Showing posts with label human ai collaboration. Show all posts

Saturday, November 14, 2026

ChatGPT: What If Every Conversation Could Generate a New Capability? | The Generative View #003

AIYING | The Generative View #003

ChatGPT: What If Every Conversation Could Generate a New Capability?

From asking questions to developing the ability to accomplish something.

Lateral Thinking × PO × Generative AI × Human Judgment

Independent AIYING exploration | November 2026

1. Reality: We Are Having More Conversations with AI

People use ChatGPT to ask questions, write messages, explain concepts, brainstorm ideas, analyze information, solve problems, and develop software.

The experience is conversational. We type or speak, ChatGPT responds, and we continue.

That interaction can be remarkably useful.

But consider what often happens:

We ask a question.
We receive an answer.
We use or read the answer.
We close the conversation.

What remains after the conversation?

Sometimes a useful document or completed task. Sometimes a better understanding. Sometimes very little that we can independently reproduce.

That observation raises a generative question:

What if we evaluated a conversation not only by the quality of its answers, but by the new capability it helped a person develop?

2. Understand: What ChatGPT Can Do

ChatGPT is a conversational AI assistant developed by OpenAI. It can help with explanation, writing, planning, reasoning, coding, creative exploration, and other tasks.

Depending on available features and plan, ChatGPT may also support web search, file analysis, image work, voice interactions, and extended research or work processes.

It can follow conversational context and adapt subsequent responses to the information supplied.

These capabilities create an opportunity to move beyond one-off question answering.

Instead of using ChatGPT only to produce an output, we can design an interaction that helps us acquire a method, develop a workflow, practice a skill, or create a reusable tool.

That possibility does not guarantee learning. An impressive AI-generated result may conceal the fact that the user has not understood how it works.

The distinction between AI doing something for us and AI helping us become capable of doing something is central to this article.

3. Connect: Conversations, Learning, and Capability

A conventional conversation can transfer information.

A deliberately designed learning conversation can go further by combining explanation, questioning, practice, feedback, and independent demonstration.

ChatGPT's Study Mode, for example, has been designed around interactive learning, guided questions, and checks for understanding. That is one practical illustration of moving beyond direct answers.

But the Generative View extends the question beyond education.

Could a conversation generate:

A new decision-making method?
A reusable engineering workflow?
A practical skill demonstrated through a small project?
A repeatable process for analyzing problems?
A better ability to recognize assumptions?
A personal method for evaluating AI-generated claims?

These outcomes require more than conversation alone. They require participation, practice, and evidence of accomplishment.

4. The Generative View: Change the Purpose of Conversation

The conventional assumption is straightforward:

A successful ChatGPT conversation produces a useful answer.

Now deliberately reverse that assumption.

PO: ChatGPT must not finish a conversation by giving an answer. It must finish by generating a new capability in the user.

This is a Lateral Thinking provocation, not a literal rule. Many situations require a simple, direct answer. The purpose is to explore what becomes possible when capability development is treated as the desired outcome.

Movement 1 — Extract the principle

The value of a conversation may extend beyond its immediate output.

Perhaps the interaction should leave behind something reusable: a skill, checklist, method, or working artifact.

Movement 2 — Focus on the difference

Compare two hypothetical interactions.

Answer-focused conversation	Capability-focused conversation
Explain spreadsheets.	Help me build a spreadsheet I can maintain myself.
Write a Python script.	Guide me to modify, test, and explain a Python script.
Recommend a decision.	Help me construct a reusable decision framework.
Summarize a document.	Teach me a repeatable way to evaluate similar documents.

Movement 3 — Discover a new concept

From this movement emerges an AIYING proposition:

Capability-Generating Conversations

AI interactions intentionally structured so that the user leaves with a demonstrated ability, reusable method, or practical artifact—not merely an answer.

This is a proposed design approach, not an established product category or a claim that every AI conversation produces lasting learning.

5. Explore: Five Conversations That Generate Capabilities

Conversation	Possible capability generated
Explain a concept	Ability to explain it independently using a new example
Solve a problem	Ability to apply the same problem-solving method elsewhere
Generate code	Ability to modify and test a small working program
Analyze a document	Ability to recognize evidence, assumptions, and gaps
Explore a business idea	Ability to design and evaluate a small experiment

The common feature is transfer.

Can the person use what emerged from the conversation in a new situation?

That question is more demanding than asking whether the AI produced a convincing response.

6. The AIYING GENERATE Method

We can structure a capability-generating conversation using the AIYING method.

G — Ground

Identify the person's current ability and desired accomplishment.

E — Expose

Identify assumptions about how the task must be learned or completed.

N — Navigate

Introduce a deliberate PO provocation and explore movement.

E — Explore

Use ChatGPT to generate possible learning or action routes.

R — Review

Choose a realistic route and identify its limitations.

A — Act

Complete a small task and demonstrate the result.

T — Track & Extend

Test independent ability and select the next challenge.

