately rather than assume they must always be performed by one person.

Stage 2 — PROVOKE: Reverse the Relationship

Edward de Bono's lateral-thinking approach uses deliberate provocations to disrupt established patterns.

For this episode, we use reversal.

The conventional arrangement is:

Human manager → Assigns work → Team performs work → AI assists

Now reverse part of the relationship:

PO: AI agent coordinates work → Humans decide priorities, approve actions and lead people

The provocation sounds uncomfortable.

Can software really be a manager?

Should an algorithm assign work to people?

Would employees lose autonomy?

Those concerns are important, but during the provocation stage, we temporarily suspend immediate rejection.

We use the unusual arrangement to search for useful ideas.

The aim is not to implement the provocation literally.

The aim is to move beyond the assumptions it challenges.

Stage 3 — MOVE: Extract New Directions

In de Bono's approach, movement means using a provocation to move toward potentially useful ideas rather than merely judging whether the provocative statement is correct.

Consider several movement techniques.

Extract a principle

The provocation suggests that routine coordination and human leadership can be separated.

An agent may support scheduling and information flow without exercising authority over people.

Explore positive aspects

Managers could gain time for coaching, innovation, customer understanding and complex decisions.

Employees could receive clearer task information and fewer repetitive status requests.

Examine consequences

If AI-generated schedules become common, teams may need better mechanisms for challenging priorities, reporting unrealistic workloads and identifying unfair recommendations.

Identify differences

A human manager understands personal circumstances, relationships, organizational culture and informal context in ways an AI system may not reliably capture.

This difference suggests complementary roles rather than interchangeable ones.

Look for special circumstances

An AI coordination assistant may be especially useful for distributed teams, recurring operational workflows or projects involving many dependencies.

However, sensitive personnel matters, performance evaluations, disciplinary decisions and consequential employment actions require stronger safeguards and human authority.

Movement turns an apparently absurd idea into several directions worth exploring.

5. GENERATE: Seven New Possibilities

Here are seven possibilities arising from the provocation. They are concepts to investigate, not claims that every organization can already implement them successfully.

Possibility 1 — The AI Coordination Partner

An agent prepares task plans, consolidates updates and flags possible delays.

Humans approve priorities and assignments.

Potential value: Less time spent collecting information and preparing routine coordination documents.

Possibility 2 — The Human Development Manager

Managers deliberately allocate more time to mentoring, coaching, skill development and resolving complex problems.

AI handles selected administrative preparation tasks.

Potential value: More attention to employee growth and team capability.

Possibility 3 — The Employee-Challenged AI Plan

AI proposes a weekly work plan.

Every employee can question an assignment, explain missing context or propose an alternative.

The human manager makes the final decision.

Potential value: Greater transparency and employee participation in planning.

Possibility 4 — The Outcome-Oriented Team

Instead of constantly reporting activities, teams define meaningful outcomes and review progress against them.

AI helps summarize evidence of progress without replacing human interpretation.

Potential value: Less emphasis on visible busyness and more emphasis on useful results.

Possibility 5 — The AI Bottleneck Investigator

An agent examines authorized workflow data to identify repeated handoffs, unnecessary approvals and recurring delays.

Managers investigate the causes and decide which changes to test.

Potential value: Better process improvement without assuming every delay is an employee performance problem.

Possibility 6 — The Human–Agent Team Designer

A new professional responsibility emerges: designing how people and AI agents collaborate.

This includes defining task boundaries, permissions, escalation procedures, quality checks and accountability.

Potential value: More reliable and understandable human–AI workflows.

Possibility 7 — The Manager as Opportunity Creator

As routine coordination becomes easier, managers devote more attention to identifying new customer needs, experimenting with services and discovering opportunities.

Potential value: Management shifts some of its effort from maintaining existing processes to improving what the organization can accomplish.

The most interesting result is that none of these possibilities requires an AI system to become a human employee's unquestionable boss.

The provocation has served its purpose by revealing alternative organizational designs.

6. JUDGE: Which Possibilities Deserve to Be Tested?

Creative possibilities need critical evaluation.