7. Review: Capability Is Not the Same as AI Output

A person may receive a perfect explanation without understanding it.

A program may work without its user knowing how to maintain it.

A decision framework may look sophisticated while depending on unsupported assumptions.

Therefore, capability-generating conversations need explicit checks.

Demonstration: Can the user perform the task?
Explanation: Can the user explain the essential steps?
Modification: Can the user change something without starting over?
Transfer: Can the method be applied to a related situation?
Verification: Can important outputs be checked independently?

Not every interaction needs all five checks. But without any check, it is easy to confuse assisted performance with acquired capability.

8. Apply: Your First Capability-Generating Conversation

Objective

Use ChatGPT to develop a reusable method for evaluating an AI-generated answer.

Tool: ChatGPT — https://chatgpt.com/

Time: Approximately 15–20 minutes.

Prerequisites: Access to ChatGPT. No programming required.

Step 1 — Start with a real capability goal

Open ChatGPT and state:

"I want to become better at checking whether an AI-generated answer is trustworthy."

Step 2 — Ask for a simple method

Request a short checklist covering factual claims, sources, assumptions, uncertainty, and missing information.

Step 3 — Apply the PO provocation

Introduce:

PO: An AI answer is not complete until the user can challenge it.

Ask ChatGPT to explore what this changes about the checklist.

Step 4 — Practice on an example

Ask ChatGPT to create a short fictional answer containing a mixture of reasonable claims, assumptions, and one unsupported assertion. It must label the example as a simulation but not reveal the issues until after your attempt.

Step 5 — Evaluate independently

Use your checklist to identify what requires verification. For real-world claims, consult reliable primary sources rather than trusting the AI's self-assessment.

Step 6 — Request feedback

Ask ChatGPT to compare your assessment with the deliberately inserted issues and explain what you missed.

Step 7 — Demonstrate transfer

Apply the checklist to a different answer, ideally one with claims you can verify using independent sources.

Reusable prompt

I want this conversation to generate
a practical capability in me,
not merely an answer.

MY GOAL:
Become better at evaluating
AI-generated answers.

Use the AIYING GENERATE method.

GROUND:
Ask about my current experience.

EXPOSE:
Identify assumptions I may have
about trusting AI answers.

NAVIGATE:
Apply this PO provocation:
"An AI answer is not complete
until the user can challenge it."

Use movement to explore what
this makes possible.

EXPLORE:
Help me develop a short,
reusable evaluation checklist.

REVIEW:
Explain the checklist's limits.

ACT:
Give me a fictional practice
example with hidden issues.
Let me evaluate it first.
Then provide feedback.

TRACK & EXTEND:
Give me a second challenge.
Ask me to explain the method
without your assistance.

Do not claim I have learned
the skill until I demonstrate it.

Expected output

A reusable AI Answer Evaluation Checklist.
One completed practice assessment.
Feedback on your reasoning.
A second challenge for independent application.

This exercise does not claim that ChatGPT can independently verify every factual assertion. External evidence remains necessary.

9. Today's Accomplishment

Your first Conversation-to-Capability Record

After completing the exercise, record:

Capability attempted: Evaluate AI-generated answers.
Artifact created: Evaluation checklist.
Practice completed: One simulated assessment.
Evidence of learning: Your independent assessment of a second example.
Next step: Apply the checklist to a real, verifiable claim.

The accomplishment is not merely having a checklist. It is beginning to demonstrate that you can use the method yourself.

10. Discoveries Worth Reflecting On

Did ChatGPT explain something, or help you develop an ability?
Which part of the process required your own judgment?
Did the PO provocation change how you approached the task?
What could you now do without asking ChatGPT to do it for you?
What further practice would make the capability more reliable?

11. The Bigger Insight

ChatGPT can be an answer engine, writing partner, research assistant, and practical tool.

But the Generative View suggests another possibility: a conversation can be deliberately designed as an opportunity for capability development.

PO: The best answer is the one that makes the next answer less necessary.

This provocation does not mean people should stop using AI. It asks whether AI assistance can also strengthen the user's independent understanding and judgment.

A conversation becomes especially valuable when it changes not only what we know, but what we are able to do.

12. References and Further Reading

OpenAI Academy — Getting Started with ChatGPT
OpenAI Help Center — ChatGPT Capabilities Overview
ChatGPT — Study Mode
OpenAI Help Center — What Is ChatGPT?
Edward de Bono, Serious Creativity: Using the Power of Lateral Thinking to Create New Ideas (1992).

Editorial note: This is an independent AIYING article, not an official OpenAI publication. Capability-Generating Conversations and the AIYING GENERATE method are proposed educational frameworks. The article does not claim that every AI interaction produces measurable learning.

Monday, November 9, 2026

AI Operacy: Beyond AI Literacy — The New Competence of Accomplishing with AI

AIYING | FOUNDATIONAL ARTICLE | AI OPERACY

AI Operacy: Beyond AI Literacy

The New Competence of Accomplishing with Artificial Intelligence

Think Constructively. Act Purposefully. Accomplish with AI.