AI-generated coordination plans can be wrong, incomplete or unfair. Agents may misunderstand context, operate on outdated information, mishandle sensitive data or create excessive monitoring.

Before experimenting, organizations should examine five questions:

Usefulness: Does the proposed workflow solve a genuine coordination problem?

Feasibility: Can the available AI system perform the task reliably with the permissions and information provided?

Human agency: Can employees understand, challenge and correct its recommendations?

Security and fairness: Are confidential information, access controls, privacy and workload equity protected?

Accountability: Who approves actions, handles mistakes and remains responsible for decisions?

A crucial distinction must remain clear:

AI may recommend and coordinate within defined boundaries. Human leaders remain responsible for consequential decisions and the treatment of people.

The most promising direction is not unlimited automation.

It is well-designed collaboration.

7. ACCOMPLISH: A Seven-Day Experiment

You do not need to reorganize your company to explore this idea.

Try a small, controlled experiment.

Day 1 — Observe

Record how much time your team spends preparing schedules, collecting updates and coordinating tasks.

Day 2 — Select

Choose one low-risk activity, such as preparing a draft weekly task plan.

Day 3 — Generate

Provide ChatGPT or another approved AI tool with a fictional or appropriately sanitized project description. Ask it to propose a task plan.

Day 4 — Review

Have team members identify missing context, unrealistic assumptions, dependencies and possible unfairness.

Day 5 — Improve

Revise the proposed plan with human input. Keep all real assignments subject to human approval.

Day 6 — Compare

Compare the AI-assisted process with the previous approach. Examine time saved, plan clarity, errors, employee experience and additional review effort.

Day 7 — Decide

Determine whether the approach should be refined, repeated or abandoned.

A successful experiment is not necessarily one that automates the most work.

It is one that produces a measurable improvement without sacrificing trust, quality or human control.

8. Your Complete ChatGPT PO Prompt

Copy the following prompt and adapt it to your team, profession or organization.

Act as my PO lateral-thinking partner, using Edward de Bono's provocation and movement methods.
I want to rethink management, teamwork and the role of AI agents in my organization.
Use this deliberate provocation:
“PO: AI agents become the managers. Humans decide what work deserves to be done.”
Guide me through the following six stages:
1. FOCUS: Help me identify what managers currently do. Separate coordination, administration, leadership, judgment, employee development and accountability. Reveal hidden assumptions.
2. PROVOKE: Explore the reversal without immediately dismissing it as unrealistic. Treat it as a thinking exercise, not a recommendation to transfer authority over people to AI.
3. MOVE: Apply principle extraction, positive aspects, consequences, differences and special circumstances to discover useful directions.
4. GENERATE: Develop at least seven genuinely different ways AI agents could transform coordination, teamwork and leadership. For each, identify the problem addressed, potential benefits and required human involvement.
5. JUDGE: Evaluate each possibility for usefulness, feasibility, reliability, security, privacy, fairness, employee autonomy and accountability. Distinguish verified capabilities from speculation.
6. ACCOMPLISH: Help me design a safe, low-cost, seven-day experiment. Include the hypothesis, required inputs, human review points, success criteria and risks.
Do not assume that AI agents have permissions or capabilities they have not been given. Do not recommend autonomous personnel decisions or unnecessary employee surveillance.
Ask questions at each stage rather than immediately giving all the answers.
Begin by asking about my team, responsibilities, workflows, available AI tools and biggest coordination challenges.

9. Beyond Managers: Who Else Could Use This PO Prompt?

Although this episode begins with project management, the underlying provocation can be explored in many settings.

Teachers might ask whether AI could coordinate routine classroom administration while teachers concentrate on learning and mentoring.

Healthcare administrators might examine AI-assisted scheduling while preserving clinical judgment and patient safety.

Entrepreneurs could explore agent-assisted operations while founders focus on customers and strategic choices.

Students might use the provocation to imagine new professional roles involving human–AI coordination.

AI engineers could explore how to design agents that are observable, interruptible, secure and accountable.

Organizational leaders could investigate which activities genuinely require managerial judgment and which could be redesigned.

The purpose is not to arrive at one universal answer.

It is to uncover new possibilities appropriate to each situation.