We are learning how to use artificial intelligence.

We are learning how to prompt it, question it, and generate content with it.

But an important question remains:

Are we becoming better at accomplishing meaningful things?

This question introduces the idea of AI Operacy.

1. The Missing Dimension of AI Competence

Artificial intelligence is making sophisticated capabilities increasingly accessible.

A student can generate explanations. A professional can draft reports. An entrepreneur can explore business ideas. A developer can generate code. A teacher can prepare learning materials.

These capabilities are valuable. Yet generating an output is not necessarily the same as achieving an outcome.

A business plan is not a functioning business. A learning summary is not demonstrated understanding. A proposed workflow is not an improved workflow. AI-generated code is not automatically a reliable application.

The distinction is between producing something and accomplishing something worthwhile.

AI education increasingly addresses practical use, evaluation and responsible application. However, there is value in explicitly organizing these capabilities around purposeful accomplishment.

AI can help generate possibilities. Human thinking and responsible action determine what becomes of them.

This is the opportunity for AI Operacy.

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2. Edward de Bono and the Meaning of Operacy

The intellectual starting point is the work of Edward de Bono, widely associated with lateral thinking, deliberate thinking tools, and constructive approaches to problem solving.

De Bono coined the term operacy to draw attention to the skills needed for doing things.

In De Bono's Thinking Course, particularly the chapters Information and Thinking and Thinking and Doing, he argues that education places considerable emphasis on knowledge while giving insufficient direct attention to the skills of action.

He proposes that operacy deserves recognition alongside literacy and numeracy.

Competence	Fundamental concern
Literacy	Reading and writing
Numeracy	Working with numbers
Operacy	The skills required for doing and making things happen

De Bono distinguishes intelligence as potential from thinking as an operating skill. He also challenges the assumption that analysis, argument, judgment and knowledge are sufficient for constructive action.

Operacy includes setting objectives, considering priorities, exploring alternatives, designing courses of action, planning and dealing with consequences.

His other works provide complementary methods. Lateral Thinking develops ways of generating alternatives. PO: Beyond Yes and No explores moving beyond judgment. Six Thinking Hats provides a structure for deliberate parallel thinking. Serious Creativity connects creative thinking with purposeful design and opportunity creation.

Attribution note: Operacy is de Bono's concept. AI Operacy, as developed in this article, is a proposed contemporary extension. The seven competencies and practice cycle introduced below are original organizing frameworks inspired by his work, not frameworks attributed to de Bono.

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3. What Is AI Operacy?

Proposed definition

AI Operacy is the learnable competence to combine human thinking and AI capabilities to purposefully design, execute, verify, and improve actions that accomplish meaningful outcomes.

This definition contains five essential ideas.

It is learnable

AI Operacy is not reserved for technical specialists or people considered exceptionally intelligent. Its component skills can be practiced and improved.

It combines human and artificial capabilities

AI can assist with information, ideas, simulations, analysis, content and workflows. Human participants remain responsible for appropriate objectives, judgment, accountability and oversight.

It is purposeful

Effective AI use begins by understanding what is worth accomplishing, not by selecting a fashionable tool.

It includes action and verification

Producing a plausible answer or attractive deliverable is not sufficient. Results need to be checked against appropriate criteria.

It is concerned with meaningful outcomes

The value of an accomplishment depends on context, affected people, resources, risks and intended benefits.

AI Literacy helps us understand and use AI. AI Operacy focuses on what we can responsibly accomplish with it.

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4. Beyond AI Literacy

AI Literacy and AI Operacy should not be treated as competing concepts.

Modern AI literacy frameworks already recognize practical use, critical evaluation, ethics and responsible participation. AI Operacy builds on these capabilities while placing demonstrated accomplishment at the center.

Dimension	AI Literacy emphasis	AI Operacy emphasis
Understanding	Capabilities, limitations and risks	Applying understanding to a purpose
Interaction	Effective AI use	Designing useful human-AI activity
Creativity	Generating and evaluating content	Developing actionable possibilities
Judgment	Evaluating AI information and outputs	Making responsible decisions and verifying results
Action	Appropriate AI application	Achieving and improving meaningful outcomes

AI Operacy is therefore proposed as an outcome-oriented competence that integrates AI understanding, thinking skills, creative design and practical action.

It does not imply that every problem requires AI. Recognizing when AI is unnecessary, inappropriate or too risky is itself an important part of good AI Operacy.

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5. The Seven Competencies of AI Operacy

The proposed framework contains seven interconnected competencies.

1. Purpose

Identify a worthwhile outcome. Clarify what needs to change, why it matters, and what success would mean.

2. Perception

Explore the situation from multiple perspectives. Identify assumptions, constraints, relevant factors, missing information and the views of affected people.

3. Possibility

Generate alternatives beyond the obvious answer. Use AI-assisted exploration together with lateral thinking techniques where appropriate.