10. The Bigger Question: What Is Management Really For?

For decades, management has often been associated with planning, organizing, directing and controlling.

AI agents invite us to examine how some of those activities might change.

If preparing schedules becomes easier, managers may have more time to understand people.

If routine reporting becomes faster, teams may have more time to discuss problems that matter.

If coordination becomes more automated, leadership may become more visibly about judgment, trust, responsibility and human development.

But these improvements will not happen automatically.

Poorly designed AI coordination could create more bureaucracy, more surveillance and less autonomy.

That is why PO thinking must be followed by careful judgment and practical experimentation.

The question is not simply whether AI can manage tasks.

The more consequential question is what humans should accomplish when managing tasks becomes easier.

11. Your PO Challenge

Imagine arriving at work tomorrow and discovering that an AI agent has prepared your schedule, organized your routine tasks and identified your project's most urgent dependencies.

You are still responsible for the decisions.

You can challenge every recommendation.

You remain accountable for how people are treated.

What would you do with the time and attention you gain?

Would you develop your team?

Solve a neglected customer problem?

Create a new service?

Improve how people collaborate?

Or discover an entirely different way of working?

Share your answer in the comments.

One unusual question may reveal a possibility that conventional thinking has overlooked.

About PO Prompts

PO Prompts — One Strange Question. A New Possibility.

PO Prompts is an AIYING series that uses deliberate lateral-thinking provocations to challenge assumptions, explore unconventional directions and turn promising ideas into practical experiments.

The series draws on Edward de Bono's PO and movement methods. The six-stage FOCUS → PROVOKE → MOVE → GENERATE → JUDGE → ACCOMPLISH process is an AIYING framework for extending those methods through AI-assisted exploration and action.

PO provokes. AI generates. Humans judge and accomplish.

One Image. One Story. One Provocation. Many Possibilities.

AIYING — Accomplishing with AI.

References and Further Reading

Microsoft, 2025 Work Trend Index Annual Report — introduction of the “agent boss” concept.
Microsoft, 2026 Work Trend Index Annual Report — developments in agent adoption and human–AI work.
Edward de Bono, PO: Beyond Yes and No (1972).
Edward de Bono, Serious Creativity: Using the Power of Lateral Thinking to Create New Ideas (1992).

Editorial note: The workplace story, proposed organizational models and seven-day experiment are illustrative. They are not presented as verified outcomes or established best practices.

PO Prompts #002 — What If Meetings Were Forbidden? | Rethinking Workplace Collaboration

What If Meetings Were Forbidden?

One Strange Question. A New Possibility.

AIYING — Accomplishing with AI

Six Meetings. One Working Day. Almost No Progress.

Monday, 9:00 AM.

Ananya, a project manager, joins her first meeting.

The team discusses progress, reviews pending tasks and identifies issues requiring attention.

At 11:00 AM, she joins another meeting to report the same progress to senior management.

At 2:00 PM, everyone gathers to prepare for tomorrow's project review.

By 5:30 PM, Ananya has attended six meetings.

Her calendar is full.

Her project work is still waiting.

She wonders:

“How can I manage my meetings more efficiently?”

Then comes a surprising proposition.

PO: What if nobody were allowed to attend meetings?

Ridiculous?

Perhaps.

But that is precisely where lateral thinking begins.

The PO Thinking Lab

Step 1 — Recognize the Problem

Ananya initially believes she has a time-management problem.

She considers shorter meetings, better agendas and stricter time limits.

These might help.

But they all assume something important:

The meetings themselves are necessary.

What if that assumption deserves examination?

Step 2 — Expose the Hidden Assumption

We often associate meetings with collaboration.

We assume that to share information, coordinate work or make decisions, people must gather at the same time.

But are all these activities fundamentally the same?

Consider four common reasons for meetings:

Sharing information
Making decisions
Solving problems
Building relationships

Each serves a different purpose.

Yet organizations frequently use the same mechanism—a meeting—for all four.

The hidden assumption: Collaboration requires meetings.

Step 3 — Introduce the PO Provocation

Edward de Bono introduced PO as a tool for provocative thinking.

Instead of immediately judging whether an idea is right or wrong, we temporarily use it to explore possibilities.