4. Design

Construct a workable approach. Consider tasks, people, processes, resources, tools, safeguards and alternative courses of action.

5. Judgment

Evaluate options against value, feasibility, evidence, risks and consequences. Do not mistake AI confidence for reliability.

6. Action

Carry out an appropriate intervention, experiment, task or workflow with suitable human control and accountability.

7. Learning

Verify what happened, reflect on the outcome, make improvements and transfer useful lessons to other situations.

These competencies are interconnected rather than rigidly sequential. A person may return to perception after testing an idea or revisit purpose when new evidence changes the original objective.

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6. The AI Operacy Practice Cycle

To translate the competencies into a practical method, we propose seven repeatable activities.

FOCUS

What do we want to accomplish?

EXPLORE

What is happening, and what do we need to understand?

PROVOKE

What alternative possibilities could change our approach?

DESIGN

What workable course of action can we construct?

ACT

What appropriate action can we take?

VERIFY

What evidence shows whether it worked?

LEARN

What should we improve, repeat or reconsider?

Different thinking tools can support different activities.

Activity	Relevant de Bono methods
Focus	AGO: Aims, Goals, Objectives
Explore	PMI, CAF, OPV
Provoke	PO, Random Entry, lateral thinking
Design	Alternatives, constructive design, Concept Fan
Act	Planning, courses of action, priorities
Verify	Consequences, evaluation, deliberate judgment
Learn	Reflection, alternative exploration, improvement

The tools do not replace domain expertise, evidence, professional standards or appropriate safeguards. They provide structures for thinking more deliberately.

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7. AI Operacy in Practice: A Job Seeker's Challenge

Consider a graduate preparing for employment.

The initial AI request might be:

Give me ten interview tips.

The AI might generate a useful list. But has the graduate become better prepared for an interview?

Not necessarily.

An AI Operacy approach would begin with a more specific accomplishment:

Complete a realistic mock interview, identify three weaknesses, and improve at least one answer through practice.

Applying the practice cycle

Focus: Improve performance in a particular type of job interview.

Explore: Examine the job description, required competencies and the candidate's current preparation.

Provoke: Challenge the assumption that interview preparation means memorizing ideal answers. Could practicing imperfect answers and improving them be more valuable?

Design: Create a five-question mock interview with criteria for evaluating responses.

Act: Conduct the mock interview, answer the questions and revise one response.

Verify: Compare the original and revised response against the agreed criteria, while recognizing that AI feedback may be imperfect.

Learn: Identify which improvements should be practiced again.

The accomplishment is no longer a list of advice. It is a completed practice activity with evidence of improvement.

AI supports the process. The learner performs the work.

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8. Lateral Thinking Meets Generative AI

There is an important intellectual opportunity at the intersection of de Bono's lateral thinking and modern generative AI.

Lateral thinking deliberately seeks to move beyond established patterns of perception. Generative AI can produce many possible formulations, combinations and candidate ideas.

But the two are not identical.

AI-generated novelty is not automatically the result of a deliberate lateral thinking process. Similarly, a provocative idea is not automatically useful merely because it is unusual.

Consider the provocation:

PO: AI must stop answering questions.

This is not a factual prediction or a proposal to prohibit answers. It is a deliberate provocation used to stimulate movement.

Possible movements include:

AI could help users discover better questions before answering.
AI could help users test their own reasoning.
AI could generate experiments instead of explanations.
AI could support decisions through structured alternatives rather than a single recommendation.

One possibility might lead to a prototype of an AI-assisted question-discovery workflow.

AI Operacy then asks whether the workflow is useful, how it should be designed, how it can be tested, and what evidence would justify adopting it.

Lateral thinking helps generate possibilities. AI can assist their exploration. Operacy connects the possibilities to purposeful action.

This relationship provides a foundation for a broader initiative connecting human creativity with AI-enabled accomplishment.

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9. How Do We Recognize AI Operacy?

AI Operacy should be demonstrated, not merely declared.

A learner who knows many AI tools may still struggle to accomplish a useful task. Another learner who uses a single tool thoughtfully may produce a meaningful improvement.

We therefore propose assessing six dimensions:

Purpose: Was the intended outcome worthwhile and clearly defined?
Thinking: Were relevant factors, alternatives and perspectives considered?
Design: Was the approach practical and appropriate?
Action: Was something actually carried out?
Verification: Was the result checked against suitable evidence?
Learning: Was there reflection, adaptation or improvement?

Responsible use, privacy, security, fairness and human oversight should be considered throughout.

This is a proposed formative assessment approach, not an established certification standard. Further development and testing would be needed before formal adoption.

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10. Your First AI Operacy Challenge

From an AI Answer to a Real Accomplishment

Time: Approximately 10 minutes for an initial exercise.

Choose one small task that matters to you. It might involve learning, communication, planning, organizing information, or improving an everyday process.

Use the following prompt with an AI assistant:

I want to accomplish something useful with AI.

My situation is:
[Describe your situation.]

Help me:

1. Define one meaningful outcome.
2. Identify three possible approaches.
3. Choose a small, practical action.
4. Identify risks and what I must verify.
5. Decide how to measure success.

Ask questions if essential information is missing.

Do not assume AI is always the best solution.

Keep the action small enough to attempt today.

Then carry out the selected action, where appropriate.

Finally, answer four questions:

What did I intend to accomplish?
What did I actually do?
What result did I achieve, and what evidence supports it?
What will I do differently next time?

Your accomplishment, not the length of the AI response, is the central result.

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11. Introducing the AI Operacy Initiative

AI Operacy is being developed as a learning initiative under AIYING — Accomplishing with AI.

Its purpose is to connect AI understanding with deliberate thinking, constructive creativity, responsible action and demonstrable outcomes.

The initiative will explore:

AI Operacy Shorts: Brief ideas that invite immediate action.
AI Operacy Insights: Thinking principles applied to everyday AI use.
AI Operacy Labs: Guided exercises with practical accomplishments.
AI Operacy Cases: Examples of purposeful human-AI collaboration.
AI Operacy Learning: Progressive skill development and assessment.

The initial competence model and learning method are proposals intended for refinement through application, learner feedback and practical evidence.

Who is AI Operacy for?

Students preparing for employment, educators developing practical learning, professionals improving their work, entrepreneurs exploring opportunities, AI practitioners designing solutions, and anyone who wants to move from AI awareness to meaningful accomplishment.

Technical expertise can be valuable, but it is not a prerequisite for beginning.

Our central proposition

AI Literacy asks: What do you understand about AI, and how can you use it responsibly?

AI Operacy asks: What can you meaningfully accomplish with AI, and how do you know you succeeded?

The future of human capability with AI should not be measured only by what AI can generate.

It should also be measured by what people can responsibly make happen.

Join the conversation

What is one meaningful thing you have accomplished with AI?

Not merely an answer you received or a prompt you wrote, but an action, improvement, decision, creation or result that made a difference.

Share your example in the comments. It may help shape future AI Operacy learning challenges.

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12. Intellectual Foundations and References

The proposed AI Operacy framework draws conceptual inspiration from the following works by Edward de Bono:

De Bono's Thinking Course — particularly the chapters on information and thinking, thinking and doing, and deliberate thinking.
Six Thinking Hats — deliberate and parallel thinking.
Lateral Thinking: A Textbook of Creativity — alternatives, restructuring patterns and generating new approaches.
Serious Creativity — provocation, movement, creative design and opportunity generation.
PO: Beyond Yes and No — the use of PO to move beyond conventional judgment.
The Mechanism of Mind — the conceptual background to pattern-based thinking.

These works inform the intellectual foundations of the initiative. The AI-specific definition, seven competencies, practice cycle and assessment model are proposed extensions developed for contemporary AI use.

AI Operacy is not presented as an officially endorsed Edward de Bono framework or an established international competency standard.

Sunday, November 8, 2026

The AI Agent That Was Paid Not to Act | The AI ThinkShift #013

The AI Assistant That Asked for Help | The AI ThinkShift #011 |

Thursday, October 29, 2026

AI Creativity #001 — What If AI Had No Prompts? | Take Away Value

What If AI Had No Prompts?

Creative Thinking Method: Take Away Value

Don't just use AI to create. Use creativity to reinvent AI.

What If We Removed the Prompt from AI?

Imagine opening ChatGPT or another AI assistant.

You don't type anything.

You don't ask a question.

You don't write a prompt.

Yet the AI helps you accomplish what matters.

PO: AI works without prompts.

At first, this sounds strange. After all, prompting has become one of the defining activities of the generative AI age.

We are encouraged to learn prompt engineering, write better instructions, provide more context, and refine our questions.

But creativity sometimes begins by questioning something everyone considers essential.

What if the prompt itself is not essential?

What if it is simply one possible way of communicating with intelligence?

Welcome to AI Creativity, a series exploring how established creative thinking methods can help us imagine and design new possibilities for the AI age.

Our first method is Take Away Value.

1. The Creative Thinking Method: Take Away Value

The principle is simple:

Take away something that appears valuable, necessary, or indispensable. Then explore what becomes possible.

This is not about proving that the removed element is useless.

It is about escaping the assumption that the element must always be present.

Consider three familiar examples:

Take away the keyboard from a computer. We begin exploring touch, voice, gesture, and other forms of interaction.
Take away the classroom from education. We begin exploring learning through communities, workplaces, simulations, and personalized digital experiences.
Take away the physical checkout counter from shopping. We begin exploring alternative ways to identify purchases and complete payment.

These examples illustrate possible creative directions, not necessarily inventions that originated from this particular method.

The important question is:

When we remove something valuable, what new value must we create to replace it?

Now apply this thinking to artificial intelligence.

2. Apply the Method: Take Away the Prompt

Today, a typical generative AI interaction follows this pattern:

Human writes a prompt → AI interprets it → AI produces a response.