Our provocation is:

PO: Attending meetings is forbidden.

This is not a recommendation to eliminate all meetings.

It is an intentionally unreasonable proposition designed to disrupt habitual thinking.

For a moment, imagine that Ananya's organization cannot hold meetings.

What would happen?

Step 4 — Move From the Impossible to New Possibilities

This is the most important stage.

We do not reject the provocation.

We explore its consequences.

Possibility A — Information without meetings

If people cannot gather to report progress, they must find another way to make progress visible.

Perhaps a shared project dashboard could show completed work, blockers, decisions and next steps.

The emerging principle:

Information should be available when needed, not only when a meeting takes place.

Possibility B — Decisions without unnecessary attendance

If meetings are unavailable, decisions cannot depend on gathering everyone.

Perhaps each decision could have a clearly identified owner, required inputs and a deadline.

The emerging principle:

Decision-making needs clarity of authority, not necessarily a room full of people.

Possibility C — Problems that demand real collaboration

Some problems involve uncertainty, competing perspectives or complex trade-offs.

Without routine meetings, these situations become more visible.

People might discover that certain conversations are genuinely valuable and deserve focused attention.

The emerging principle:

Meet when interaction creates value that other methods cannot provide.

Possibility D — Collaboration without constant interruption

If routine meetings disappear, professionals gain longer periods for focused work.

But they also need reliable ways to ask questions, raise concerns and coordinate dependencies.

The emerging principle:

Collaboration should support productive work, not continually interrupt it.

Step 5 — Discover the Aha Moment

Ananya realizes something important.

Her original question was:

“How can I manage my meetings more efficiently?”

Now she asks:

“Which outcomes actually require a meeting?”

That is the thinking shift.

The discovery is not that meetings are bad.

It is that meetings are one possible mechanism for collaboration—not collaboration itself.

The goal is to design the most appropriate way to achieve each outcome.

Step 6 — Accomplish: The One-Week PO Experiment

Choose one recurring status meeting.

For one week, replace it with a simple written update containing:

Progress since the last update
Current blockers
Decisions required
Responsible owners and deadlines

Allow team members to request a focused discussion when a problem genuinely requires interaction.

At the end of the week, evaluate:

Was information equally or more accessible?
Were decisions made on time?
Did team members gain useful focus time?
Did any communication problems emerge?
Should the original meeting return, change or disappear?

The purpose is not to prove that meetings are unnecessary. It is to discover which forms of collaboration work best.

Try the PO Thinking Lab With AI

AI can help us explore provocative ideas without replacing human judgment.

Copy this prompt into ChatGPT:

PO Thinking Partner Prompt

"Act as PO, my lateral-thinking companion, using Edward de Bono's approach.

I want to challenge the assumption that effective collaboration requires meetings.

Start with this provocation: 'Attending meetings is forbidden.'

Do not immediately offer conventional productivity advice.

First, help me identify the assumptions behind our existing meeting practices.

Next, guide me through the consequences of the provocation. Ask what would happen to information sharing, decision-making, problem-solving and relationships.

Help me extract useful principles from those consequences.

Then generate at least three alternative ways of collaborating.

Finally, help me design a realistic one-week experiment with measurable outcomes.

Ask questions throughout so I can make the discoveries and decisions myself."

Your PO Challenge

Imagine your organization announces:

“For the next seven days, nobody may attend a meeting.”

What would stop working?

What might work better?

And what entirely new way of collaborating might emerge?

Don't immediately search for the correct answer.

Explore the possibilities.

Share your most surprising discovery in the comments.

About PO Prompts

PO Prompts is a series exploring how surprising provocations, human stories and deliberate lateral thinking can reveal possibilities that conventional problem-solving may overlook.

Inspired by Edward de Bono's PO approach, each episode begins with a familiar challenge and invites readers to think beyond established assumptions.

AI serves as a thinking partner—helping us question, explore, experiment and learn.

PO Prompts
One Strange Question. A New Possibility.

AIYING — Accomplishing with AI.

PO Prompts #006 — What If AI Agents Became the Managers?

What If AI Agents Became the Managers?