The prompt acts as the starting point.

Let's deliberately remove it.

PO: The user never writes a prompt.

Don't immediately object that AI needs instructions. That objection reflects the existing model.

Instead, ask:

How might AI recognize that help is needed?
Where could the necessary context come from?
Could a system respond to events rather than questions?
Could users communicate intentions without composing instructions?
Could AI offer assistance while preserving human control?

Suddenly, we are no longer discussing how to write better prompts.

We are exploring new ways of designing human–AI interaction.

3. Example One: The Student Who Never Asks

Imagine a student studying mathematics.

Today, the student might write:

"Explain quadratic equations with three examples."

In our prompt-free scenario, the student is working in a learning environment that can recognize repeated mistakes, with appropriate permissions.

The AI notices that the student repeatedly makes errors when factoring quadratic expressions.

It offers:

"You seem to be having difficulty with this step. Would you like a visual explanation?"

The student clicks Yes.

No written prompt was required.

The creative possibility

AI could shift from answering explicit questions to offering timely, context-sensitive learning support.

The student remains in control of whether to accept that support.

New design direction: Assistance triggered by learning difficulties rather than manually written requests.

4. Example Two: The Professional Who Never Requests a Report

Imagine a project manager preparing for a weekly review.

Normally, the manager writes:

"Summarize project progress, identify delays, and prepare a status report."

Now remove the prompt.

A properly authorized AI system has access to selected project updates, deadlines, and task records.

Before the scheduled review, it prepares a draft containing:

Completed milestones
Delayed activities
Decisions requiring attention
Possible next actions

The manager reviews and approves the report.

The creative possibility

AI becomes part of a workflow rather than a separate destination where people must repeatedly enter instructions.

New design direction: Event-triggered assistance integrated into everyday work.

The important distinction is that preparation can be automatic while consequential decisions remain with accountable people.

5. Example Three: The Senior Citizen Who Never Learns Prompt Engineering

Imagine an older adult who finds typing difficult.

Traditional AI adoption may require learning how to ask questions effectively.

But why should useful intelligence depend on the ability to write good prompts?

Suppose a simple, consent-based assistant presents a few relevant choices:

"Would you like help understanding this message?"

"Would you like me to read this document aloud?"

"Would you like to contact someone?"

The user simply selects an option.

The creative possibility

We stop treating prompt-writing ability as a prerequisite for benefiting from AI.

New design direction: Accessible AI built around understandable choices, voice, and everyday activities.

6. Example Four: The AI Engineer Who Never Writes a Diagnostic Prompt

An AI engineer monitors a deployed application.

Currently, the engineer might ask:

"Analyze yesterday's failed requests and identify the likely causes."

Take away the prompt.

An observability system detects an unusual increase in failed requests.

An authorized AI diagnostic component automatically prepares:

Incident detected: Failure rate above the configured threshold.

Evidence: Errors concentrated in one service after a recent deployment.

Possible causes: Configuration change, dependency failure, or deployment regression.

Recommended next step: Review the deployment diff and affected service logs.

The engineer investigates before authorizing any corrective action.

The creative possibility

AI assistance can be activated by operational signals rather than conversational instructions.

New design direction: Observable, event-driven AI engineering support.

This is especially important because prompt-free interaction should not become permission-free execution.

7. What New Ideas Emerge?

Our original provocation was:

PO: AI works without prompts.

From that single creative move, several directions emerge.

New direction	What changes?
Context-aware AI	Relevant context reduces repeated instructions
Event-driven AI	Assistance begins when a defined event occurs
Intent-driven interfaces	Users select goals instead of composing prompts
Ambient assistance	Help becomes available within existing activities
Workflow-integrated AI	AI participates in processes rather than isolated chats
Accessible AI	People benefit without mastering prompt engineering

None of these directions requires eliminating prompts from every AI system.

The provocation helps us discover alternatives that might otherwise be overlooked.

8. The Important Question: What Do We Lose?

A creative idea becomes useful only when we examine its consequences.

Removing explicit prompts can introduce significant problems.

Privacy: How much personal or organizational context should AI observe?

Consent: Who decides when the system may intervene?

Accuracy: What happens when AI misunderstands the user's intentions?

Autonomy: Could proactive assistance become intrusive?

Security: Could untrusted data trigger inappropriate actions?

Accountability: Who remains responsible when an AI system initiates work?

These are not reasons to abandon the idea.

They are design challenges that must be addressed before implementation.

A responsible prompt-free system should use explicit permissions, limited context, understandable triggers, and human confirmation for consequential actions.

9. Try It Yourself: A Five-Minute AI Creativity Experiment

Choose one activity where you regularly write prompts.

For example:

"I ask AI every morning to help organize my tasks."

Now follow this experiment.

Step 1 — Identify the value

What does the prompt provide?

In this case, it tells AI that you want help organizing your day.

Step 2 — Take it away

Imagine you are not allowed to type or speak that request.

Step 3 — Generate alternatives

How else could the assistance begin?

Perhaps:

A scheduled daily planning view
A button labeled "Plan My Day"
A calendar-triggered draft
A task application with built-in AI assistance

Step 4 — Choose one idea

Select an option that offers clear value without requiring excessive permissions.

Step 5 — Test it

Create a small prototype or simulate the workflow. Compare the time saved, usefulness, and level of user control against your existing prompting approach.

You have now used a creative thinking method to generate an alternative AI interaction design.

10. A Reusable AI Creativity Prompt

Ironically, we can use a prompt to explore a future without prompts!

Copy the following into your preferred AI assistant:

Act as my creative thinking partner.
I want to apply the Take Away Value method to an AI-related problem.
My current situation is: [describe the situation].
Identify something valuable that people assume must be present.
Deliberately remove it.
Do not immediately reject the resulting idea as impractical.
Generate five alternative ways the original purpose could still be accomplished.
Identify the most surprising direction, its potential value, its risks, and one small experiment to test it.
Clearly distinguish creative possibilities from currently available capabilities.

Try it with prompting, AI agents, education, customer service, software engineering, or any other AI-age activity.

11. The Bigger Insight

The future of AI may not be defined only by better models, larger context windows, or more sophisticated prompts.

It may also depend on our ability to question the assumptions built into today's interfaces and workflows.

Prompt engineering is valuable.

But creativity invites us to imagine what comes after the prompt.

The most interesting question may not be "How do we prompt AI better?"

It may be "Why must we prompt AI at all?"

That is the power of taking away something valuable: it forces us to discover new ways of creating value.

Your Turn

What would you remove from today's AI to discover something entirely new?

The prompt? The screen? The chatbot? The need for an app? Or something else?

Share your most unexpected idea.

Next episode: AI Creativity #002 — What If AI Never Gave Answers?

Creative Thinking Method: Take Away Value

AI Creativity — Don't just use AI to create. Use creativity to reinvent AI.

AIYING — Accomplishing with AI.

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 #003 — What If AI Were Forbidden to Give Answers? | From Answers to Possibilities

What If AI Were Forbidden to Give Answers?

What if the greatest potential of Generative AI is not generating answers, but generating possibilities?

We are living in an extraordinary age.

Ask AI to write an email, and it produces one. Ask it to summarize a report, and it responds in seconds. Ask it to recommend a strategy, and it offers a structured plan.

The better AI becomes at answering, the more we rely on it.

But consider a strange possibility:

What if AI were forbidden to give answers?

Would it become useless?

Or would we discover an entirely different kind of intelligence partnership?

Welcome to PO Prompts #003, where we use Edward de Bono's provocative thinking approach to explore what AI could become when we stop treating it merely as an answer machine.

1. The Story: The Professional Who Had All the Answers

Arjun is a young professional working in a technology company.

His day begins with AI.

He uses it to draft emails, summarize meetings, prepare presentations, analyze documents and recommend solutions.

His productivity has improved dramatically.

One afternoon, during a strategy meeting, his manager asks a simple question:

“What possibilities have we not considered?”

Arjun opens his AI assistant.

Within seconds, he receives ten recommendations.

They are sensible, well-structured and professionally presented.

But something bothers him.

Most of the suggestions are variations of ideas the team has already discussed.

The manager asks another question:

“What if our entire approach to this problem is wrong?”

Arjun hesitates.

For the first time, he realizes that having more answers does not necessarily mean discovering more possibilities.

Perhaps he has been asking AI the wrong kind of questions.

2. The Hidden Assumption

Most interactions with AI follow a familiar pattern:

Human asks → AI answers → Human acts.

Behind this pattern lies an assumption:

The primary value of AI is its ability to provide useful answers.

This assumption is understandable. Answer generation is one of AI's most visible capabilities.

But it is not the only way AI can contribute to thinking.

AI can also help us explore alternatives, construct hypothetical scenarios, examine assumptions and develop ideas that would otherwise remain unexplored.

What happens when we deliberately remove the expectation of an immediate answer?

3. Enter PO: What If AI Were Forbidden to Give Answers?

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

Unlike an ordinary question, a PO statement can deliberately introduce an unreasonable or impossible condition to disrupt habitual thinking.

The purpose is not to prove the statement correct.

It is to use the provocation as a stepping stone toward new ideas.

Our provocation is:

PO: AI is forbidden to give answers.

Imagine that tomorrow, every AI assistant receives a new instruction:

You may not provide direct answers or recommendations.

Instead, AI must help users explore what they have not yet considered.

What could such an AI do?

It might challenge assumptions rather than accept them.

It might ask questions that expose overlooked possibilities.

It might introduce deliberately unusual perspectives.

It might help people imagine alternative futures.

And it might turn surprising ideas into experiments.

The apparent restriction could reveal an entirely different source of value.

AI becomes a possibility engine rather than only an answer engine.

4. PO Thinking Lab — From Answers to Possibilities

This is where the reader becomes an active participant.

The goal is not merely to appreciate the provocation. It is to use it.

Step 1 — Recognize the conventional thinking

Consider a problem you are currently facing.

Perhaps you are planning a project, making a career decision, improving a business process or designing a new product.

Your usual AI prompt might be:

“What is the best solution to this problem?”

Notice what this question assumes.

It assumes that the problem is already correctly defined and that the next step is to identify a solution.

But what if the problem itself needs to be questioned?

Step 2 — Challenge the assumption

Ask yourself:

What am I taking for granted?
Why do I believe this is the problem?
Which alternatives have I already excluded?
What would someone from an entirely different field notice?

The objective is not to reject established knowledge. It is to expose assumptions that may be unnecessarily limiting our thinking.

Step 3 — Introduce the PO provocation

Now impose the unusual rule:

PO: AI cannot give me an answer.

It can only help me discover possibilities.

Instead of asking, “What should I do?”, ask:

“What possibilities am I failing to see?”

This changes the direction of the conversation.

Step 4 — Move through five possibilities

Consider five potential roles for an answer-free AI.

Possibility A — The Assumption Challenger

AI identifies assumptions embedded in the user's problem and explores what might happen if they were removed.

Possibility B — The Question Inventor

Instead of producing solutions, AI generates unfamiliar, provocative questions that open new directions.

Possibility C — The Alternative-Future Explorer

AI helps users imagine different scenarios, including ones that seem improbable today.

Possibility D — The Perspective Shifter

AI examines a problem from the viewpoints of customers, competitors, beginners, future generations or entirely different professions.

Possibility E — The Experiment Designer

AI helps transform unconventional possibilities into small, testable experiments without prematurely claiming that an idea will work.

These are not predictions about what every AI system should become.

They are possibilities revealed by the provocation.

Step 5 — Discover the movement

We began with a seemingly absurd restriction:

AI is forbidden to give answers.

But the restriction reveals an important distinction.

Answer generation helps us work within a problem frame.

Possibility exploration helps us reconsider the frame itself.

Both are valuable.

The breakthrough is recognizing when each is needed.

Step 6 — Run a real experiment

Choose one decision you are facing this week.

For your first AI conversation about it, prohibit immediate recommendations.

Ask AI to identify assumptions, generate five distinct ways of framing the issue and explore their implications.

Then select one surprising but plausible direction.

Only after this exploration should you ask AI to help evaluate and implement it.

Compare the experience with your usual answer-seeking approach.

Did you discover a possibility you would otherwise have missed?

5. The Complete ChatGPT PO Prompt

Copy this prompt into ChatGPT and try it with a real problem.

Act as my PO lateral-thinking partner, inspired by Edward de Bono's concept of provocation.
For this conversation, do not begin by giving me answers or recommendations.
Instead:
Help me identify the hidden assumptions behind my problem.
Introduce one deliberately provocative PO statement that challenges those assumptions.
Explore at least five distinct possibilities that emerge from the provocation.
Help me move from unusual ideas toward potentially useful concepts.
Challenge me with questions that expand my thinking.
Help me select one possibility for a small, practical experiment.
Distinguish speculative possibilities from established facts. Do not evaluate ideas too early.
Begin by asking: “What problem, decision, or opportunity would you like to explore differently?”

The purpose of this prompt is not to eliminate answers forever.

It is to create space for exploration before committing to an answer.

6. PO and Generative AI: A Powerful Partnership

Generative AI can produce many ideas, scenarios and variations.

But generating more content does not automatically produce genuinely new thinking.

If our prompts remain trapped within familiar assumptions, AI may simply generate more sophisticated variations of familiar solutions.

This is where PO becomes especially relevant.

PO disrupts the direction of thinking.

AI expands the possibilities that emerge.

Human judgment evaluates their usefulness.

Experimentation turns promising possibilities into accomplishments.

We can express this partnership as:

PROVOKE → GENERATE → MOVE → ACCOMPLISH

This is not a claim that every provocative idea is valuable or that AI-generated possibilities are automatically correct.

It is a practical approach for combining creative exploration with responsible judgment.

And it connects directly to the philosophy of AIYING — Accomplishing with AI.

7. The Bigger Insight

We often ask:

How can AI help me do this better?

PO invites a different question:

What if I should be doing something entirely different?

That is a fundamental shift.

The first question improves an existing direction.

The second opens the possibility of discovering a new one.

The most valuable AI conversations may involve both.

First, expand what is possible.

Then, determine what is useful, responsible and achievable.

8. Your PO Challenge

For your next important decision, try this:

Spend five minutes with AI without asking for a single solution.

Ask only for assumptions, provocations, alternative perspectives and unexplored possibilities.

Then reflect:

Did AI give you something more valuable than an answer — a new direction?

Share the most surprising possibility you discovered.

PO Prompts — One Strange Question. A New Possibility.

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

AIYING — Accomplishing with AI.

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

What If AI Agents Became the Managers?